Laurence D. Higgins, MD, MBA

Take-Home Points

• Shared decision-making tools, such as predictive models, can help empower the patient to make decisions for or against surgery equipped with more information about the expected outcome.
• There is a role for preoperative collection of PROMs in the clinical decision-making process.
• Mental health state, as reported by the VR-12 MCS, is a significant predictor of postoperative pain and function as reported by the VAS pain and ASES function scores.
• A significant portion of the predictive ability of this model comes from the fact that at 1-year postoperatively, patients receiving a rTSA will on average have a 3.8 point lower on ASES function score than those receiving a TSA (P < .001, ω²=.083).
• Future studies to discern the role of different modalities to improve a patient’s emotional health preoperatively will be beneficial as the healthcare industry trends toward value based medicine collecting PROMs as part of reimbursement schemes.

Over the past few decades, decisions regarding patients’ care have gradually transitioned from a paternalistic model to a more cooperative exchange between patient and physician. Shared decision-making provides patients a measure of autonomy in making choices for their health and their future. Patient participation may mitigate uncertainty and discomfort during selection of a course of treatment, which may lead to increased satisfaction levels after surgery.¹ Moreover, shared decision-making may help patients better manage postoperative expectations through evidenced-based discussions of preoperative health levels and their corresponding outcomes. Patient-reported outcome measures (PROMs) use clinically sensitive and specific metrics to evaluate a patient’s self-reported pain, functional ability, and mental state.² These metrics are useful in setting patient expectations for potential outcomes of treatment options. Use of evidence-based clinical decision-making tools, such as PROM-based predictive models, can facilitate a collaborative decision-making environment for patient and physician. Given the present cost-containment era, and the need for preoperative metrics that can assist in predictive analysis of postoperative improvement, models are clearly valuable.

In attempts to help patients set well-informed and reasonable expectations, physicians have turned to PROMs to facilitate preoperative evidence-based discussions. Although PROMs have been in use for almost 30 years, only recently have they been used to create tools that can aid quantitatively in the surgical decision-making process.^2-6 Combining physical examination findings, imaging studies, comorbidities, and quantitative tools, such as this model, may enhance patients’ understanding of their preoperative condition and expected prognosis and thereby guide their surgical decisions.

We conducted a study to determine whether certain preoperative PROMs can predict 1-year postoperative visual analog scale (VAS) pain scores and American Shoulder and Elbow Surgeons (ASES) Function scores in total shoulder arthroplasty (TSA) and reverse TSA (rTSA). We hypothesized that preoperative mental health status as captured by Veterans RAND 12-Item Health Survey (VR-12) mental health component summary (MCS) score and preoperative VAS pain score would predict both VAS pain score and ASES Function score 1 year after surgery. Specifically, we hypothesized that a higher preoperative VR-12 MCS score would predict less pain and better function 1 year after surgery and that a higher preoperative VAS pain score would predict more pain and worse function 1 year after surgery.

Methods

This study was approved by the Institutional Review Board of Partners Healthcare. The study used the Surgical Outcome System (Arthrex), a comprehensive prospective database that stores preoperative and 1-year postoperative patient demographics and TSA-PROM data. Surveys are emailed to all enrolled patients before surgery and 1 year after surgery. As indicated by the Institutional Review Boards of all participating institutions, patients in the Surgical Outcome System have to sign a consent form to permit use of their responses in research.

The database includes patient data from 42 orthopedic surgeons across the United States. All primary TSAs and primary rTSAs in the database were included in this study, regardless of arthroplasty indication. Revisions were excluded. Also excluded were cases in which the 1-year postoperative questionnaire was not completed. Of the 1681 patients eligible for 1-year follow-up, 1225 (73%) completed the 1-year postoperative questionnaire. PROMs used in the study were VAS pain score, ASES Function score, VR-12 MCS score, and Single Assessment Numerical Evaluation (SANE). Unfortunately, not all surgeons use every measure in the 1-year postoperative questionnaire set. Thus, in our complete models, total number of observations was 1004 for modeling 1-year postoperative VAS pain scores and 986 for modeling 1-year postoperative ASES Function scores. 

Metrics

On VAS, pain is rated from 0 (no pain) to 10 (pain as bad as it can be). Tashjian and colleagues⁷ estimated that the minimal clinically important difference (MCID) for postoperative VAS pain scores was 1.4 in a cohort of 326 patients who had TSA, rTSA, or shoulder hemiarthroplasty. ASES Function score is scaled from 0 to 30, with 30 representing best function.⁸ Wong and colleagues⁹ identified an MCID of 6.5 for ASES Function scores in a cohort of 107 patients who had TSA or rTSA. SANE ratings range from 0% to 100%, with 100% indicating the patient’s shoulder was totally “normal.”10 VR-12 MCS scores appear on a logarithmic scale, with higher numbers representing better mental health. The population mean estimate for VR-12 MCS scores is 50.1 (SD, 11.49; overall possible range, –2.47 to 76.1).11 Our patient population’s scores ranged from 12.5 to 73.8.

Statistical Analysis

Simple bivariate and multivariate linear regressions were performed to evaluate the predictive value of each of the outlined PROMs. Our complete model controls for patient sex, age, and type of arthroplasty. Categorical variables were dummy-coded. Both 1-year postoperative VAS pain score and 1-year postoperative ASES Function score were investigated as dependent variables. Regression coefficients and P and ω² values are reported. Omega square represents how much of the variance in an outcome variable a model explains, like R², and ω² values can also be calculated for individual factors to see how much variance a given factor accounts for. For a simple relative risk calculation, we divided our cohort into 3 equal-sized groups based on preoperative VR-12 MCS scores and compared the risk that patients with scores in the top third (better mental health) would end up below certain ASES Total scores with the risk of patients with scores in the bottom third (worse mental health). All statistical analyses were performed with Stata (StataCorp).

Results

Table 1 lists summary statistics for the population used in these models.

Predicting VAS Pain Score (Table 2)

Preoperative VAS pain score and VR-12 MCS score both predicted 1-year postoperative VAS pain score (P < .001). Preoperative ASES Function score did not predict pain 1 year after surgery. By contrast, higher preoperative VAS pain scores were associated with higher VAS pain scores 1 year after surgery. Higher preoperative VR-12 MCS scores were significantly associated with lower VAS pain scores 1 year after surgery, indicating that better preoperative mental health is significantly associated with better self-reported outcomes in terms of pain 1 year after surgery. These associations remained statistically significant when controlling for age at time of surgery, sex, and type of arthroplasty.

Preoperative VR-12 MCS score was more predictive of 1-year postoperative VAS pain score than preoperative VAS pain score was. In other words, preoperative VR-12 MCS score accounted for more variability in outcome for 1-year postoperative VAS pain score (2.4%; ω² = .023) than preoperative VAS pain score did (1.6%; ω² = .015). 

Predicting ASES Function Score (Table 3)

By contrast, preoperative VAS pain score did not predict 1-year postoperative ASES Function score. Preoperative ASES Function and VR-12 MCS scores both predicted 1-year postoperative ASES Function score (P < .001). Higher preoperative ASES Function scores were associated with higher 1-year postoperative ASES Function scores. In other words, reporting better shoulder function before surgery was associated with reporting better shoulder function after surgery.

An example gives a sense of the effect size associated with the coefficient for preoperative ASES Function score. Our model predicts that, compared with a patient who reports 5 points lower on preoperative ASES Function (which ranges from 0-30), a patient who reports 5 points higher will report on average about 1 point higher on 1-year postoperative ASES Function. As in the model for postoperative pain, these associations with preoperative function and mental health scores held when controlling for age, sex, and type of arthroplasty. 

As in the postoperative pain model, preoperative VR-12 MCS score was more predictive of 1-year postoperative ASES Function score than preoperative ASES Function score was. Preoperative VR-12 MCS score accounted for more of the variation that our model predicts (ω² = .029) than preoperative ASES Function score did (ω² = .020). We compared the risk that patients with high preoperative VR-12 MCS scores (top third of cohort) would end up with ASES Total scores below 70, below 80, or below 90 with the risk of patients with low preoperative VR-12 MCS scores (bottom third). Results appear in Table 4.

A significant part of the predictive ability of our model for postoperative ASES Function scores stems from the fact that a patient who undergoes rTSA (vs TSA) is predicted to have an ASES Function score 3.8 points lower 1 year after surgery (P < .001, ω² = .083). With type of arthroplasty controlled for, female sex is associated with an ASES Function score 1.6 points lower 1 year after surgery (P < .001, ω² = .016).

Preoperative SANE score did not predict 1-year postoperative VAS pain score or ASES Function score. In addition, when our complete model was run with 1-year postoperative SANE score as the dependent variable, preoperative SANE score did not predict 1-year postoperative SANE score. Our data provide no supporting evidence for the use of SANE scores for predictive modeling for shoulder arthroplasty.

Discussion

We prospectively gathered data to determine which factors would predict patient subjective outcomes of primary TSA and primary rTSA. We hypothesized that preoperative VR-12 MCS scores and preoperative VAS pain scores would predict postoperative pain and function as measured with those PROMs. Second, we hypothesized that better preoperative mental health (as measured with VR-12 MCS scores) would predict lower postoperative pain (VAS pain scores) and better postoperative function (ASES Function scores). Third, we hypothesized that higher preoperative pain (VAS pain scores) would correlate with higher postoperative pain (VAS pain scores) and worse postoperative function (ASES Function scores).

Our main goal is to provide patients and surgeons with a predictive model that generates insights into what patients can expect after surgery. This model is not intended to be a screening tool for operative indications, but a clinical tool for helping set expectations.

Our results showed that patients with more pain before surgery were more likely to have more pain 1 year after surgery—confirming the hypothesized relationship between pain before and after surgery. Contrary to the hypothesis, however, degree of pain before surgery was not associated with function 1 year after surgery. Our mental health hypothesis was confirmed: Patients with better preoperative mental health scores had on average less pain and better function 1 year after surgery. Not surprisingly, our model demonstrated that patients with better self-reported function before surgery had better self-reported function after surgery. Patient-reported function before surgery did not significantly affect how much pain the patient had 1 year after surgery. Although we did not hypothesize about the role of function in predicting 1-year outcomes, function is a significant factor to be considered when setting patient expectations regarding shoulder arthroplasty outcomes (Table 5).

Although the effect sizes of each analyzed factor are small, together our models for 1-year postoperative pain and function provide significant insight into patients’ likely outcomes 1 year after TSA and rTSA.

Many studies have found that mental health correlated with pain and function during recovery from orthopedic trauma.^12-18 For example, Wylie and colleagues¹⁹ found that preoperative mental health, as measured with the 36-Item Short Form Health Survey (SF-36) MCS score, predicted patient-reported pain and function in the setting of rotator cuff injury, regardless of treatment type (operative, nonoperative). Others have found that mental health may play a role in how patients report their pain and function on various PROMs.^20,21 Modalities for improving patients’ emotional health baseline may even become a preoperative requirement as the healthcare industry moves toward value-based medicine and collection of patient-related outcomes as part of reimbursement schemes. 

By contrast, some studies have found that preoperative mental health did not predict postoperative outcomes. For example, Kennedy and colleagues²² found that preoperative mental health (as measured with SF-36 MCS scores) did not predict functional outcome in patients with ankle arthritis treated with ankle arthroplasty or arthrodesis. Likewise, Styron and colleagues²³ found no correlation between preoperative mental health (SF-12 MCS scores) and postoperative mental health and function in TSA. Their findings contradict those of the present study and many other studies.^12-18 The contradiction in findings demonstrates the need for well-designed, sufficiently powered studies of the link between preoperative mental health and postoperative outcome. Our study, with its large sample and heterogeneous population, is a start.

Two other groups (Simmen and colleagues,¹⁸ Matsen and colleagues²⁴) have attempted to develop a model predicting outcomes of shoulder arthroplasty. Simmen and colleagues¹⁸ estimated the probability of “treatment success” 1 year after TSA. Their model had 4 factors predictive of patient outcomes. Previous shoulder surgery and age over 75 years were significantly associated with lower probability of success, whereas higher preoperative SF-36 MCS scores and higher preoperative DASH (Disabilities of the Arm, Shoulder, and Hand) Function scores were associated with higher probability of success. The authors deemed TSA successful if the patient achieved a Constant score of ≥80 out of 100. Their model predicts probability of TSA “success,” whereas our models predict particular outcome scores. Both their results and ours support the hypothesis that preoperative mental health and function scores can predict how well a patient fares after surgery. Simmen and colleagues18 based their model on a cohort of only 140 patients and reported a 33.6% success rate (47/140 surgeries).

Matsen and colleagues²⁴ used a 1-practice cohort of 337 patients who underwent different types of arthroplasties, including TSA, rTSA, hemiarthroplasty, and ream-and-run arthroplasty. Although their focus was not preoperative PROMs predicting postoperative PROMs, they used the Simple Shoulder Test (SST) baseline score as a predictive variable. They found that 6 baseline characteristics—American Society of Anesthesiologists class I, shoulder problem unrelated to work, no prior shoulder surgery, glenoid type other than A1, humeral head not superiorly displaced on anteroposterior radiograph, and lower baseline SST score—were statistically associated with better outcomes, and they developed a model driven by these characteristics. They urged other investigators to perform the same kind of analysis with larger patient populations from multiple practices. One of the strengths of our study is its large patient population. We collected data on 1004 patients for modeling 1-year postoperative VAS pain scores and 986 patients for modeling 1-year postoperative ASES Function scores.

Our study had several limitations. First, its data came from a 42-surgeon database, and there may be variations in how these surgeons enroll patients in the registry. If some surgeons did not enroll all their surgical patients, our sample could have been subject to selection bias. Second, in developing our model, we used only patient characteristics that were available in the database. On the other hand, the heterogeneity of the surgeon sample lended external validity to the model. A third limitation was that the model always predicts better pain and function outcomes after TSA than after rTSA. In other words, it does not consider whether TSA is appropriate for a particular patient. Instead, it predicts 1-year shoulder arthroplasty outcomes. 

