Robert L. Owens, MD

Obstructive sleep apnea (OSA) contributes a major health burden to society due to its high prevalence and substantial neurocognitive and cardiovascular consequences. Estimates suggest that at least 10% of adults in North America are afflicted with OSA, making it probably the most common respiratory disease in the developed world (Peppard et al. Am J Epidemiol. 2013;177[9]:1006). Nasal CPAP is a highly efficacious therapy that has been shown to improve neurocognitive and cardiovascular outcomes. However, CPAP is not always well tolerated. Alternative therapies, such as oral appliances and upper airway surgery, have highly variable efficacy, and evidence of important clinical benefits are uncertain. Therefore, efforts are ongoing to determine optimal alternative strategies for therapy.

In order to treat any condition optimally, one needs to be able to predict who is at highest risk of developing the condition, then to assess the consequences if left untreated, and finally to be able to predict response to various treatment options. Currently, the OSA field is still in its early stages of our understanding. Clinically, we are often faced with patients who have varying presentations and manifestations, but, for reasons that are unclear. For instance, two individuals with the same body mass index may have very different clinical manifestations, one with severe OSA and one without any OSA. Similarly, two individuals with an apnea hypopnea index of 40 events per hour (ie, severe OSA) may have very different symptoms attributable to OSA, eg, one could be asymptomatic and the other could be debilitated from sleepiness. We and others have been making efforts to determine why these phenomenon occur. At present, the techniques to define mechanisms underlying OSA are labor-intensive, requiring one or two overnight experiments to gather meaningful data. Although we are gathering new insights based on these techniques, efforts are ongoing to simplify these approaches and to make assessment of pathophysiologic characteristics more accessible to the clinician (Orr et al. Am J Respir Crit Care Med. 2017 Nov 30. doi: 10.1164/rccm.201707-1357LE. [Epub ahead of print]).

We ultimately believe that a thorough analysis of a sleep recording combined with demographic data and other readily available clinical data (perhaps plasma biomarkers) may yield sufficient information for us to know why OSA is occurring and what interventions might be helpful for an individual patient. Currently, our use of the polysomnogram to derive only an apnea hypopnea index does not take full advantage of the available data. An apnea hypopnea index can be readily obtained from home sleep testing and does not truly provide much insight into why a given individual has OSA, what symptoms are attributable to OSA, and what interventions might be considered for the afflicted individual. By analogy, if the only useful data derived from an ECG were a heart rate, the test would rapidly become obsolete. Along these lines, if the only role for the sleep clinician was to prescribe CPAP to everyone with an AHI greater than 5/h, there would be little need or interest in specialized training. In contrast, we suggest that rich insights regarding pathophysiology and mechanisms should be gathered and may influence clinical management of patients afflicted with OSA. Thus, we encourage more thorough analyses of available data to maximize information gleaned and, ultimately, to optimize clinical outcomes.

Recent studies suggest that sleep apnea occurs for varying reasons, a concept that is now thought to be clinically important (Jordan et al. Lancet. 2014;383[9918]:736). We draw a crucial distinction between endotypes (mechanisms underlying disease) and phenotypes (clinical expression of disease). Important endotypes include compromised upper airway anatomy, dysfunction in pharyngeal dilator muscles, unstable ventilatory control (high loop gain), and low arousal threshold (wake up easily), among others. Important phenotypes of sleep apnea are emerging and still evolving to include minimally symptomatic OSA, OSA with daytime sleepiness, and OSA with major cardiometabolic risk, among others. Several important concepts have emerged regarding different OSA endotypes and phenotypes:

1 The mechanism underlying OSA may predict potential response to therapeutic interventions. For instance, the endotype of OSA with unstable ventilatory control (high loop gain) may respond to agents such as oxygen and acetazolamide, which serve to stabilize control of breathing. In patients with anatomical compromise at the level of the velopharynx, uvulopalatopharyngoplasty may be an effective intervention. For patients with multiple pathophysiologic abnormalities, combination therapy may be required to alleviate OSA (Edwards et al. Sleep. 2016;9[11]:1973).

