For the final dataset of 426 patients, 30-day mortality rate, readmission rate, and rate of the combined death/readmission outcome were 6.1% (26 patients), 15% (64 patients) and 19.7% (84 patients), respectively. A total of 6 patients were readmitted and died within 30 days after the initial discharge. The number of patients that had an I-MOVE score greater than or equal to 8 was 232 (54.4%). Table 2 presents the mean, standard deviation, and median I-MOVE score by patient discharge destination. Patients discharged home had an average I-MOVE score of 11.98, versus 7.24 for patients discharge to a skilled nursing facility ( P = 0.2).
Analysis
Table 3 presents the odds ratios and coefficient estimate of the models. In the univariate analysis, an I-MOVE score greater than or equal to 8 was significantly correlated with 30-day mortality ( P < 0.001), and the combined outcome ( P = 0.044) but not with 30-day readmission ( P = 0.76). After controlling for confounding variables, I-MOVE greater than or equal to 8 was a significant predictor of 30-day mortality ( P < 0.01) but not 30-day readmission ( P = 0.75)
Discussion
An I-MOVE score of less than 8 (inability to transfer from bed to a chair unassisted) is a statistically significant predictor of 30-day post-discharge mortality but not readmission or the combined outcome of death/readmission.
A recent review that evaluated published models that attempted to predict readmissions concluded that most current readmission risk prediction models designed for either comparative or clinical purposes perform poorly and that efforts are needed to improve their performance as use becomes more widespread [8]. Health care providers’ ability to predict which patients would be readmitted within 30 days was also shown by a recent study to be very poor, with C-statistics around 0.60 [9]. This inability of both experts and statistical methods to accurately predict readmissions may reflect some inherent randomness or unpredictability of readmissions, or the fact that a paradigm shift is still needed in the identification of the most important risk factors for readmissions. Along the same line, a recent evaluation of interventions aimed at reducing readmissions found that none of those identified in the literature managed to consistently reduce readmission rates long-term [10]. In addition, hospitals with greater adherence to recommended care processes did not achieve meaningfully better 30-day hospital readmission rates compared to those with lower levels of performance.
Conceptually, readmissions are an example of what is called “complexity science,” where many agents or factors (including the patient’s underlying illness, quality of care delivered, continuity and coordination of care, and resources available in the patient’s environment) and their interactions all play a role in the outcome [11,12]. Since I-MOVE primarily evaluates the physical capacity of the patient, and not any of the other variables that strongly affect readmission, it is perhaps not surprising that it did not predict readmission. It can be argued, on the other hand, that short term (30-day) mortality is more dependent on the patient’s physical and functional status [13] and so more likely to correlate with a measure such as I-MOVE. Inouye et al [13] found that pre-hospital, self-reported need for assistance in 7 basic “activities of daily living” (among which are transfers and ambulation) correlated with 90-day, and 2-year, post-hospital mortality.