Our goal here is not to provide outcomes information or a surgical screening tool, but to report on our use of a simple data-driven tool for setting expectations. When appropriate data become available, tools like this should be expanded to predict longer-term shoulder arthroplasty outcomes. We need more studies that combine preoperative PROMs, more baseline clinical and patient characteristics (following the Matsen and colleagues²⁴ model), and large sample sizes.

Conclusion

The educational models presented here can help patients and surgeons learn what to expect after surgery. These models reveal the value in collecting preoperative subjective PROMs and show how a quantitative tool can help facilitate shared decision-making and set patient expectations. Separately, the effect size of each factor is small, but together a patient’s preoperative VAS pain score, ASES Function score, VR-12 MCS score, age, sex, and type of arthroplasty can provide information predictive of the patient’s self-reported pain and function 1 year after surgery.

Take-Home Points

• Shared decision-making tools, such as predictive models, can help empower the patient to make decisions for or against surgery equipped with more information about the expected outcome.
• There is a role for preoperative collection of PROMs in the clinical decision-making process.
• Mental health state, as reported by the VR-12 MCS, is a significant predictor of postoperative pain and function as reported by the VAS pain and ASES function scores.
• A significant portion of the predictive ability of this model comes from the fact that at 1-year postoperatively, patients receiving a rTSA will on average have a 3.8 point lower on ASES function score than those receiving a TSA (P < .001, ω²=.083).
• Future studies to discern the role of different modalities to improve a patient’s emotional health preoperatively will be beneficial as the healthcare industry trends toward value based medicine collecting PROMs as part of reimbursement schemes.

Over the past few decades, decisions regarding patients’ care have gradually transitioned from a paternalistic model to a more cooperative exchange between patient and physician. Shared decision-making provides patients a measure of autonomy in making choices for their health and their future. Patient participation may mitigate uncertainty and discomfort during selection of a course of treatment, which may lead to increased satisfaction levels after surgery.¹ Moreover, shared decision-making may help patients better manage postoperative expectations through evidenced-based discussions of preoperative health levels and their corresponding outcomes. Patient-reported outcome measures (PROMs) use clinically sensitive and specific metrics to evaluate a patient’s self-reported pain, functional ability, and mental state.² These metrics are useful in setting patient expectations for potential outcomes of treatment options. Use of evidence-based clinical decision-making tools, such as PROM-based predictive models, can facilitate a collaborative decision-making environment for patient and physician. Given the present cost-containment era, and the need for preoperative metrics that can assist in predictive analysis of postoperative improvement, models are clearly valuable.

In attempts to help patients set well-informed and reasonable expectations, physicians have turned to PROMs to facilitate preoperative evidence-based discussions. Although PROMs have been in use for almost 30 years, only recently have they been used to create tools that can aid quantitatively in the surgical decision-making process.^2-6 Combining physical examination findings, imaging studies, comorbidities, and quantitative tools, such as this model, may enhance patients’ understanding of their preoperative condition and expected prognosis and thereby guide their surgical decisions.

We conducted a study to determine whether certain preoperative PROMs can predict 1-year postoperative visual analog scale (VAS) pain scores and American Shoulder and Elbow Surgeons (ASES) Function scores in total shoulder arthroplasty (TSA) and reverse TSA (rTSA). We hypothesized that preoperative mental health status as captured by Veterans RAND 12-Item Health Survey (VR-12) mental health component summary (MCS) score and preoperative VAS pain score would predict both VAS pain score and ASES Function score 1 year after surgery. Specifically, we hypothesized that a higher preoperative VR-12 MCS score would predict less pain and better function 1 year after surgery and that a higher preoperative VAS pain score would predict more pain and worse function 1 year after surgery.

Methods

This study was approved by the Institutional Review Board of Partners Healthcare. The study used the Surgical Outcome System (Arthrex), a comprehensive prospective database that stores preoperative and 1-year postoperative patient demographics and TSA-PROM data. Surveys are emailed to all enrolled patients before surgery and 1 year after surgery. As indicated by the Institutional Review Boards of all participating institutions, patients in the Surgical Outcome System have to sign a consent form to permit use of their responses in research.

The database includes patient data from 42 orthopedic surgeons across the United States. All primary TSAs and primary rTSAs in the database were included in this study, regardless of arthroplasty indication. Revisions were excluded. Also excluded were cases in which the 1-year postoperative questionnaire was not completed. Of the 1681 patients eligible for 1-year follow-up, 1225 (73%) completed the 1-year postoperative questionnaire. PROMs used in the study were VAS pain score, ASES Function score, VR-12 MCS score, and Single Assessment Numerical Evaluation (SANE). Unfortunately, not all surgeons use every measure in the 1-year postoperative questionnaire set. Thus, in our complete models, total number of observations was 1004 for modeling 1-year postoperative VAS pain scores and 986 for modeling 1-year postoperative ASES Function scores. 

Metrics

On VAS, pain is rated from 0 (no pain) to 10 (pain as bad as it can be). Tashjian and colleagues⁷ estimated that the minimal clinically important difference (MCID) for postoperative VAS pain scores was 1.4 in a cohort of 326 patients who had TSA, rTSA, or shoulder hemiarthroplasty. ASES Function score is scaled from 0 to 30, with 30 representing best function.⁸ Wong and colleagues⁹ identified an MCID of 6.5 for ASES Function scores in a cohort of 107 patients who had TSA or rTSA. SANE ratings range from 0% to 100%, with 100% indicating the patient’s shoulder was totally “normal.”10 VR-12 MCS scores appear on a logarithmic scale, with higher numbers representing better mental health. The population mean estimate for VR-12 MCS scores is 50.1 (SD, 11.49; overall possible range, –2.47 to 76.1).11 Our patient population’s scores ranged from 12.5 to 73.8.

Statistical Analysis

Simple bivariate and multivariate linear regressions were performed to evaluate the predictive value of each of the outlined PROMs. Our complete model controls for patient sex, age, and type of arthroplasty. Categorical variables were dummy-coded. Both 1-year postoperative VAS pain score and 1-year postoperative ASES Function score were investigated as dependent variables. Regression coefficients and P and ω² values are reported. Omega square represents how much of the variance in an outcome variable a model explains, like R², and ω² values can also be calculated for individual factors to see how much variance a given factor accounts for. For a simple relative risk calculation, we divided our cohort into 3 equal-sized groups based on preoperative VR-12 MCS scores and compared the risk that patients with scores in the top third (better mental health) would end up below certain ASES Total scores with the risk of patients with scores in the bottom third (worse mental health). All statistical analyses were performed with Stata (StataCorp).

Results

Table 1 lists summary statistics for the population used in these models.

Predicting VAS Pain Score (Table 2)

Preoperative VAS pain score and VR-12 MCS score both predicted 1-year postoperative VAS pain score (P < .001). Preoperative ASES Function score did not predict pain 1 year after surgery. By contrast, higher preoperative VAS pain scores were associated with higher VAS pain scores 1 year after surgery. Higher preoperative VR-12 MCS scores were significantly associated with lower VAS pain scores 1 year after surgery, indicating that better preoperative mental health is significantly associated with better self-reported outcomes in terms of pain 1 year after surgery. These associations remained statistically significant when controlling for age at time of surgery, sex, and type of arthroplasty.

Preoperative VR-12 MCS score was more predictive of 1-year postoperative VAS pain score than preoperative VAS pain score was. In other words, preoperative VR-12 MCS score accounted for more variability in outcome for 1-year postoperative VAS pain score (2.4%; ω² = .023) than preoperative VAS pain score did (1.6%; ω² = .015). 

Predicting ASES Function Score (Table 3)

By contrast, preoperative VAS pain score did not predict 1-year postoperative ASES Function score. Preoperative ASES Function and VR-12 MCS scores both predicted 1-year postoperative ASES Function score (P < .001). Higher preoperative ASES Function scores were associated with higher 1-year postoperative ASES Function scores. In other words, reporting better shoulder function before surgery was associated with reporting better shoulder function after surgery.

An example gives a sense of the effect size associated with the coefficient for preoperative ASES Function score. Our model predicts that, compared with a patient who reports 5 points lower on preoperative ASES Function (which ranges from 0-30), a patient who reports 5 points higher will report on average about 1 point higher on 1-year postoperative ASES Function. As in the model for postoperative pain, these associations with preoperative function and mental health scores held when controlling for age, sex, and type of arthroplasty. 

As in the postoperative pain model, preoperative VR-12 MCS score was more predictive of 1-year postoperative ASES Function score than preoperative ASES Function score was. Preoperative VR-12 MCS score accounted for more of the variation that our model predicts (ω² = .029) than preoperative ASES Function score did (ω² = .020). We compared the risk that patients with high preoperative VR-12 MCS scores (top third of cohort) would end up with ASES Total scores below 70, below 80, or below 90 with the risk of patients with low preoperative VR-12 MCS scores (bottom third). Results appear in Table 4.

A significant part of the predictive ability of our model for postoperative ASES Function scores stems from the fact that a patient who undergoes rTSA (vs TSA) is predicted to have an ASES Function score 3.8 points lower 1 year after surgery (P < .001, ω² = .083). With type of arthroplasty controlled for, female sex is associated with an ASES Function score 1.6 points lower 1 year after surgery (P < .001, ω² = .016).

Preoperative SANE score did not predict 1-year postoperative VAS pain score or ASES Function score. In addition, when our complete model was run with 1-year postoperative SANE score as the dependent variable, preoperative SANE score did not predict 1-year postoperative SANE score. Our data provide no supporting evidence for the use of SANE scores for predictive modeling for shoulder arthroplasty.

Discussion

We prospectively gathered data to determine which factors would predict patient subjective outcomes of primary TSA and primary rTSA. We hypothesized that preoperative VR-12 MCS scores and preoperative VAS pain scores would predict postoperative pain and function as measured with those PROMs. Second, we hypothesized that better preoperative mental health (as measured with VR-12 MCS scores) would predict lower postoperative pain (VAS pain scores) and better postoperative function (ASES Function scores). Third, we hypothesized that higher preoperative pain (VAS pain scores) would correlate with higher postoperative pain (VAS pain scores) and worse postoperative function (ASES Function scores).

Our main goal is to provide patients and surgeons with a predictive model that generates insights into what patients can expect after surgery. This model is not intended to be a screening tool for operative indications, but a clinical tool for helping set expectations.

Our results showed that patients with more pain before surgery were more likely to have more pain 1 year after surgery—confirming the hypothesized relationship between pain before and after surgery. Contrary to the hypothesis, however, degree of pain before surgery was not associated with function 1 year after surgery. Our mental health hypothesis was confirmed: Patients with better preoperative mental health scores had on average less pain and better function 1 year after surgery. Not surprisingly, our model demonstrated that patients with better self-reported function before surgery had better self-reported function after surgery. Patient-reported function before surgery did not significantly affect how much pain the patient had 1 year after surgery. Although we did not hypothesize about the role of function in predicting 1-year outcomes, function is a significant factor to be considered when setting patient expectations regarding shoulder arthroplasty outcomes (Table 5).

Although the effect sizes of each analyzed factor are small, together our models for 1-year postoperative pain and function provide significant insight into patients’ likely outcomes 1 year after TSA and rTSA.

Many studies have found that mental health correlated with pain and function during recovery from orthopedic trauma.^12-18 For example, Wylie and colleagues¹⁹ found that preoperative mental health, as measured with the 36-Item Short Form Health Survey (SF-36) MCS score, predicted patient-reported pain and function in the setting of rotator cuff injury, regardless of treatment type (operative, nonoperative). Others have found that mental health may play a role in how patients report their pain and function on various PROMs.^20,21 Modalities for improving patients’ emotional health baseline may even become a preoperative requirement as the healthcare industry moves toward value-based medicine and collection of patient-related outcomes as part of reimbursement schemes. 

By contrast, some studies have found that preoperative mental health did not predict postoperative outcomes. For example, Kennedy and colleagues²² found that preoperative mental health (as measured with SF-36 MCS scores) did not predict functional outcome in patients with ankle arthritis treated with ankle arthroplasty or arthrodesis. Likewise, Styron and colleagues²³ found no correlation between preoperative mental health (SF-12 MCS scores) and postoperative mental health and function in TSA. Their findings contradict those of the present study and many other studies.^12-18 The contradiction in findings demonstrates the need for well-designed, sufficiently powered studies of the link between preoperative mental health and postoperative outcome. Our study, with its large sample and heterogeneous population, is a start.

Two other groups (Simmen and colleagues,¹⁸ Matsen and colleagues²⁴) have attempted to develop a model predicting outcomes of shoulder arthroplasty. Simmen and colleagues¹⁸ estimated the probability of “treatment success” 1 year after TSA. Their model had 4 factors predictive of patient outcomes. Previous shoulder surgery and age over 75 years were significantly associated with lower probability of success, whereas higher preoperative SF-36 MCS scores and higher preoperative DASH (Disabilities of the Arm, Shoulder, and Hand) Function scores were associated with higher probability of success. The authors deemed TSA successful if the patient achieved a Constant score of ≥80 out of 100. Their model predicts probability of TSA “success,” whereas our models predict particular outcome scores. Both their results and ours support the hypothesis that preoperative mental health and function scores can predict how well a patient fares after surgery. Simmen and colleagues18 based their model on a cohort of only 140 patients and reported a 33.6% success rate (47/140 surgeries).

Matsen and colleagues²⁴ used a 1-practice cohort of 337 patients who underwent different types of arthroplasties, including TSA, rTSA, hemiarthroplasty, and ream-and-run arthroplasty. Although their focus was not preoperative PROMs predicting postoperative PROMs, they used the Simple Shoulder Test (SST) baseline score as a predictive variable. They found that 6 baseline characteristics—American Society of Anesthesiologists class I, shoulder problem unrelated to work, no prior shoulder surgery, glenoid type other than A1, humeral head not superiorly displaced on anteroposterior radiograph, and lower baseline SST score—were statistically associated with better outcomes, and they developed a model driven by these characteristics. They urged other investigators to perform the same kind of analysis with larger patient populations from multiple practices. One of the strengths of our study is its large patient population. We collected data on 1004 patients for modeling 1-year postoperative VAS pain scores and 986 patients for modeling 1-year postoperative ASES Function scores.