2 Given that OSA has many underlying etiologies, efforts are underway to determine whether individuals with different risk factors for OSA develop their disease based on varying mechanisms. As an example, people with posttraumatic stress disorder (PTSD) may be at increased risk of OSA perhaps on the basis of a low threshold for arousal (Orr et al. JCSM. 2017, 13[1]: 57-63). Another example would be patients with neuromuscular disease who may be at risk of OSA primarily based on impaired pharyngeal dilator muscle function.

3 A new concept is emerging whereby endotypes of OSA may actually predict differing OSA phenotypes. In theory, loop gain-driven OSA may have different consequences from OSA driven by compromise of pharyngeal anatomy. To this point, data suggest that OSA in the elderly may not have as many consequences as OSA in younger people matched on severity of illness. OSA in the elderly has lower loop gain than OSA in younger people and is associated with less negative intrathoracic pressure at the time of arousal as compared with younger individuals with OSA (Kobayashi et al. Chest. 2010; 137[6]:1310). As such, the endotype of OSA in the elderly may explain why the clinical consequences are fewer than in the younger OSA counterparts.

4 The mechanism underlying OSA may be important in determining response to clinical interventions, such as nasal CPAP. Patients with a low arousal threshold may be prone to insomnia when placed on CPAP and could theoretically be poorly tolerant of therapy based on disrupted sleep architecture. Such patients may benefit from non-myorelaxant hypnotic therapy to consolidate sleep and improve CPAP adherence. In addition, patients with high loop gain (unstable ventilatory control) may be prone to develop central apneas when placed on CPAP therapy (Stanchina et al. Ann Am Thorac Soc. 2015;12[9]:1351). These patients may benefit from newer technologies, eg, auto or adaptive servo ventilation - ASV. High loop gain has also been shown to predict failure of upper airway surgery as a treatment for OSA by several groups (Li et al. JCSM. 2017;13[9]:1029). Such patients should, perhaps, undergo nonsurgical therapies for OSA.

We emphasize that some of the points being made are somewhat speculative and, thus, encourage further basic and clinical research to test our assumptions. Robust, multicenter clinical trials assessing hard outcomes will ultimately be required to change the current standard of care. Nonetheless, we believe that a more thorough understanding of OSA pathogenesis can help guide clinical care today and will be critical to the optimal treatment of afflicted individuals tomorrow.

Dr. Owens is Assistant Clinical Professor of Medicine; Dr. Deacon is a Post-Doctoral Research Scholar; and Dr. Malhotra is Kenneth M. Moser Professor of Medicine and Chief, Division of Pulmonary, Critical Care and Sleep Medicine, University of California San Diego.

Obstructive sleep apnea (OSA) contributes a major health burden to society due to its high prevalence and substantial neurocognitive and cardiovascular consequences. Estimates suggest that at least 10% of adults in North America are afflicted with OSA, making it probably the most common respiratory disease in the developed world (Peppard et al. Am J Epidemiol. 2013;177[9]:1006). Nasal CPAP is a highly efficacious therapy that has been shown to improve neurocognitive and cardiovascular outcomes. However, CPAP is not always well tolerated. Alternative therapies, such as oral appliances and upper airway surgery, have highly variable efficacy, and evidence of important clinical benefits are uncertain. Therefore, efforts are ongoing to determine optimal alternative strategies for therapy.

In order to treat any condition optimally, one needs to be able to predict who is at highest risk of developing the condition, then to assess the consequences if left untreated, and finally to be able to predict response to various treatment options. Currently, the OSA field is still in its early stages of our understanding. Clinically, we are often faced with patients who have varying presentations and manifestations, but, for reasons that are unclear. For instance, two individuals with the same body mass index may have very different clinical manifestations, one with severe OSA and one without any OSA. Similarly, two individuals with an apnea hypopnea index of 40 events per hour (ie, severe OSA) may have very different symptoms attributable to OSA, eg, one could be asymptomatic and the other could be debilitated from sleepiness. We and others have been making efforts to determine why these phenomenon occur. At present, the techniques to define mechanisms underlying OSA are labor-intensive, requiring one or two overnight experiments to gather meaningful data. Although we are gathering new insights based on these techniques, efforts are ongoing to simplify these approaches and to make assessment of pathophysiologic characteristics more accessible to the clinician (Orr et al. Am J Respir Crit Care Med. 2017 Nov 30. doi: 10.1164/rccm.201707-1357LE. [Epub ahead of print]).

We ultimately believe that a thorough analysis of a sleep recording combined with demographic data and other readily available clinical data (perhaps plasma biomarkers) may yield sufficient information for us to know why OSA is occurring and what interventions might be helpful for an individual patient. Currently, our use of the polysomnogram to derive only an apnea hypopnea index does not take full advantage of the available data. An apnea hypopnea index can be readily obtained from home sleep testing and does not truly provide much insight into why a given individual has OSA, what symptoms are attributable to OSA, and what interventions might be considered for the afflicted individual. By analogy, if the only useful data derived from an ECG were a heart rate, the test would rapidly become obsolete. Along these lines, if the only role for the sleep clinician was to prescribe CPAP to everyone with an AHI greater than 5/h, there would be little need or interest in specialized training. In contrast, we suggest that rich insights regarding pathophysiology and mechanisms should be gathered and may influence clinical management of patients afflicted with OSA. Thus, we encourage more thorough analyses of available data to maximize information gleaned and, ultimately, to optimize clinical outcomes.

Recent studies suggest that sleep apnea occurs for varying reasons, a concept that is now thought to be clinically important (Jordan et al. Lancet. 2014;383[9918]:736). We draw a crucial distinction between endotypes (mechanisms underlying disease) and phenotypes (clinical expression of disease). Important endotypes include compromised upper airway anatomy, dysfunction in pharyngeal dilator muscles, unstable ventilatory control (high loop gain), and low arousal threshold (wake up easily), among others. Important phenotypes of sleep apnea are emerging and still evolving to include minimally symptomatic OSA, OSA with daytime sleepiness, and OSA with major cardiometabolic risk, among others. Several important concepts have emerged regarding different OSA endotypes and phenotypes:

1 The mechanism underlying OSA may predict potential response to therapeutic interventions. For instance, the endotype of OSA with unstable ventilatory control (high loop gain) may respond to agents such as oxygen and acetazolamide, which serve to stabilize control of breathing. In patients with anatomical compromise at the level of the velopharynx, uvulopalatopharyngoplasty may be an effective intervention. For patients with multiple pathophysiologic abnormalities, combination therapy may be required to alleviate OSA (Edwards et al. Sleep. 2016;9[11]:1973).

2 Given that OSA has many underlying etiologies, efforts are underway to determine whether individuals with different risk factors for OSA develop their disease based on varying mechanisms. As an example, people with posttraumatic stress disorder (PTSD) may be at increased risk of OSA perhaps on the basis of a low threshold for arousal (Orr et al. JCSM. 2017, 13[1]: 57-63). Another example would be patients with neuromuscular disease who may be at risk of OSA primarily based on impaired pharyngeal dilator muscle function.

3 A new concept is emerging whereby endotypes of OSA may actually predict differing OSA phenotypes. In theory, loop gain-driven OSA may have different consequences from OSA driven by compromise of pharyngeal anatomy. To this point, data suggest that OSA in the elderly may not have as many consequences as OSA in younger people matched on severity of illness. OSA in the elderly has lower loop gain than OSA in younger people and is associated with less negative intrathoracic pressure at the time of arousal as compared with younger individuals with OSA (Kobayashi et al. Chest. 2010; 137[6]:1310). As such, the endotype of OSA in the elderly may explain why the clinical consequences are fewer than in the younger OSA counterparts.