Our study had several limitations. First, its data came from a 42-surgeon database, and there may be variations in how these surgeons enroll patients in the registry. If some surgeons did not enroll all their surgical patients, our sample could have been subject to selection bias. Second, in developing our model, we used only patient characteristics that were available in the database. On the other hand, the heterogeneity of the surgeon sample lended external validity to the model. A third limitation was that the model always predicts better pain and function outcomes after TSA than after rTSA. In other words, it does not consider whether TSA is appropriate for a particular patient. Instead, it predicts 1-year shoulder arthroplasty outcomes. 

Our goal here is not to provide outcomes information or a surgical screening tool, but to report on our use of a simple data-driven tool for setting expectations. When appropriate data become available, tools like this should be expanded to predict longer-term shoulder arthroplasty outcomes. We need more studies that combine preoperative PROMs, more baseline clinical and patient characteristics (following the Matsen and colleagues²⁴ model), and large sample sizes.

Conclusion

The educational models presented here can help patients and surgeons learn what to expect after surgery. These models reveal the value in collecting preoperative subjective PROMs and show how a quantitative tool can help facilitate shared decision-making and set patient expectations. Separately, the effect size of each factor is small, but together a patient’s preoperative VAS pain score, ASES Function score, VR-12 MCS score, age, sex, and type of arthroplasty can provide information predictive of the patient’s self-reported pain and function 1 year after surgery.

Take-Home Points

• Shared decision-making tools, such as predictive models, can help empower the patient to make decisions for or against surgery equipped with more information about the expected outcome.
• There is a role for preoperative collection of PROMs in the clinical decision-making process.
• Mental health state, as reported by the VR-12 MCS, is a significant predictor of postoperative pain and function as reported by the VAS pain and ASES function scores.
• A significant portion of the predictive ability of this model comes from the fact that at 1-year postoperatively, patients receiving a rTSA will on average have a 3.8 point lower on ASES function score than those receiving a TSA (P < .001, ω²=.083).
• Future studies to discern the role of different modalities to improve a patient’s emotional health preoperatively will be beneficial as the healthcare industry trends toward value based medicine collecting PROMs as part of reimbursement schemes.

Over the past few decades, decisions regarding patients’ care have gradually transitioned from a paternalistic model to a more cooperative exchange between patient and physician. Shared decision-making provides patients a measure of autonomy in making choices for their health and their future. Patient participation may mitigate uncertainty and discomfort during selection of a course of treatment, which may lead to increased satisfaction levels after surgery.¹ Moreover, shared decision-making may help patients better manage postoperative expectations through evidenced-based discussions of preoperative health levels and their corresponding outcomes. Patient-reported outcome measures (PROMs) use clinically sensitive and specific metrics to evaluate a patient’s self-reported pain, functional ability, and mental state.² These metrics are useful in setting patient expectations for potential outcomes of treatment options. Use of evidence-based clinical decision-making tools, such as PROM-based predictive models, can facilitate a collaborative decision-making environment for patient and physician. Given the present cost-containment era, and the need for preoperative metrics that can assist in predictive analysis of postoperative improvement, models are clearly valuable.

In attempts to help patients set well-informed and reasonable expectations, physicians have turned to PROMs to facilitate preoperative evidence-based discussions. Although PROMs have been in use for almost 30 years, only recently have they been used to create tools that can aid quantitatively in the surgical decision-making process.^2-6 Combining physical examination findings, imaging studies, comorbidities, and quantitative tools, such as this model, may enhance patients’ understanding of their preoperative condition and expected prognosis and thereby guide their surgical decisions.

We conducted a study to determine whether certain preoperative PROMs can predict 1-year postoperative visual analog scale (VAS) pain scores and American Shoulder and Elbow Surgeons (ASES) Function scores in total shoulder arthroplasty (TSA) and reverse TSA (rTSA). We hypothesized that preoperative mental health status as captured by Veterans RAND 12-Item Health Survey (VR-12) mental health component summary (MCS) score and preoperative VAS pain score would predict both VAS pain score and ASES Function score 1 year after surgery. Specifically, we hypothesized that a higher preoperative VR-12 MCS score would predict less pain and better function 1 year after surgery and that a higher preoperative VAS pain score would predict more pain and worse function 1 year after surgery.

Methods

This study was approved by the Institutional Review Board of Partners Healthcare. The study used the Surgical Outcome System (Arthrex), a comprehensive prospective database that stores preoperative and 1-year postoperative patient demographics and TSA-PROM data. Surveys are emailed to all enrolled patients before surgery and 1 year after surgery. As indicated by the Institutional Review Boards of all participating institutions, patients in the Surgical Outcome System have to sign a consent form to permit use of their responses in research.

The database includes patient data from 42 orthopedic surgeons across the United States. All primary TSAs and primary rTSAs in the database were included in this study, regardless of arthroplasty indication. Revisions were excluded. Also excluded were cases in which the 1-year postoperative questionnaire was not completed. Of the 1681 patients eligible for 1-year follow-up, 1225 (73%) completed the 1-year postoperative questionnaire. PROMs used in the study were VAS pain score, ASES Function score, VR-12 MCS score, and Single Assessment Numerical Evaluation (SANE). Unfortunately, not all surgeons use every measure in the 1-year postoperative questionnaire set. Thus, in our complete models, total number of observations was 1004 for modeling 1-year postoperative VAS pain scores and 986 for modeling 1-year postoperative ASES Function scores. 

Metrics

On VAS, pain is rated from 0 (no pain) to 10 (pain as bad as it can be). Tashjian and colleagues⁷ estimated that the minimal clinically important difference (MCID) for postoperative VAS pain scores was 1.4 in a cohort of 326 patients who had TSA, rTSA, or shoulder hemiarthroplasty. ASES Function score is scaled from 0 to 30, with 30 representing best function.⁸ Wong and colleagues⁹ identified an MCID of 6.5 for ASES Function scores in a cohort of 107 patients who had TSA or rTSA. SANE ratings range from 0% to 100%, with 100% indicating the patient’s shoulder was totally “normal.”10 VR-12 MCS scores appear on a logarithmic scale, with higher numbers representing better mental health. The population mean estimate for VR-12 MCS scores is 50.1 (SD, 11.49; overall possible range, –2.47 to 76.1).11 Our patient population’s scores ranged from 12.5 to 73.8.

Statistical Analysis

Simple bivariate and multivariate linear regressions were performed to evaluate the predictive value of each of the outlined PROMs. Our complete model controls for patient sex, age, and type of arthroplasty. Categorical variables were dummy-coded. Both 1-year postoperative VAS pain score and 1-year postoperative ASES Function score were investigated as dependent variables. Regression coefficients and P and ω² values are reported. Omega square represents how much of the variance in an outcome variable a model explains, like R², and ω² values can also be calculated for individual factors to see how much variance a given factor accounts for. For a simple relative risk calculation, we divided our cohort into 3 equal-sized groups based on preoperative VR-12 MCS scores and compared the risk that patients with scores in the top third (better mental health) would end up below certain ASES Total scores with the risk of patients with scores in the bottom third (worse mental health). All statistical analyses were performed with Stata (StataCorp).

Results

Table 1 lists summary statistics for the population used in these models.

Predicting VAS Pain Score (Table 2)

Preoperative VAS pain score and VR-12 MCS score both predicted 1-year postoperative VAS pain score (P < .001). Preoperative ASES Function score did not predict pain 1 year after surgery. By contrast, higher preoperative VAS pain scores were associated with higher VAS pain scores 1 year after surgery. Higher preoperative VR-12 MCS scores were significantly associated with lower VAS pain scores 1 year after surgery, indicating that better preoperative mental health is significantly associated with better self-reported outcomes in terms of pain 1 year after surgery. These associations remained statistically significant when controlling for age at time of surgery, sex, and type of arthroplasty.

Preoperative VR-12 MCS score was more predictive of 1-year postoperative VAS pain score than preoperative VAS pain score was. In other words, preoperative VR-12 MCS score accounted for more variability in outcome for 1-year postoperative VAS pain score (2.4%; ω² = .023) than preoperative VAS pain score did (1.6%; ω² = .015). 

Predicting ASES Function Score (Table 3)

By contrast, preoperative VAS pain score did not predict 1-year postoperative ASES Function score. Preoperative ASES Function and VR-12 MCS scores both predicted 1-year postoperative ASES Function score (P < .001). Higher preoperative ASES Function scores were associated with higher 1-year postoperative ASES Function scores. In other words, reporting better shoulder function before surgery was associated with reporting better shoulder function after surgery.

An example gives a sense of the effect size associated with the coefficient for preoperative ASES Function score. Our model predicts that, compared with a patient who reports 5 points lower on preoperative ASES Function (which ranges from 0-30), a patient who reports 5 points higher will report on average about 1 point higher on 1-year postoperative ASES Function. As in the model for postoperative pain, these associations with preoperative function and mental health scores held when controlling for age, sex, and type of arthroplasty. 

As in the postoperative pain model, preoperative VR-12 MCS score was more predictive of 1-year postoperative ASES Function score than preoperative ASES Function score was. Preoperative VR-12 MCS score accounted for more of the variation that our model predicts (ω² = .029) than preoperative ASES Function score did (ω² = .020). We compared the risk that patients with high preoperative VR-12 MCS scores (top third of cohort) would end up with ASES Total scores below 70, below 80, or below 90 with the risk of patients with low preoperative VR-12 MCS scores (bottom third). Results appear in Table 4.

A significant part of the predictive ability of our model for postoperative ASES Function scores stems from the fact that a patient who undergoes rTSA (vs TSA) is predicted to have an ASES Function score 3.8 points lower 1 year after surgery (P < .001, ω² = .083). With type of arthroplasty controlled for, female sex is associated with an ASES Function score 1.6 points lower 1 year after surgery (P < .001, ω² = .016).

Preoperative SANE score did not predict 1-year postoperative VAS pain score or ASES Function score. In addition, when our complete model was run with 1-year postoperative SANE score as the dependent variable, preoperative SANE score did not predict 1-year postoperative SANE score. Our data provide no supporting evidence for the use of SANE scores for predictive modeling for shoulder arthroplasty.

Discussion

We prospectively gathered data to determine which factors would predict patient subjective outcomes of primary TSA and primary rTSA. We hypothesized that preoperative VR-12 MCS scores and preoperative VAS pain scores would predict postoperative pain and function as measured with those PROMs. Second, we hypothesized that better preoperative mental health (as measured with VR-12 MCS scores) would predict lower postoperative pain (VAS pain scores) and better postoperative function (ASES Function scores). Third, we hypothesized that higher preoperative pain (VAS pain scores) would correlate with higher postoperative pain (VAS pain scores) and worse postoperative function (ASES Function scores).

Our main goal is to provide patients and surgeons with a predictive model that generates insights into what patients can expect after surgery. This model is not intended to be a screening tool for operative indications, but a clinical tool for helping set expectations.

Our results showed that patients with more pain before surgery were more likely to have more pain 1 year after surgery—confirming the hypothesized relationship between pain before and after surgery. Contrary to the hypothesis, however, degree of pain before surgery was not associated with function 1 year after surgery. Our mental health hypothesis was confirmed: Patients with better preoperative mental health scores had on average less pain and better function 1 year after surgery. Not surprisingly, our model demonstrated that patients with better self-reported function before surgery had better self-reported function after surgery. Patient-reported function before surgery did not significantly affect how much pain the patient had 1 year after surgery. Although we did not hypothesize about the role of function in predicting 1-year outcomes, function is a significant factor to be considered when setting patient expectations regarding shoulder arthroplasty outcomes (Table 5).

Although the effect sizes of each analyzed factor are small, together our models for 1-year postoperative pain and function provide significant insight into patients’ likely outcomes 1 year after TSA and rTSA.

Many studies have found that mental health correlated with pain and function during recovery from orthopedic trauma.^12-18 For example, Wylie and colleagues¹⁹ found that preoperative mental health, as measured with the 36-Item Short Form Health Survey (SF-36) MCS score, predicted patient-reported pain and function in the setting of rotator cuff injury, regardless of treatment type (operative, nonoperative). Others have found that mental health may play a role in how patients report their pain and function on various PROMs.^20,21 Modalities for improving patients’ emotional health baseline may even become a preoperative requirement as the healthcare industry moves toward value-based medicine and collection of patient-related outcomes as part of reimbursement schemes. 

By contrast, some studies have found that preoperative mental health did not predict postoperative outcomes. For example, Kennedy and colleagues²² found that preoperative mental health (as measured with SF-36 MCS scores) did not predict functional outcome in patients with ankle arthritis treated with ankle arthroplasty or arthrodesis. Likewise, Styron and colleagues²³ found no correlation between preoperative mental health (SF-12 MCS scores) and postoperative mental health and function in TSA. Their findings contradict those of the present study and many other studies.^12-18 The contradiction in findings demonstrates the need for well-designed, sufficiently powered studies of the link between preoperative mental health and postoperative outcome. Our study, with its large sample and heterogeneous population, is a start.

Two other groups (Simmen and colleagues,¹⁸ Matsen and colleagues²⁴) have attempted to develop a model predicting outcomes of shoulder arthroplasty. Simmen and colleagues¹⁸ estimated the probability of “treatment success” 1 year after TSA. Their model had 4 factors predictive of patient outcomes. Previous shoulder surgery and age over 75 years were significantly associated with lower probability of success, whereas higher preoperative SF-36 MCS scores and higher preoperative DASH (Disabilities of the Arm, Shoulder, and Hand) Function scores were associated with higher probability of success. The authors deemed TSA successful if the patient achieved a Constant score of ≥80 out of 100. Their model predicts probability of TSA “success,” whereas our models predict particular outcome scores. Both their results and ours support the hypothesis that preoperative mental health and function scores can predict how well a patient fares after surgery. Simmen and colleagues18 based their model on a cohort of only 140 patients and reported a 33.6% success rate (47/140 surgeries).

Matsen and colleagues²⁴ used a 1-practice cohort of 337 patients who underwent different types of arthroplasties, including TSA, rTSA, hemiarthroplasty, and ream-and-run arthroplasty. Although their focus was not preoperative PROMs predicting postoperative PROMs, they used the Simple Shoulder Test (SST) baseline score as a predictive variable. They found that 6 baseline characteristics—American Society of Anesthesiologists class I, shoulder problem unrelated to work, no prior shoulder surgery, glenoid type other than A1, humeral head not superiorly displaced on anteroposterior radiograph, and lower baseline SST score—were statistically associated with better outcomes, and they developed a model driven by these characteristics. They urged other investigators to perform the same kind of analysis with larger patient populations from multiple practices. One of the strengths of our study is its large patient population. We collected data on 1004 patients for modeling 1-year postoperative VAS pain scores and 986 patients for modeling 1-year postoperative ASES Function scores.

Our study had several limitations. First, its data came from a 42-surgeon database, and there may be variations in how these surgeons enroll patients in the registry. If some surgeons did not enroll all their surgical patients, our sample could have been subject to selection bias. Second, in developing our model, we used only patient characteristics that were available in the database. On the other hand, the heterogeneity of the surgeon sample lended external validity to the model. A third limitation was that the model always predicts better pain and function outcomes after TSA than after rTSA. In other words, it does not consider whether TSA is appropriate for a particular patient. Instead, it predicts 1-year shoulder arthroplasty outcomes. 

Our goal here is not to provide outcomes information or a surgical screening tool, but to report on our use of a simple data-driven tool for setting expectations. When appropriate data become available, tools like this should be expanded to predict longer-term shoulder arthroplasty outcomes. We need more studies that combine preoperative PROMs, more baseline clinical and patient characteristics (following the Matsen and colleagues²⁴ model), and large sample sizes.

Conclusion

The educational models presented here can help patients and surgeons learn what to expect after surgery. These models reveal the value in collecting preoperative subjective PROMs and show how a quantitative tool can help facilitate shared decision-making and set patient expectations. Separately, the effect size of each factor is small, but together a patient’s preoperative VAS pain score, ASES Function score, VR-12 MCS score, age, sex, and type of arthroplasty can provide information predictive of the patient’s self-reported pain and function 1 year after surgery.

Take-Home Points

• There is no difference in PROMs following knotless or knotted labral repair.
• Operative time is shorter for knotless compared to knotted glenoid labral tears.
• Knotless constructs may be more predictable than knotted constructs biomechanically.

Orthopedic surgeons often encounter labral pathology, and labral tears historically have required open techniques.^1-3 Arthroscopy allows for advanced visualization and treatment of shoulder lesions,^4,5 including anterior, posterior, and superior labrum anterior to posterior (SLAP) lesions.⁶

The goal of arthroscopic labral repair is to restore joint stability while maintaining range of motion. Arthroscopically repairing the labrum with suture anchors has become the standard technique, and several studies have reported satisfactory biomechanical and clinical results.^1,7-12 Surgeons traditionally have been required to tie knots for these anchors, but knot security varies significantly among experienced arthroscopic surgeons.¹³ In addition, knots can migrate,¹⁴ and bulky knots can cause chondral abrasion.^15,16 Several manufacturers have introduced knotless anchors for soft-tissue fixation.^15,17 The knotless technique provides a low-profile repair with potentially less operating time.⁸ These factors may warrant switching from knotted to knotless techniques if outcomes are clinically acceptable. However, few studies have compared knotted and knotless techniques for glenohumeral labral repair.^8,15,18-21

We conducted a study to compare the clinical results and operative times of knotless and knotted fixation of anterior and posterior glenohumeral labral repairs and SLAP repairs. We hypothesized there would be no difference in patient-reported outcome measures (PROMs) between knotted and knotless techniques.

Methods

We retrospectively evaluated data that had been prospectively collected between 2012 and 2016 in a Surgical Outcomes System (SOS; Arthrex) database. Participation in this registry is elective, and enrollment can occur on a case-by-case basis. The database stores data on basic demographics, PROMs, and operative time. Data for our specific analysis were available for surgeries performed by 115 different surgeons. Inclusion criteria included primary isolated arthroscopic anterior, isolated posterior, and isolated SLAP repair with completely knotted or completely knotless labral repair and minimum 1-year follow-up. Exclusion criteria included hybrid knotted–knotless repair, rotator cuff repair, revision surgery, open surgery, and lack of complete follow-up data.

SOS is a proprietary registry that allows for the collection of basic patient demographics, diagnostic and operative data, and PROMs. PROMs in the SOS shoulder arthroscopy module include Veterans RAND 12-Item Health Survey (VR-12) mental health and physical health component summary scores, visual analog scale (VAS) pain scores, and American Shoulder and Elbow Surgeons (ASES) scores. For this study, PROMs were reviewed before surgery and 6 and 12 months after surgery. In addition, operative times of all procedures were collected.

For the analysis, completely knotted and completely knotless techniques were compared for anterior repair, posterior repair, and SLAP repair. A t test was used to compare the techniques on PROMs, and χ2 test was used to evaluate proportion differences. Statistical significance was set at P < .05. 

Results

Anterior Labral Repairs

Of the 102 knotted anterior labral repairs that met the study criteria, 26 (25%) had minimum 1-year follow-up. Of the 122 knotless labral repairs, 33 (27%) had minimum 1-year follow-up. Seventy-five percent of knotted repairs and 80% of knotless repairs were performed in men. Mean (SD) age was 25.3 (11.7) years for the knotted group and 26.9 (10.6) years for the knotless group (P = .109). Anterior labral repairs did not differ in PROMs at any point (Table 1).

A mean of 2.8 anchors was used for knotted repairs, and a mean of 3.1 anchors was used for knotless repairs. Mean operative time was 75.8 minutes for knotted repairs and 67.5 minutes for knotless repairs. Mean (SD) time per anchor was 30.9 (13.9) minutes for knotted repairs and 25.6 (19.5) minutes for knotless repairs (P = .021). 

Posterior Labral Repairs

Of the 165 knotted posterior labral repairs that met the study criteria, 39 (29%) had minimum 1-year follow-up. Of the 229 knotless labral repairs, 56 (24%) had minimum 1-year follow-up. Eighty-five percent of knotted repairs and 74% of knotless repairs were performed in men. Mean (SD) age was 29.1 (12.0) years for the knotted group and 27.5 (11.9) years for the knotless group (P = .148). Posterior labral repairs did not differ in PROMs before surgery or 1 year after surgery; 6 months after surgery, these repairs differed only in ASES scores (Table 2). 

A mean of 3.6 anchors was used for knotted repairs, and a mean of 3.0 anchors was used for knotless repairs. Mean operative time was 67.0 minutes for knotted repairs and 43.1 minutes for knotless repairs. Mean (SD) time per anchor was 21.1 (10.7) minutes for knotted repairs and 17.5 (14.7) minutes for knotless repairs (P = .031).

SLAP Repairs

Of the 54 knotted SLAP repairs that met the study criteria, 24 (44%) had minimum 1-year follow-up. Of the 138 knotless SLAP repairs, 48 (35%) had minimum 1-year follow-up. Seventy-two percent of knotted repairs and 72% of knotless repairs were performed in men. Mean (SD) age was 32.1 (11.6) years for the knotted group and 35.0 (12.8) years for the knotless group (P = .246). SLAP repairs did not differ in PROMs at any point (Table 3).

A mean of 1.9 anchors was used for knotted repairs, and a mean of 2.1 anchors was used for knotless repairs. Mean operative time was 59.0 minutes for knotted repairs and 40.9 minutes for knotless repairs. Mean (SD) time per anchor was 36.6 (22.4) minutes for knotted repairs and 26.3 (14.0) minutes for knotless repairs (P = .080).

Discussion

Our hypothesis that there would be no difference in PROMs between knotted and knotless labral repairs was confirmed. Our findings are important because this study compared the gold standard of knotted suture anchor with the alternative knotless suture anchor in glenohumeral labral repair. These findings have several important implications for labral repair.

Knot tying traditionally has been used to achieve fixation with an anchor. Although simple in concept, knot tying can be challenging and its quality variable. Thal¹⁵ wrote that good-quality arthroscopic suture anchor repair is difficult to achieve because satisfactory knot tying requires significant practice with certain devices designed specifically for knot tying. Multiple surgeons have noted a significant learning curve associated with knot tying, and there is no agreement on which knot is superior.^22-26 Leedle and Miller¹⁷ even suggested that, because knot tying is difficult, tying knots arthroscopically can lead to knot failure. In their study, they concluded that the knot is consistently the weakest link in suture repair of an anterior labrum construct. In a controlled laboratory study, Hanypsiak and colleagues¹³ found considerable knot-strength variability among expert arthroscopists. Only 65 (18%) of 365 knots tied fell within 20% of the mean for ultimate load failure, and only 128 (36%) of 365 fell within 20% of the mean for clinical failure (3 mm of displacement). These data suggested expert arthroscopists were unable to tie 5 consecutive knots of the same type consistently. Even among experts, it seems, knot strength varies significantly, and knot-strength issues may affect the rates of labral repair failure.

Multiple authors have also reported that bulky knots can cause chondral abrasion or that knots can migrate.^25,27 Rhee and Ha²⁷ reported that, when another knot (eg, a half-hitch knot) is tied to prevent knot failure, the resulting overall knot can be too bulky for a limited space, and chondral abrasion can result. In addition, regardless of size, a knot can migrate and, in its new position, start rubbing against the head of the humerus. Kim and colleagues¹⁴ found that, even when a knot is placed away from the humeral head, migration and repeated contact with the head are possible. Park and colleagues²⁸ found that a significant number of knotted SLAP repairs required arthroscopic knot removal for relief of knot-induced pain and clicking.

Knotless constructs have several theoretical advantages over knotted constructs. Compared with a knotted technique, a knotless technique appears to provide more predictable strength, as variability in knot tying is eliminated (unpublished data). A knotless repair also has a lower profile,8 which should lead to less contact with the humeral head.¹⁹ Last, a knotless repair is more efficient—it takes less time to perform. In our study, operative time was reduced by a mean of 5.3 minutes per anchor for anterior labral repair. Assuming a mean of 3 anchors, this reduction equates to 16 minutes per case. Therefore, a surgeon who performs 25 labral repairs a year can save 6.7 hours a year. Reduced operative time benefits the patient (ie, lower risk of infection and other complications²⁹), the surgeon, and the healthcare system (ie, cost savings). Macario³⁰ found that operating room costs averaged $62 per minute (range, $22-$133 per minute). Therefore, saving 16 minutes per case could lead to saving $992 per case. In summary, a knotless technique appears to be clinically and financially advantageous as long as its results are the same as or better than those of a knotted technique.

A few other studies have compared knotted and knotless techniques. In a cadaveric study, Slabaugh and colleagues²⁰ found no difference in labral height between traditional and knotless suture anchors. Leedle and Miller¹⁷ found that knotless constructs are biomechanically stronger than knotted constructs in anterior labral repair. In a level 3 clinical study, Yang and colleagues²¹ compared a conventional vertical knot with a knotless horizontal mattress suture in 41 patients who underwent SLAP repair. Functional outcome was no different between the 2 groups, but postoperative range of motion was improved in the knotless group. Ng and Kumar³¹ compared 45 patients who had knotted Bankart repair with 42 patients who had knotless Bankart repair and found no difference in functional outcome or rate of recurrent dislocation. Similarly, Kocaoglu and colleagues²² found no difference in recurrence rate between 18 patients who underwent a knotted technique for arthroscopic Bankart repair and 20 patients who underwent a knotless technique. Our findings corroborate the findings of these studies and further support the idea that there is no difference between knotted and knotless constructs with respect to PROMs.

Study Limitations

The major strength of this study was its large cohort and large population of surgeons. However, there were several study limitations. First, we could not detail specific repair techniques, such as simple or horizontal mattress orientation, and rehabilitation protocols and other variables are likely as well. Second, the repair technique was not randomized, and therefore there may have been a selection bias based on tissue quality. Although we cannot prove no bias, we think it was unlikely given that the groups were similar in age. Third, our data did not include information on range of motion or recurrent instability. Our goal was simply to evaluate PROMs among multiple surgeons using the 2 techniques. Fourth, there was substantial follow-up loss, which introduced potential selection bias. Last, there may have been conditions under which a hybrid technique with inferior knot tying, combined with a hybrid knotless construct, could have proved advantageous.

Conclusion

Our data showed that the advantages of knotless repair are not compromised in clinical situations. Although the data showed no significant difference in clinical outcomes, knotless repairs may provide surgeons with shorter surgeries, simpler constructs, less potential for chondral damage, and more consistent suture tensioning. Additional studies may further confirm these results.

Take-Home Points

• There is no difference in PROMs following knotless or knotted labral repair.
• Operative time is shorter for knotless compared to knotted glenoid labral tears.
• Knotless constructs may be more predictable than knotted constructs biomechanically.

Orthopedic surgeons often encounter labral pathology, and labral tears historically have required open techniques.^1-3 Arthroscopy allows for advanced visualization and treatment of shoulder lesions,^4,5 including anterior, posterior, and superior labrum anterior to posterior (SLAP) lesions.⁶

The goal of arthroscopic labral repair is to restore joint stability while maintaining range of motion. Arthroscopically repairing the labrum with suture anchors has become the standard technique, and several studies have reported satisfactory biomechanical and clinical results.^1,7-12 Surgeons traditionally have been required to tie knots for these anchors, but knot security varies significantly among experienced arthroscopic surgeons.¹³ In addition, knots can migrate,¹⁴ and bulky knots can cause chondral abrasion.^15,16 Several manufacturers have introduced knotless anchors for soft-tissue fixation.^15,17 The knotless technique provides a low-profile repair with potentially less operating time.⁸ These factors may warrant switching from knotted to knotless techniques if outcomes are clinically acceptable. However, few studies have compared knotted and knotless techniques for glenohumeral labral repair.^8,15,18-21

We conducted a study to compare the clinical results and operative times of knotless and knotted fixation of anterior and posterior glenohumeral labral repairs and SLAP repairs. We hypothesized there would be no difference in patient-reported outcome measures (PROMs) between knotted and knotless techniques.