4 The mechanism underlying OSA may be important in determining response to clinical interventions, such as nasal CPAP. Patients with a low arousal threshold may be prone to insomnia when placed on CPAP and could theoretically be poorly tolerant of therapy based on disrupted sleep architecture. Such patients may benefit from non-myorelaxant hypnotic therapy to consolidate sleep and improve CPAP adherence. In addition, patients with high loop gain (unstable ventilatory control) may be prone to develop central apneas when placed on CPAP therapy (Stanchina et al. Ann Am Thorac Soc. 2015;12[9]:1351). These patients may benefit from newer technologies, eg, auto or adaptive servo ventilation - ASV. High loop gain has also been shown to predict failure of upper airway surgery as a treatment for OSA by several groups (Li et al. JCSM. 2017;13[9]:1029). Such patients should, perhaps, undergo nonsurgical therapies for OSA.

We emphasize that some of the points being made are somewhat speculative and, thus, encourage further basic and clinical research to test our assumptions. Robust, multicenter clinical trials assessing hard outcomes will ultimately be required to change the current standard of care. Nonetheless, we believe that a more thorough understanding of OSA pathogenesis can help guide clinical care today and will be critical to the optimal treatment of afflicted individuals tomorrow.

Dr. Owens is Assistant Clinical Professor of Medicine; Dr. Deacon is a Post-Doctoral Research Scholar; and Dr. Malhotra is Kenneth M. Moser Professor of Medicine and Chief, Division of Pulmonary, Critical Care and Sleep Medicine, University of California San Diego.

Obstructive sleep apnea (OSA) contributes a major health burden to society due to its high prevalence and substantial neurocognitive and cardiovascular consequences. Estimates suggest that at least 10% of adults in North America are afflicted with OSA, making it probably the most common respiratory disease in the developed world (Peppard et al. Am J Epidemiol. 2013;177[9]:1006). Nasal CPAP is a highly efficacious therapy that has been shown to improve neurocognitive and cardiovascular outcomes. However, CPAP is not always well tolerated. Alternative therapies, such as oral appliances and upper airway surgery, have highly variable efficacy, and evidence of important clinical benefits are uncertain. Therefore, efforts are ongoing to determine optimal alternative strategies for therapy.

In order to treat any condition optimally, one needs to be able to predict who is at highest risk of developing the condition, then to assess the consequences if left untreated, and finally to be able to predict response to various treatment options. Currently, the OSA field is still in its early stages of our understanding. Clinically, we are often faced with patients who have varying presentations and manifestations, but, for reasons that are unclear. For instance, two individuals with the same body mass index may have very different clinical manifestations, one with severe OSA and one without any OSA. Similarly, two individuals with an apnea hypopnea index of 40 events per hour (ie, severe OSA) may have very different symptoms attributable to OSA, eg, one could be asymptomatic and the other could be debilitated from sleepiness. We and others have been making efforts to determine why these phenomenon occur. At present, the techniques to define mechanisms underlying OSA are labor-intensive, requiring one or two overnight experiments to gather meaningful data. Although we are gathering new insights based on these techniques, efforts are ongoing to simplify these approaches and to make assessment of pathophysiologic characteristics more accessible to the clinician (Orr et al. Am J Respir Crit Care Med. 2017 Nov 30. doi: 10.1164/rccm.201707-1357LE. [Epub ahead of print]).

We ultimately believe that a thorough analysis of a sleep recording combined with demographic data and other readily available clinical data (perhaps plasma biomarkers) may yield sufficient information for us to know why OSA is occurring and what interventions might be helpful for an individual patient. Currently, our use of the polysomnogram to derive only an apnea hypopnea index does not take full advantage of the available data. An apnea hypopnea index can be readily obtained from home sleep testing and does not truly provide much insight into why a given individual has OSA, what symptoms are attributable to OSA, and what interventions might be considered for the afflicted individual. By analogy, if the only useful data derived from an ECG were a heart rate, the test would rapidly become obsolete. Along these lines, if the only role for the sleep clinician was to prescribe CPAP to everyone with an AHI greater than 5/h, there would be little need or interest in specialized training. In contrast, we suggest that rich insights regarding pathophysiology and mechanisms should be gathered and may influence clinical management of patients afflicted with OSA. Thus, we encourage more thorough analyses of available data to maximize information gleaned and, ultimately, to optimize clinical outcomes.

Recent studies suggest that sleep apnea occurs for varying reasons, a concept that is now thought to be clinically important (Jordan et al. Lancet. 2014;383[9918]:736). We draw a crucial distinction between endotypes (mechanisms underlying disease) and phenotypes (clinical expression of disease). Important endotypes include compromised upper airway anatomy, dysfunction in pharyngeal dilator muscles, unstable ventilatory control (high loop gain), and low arousal threshold (wake up easily), among others. Important phenotypes of sleep apnea are emerging and still evolving to include minimally symptomatic OSA, OSA with daytime sleepiness, and OSA with major cardiometabolic risk, among others. Several important concepts have emerged regarding different OSA endotypes and phenotypes:

1 The mechanism underlying OSA may predict potential response to therapeutic interventions. For instance, the endotype of OSA with unstable ventilatory control (high loop gain) may respond to agents such as oxygen and acetazolamide, which serve to stabilize control of breathing. In patients with anatomical compromise at the level of the velopharynx, uvulopalatopharyngoplasty may be an effective intervention. For patients with multiple pathophysiologic abnormalities, combination therapy may be required to alleviate OSA (Edwards et al. Sleep. 2016;9[11]:1973).

2 Given that OSA has many underlying etiologies, efforts are underway to determine whether individuals with different risk factors for OSA develop their disease based on varying mechanisms. As an example, people with posttraumatic stress disorder (PTSD) may be at increased risk of OSA perhaps on the basis of a low threshold for arousal (Orr et al. JCSM. 2017, 13[1]: 57-63). Another example would be patients with neuromuscular disease who may be at risk of OSA primarily based on impaired pharyngeal dilator muscle function.

3 A new concept is emerging whereby endotypes of OSA may actually predict differing OSA phenotypes. In theory, loop gain-driven OSA may have different consequences from OSA driven by compromise of pharyngeal anatomy. To this point, data suggest that OSA in the elderly may not have as many consequences as OSA in younger people matched on severity of illness. OSA in the elderly has lower loop gain than OSA in younger people and is associated with less negative intrathoracic pressure at the time of arousal as compared with younger individuals with OSA (Kobayashi et al. Chest. 2010; 137[6]:1310). As such, the endotype of OSA in the elderly may explain why the clinical consequences are fewer than in the younger OSA counterparts.

4 The mechanism underlying OSA may be important in determining response to clinical interventions, such as nasal CPAP. Patients with a low arousal threshold may be prone to insomnia when placed on CPAP and could theoretically be poorly tolerant of therapy based on disrupted sleep architecture. Such patients may benefit from non-myorelaxant hypnotic therapy to consolidate sleep and improve CPAP adherence. In addition, patients with high loop gain (unstable ventilatory control) may be prone to develop central apneas when placed on CPAP therapy (Stanchina et al. Ann Am Thorac Soc. 2015;12[9]:1351). These patients may benefit from newer technologies, eg, auto or adaptive servo ventilation - ASV. High loop gain has also been shown to predict failure of upper airway surgery as a treatment for OSA by several groups (Li et al. JCSM. 2017;13[9]:1029). Such patients should, perhaps, undergo nonsurgical therapies for OSA.

We emphasize that some of the points being made are somewhat speculative and, thus, encourage further basic and clinical research to test our assumptions. Robust, multicenter clinical trials assessing hard outcomes will ultimately be required to change the current standard of care. Nonetheless, we believe that a more thorough understanding of OSA pathogenesis can help guide clinical care today and will be critical to the optimal treatment of afflicted individuals tomorrow.