Methods

We retrospectively evaluated data that had been prospectively collected between 2012 and 2016 in a Surgical Outcomes System (SOS; Arthrex) database. Participation in this registry is elective, and enrollment can occur on a case-by-case basis. The database stores data on basic demographics, PROMs, and operative time. Data for our specific analysis were available for surgeries performed by 115 different surgeons. Inclusion criteria included primary isolated arthroscopic anterior, isolated posterior, and isolated SLAP repair with completely knotted or completely knotless labral repair and minimum 1-year follow-up. Exclusion criteria included hybrid knotted–knotless repair, rotator cuff repair, revision surgery, open surgery, and lack of complete follow-up data.

SOS is a proprietary registry that allows for the collection of basic patient demographics, diagnostic and operative data, and PROMs. PROMs in the SOS shoulder arthroscopy module include Veterans RAND 12-Item Health Survey (VR-12) mental health and physical health component summary scores, visual analog scale (VAS) pain scores, and American Shoulder and Elbow Surgeons (ASES) scores. For this study, PROMs were reviewed before surgery and 6 and 12 months after surgery. In addition, operative times of all procedures were collected.

For the analysis, completely knotted and completely knotless techniques were compared for anterior repair, posterior repair, and SLAP repair. A t test was used to compare the techniques on PROMs, and χ2 test was used to evaluate proportion differences. Statistical significance was set at P < .05. 

Results

Anterior Labral Repairs

Of the 102 knotted anterior labral repairs that met the study criteria, 26 (25%) had minimum 1-year follow-up. Of the 122 knotless labral repairs, 33 (27%) had minimum 1-year follow-up. Seventy-five percent of knotted repairs and 80% of knotless repairs were performed in men. Mean (SD) age was 25.3 (11.7) years for the knotted group and 26.9 (10.6) years for the knotless group (P = .109). Anterior labral repairs did not differ in PROMs at any point (Table 1).

A mean of 2.8 anchors was used for knotted repairs, and a mean of 3.1 anchors was used for knotless repairs. Mean operative time was 75.8 minutes for knotted repairs and 67.5 minutes for knotless repairs. Mean (SD) time per anchor was 30.9 (13.9) minutes for knotted repairs and 25.6 (19.5) minutes for knotless repairs (P = .021). 

Posterior Labral Repairs

Of the 165 knotted posterior labral repairs that met the study criteria, 39 (29%) had minimum 1-year follow-up. Of the 229 knotless labral repairs, 56 (24%) had minimum 1-year follow-up. Eighty-five percent of knotted repairs and 74% of knotless repairs were performed in men. Mean (SD) age was 29.1 (12.0) years for the knotted group and 27.5 (11.9) years for the knotless group (P = .148). Posterior labral repairs did not differ in PROMs before surgery or 1 year after surgery; 6 months after surgery, these repairs differed only in ASES scores (Table 2). 

A mean of 3.6 anchors was used for knotted repairs, and a mean of 3.0 anchors was used for knotless repairs. Mean operative time was 67.0 minutes for knotted repairs and 43.1 minutes for knotless repairs. Mean (SD) time per anchor was 21.1 (10.7) minutes for knotted repairs and 17.5 (14.7) minutes for knotless repairs (P = .031).

SLAP Repairs

Of the 54 knotted SLAP repairs that met the study criteria, 24 (44%) had minimum 1-year follow-up. Of the 138 knotless SLAP repairs, 48 (35%) had minimum 1-year follow-up. Seventy-two percent of knotted repairs and 72% of knotless repairs were performed in men. Mean (SD) age was 32.1 (11.6) years for the knotted group and 35.0 (12.8) years for the knotless group (P = .246). SLAP repairs did not differ in PROMs at any point (Table 3).

A mean of 1.9 anchors was used for knotted repairs, and a mean of 2.1 anchors was used for knotless repairs. Mean operative time was 59.0 minutes for knotted repairs and 40.9 minutes for knotless repairs. Mean (SD) time per anchor was 36.6 (22.4) minutes for knotted repairs and 26.3 (14.0) minutes for knotless repairs (P = .080).

Discussion

Our hypothesis that there would be no difference in PROMs between knotted and knotless labral repairs was confirmed. Our findings are important because this study compared the gold standard of knotted suture anchor with the alternative knotless suture anchor in glenohumeral labral repair. These findings have several important implications for labral repair.

Knot tying traditionally has been used to achieve fixation with an anchor. Although simple in concept, knot tying can be challenging and its quality variable. Thal¹⁵ wrote that good-quality arthroscopic suture anchor repair is difficult to achieve because satisfactory knot tying requires significant practice with certain devices designed specifically for knot tying. Multiple surgeons have noted a significant learning curve associated with knot tying, and there is no agreement on which knot is superior.^22-26 Leedle and Miller¹⁷ even suggested that, because knot tying is difficult, tying knots arthroscopically can lead to knot failure. In their study, they concluded that the knot is consistently the weakest link in suture repair of an anterior labrum construct. In a controlled laboratory study, Hanypsiak and colleagues¹³ found considerable knot-strength variability among expert arthroscopists. Only 65 (18%) of 365 knots tied fell within 20% of the mean for ultimate load failure, and only 128 (36%) of 365 fell within 20% of the mean for clinical failure (3 mm of displacement). These data suggested expert arthroscopists were unable to tie 5 consecutive knots of the same type consistently. Even among experts, it seems, knot strength varies significantly, and knot-strength issues may affect the rates of labral repair failure.

Multiple authors have also reported that bulky knots can cause chondral abrasion or that knots can migrate.^25,27 Rhee and Ha²⁷ reported that, when another knot (eg, a half-hitch knot) is tied to prevent knot failure, the resulting overall knot can be too bulky for a limited space, and chondral abrasion can result. In addition, regardless of size, a knot can migrate and, in its new position, start rubbing against the head of the humerus. Kim and colleagues¹⁴ found that, even when a knot is placed away from the humeral head, migration and repeated contact with the head are possible. Park and colleagues²⁸ found that a significant number of knotted SLAP repairs required arthroscopic knot removal for relief of knot-induced pain and clicking.

Knotless constructs have several theoretical advantages over knotted constructs. Compared with a knotted technique, a knotless technique appears to provide more predictable strength, as variability in knot tying is eliminated (unpublished data). A knotless repair also has a lower profile,8 which should lead to less contact with the humeral head.¹⁹ Last, a knotless repair is more efficient—it takes less time to perform. In our study, operative time was reduced by a mean of 5.3 minutes per anchor for anterior labral repair. Assuming a mean of 3 anchors, this reduction equates to 16 minutes per case. Therefore, a surgeon who performs 25 labral repairs a year can save 6.7 hours a year. Reduced operative time benefits the patient (ie, lower risk of infection and other complications²⁹), the surgeon, and the healthcare system (ie, cost savings). Macario³⁰ found that operating room costs averaged $62 per minute (range, $22-$133 per minute). Therefore, saving 16 minutes per case could lead to saving $992 per case. In summary, a knotless technique appears to be clinically and financially advantageous as long as its results are the same as or better than those of a knotted technique.

A few other studies have compared knotted and knotless techniques. In a cadaveric study, Slabaugh and colleagues²⁰ found no difference in labral height between traditional and knotless suture anchors. Leedle and Miller¹⁷ found that knotless constructs are biomechanically stronger than knotted constructs in anterior labral repair. In a level 3 clinical study, Yang and colleagues²¹ compared a conventional vertical knot with a knotless horizontal mattress suture in 41 patients who underwent SLAP repair. Functional outcome was no different between the 2 groups, but postoperative range of motion was improved in the knotless group. Ng and Kumar³¹ compared 45 patients who had knotted Bankart repair with 42 patients who had knotless Bankart repair and found no difference in functional outcome or rate of recurrent dislocation. Similarly, Kocaoglu and colleagues²² found no difference in recurrence rate between 18 patients who underwent a knotted technique for arthroscopic Bankart repair and 20 patients who underwent a knotless technique. Our findings corroborate the findings of these studies and further support the idea that there is no difference between knotted and knotless constructs with respect to PROMs.

Study Limitations

The major strength of this study was its large cohort and large population of surgeons. However, there were several study limitations. First, we could not detail specific repair techniques, such as simple or horizontal mattress orientation, and rehabilitation protocols and other variables are likely as well. Second, the repair technique was not randomized, and therefore there may have been a selection bias based on tissue quality. Although we cannot prove no bias, we think it was unlikely given that the groups were similar in age. Third, our data did not include information on range of motion or recurrent instability. Our goal was simply to evaluate PROMs among multiple surgeons using the 2 techniques. Fourth, there was substantial follow-up loss, which introduced potential selection bias. Last, there may have been conditions under which a hybrid technique with inferior knot tying, combined with a hybrid knotless construct, could have proved advantageous.

Conclusion

Our data showed that the advantages of knotless repair are not compromised in clinical situations. Although the data showed no significant difference in clinical outcomes, knotless repairs may provide surgeons with shorter surgeries, simpler constructs, less potential for chondral damage, and more consistent suture tensioning. Additional studies may further confirm these results.

Take-Home Points

• There is no difference in PROMs following knotless or knotted labral repair.
• Operative time is shorter for knotless compared to knotted glenoid labral tears.
• Knotless constructs may be more predictable than knotted constructs biomechanically.

Orthopedic surgeons often encounter labral pathology, and labral tears historically have required open techniques.^1-3 Arthroscopy allows for advanced visualization and treatment of shoulder lesions,^4,5 including anterior, posterior, and superior labrum anterior to posterior (SLAP) lesions.⁶

The goal of arthroscopic labral repair is to restore joint stability while maintaining range of motion. Arthroscopically repairing the labrum with suture anchors has become the standard technique, and several studies have reported satisfactory biomechanical and clinical results.^1,7-12 Surgeons traditionally have been required to tie knots for these anchors, but knot security varies significantly among experienced arthroscopic surgeons.¹³ In addition, knots can migrate,¹⁴ and bulky knots can cause chondral abrasion.^15,16 Several manufacturers have introduced knotless anchors for soft-tissue fixation.^15,17 The knotless technique provides a low-profile repair with potentially less operating time.⁸ These factors may warrant switching from knotted to knotless techniques if outcomes are clinically acceptable. However, few studies have compared knotted and knotless techniques for glenohumeral labral repair.^8,15,18-21

We conducted a study to compare the clinical results and operative times of knotless and knotted fixation of anterior and posterior glenohumeral labral repairs and SLAP repairs. We hypothesized there would be no difference in patient-reported outcome measures (PROMs) between knotted and knotless techniques.

Methods

We retrospectively evaluated data that had been prospectively collected between 2012 and 2016 in a Surgical Outcomes System (SOS; Arthrex) database. Participation in this registry is elective, and enrollment can occur on a case-by-case basis. The database stores data on basic demographics, PROMs, and operative time. Data for our specific analysis were available for surgeries performed by 115 different surgeons. Inclusion criteria included primary isolated arthroscopic anterior, isolated posterior, and isolated SLAP repair with completely knotted or completely knotless labral repair and minimum 1-year follow-up. Exclusion criteria included hybrid knotted–knotless repair, rotator cuff repair, revision surgery, open surgery, and lack of complete follow-up data.

SOS is a proprietary registry that allows for the collection of basic patient demographics, diagnostic and operative data, and PROMs. PROMs in the SOS shoulder arthroscopy module include Veterans RAND 12-Item Health Survey (VR-12) mental health and physical health component summary scores, visual analog scale (VAS) pain scores, and American Shoulder and Elbow Surgeons (ASES) scores. For this study, PROMs were reviewed before surgery and 6 and 12 months after surgery. In addition, operative times of all procedures were collected.

For the analysis, completely knotted and completely knotless techniques were compared for anterior repair, posterior repair, and SLAP repair. A t test was used to compare the techniques on PROMs, and χ2 test was used to evaluate proportion differences. Statistical significance was set at P < .05. 

Results

Anterior Labral Repairs

Of the 102 knotted anterior labral repairs that met the study criteria, 26 (25%) had minimum 1-year follow-up. Of the 122 knotless labral repairs, 33 (27%) had minimum 1-year follow-up. Seventy-five percent of knotted repairs and 80% of knotless repairs were performed in men. Mean (SD) age was 25.3 (11.7) years for the knotted group and 26.9 (10.6) years for the knotless group (P = .109). Anterior labral repairs did not differ in PROMs at any point (Table 1).

A mean of 2.8 anchors was used for knotted repairs, and a mean of 3.1 anchors was used for knotless repairs. Mean operative time was 75.8 minutes for knotted repairs and 67.5 minutes for knotless repairs. Mean (SD) time per anchor was 30.9 (13.9) minutes for knotted repairs and 25.6 (19.5) minutes for knotless repairs (P = .021). 

Posterior Labral Repairs

Of the 165 knotted posterior labral repairs that met the study criteria, 39 (29%) had minimum 1-year follow-up. Of the 229 knotless labral repairs, 56 (24%) had minimum 1-year follow-up. Eighty-five percent of knotted repairs and 74% of knotless repairs were performed in men. Mean (SD) age was 29.1 (12.0) years for the knotted group and 27.5 (11.9) years for the knotless group (P = .148). Posterior labral repairs did not differ in PROMs before surgery or 1 year after surgery; 6 months after surgery, these repairs differed only in ASES scores (Table 2). 

A mean of 3.6 anchors was used for knotted repairs, and a mean of 3.0 anchors was used for knotless repairs. Mean operative time was 67.0 minutes for knotted repairs and 43.1 minutes for knotless repairs. Mean (SD) time per anchor was 21.1 (10.7) minutes for knotted repairs and 17.5 (14.7) minutes for knotless repairs (P = .031).

SLAP Repairs

Of the 54 knotted SLAP repairs that met the study criteria, 24 (44%) had minimum 1-year follow-up. Of the 138 knotless SLAP repairs, 48 (35%) had minimum 1-year follow-up. Seventy-two percent of knotted repairs and 72% of knotless repairs were performed in men. Mean (SD) age was 32.1 (11.6) years for the knotted group and 35.0 (12.8) years for the knotless group (P = .246). SLAP repairs did not differ in PROMs at any point (Table 3).

A mean of 1.9 anchors was used for knotted repairs, and a mean of 2.1 anchors was used for knotless repairs. Mean operative time was 59.0 minutes for knotted repairs and 40.9 minutes for knotless repairs. Mean (SD) time per anchor was 36.6 (22.4) minutes for knotted repairs and 26.3 (14.0) minutes for knotless repairs (P = .080).

Discussion

Our hypothesis that there would be no difference in PROMs between knotted and knotless labral repairs was confirmed. Our findings are important because this study compared the gold standard of knotted suture anchor with the alternative knotless suture anchor in glenohumeral labral repair. These findings have several important implications for labral repair.

Knot tying traditionally has been used to achieve fixation with an anchor. Although simple in concept, knot tying can be challenging and its quality variable. Thal¹⁵ wrote that good-quality arthroscopic suture anchor repair is difficult to achieve because satisfactory knot tying requires significant practice with certain devices designed specifically for knot tying. Multiple surgeons have noted a significant learning curve associated with knot tying, and there is no agreement on which knot is superior.^22-26 Leedle and Miller¹⁷ even suggested that, because knot tying is difficult, tying knots arthroscopically can lead to knot failure. In their study, they concluded that the knot is consistently the weakest link in suture repair of an anterior labrum construct. In a controlled laboratory study, Hanypsiak and colleagues¹³ found considerable knot-strength variability among expert arthroscopists. Only 65 (18%) of 365 knots tied fell within 20% of the mean for ultimate load failure, and only 128 (36%) of 365 fell within 20% of the mean for clinical failure (3 mm of displacement). These data suggested expert arthroscopists were unable to tie 5 consecutive knots of the same type consistently. Even among experts, it seems, knot strength varies significantly, and knot-strength issues may affect the rates of labral repair failure.

Multiple authors have also reported that bulky knots can cause chondral abrasion or that knots can migrate.^25,27 Rhee and Ha²⁷ reported that, when another knot (eg, a half-hitch knot) is tied to prevent knot failure, the resulting overall knot can be too bulky for a limited space, and chondral abrasion can result. In addition, regardless of size, a knot can migrate and, in its new position, start rubbing against the head of the humerus. Kim and colleagues¹⁴ found that, even when a knot is placed away from the humeral head, migration and repeated contact with the head are possible. Park and colleagues²⁸ found that a significant number of knotted SLAP repairs required arthroscopic knot removal for relief of knot-induced pain and clicking.

Knotless constructs have several theoretical advantages over knotted constructs. Compared with a knotted technique, a knotless technique appears to provide more predictable strength, as variability in knot tying is eliminated (unpublished data). A knotless repair also has a lower profile,8 which should lead to less contact with the humeral head.¹⁹ Last, a knotless repair is more efficient—it takes less time to perform. In our study, operative time was reduced by a mean of 5.3 minutes per anchor for anterior labral repair. Assuming a mean of 3 anchors, this reduction equates to 16 minutes per case. Therefore, a surgeon who performs 25 labral repairs a year can save 6.7 hours a year. Reduced operative time benefits the patient (ie, lower risk of infection and other complications²⁹), the surgeon, and the healthcare system (ie, cost savings). Macario³⁰ found that operating room costs averaged $62 per minute (range, $22-$133 per minute). Therefore, saving 16 minutes per case could lead to saving $992 per case. In summary, a knotless technique appears to be clinically and financially advantageous as long as its results are the same as or better than those of a knotted technique.

A few other studies have compared knotted and knotless techniques. In a cadaveric study, Slabaugh and colleagues²⁰ found no difference in labral height between traditional and knotless suture anchors. Leedle and Miller¹⁷ found that knotless constructs are biomechanically stronger than knotted constructs in anterior labral repair. In a level 3 clinical study, Yang and colleagues²¹ compared a conventional vertical knot with a knotless horizontal mattress suture in 41 patients who underwent SLAP repair. Functional outcome was no different between the 2 groups, but postoperative range of motion was improved in the knotless group. Ng and Kumar³¹ compared 45 patients who had knotted Bankart repair with 42 patients who had knotless Bankart repair and found no difference in functional outcome or rate of recurrent dislocation. Similarly, Kocaoglu and colleagues²² found no difference in recurrence rate between 18 patients who underwent a knotted technique for arthroscopic Bankart repair and 20 patients who underwent a knotless technique. Our findings corroborate the findings of these studies and further support the idea that there is no difference between knotted and knotless constructs with respect to PROMs.

Study Limitations

The major strength of this study was its large cohort and large population of surgeons. However, there were several study limitations. First, we could not detail specific repair techniques, such as simple or horizontal mattress orientation, and rehabilitation protocols and other variables are likely as well. Second, the repair technique was not randomized, and therefore there may have been a selection bias based on tissue quality. Although we cannot prove no bias, we think it was unlikely given that the groups were similar in age. Third, our data did not include information on range of motion or recurrent instability. Our goal was simply to evaluate PROMs among multiple surgeons using the 2 techniques. Fourth, there was substantial follow-up loss, which introduced potential selection bias. Last, there may have been conditions under which a hybrid technique with inferior knot tying, combined with a hybrid knotless construct, could have proved advantageous.

Conclusion

Our data showed that the advantages of knotless repair are not compromised in clinical situations. Although the data showed no significant difference in clinical outcomes, knotless repairs may provide surgeons with shorter surgeries, simpler constructs, less potential for chondral damage, and more consistent suture tensioning. Additional studies may further confirm these results.

Take-Home Points

• Systematic use of PROMs allows physicians to review data on pain, physical function, and psychological status to aid in clinical decision-making and best practices.
• PROMs should include both general outcome measures (VAS, SF-36, or EQ-5D) and reliable, valid, and responsive disease specific measures.
• PROM questionnaires should collect pertinent information while limiting the length to maximize patient compliance and reliability.
• PROMIS has been developed to standardize questionnaires, but generality for specific orthopedic procedures may result in less effective measures.
• PROMs can also be used for predictive modeling, which has the potential to help develop more cost-effective care and predict expected outcomes and recovery trajectories for individual patients.

Owing to their unique ability to recognize patients as stakeholders in their own healthcare, patient-reported outcome measures (PROMs) are becoming increasingly popular in the assessment of medical and surgical outcomes.¹ PROMs are an outcome measures subset in which patients complete questionnaires about their perceptions of their overall health status and specific health limitations. By systematically using PROMs before and after a clearly defined episode of care, clinicians can collect data on perceived pain level, physical function, and psychological status and use the data to validate use of surgical procedures and shape clinical decisions about best practices.^2-4 Although mortality and morbidity rates and other traditional measures are valuable in assessing outcomes, they do not represent or communicate the larger impact of an episode of care. As many orthopedic procedures are elective, and some are low-risk, the evaluation of changes in quality of life and self-reported functional improvement is an important addition to morbidity and mortality rates in capturing the true impact of a surgical procedure and recovery. The patient’s preoperative and postoperative perspectives on his or her health status have become important as well; our healthcare system has been placing more emphasis on patient-centered quality care.^2,5

Although PROMs have many benefits, implementation in an orthopedic surgery practice has its challenges. With so many PROMs available, selecting those that fit the patient population for a specialized orthopedic surgery practice can be difficult. In addition, although PROM data are essential for research and for measuring individual or institutional recovery trajectories for surgical procedures, in a busy practice getting patients to provide these data can be difficult.

PROMs are heavily used for outcomes assessment in the orthopedics literature, but there are few resources for orthopedic surgeons who want to implement PROMs in their practices. In this article, we review the literature on the challenges of effectively implementing PROMs in an orthopedic surgery practice.

PROM Selection Considerations

PROMs can be categorized as either generic or disease-specific,⁴ but together they are used to adequately capture the impact, both broad and local, of an orthopedic condition.

Generic Outcome Measures

Generic outcome measures apply to a range of subspecialties or anatomical regions, allowing for evaluation of a patient’s overall health or quality of life. The most widely accepted measure of pain is the visual analog scale (VAS). The VAS for pain quantifies the level of pain a patient experiences at a given time on a graphic sliding scale from 0 (no pain) to 10 (worst possible pain). The VAS is used in clinical evaluation of pain and in reported outcomes literature.^6,7

Many generic PROMs assess mental health status in addition to physical limitations. Poor preoperative mental health status has been recognized as a predictor of worse outcomes across a variety of orthopedic procedures.^8,9 Therefore, to assess the overall influence of an orthopedic condition, it is important to include at least 1 generic PROM that assesses mental health status before and after an episode of care. Generic PROMs commonly used in orthopedic surgery include the 36-Item Short Form Health Survey (SF-36), the shorter SF-12, the Veterans RAND 12-Item Health Survey (VR-12), the World Health Organization Disability Assessment Schedule (WHODAS), the European Quality of Life-5 Dimensions (EQ-5D) index, and the 10-item Patient-Reported Outcomes Measurement Information System Global Health (PROMIS-10) scale.^10-14

Some generic outcome measures (eg, the EQ-5D index) offer the “utility” calculation, which represents a preference for a patient’s desired health status. Such utilities allow for a measurement of quality of life, represented by quality-adjusted life years (QALY), which is a standardized measure of disease burden. Calculated QALY from measures such as the EQ-5D can be used in cost-effectiveness analyses of surgical interventions and have been used to validate use of procedures, particularly in arthroplasty.^15-17

Disease-Specific Outcome Measures

Likewise, there is a range of disease-specific PROMs validated for use in orthopedic surgery, and providers select PROMs that fit their scope of practice. In anatomical regions such as the knee, hip, and shoulder, disease-specific outcome measures vary significantly by subspecialty and patient population. When selecting disease-specific PROMs, providers must consider tools such as reliability, validity, responsiveness, and available population norms. One study used Evaluating Measures of Patient-Reported Outcomes (EMPRO) to assess the quality of a PROM in shoulders and concluded that the American Shoulder and Elbow Surgeons (ASES) index, the Simple Shoulder Test (SST), and the Oxford Shoulder Score (OSS) were all supported for use in practice.¹⁸ It is important to note that reliability, validity, and responsiveness of a PROM may vary with the diagnosis or the patient population studied. For example, the SST was found to be responsive in assessing rotator cuff injury but not as useful in assessing shoulder instability or arthritis.¹⁹ Variable responsiveness highlights the need for a diagnosis-based level of PROM customization. For example, patients who undergo a surgical intervention for shoulder instability are given a customized survey, which includes PROMs specific to their condition, such as the Western Ontario Shoulder Instability (WOSI) index.²⁰ For patients with knee instability, similar considerations apply; specific measures such as the Lysholm score and the Tenger Activity Scale capture the impact of injury in physically demanding activities.²¹ When selecting disease-specific PROMs, providers should consult articles like those by Davidson and Keating²² and Bent and colleagues,²³ who present provider-friendly tools that can be used to examine the effectiveness of a PROM, and provide additional background information on selecting disease-specific PROMs. For hip and knee arthroplasty subspecialties, the International Society of Arthroplasty Registries (ISAR) created a working group that determines best practices for PROM collection and identifies PROMs most commonly reported in arthroplasty.²⁴

Questionnaire Length Considerations

When PROMs are used in a practice, a balance must be struck between gathering enough information to determine functionality and limiting the patient burden of questionnaire length. A decision to use several PROMs all at once, at a single data collection point, can lengthen the questionnaire significantly. One study found that, with use of longer questionnaires, patients may lose interest, resulting in decreased reliability and compliance.²⁵ For example, providers who use the long (42-item) Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire to assess knee function are often limited in what other PROMs they may administer at the same time. Efforts to shorten this questionnaire while still capturing necessary information led to the development of the 7-item KOOS Jr, which was validated for use in knee arthroplasty and had its 7 items drawn from the original 42.²⁶ Similarly, the 40-item Hip Disability and Osteoarthritis Outcome Score (HOOS) questionnaire was shortened to the 6-item HOOS Jr, which was validated for use in hip arthroplasty,²⁷ and the generic SF-36 was shortened to the SF-12.¹¹ Providers trying to build an outcomes database while minimizing patient burden should consider using the shorter versions of these questionnaires but should also consider their validity, as KOOS Jr and HOOS Jr have been validated for use only in knee and hip arthroplasty and not in other knee and hip conditions.

PROM Data Collection Considerations

Comprehensive collection of longitudinal PROM data poses many challenges for providers and patients. For providers, the greatest challenges are infrastructure, technology, and the personnel needed to administer and store paper or electronic surveys. For patients, the most common survey completion barriers are questionnaire length, confusing or irrelevant content, and, in the case of some older adults, inability to complete surveys electronically.²⁵

Identifying a nonresponsive or noncompliant patient population is an important issue in collecting PROM data for research or other purposes. A study of factors associated with higher nonresponse rates in elective surgery patients (N = 135,474) found that noncompliance was higher for males, patients under age 55 years, nonwhites, patients in the lowest socioeconomic quintile, patients living alone, patients needing assistance in completing questionnaires, and patients who previously underwent surgery for their condition.²⁸ In a systematic review of methods that increased the response rates of postal and electronic surveys, Edwards and colleagues²⁹ found significantly higher odds of response for patients who were prenotified of the survey, given shorter questionnaires, or given a deadline for survey completion. Of note, response rates were lower when the word survey was used in the subject line of an email. 

PROM distribution has evolved with the rise of technological advances that allow for electronic survey distribution and data capture. Several studies have found that electronically administered PROMs have high response rates.^3,30,31 In a study of patients who underwent total hip arthroplasty, Rolfson and colleagues³² found that response rates were significantly higher for those who were surveyed on paper than for those surveyed over the internet. A randomized controlled study found that, compared with paper surveys, digital tablet surveys effectively and reliably collected PROM data; in addition, digital tablets provided instant data storage, and improved survey completion by requiring that all questions be answered before the survey could be submitted.33 However, age, race/ethnicity, and income disparities in technology use must be considered when administering internet-based follow-up surveys and analyzing data collected with web-based methods.³⁴ A study of total joint arthroplasty candidates found that several groups were less likely to complete electronic PROM questionnaires: patients over age 75 years, Hispanic or black patients, patients with Medicare or Medicaid, patients who previously underwent orthopedic surgery, patients undergoing revision total joint arthroplasty, patients with other comorbidities, and patients whose primary language was not English.³⁵ Providers interested in implementing PROMs must consider their patient population when selecting a method for survey distribution and follow-up. A study found that a majority of PROMs were written at a level many patients may not have understood, because of their literacy level or age; this lack of understanding created a barrier to compliance in many patient populations.³⁶

PROM Limitations and PROMIS Use

Use of PROMs has its limitations. The large variety of PROMs available for use in orthopedic surgery has led to several standardization initiatives. The National Institutes of Health funded the development of PROMIS, a person-centered measures database that evaluates and monitors the physical, social, and emotional health of adults and children.³⁷ The goal of PROMIS is to develop a standardized method of selecting PROMs, so that all medical disciplines and subspecialties can choose an applicable set of questions from the PROMIS question bank and use it in practice. Orthopedic surgery can use questions pertaining to physical functioning of the lower and upper extremities as well as quality of life and mental health. PROMIS physical function questions have been validated for use in several areas of orthopedic surgery.^38-40 A disadvantage of PROMIS is the overgenerality of its questions, which may not be as effective in capturing the implications of specific diagnoses. For example, it is difficult to use generalized questions to determine the implications of a diagnosis such as shoulder instability, which may affect only higher functioning activities or sports. More research on best PROM selection practices is needed in order to either standardize PROMs or move toward use of a single database such as PROMIS.

Future Directions in PROM Applications

PROMs are being used for research and patient engagement, but there are many other applications on the horizon. As already mentioned, predictive modeling is of particular interest. The existence of vast collaborative PROM databases that capture a diverse patient population introduces the possibility of creating models capable of predicting a patient outcome and enhancing shared decision-making.³ Predicting good or excellent patient outcomes for specific patient populations may allow elimination of certain postoperative visits, thereby creating more cost-effective care and reducing the burden of unnecessary clinic visits for both patients and physicians.

As with other healthcare areas, PROM data collection technology is rapidly advancing. Not only has electronic technology almost entirely replaced paper-and-pencil collection methods, but a new method of outcome data collection has been developed: computerized adaptive testing (CAT). CAT uses item-response theory to minimize the number of questions patients must answer in order for validated and reliable outcome scores to be calculated. According to multiple studies, CAT used across several questionnaires has reliably assessed PROMs while minimizing floor and ceiling effects, eliminating irrelevant questions, and shortening survey completion time.^41-43

Besides becoming more patient-friendly and accessible across multiple interfaces (mobile devices and computers), PROMs are also beginning to be integrated into the electronic medical record, allowing easier access to information during chart reviews. Use of statistical and predictive modeling, as described by Chang,³ could give PROMs a role in clinical decision-making. Informing patients of their expected outcome and recovery trajectory—based on demographics, comorbidities, preoperative functional status, and other factors—could influence their decision to undergo surgical intervention. As Halawi and colleagues⁴⁴ pointed out, it is important to discuss patient expectations before surgery, as unrealistic ones can negatively affect outcomes and lead to dissatisfaction. With clinicians having ready access to statistics and models in patient charts, we may see a transformation in clinical practices and surgical decision-making.

Conclusion

PROMs offer many ways to improve research and clinical care in orthopedic surgery. However, implementing PROMs in practice is not without challenges. Interested orthopedic surgeons should select the PROMs that are most appropriate—reliable, validated, and responsive to their patient population. Electronic distribution of PROM questionnaires is effective and allows data to be stored on entry, but orthopedic surgeons must consider their patient population to ensure accurate data capture and compliance in longitudinal surveys. Proper implementation of PROMs in a practice can allow clinicians to formulate expectations for postoperative recovery and set reasonable postoperative goals while engaging patients in improving quality of care.

Take-Home Points

• Systematic use of PROMs allows physicians to review data on pain, physical function, and psychological status to aid in clinical decision-making and best practices.
• PROMs should include both general outcome measures (VAS, SF-36, or EQ-5D) and reliable, valid, and responsive disease specific measures.
• PROM questionnaires should collect pertinent information while limiting the length to maximize patient compliance and reliability.
• PROMIS has been developed to standardize questionnaires, but generality for specific orthopedic procedures may result in less effective measures.
• PROMs can also be used for predictive modeling, which has the potential to help develop more cost-effective care and predict expected outcomes and recovery trajectories for individual patients.

Owing to their unique ability to recognize patients as stakeholders in their own healthcare, patient-reported outcome measures (PROMs) are becoming increasingly popular in the assessment of medical and surgical outcomes.¹ PROMs are an outcome measures subset in which patients complete questionnaires about their perceptions of their overall health status and specific health limitations. By systematically using PROMs before and after a clearly defined episode of care, clinicians can collect data on perceived pain level, physical function, and psychological status and use the data to validate use of surgical procedures and shape clinical decisions about best practices.^2-4 Although mortality and morbidity rates and other traditional measures are valuable in assessing outcomes, they do not represent or communicate the larger impact of an episode of care. As many orthopedic procedures are elective, and some are low-risk, the evaluation of changes in quality of life and self-reported functional improvement is an important addition to morbidity and mortality rates in capturing the true impact of a surgical procedure and recovery. The patient’s preoperative and postoperative perspectives on his or her health status have become important as well; our healthcare system has been placing more emphasis on patient-centered quality care.^2,5

Although PROMs have many benefits, implementation in an orthopedic surgery practice has its challenges. With so many PROMs available, selecting those that fit the patient population for a specialized orthopedic surgery practice can be difficult. In addition, although PROM data are essential for research and for measuring individual or institutional recovery trajectories for surgical procedures, in a busy practice getting patients to provide these data can be difficult.

PROMs are heavily used for outcomes assessment in the orthopedics literature, but there are few resources for orthopedic surgeons who want to implement PROMs in their practices. In this article, we review the literature on the challenges of effectively implementing PROMs in an orthopedic surgery practice.

PROM Selection Considerations

PROMs can be categorized as either generic or disease-specific,⁴ but together they are used to adequately capture the impact, both broad and local, of an orthopedic condition.

Generic Outcome Measures

Generic outcome measures apply to a range of subspecialties or anatomical regions, allowing for evaluation of a patient’s overall health or quality of life. The most widely accepted measure of pain is the visual analog scale (VAS). The VAS for pain quantifies the level of pain a patient experiences at a given time on a graphic sliding scale from 0 (no pain) to 10 (worst possible pain). The VAS is used in clinical evaluation of pain and in reported outcomes literature.^6,7

Many generic PROMs assess mental health status in addition to physical limitations. Poor preoperative mental health status has been recognized as a predictor of worse outcomes across a variety of orthopedic procedures.^8,9 Therefore, to assess the overall influence of an orthopedic condition, it is important to include at least 1 generic PROM that assesses mental health status before and after an episode of care. Generic PROMs commonly used in orthopedic surgery include the 36-Item Short Form Health Survey (SF-36), the shorter SF-12, the Veterans RAND 12-Item Health Survey (VR-12), the World Health Organization Disability Assessment Schedule (WHODAS), the European Quality of Life-5 Dimensions (EQ-5D) index, and the 10-item Patient-Reported Outcomes Measurement Information System Global Health (PROMIS-10) scale.^10-14

Some generic outcome measures (eg, the EQ-5D index) offer the “utility” calculation, which represents a preference for a patient’s desired health status. Such utilities allow for a measurement of quality of life, represented by quality-adjusted life years (QALY), which is a standardized measure of disease burden. Calculated QALY from measures such as the EQ-5D can be used in cost-effectiveness analyses of surgical interventions and have been used to validate use of procedures, particularly in arthroplasty.^15-17

Disease-Specific Outcome Measures

Likewise, there is a range of disease-specific PROMs validated for use in orthopedic surgery, and providers select PROMs that fit their scope of practice. In anatomical regions such as the knee, hip, and shoulder, disease-specific outcome measures vary significantly by subspecialty and patient population. When selecting disease-specific PROMs, providers must consider tools such as reliability, validity, responsiveness, and available population norms. One study used Evaluating Measures of Patient-Reported Outcomes (EMPRO) to assess the quality of a PROM in shoulders and concluded that the American Shoulder and Elbow Surgeons (ASES) index, the Simple Shoulder Test (SST), and the Oxford Shoulder Score (OSS) were all supported for use in practice.¹⁸ It is important to note that reliability, validity, and responsiveness of a PROM may vary with the diagnosis or the patient population studied. For example, the SST was found to be responsive in assessing rotator cuff injury but not as useful in assessing shoulder instability or arthritis.¹⁹ Variable responsiveness highlights the need for a diagnosis-based level of PROM customization. For example, patients who undergo a surgical intervention for shoulder instability are given a customized survey, which includes PROMs specific to their condition, such as the Western Ontario Shoulder Instability (WOSI) index.²⁰ For patients with knee instability, similar considerations apply; specific measures such as the Lysholm score and the Tenger Activity Scale capture the impact of injury in physically demanding activities.²¹ When selecting disease-specific PROMs, providers should consult articles like those by Davidson and Keating²² and Bent and colleagues,²³ who present provider-friendly tools that can be used to examine the effectiveness of a PROM, and provide additional background information on selecting disease-specific PROMs. For hip and knee arthroplasty subspecialties, the International Society of Arthroplasty Registries (ISAR) created a working group that determines best practices for PROM collection and identifies PROMs most commonly reported in arthroplasty.²⁴

Questionnaire Length Considerations

When PROMs are used in a practice, a balance must be struck between gathering enough information to determine functionality and limiting the patient burden of questionnaire length. A decision to use several PROMs all at once, at a single data collection point, can lengthen the questionnaire significantly. One study found that, with use of longer questionnaires, patients may lose interest, resulting in decreased reliability and compliance.²⁵ For example, providers who use the long (42-item) Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire to assess knee function are often limited in what other PROMs they may administer at the same time. Efforts to shorten this questionnaire while still capturing necessary information led to the development of the 7-item KOOS Jr, which was validated for use in knee arthroplasty and had its 7 items drawn from the original 42.²⁶ Similarly, the 40-item Hip Disability and Osteoarthritis Outcome Score (HOOS) questionnaire was shortened to the 6-item HOOS Jr, which was validated for use in hip arthroplasty,²⁷ and the generic SF-36 was shortened to the SF-12.¹¹ Providers trying to build an outcomes database while minimizing patient burden should consider using the shorter versions of these questionnaires but should also consider their validity, as KOOS Jr and HOOS Jr have been validated for use only in knee and hip arthroplasty and not in other knee and hip conditions.

PROM Data Collection Considerations

Comprehensive collection of longitudinal PROM data poses many challenges for providers and patients. For providers, the greatest challenges are infrastructure, technology, and the personnel needed to administer and store paper or electronic surveys. For patients, the most common survey completion barriers are questionnaire length, confusing or irrelevant content, and, in the case of some older adults, inability to complete surveys electronically.²⁵

Identifying a nonresponsive or noncompliant patient population is an important issue in collecting PROM data for research or other purposes. A study of factors associated with higher nonresponse rates in elective surgery patients (N = 135,474) found that noncompliance was higher for males, patients under age 55 years, nonwhites, patients in the lowest socioeconomic quintile, patients living alone, patients needing assistance in completing questionnaires, and patients who previously underwent surgery for their condition.²⁸ In a systematic review of methods that increased the response rates of postal and electronic surveys, Edwards and colleagues²⁹ found significantly higher odds of response for patients who were prenotified of the survey, given shorter questionnaires, or given a deadline for survey completion. Of note, response rates were lower when the word survey was used in the subject line of an email. 

PROM distribution has evolved with the rise of technological advances that allow for electronic survey distribution and data capture. Several studies have found that electronically administered PROMs have high response rates.^3,30,31 In a study of patients who underwent total hip arthroplasty, Rolfson and colleagues³² found that response rates were significantly higher for those who were surveyed on paper than for those surveyed over the internet. A randomized controlled study found that, compared with paper surveys, digital tablet surveys effectively and reliably collected PROM data; in addition, digital tablets provided instant data storage, and improved survey completion by requiring that all questions be answered before the survey could be submitted.33 However, age, race/ethnicity, and income disparities in technology use must be considered when administering internet-based follow-up surveys and analyzing data collected with web-based methods.³⁴ A study of total joint arthroplasty candidates found that several groups were less likely to complete electronic PROM questionnaires: patients over age 75 years, Hispanic or black patients, patients with Medicare or Medicaid, patients who previously underwent orthopedic surgery, patients undergoing revision total joint arthroplasty, patients with other comorbidities, and patients whose primary language was not English.³⁵ Providers interested in implementing PROMs must consider their patient population when selecting a method for survey distribution and follow-up. A study found that a majority of PROMs were written at a level many patients may not have understood, because of their literacy level or age; this lack of understanding created a barrier to compliance in many patient populations.³⁶

PROM Limitations and PROMIS Use

Use of PROMs has its limitations. The large variety of PROMs available for use in orthopedic surgery has led to several standardization initiatives. The National Institutes of Health funded the development of PROMIS, a person-centered measures database that evaluates and monitors the physical, social, and emotional health of adults and children.³⁷ The goal of PROMIS is to develop a standardized method of selecting PROMs, so that all medical disciplines and subspecialties can choose an applicable set of questions from the PROMIS question bank and use it in practice. Orthopedic surgery can use questions pertaining to physical functioning of the lower and upper extremities as well as quality of life and mental health. PROMIS physical function questions have been validated for use in several areas of orthopedic surgery.^38-40 A disadvantage of PROMIS is the overgenerality of its questions, which may not be as effective in capturing the implications of specific diagnoses. For example, it is difficult to use generalized questions to determine the implications of a diagnosis such as shoulder instability, which may affect only higher functioning activities or sports. More research on best PROM selection practices is needed in order to either standardize PROMs or move toward use of a single database such as PROMIS.

Future Directions in PROM Applications

PROMs are being used for research and patient engagement, but there are many other applications on the horizon. As already mentioned, predictive modeling is of particular interest. The existence of vast collaborative PROM databases that capture a diverse patient population introduces the possibility of creating models capable of predicting a patient outcome and enhancing shared decision-making.³ Predicting good or excellent patient outcomes for specific patient populations may allow elimination of certain postoperative visits, thereby creating more cost-effective care and reducing the burden of unnecessary clinic visits for both patients and physicians.

As with other healthcare areas, PROM data collection technology is rapidly advancing. Not only has electronic technology almost entirely replaced paper-and-pencil collection methods, but a new method of outcome data collection has been developed: computerized adaptive testing (CAT). CAT uses item-response theory to minimize the number of questions patients must answer in order for validated and reliable outcome scores to be calculated. According to multiple studies, CAT used across several questionnaires has reliably assessed PROMs while minimizing floor and ceiling effects, eliminating irrelevant questions, and shortening survey completion time.^41-43

Besides becoming more patient-friendly and accessible across multiple interfaces (mobile devices and computers), PROMs are also beginning to be integrated into the electronic medical record, allowing easier access to information during chart reviews. Use of statistical and predictive modeling, as described by Chang,³ could give PROMs a role in clinical decision-making. Informing patients of their expected outcome and recovery trajectory—based on demographics, comorbidities, preoperative functional status, and other factors—could influence their decision to undergo surgical intervention. As Halawi and colleagues⁴⁴ pointed out, it is important to discuss patient expectations before surgery, as unrealistic ones can negatively affect outcomes and lead to dissatisfaction. With clinicians having ready access to statistics and models in patient charts, we may see a transformation in clinical practices and surgical decision-making.

Conclusion

PROMs offer many ways to improve research and clinical care in orthopedic surgery. However, implementing PROMs in practice is not without challenges. Interested orthopedic surgeons should select the PROMs that are most appropriate—reliable, validated, and responsive to their patient population. Electronic distribution of PROM questionnaires is effective and allows data to be stored on entry, but orthopedic surgeons must consider their patient population to ensure accurate data capture and compliance in longitudinal surveys. Proper implementation of PROMs in a practice can allow clinicians to formulate expectations for postoperative recovery and set reasonable postoperative goals while engaging patients in improving quality of care.

Take-Home Points

• Systematic use of PROMs allows physicians to review data on pain, physical function, and psychological status to aid in clinical decision-making and best practices.
• PROMs should include both general outcome measures (VAS, SF-36, or EQ-5D) and reliable, valid, and responsive disease specific measures.
• PROM questionnaires should collect pertinent information while limiting the length to maximize patient compliance and reliability.
• PROMIS has been developed to standardize questionnaires, but generality for specific orthopedic procedures may result in less effective measures.
• PROMs can also be used for predictive modeling, which has the potential to help develop more cost-effective care and predict expected outcomes and recovery trajectories for individual patients.

Owing to their unique ability to recognize patients as stakeholders in their own healthcare, patient-reported outcome measures (PROMs) are becoming increasingly popular in the assessment of medical and surgical outcomes.¹ PROMs are an outcome measures subset in which patients complete questionnaires about their perceptions of their overall health status and specific health limitations. By systematically using PROMs before and after a clearly defined episode of care, clinicians can collect data on perceived pain level, physical function, and psychological status and use the data to validate use of surgical procedures and shape clinical decisions about best practices.^2-4 Although mortality and morbidity rates and other traditional measures are valuable in assessing outcomes, they do not represent or communicate the larger impact of an episode of care. As many orthopedic procedures are elective, and some are low-risk, the evaluation of changes in quality of life and self-reported functional improvement is an important addition to morbidity and mortality rates in capturing the true impact of a surgical procedure and recovery. The patient’s preoperative and postoperative perspectives on his or her health status have become important as well; our healthcare system has been placing more emphasis on patient-centered quality care.^2,5

Although PROMs have many benefits, implementation in an orthopedic surgery practice has its challenges. With so many PROMs available, selecting those that fit the patient population for a specialized orthopedic surgery practice can be difficult. In addition, although PROM data are essential for research and for measuring individual or institutional recovery trajectories for surgical procedures, in a busy practice getting patients to provide these data can be difficult.

PROMs are heavily used for outcomes assessment in the orthopedics literature, but there are few resources for orthopedic surgeons who want to implement PROMs in their practices. In this article, we review the literature on the challenges of effectively implementing PROMs in an orthopedic surgery practice.

PROM Selection Considerations

PROMs can be categorized as either generic or disease-specific,⁴ but together they are used to adequately capture the impact, both broad and local, of an orthopedic condition.

Generic Outcome Measures

Generic outcome measures apply to a range of subspecialties or anatomical regions, allowing for evaluation of a patient’s overall health or quality of life. The most widely accepted measure of pain is the visual analog scale (VAS). The VAS for pain quantifies the level of pain a patient experiences at a given time on a graphic sliding scale from 0 (no pain) to 10 (worst possible pain). The VAS is used in clinical evaluation of pain and in reported outcomes literature.^6,7

Many generic PROMs assess mental health status in addition to physical limitations. Poor preoperative mental health status has been recognized as a predictor of worse outcomes across a variety of orthopedic procedures.^8,9 Therefore, to assess the overall influence of an orthopedic condition, it is important to include at least 1 generic PROM that assesses mental health status before and after an episode of care. Generic PROMs commonly used in orthopedic surgery include the 36-Item Short Form Health Survey (SF-36), the shorter SF-12, the Veterans RAND 12-Item Health Survey (VR-12), the World Health Organization Disability Assessment Schedule (WHODAS), the European Quality of Life-5 Dimensions (EQ-5D) index, and the 10-item Patient-Reported Outcomes Measurement Information System Global Health (PROMIS-10) scale.^10-14

Some generic outcome measures (eg, the EQ-5D index) offer the “utility” calculation, which represents a preference for a patient’s desired health status. Such utilities allow for a measurement of quality of life, represented by quality-adjusted life years (QALY), which is a standardized measure of disease burden. Calculated QALY from measures such as the EQ-5D can be used in cost-effectiveness analyses of surgical interventions and have been used to validate use of procedures, particularly in arthroplasty.^15-17

Disease-Specific Outcome Measures

Likewise, there is a range of disease-specific PROMs validated for use in orthopedic surgery, and providers select PROMs that fit their scope of practice. In anatomical regions such as the knee, hip, and shoulder, disease-specific outcome measures vary significantly by subspecialty and patient population. When selecting disease-specific PROMs, providers must consider tools such as reliability, validity, responsiveness, and available population norms. One study used Evaluating Measures of Patient-Reported Outcomes (EMPRO) to assess the quality of a PROM in shoulders and concluded that the American Shoulder and Elbow Surgeons (ASES) index, the Simple Shoulder Test (SST), and the Oxford Shoulder Score (OSS) were all supported for use in practice.¹⁸ It is important to note that reliability, validity, and responsiveness of a PROM may vary with the diagnosis or the patient population studied. For example, the SST was found to be responsive in assessing rotator cuff injury but not as useful in assessing shoulder instability or arthritis.¹⁹ Variable responsiveness highlights the need for a diagnosis-based level of PROM customization. For example, patients who undergo a surgical intervention for shoulder instability are given a customized survey, which includes PROMs specific to their condition, such as the Western Ontario Shoulder Instability (WOSI) index.²⁰ For patients with knee instability, similar considerations apply; specific measures such as the Lysholm score and the Tenger Activity Scale capture the impact of injury in physically demanding activities.²¹ When selecting disease-specific PROMs, providers should consult articles like those by Davidson and Keating²² and Bent and colleagues,²³ who present provider-friendly tools that can be used to examine the effectiveness of a PROM, and provide additional background information on selecting disease-specific PROMs. For hip and knee arthroplasty subspecialties, the International Society of Arthroplasty Registries (ISAR) created a working group that determines best practices for PROM collection and identifies PROMs most commonly reported in arthroplasty.²⁴

Questionnaire Length Considerations

When PROMs are used in a practice, a balance must be struck between gathering enough information to determine functionality and limiting the patient burden of questionnaire length. A decision to use several PROMs all at once, at a single data collection point, can lengthen the questionnaire significantly. One study found that, with use of longer questionnaires, patients may lose interest, resulting in decreased reliability and compliance.²⁵ For example, providers who use the long (42-item) Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire to assess knee function are often limited in what other PROMs they may administer at the same time. Efforts to shorten this questionnaire while still capturing necessary information led to the development of the 7-item KOOS Jr, which was validated for use in knee arthroplasty and had its 7 items drawn from the original 42.²⁶ Similarly, the 40-item Hip Disability and Osteoarthritis Outcome Score (HOOS) questionnaire was shortened to the 6-item HOOS Jr, which was validated for use in hip arthroplasty,²⁷ and the generic SF-36 was shortened to the SF-12.¹¹ Providers trying to build an outcomes database while minimizing patient burden should consider using the shorter versions of these questionnaires but should also consider their validity, as KOOS Jr and HOOS Jr have been validated for use only in knee and hip arthroplasty and not in other knee and hip conditions.

PROM Data Collection Considerations

Comprehensive collection of longitudinal PROM data poses many challenges for providers and patients. For providers, the greatest challenges are infrastructure, technology, and the personnel needed to administer and store paper or electronic surveys. For patients, the most common survey completion barriers are questionnaire length, confusing or irrelevant content, and, in the case of some older adults, inability to complete surveys electronically.²⁵

Identifying a nonresponsive or noncompliant patient population is an important issue in collecting PROM data for research or other purposes. A study of factors associated with higher nonresponse rates in elective surgery patients (N = 135,474) found that noncompliance was higher for males, patients under age 55 years, nonwhites, patients in the lowest socioeconomic quintile, patients living alone, patients needing assistance in completing questionnaires, and patients who previously underwent surgery for their condition.²⁸ In a systematic review of methods that increased the response rates of postal and electronic surveys, Edwards and colleagues²⁹ found significantly higher odds of response for patients who were prenotified of the survey, given shorter questionnaires, or given a deadline for survey completion. Of note, response rates were lower when the word survey was used in the subject line of an email. 

PROM distribution has evolved with the rise of technological advances that allow for electronic survey distribution and data capture. Several studies have found that electronically administered PROMs have high response rates.^3,30,31 In a study of patients who underwent total hip arthroplasty, Rolfson and colleagues³² found that response rates were significantly higher for those who were surveyed on paper than for those surveyed over the internet. A randomized controlled study found that, compared with paper surveys, digital tablet surveys effectively and reliably collected PROM data; in addition, digital tablets provided instant data storage, and improved survey completion by requiring that all questions be answered before the survey could be submitted.33 However, age, race/ethnicity, and income disparities in technology use must be considered when administering internet-based follow-up surveys and analyzing data collected with web-based methods.³⁴ A study of total joint arthroplasty candidates found that several groups were less likely to complete electronic PROM questionnaires: patients over age 75 years, Hispanic or black patients, patients with Medicare or Medicaid, patients who previously underwent orthopedic surgery, patients undergoing revision total joint arthroplasty, patients with other comorbidities, and patients whose primary language was not English.³⁵ Providers interested in implementing PROMs must consider their patient population when selecting a method for survey distribution and follow-up. A study found that a majority of PROMs were written at a level many patients may not have understood, because of their literacy level or age; this lack of understanding created a barrier to compliance in many patient populations.³⁶

PROM Limitations and PROMIS Use

Use of PROMs has its limitations. The large variety of PROMs available for use in orthopedic surgery has led to several standardization initiatives. The National Institutes of Health funded the development of PROMIS, a person-centered measures database that evaluates and monitors the physical, social, and emotional health of adults and children.³⁷ The goal of PROMIS is to develop a standardized method of selecting PROMs, so that all medical disciplines and subspecialties can choose an applicable set of questions from the PROMIS question bank and use it in practice. Orthopedic surgery can use questions pertaining to physical functioning of the lower and upper extremities as well as quality of life and mental health. PROMIS physical function questions have been validated for use in several areas of orthopedic surgery.^38-40 A disadvantage of PROMIS is the overgenerality of its questions, which may not be as effective in capturing the implications of specific diagnoses. For example, it is difficult to use generalized questions to determine the implications of a diagnosis such as shoulder instability, which may affect only higher functioning activities or sports. More research on best PROM selection practices is needed in order to either standardize PROMs or move toward use of a single database such as PROMIS.

Future Directions in PROM Applications

PROMs are being used for research and patient engagement, but there are many other applications on the horizon. As already mentioned, predictive modeling is of particular interest. The existence of vast collaborative PROM databases that capture a diverse patient population introduces the possibility of creating models capable of predicting a patient outcome and enhancing shared decision-making.³ Predicting good or excellent patient outcomes for specific patient populations may allow elimination of certain postoperative visits, thereby creating more cost-effective care and reducing the burden of unnecessary clinic visits for both patients and physicians.

As with other healthcare areas, PROM data collection technology is rapidly advancing. Not only has electronic technology almost entirely replaced paper-and-pencil collection methods, but a new method of outcome data collection has been developed: computerized adaptive testing (CAT). CAT uses item-response theory to minimize the number of questions patients must answer in order for validated and reliable outcome scores to be calculated. According to multiple studies, CAT used across several questionnaires has reliably assessed PROMs while minimizing floor and ceiling effects, eliminating irrelevant questions, and shortening survey completion time.^41-43

Besides becoming more patient-friendly and accessible across multiple interfaces (mobile devices and computers), PROMs are also beginning to be integrated into the electronic medical record, allowing easier access to information during chart reviews. Use of statistical and predictive modeling, as described by Chang,³ could give PROMs a role in clinical decision-making. Informing patients of their expected outcome and recovery trajectory—based on demographics, comorbidities, preoperative functional status, and other factors—could influence their decision to undergo surgical intervention. As Halawi and colleagues⁴⁴ pointed out, it is important to discuss patient expectations before surgery, as unrealistic ones can negatively affect outcomes and lead to dissatisfaction. With clinicians having ready access to statistics and models in patient charts, we may see a transformation in clinical practices and surgical decision-making.

Conclusion

PROMs offer many ways to improve research and clinical care in orthopedic surgery. However, implementing PROMs in practice is not without challenges. Interested orthopedic surgeons should select the PROMs that are most appropriate—reliable, validated, and responsive to their patient population. Electronic distribution of PROM questionnaires is effective and allows data to be stored on entry, but orthopedic surgeons must consider their patient population to ensure accurate data capture and compliance in longitudinal surveys. Proper implementation of PROMs in a practice can allow clinicians to formulate expectations for postoperative recovery and set reasonable postoperative goals while engaging patients in improving quality of care.