Dr. Owens is Assistant Clinical Professor of Medicine; Dr. Deacon is a Post-Doctoral Research Scholar; and Dr. Malhotra is Kenneth M. Moser Professor of Medicine and Chief, Division of Pulmonary, Critical Care and Sleep Medicine, University of California San Diego.

BACKGROUND: Hospitalized patients frequently report poor sleep, partly due to the inpatient environment. In-hospital sound and light levels are not well described on non–intensive care unit (non-ICU) wards. Although non-ICU wards may have lower average and peak noise levels, sound level changes (SLCs), which are important in disrupting sleep, may still be a substantial problem.

OBJECTIVE: To compare ambient sound and light levels, including SLCs, in ICU and non-ICU environments.

DESIGN: Observational study.

SETTING: Tertiary-care hospital.

MEASUREMENTS: Sound measurements of 0.5 Hz were analyzed to provide average hourly sound levels, sound peaks, and SLCs greater than or equal to 17.5 decibels (dB). For light data, measurements taken at 2-minute intervals provided average and maximum light levels.

RESULTS: The ICU rooms were louder than non-ICU wards; hourly averages ranged from 56.1 plus or minus 1.3 dB to 60.3 plus or minus 1.7 dB in the ICU, 47.3 plus or minus 3.7 dB to 55.1 plus or minus 3.7 dB on the telemetry floor, and 44.6 plus or minus 2.1 dB to 53.7 plus or minus 3.6 dB on the general ward. However, SLCs greater than or equal to 17.5 dB were not statistically different (ICU, 203.9 plus or minus 28.8 times; non-ICU, 270.9 plus or minus 39.5; P = 0.11). In both ICU and non-ICU wards, average daytime light levels were less than 250 lux, and peak light levels occurred in the afternoon and early evening.

CONCLUSIONS: While quieter, non-ICU wards have as many SLCs as ICUs do, which has implications for quality improvement measurements. Efforts to further reduce average noise levels might be counterproductive. Light levels in the hospital (ICU and non-ICU) may not be optimal for maintenance of a normal circadian rhythm for most people.

Read the entire article in the Journal of Hospital Medicine.
 

Also in JHM this month

Associations of physician empathy with patient anxiety and ratings of communication in hospital admission encounters

AUTHORS: Rachel Weiss, MD, Eric Vittinghoff, PhD, MPH, Margaret C. Fang, MD, MPH, Jenica E. W. Cimino, Kristen Adams Chasteen, MD, Robert M. Arnold, MD, Andrew D. Auerbach, MD, Wendy G. Anderson, MD, MS


A concise tool for measuring care coordination from the provider’s perspective in the hospital setting

AUTHORS: Christine M. Weston, PhD, and Sehyo Yune, MD, Eric B. Bass, MD, MPH, Scott A. Berkowitz, MD, MBA, Daniel J. Brotman, MD, Amy Deutschendorf, MS, RN, ACNS-BC, Eric E. Howell, MD, Melissa B. Richardson, MBA Carol Sylvester, RN, MS, Albert W. Wu, MD, MPH


Post–intensive care unit psychiatric comorbidity and quality of life

AUTHORS: Sophia Wang, MD, and Chris Mosher, MD, Anthony J. Perkins, MS, Sujuan Gao, PhD, Sue Lasiter, RN, PhD, Sikandar Khan, MD, Malaz Boustani, MD, MPH, Babar Khan, MD, MS


An opportunity to improve Medicare’s planned readmissions measure

AUTHORS: Chad Ellimoottil, MD, MS, Roger K. Khouri Jr., MD, Apoorv Dhir, BA, Hechuan Hou, MS, David C. Miller, MD, MPH, James M. Dupree, MD, MPH


Against medical advice discharges

AUTHORS: David Alfandre, MD, MSPH, Jay Brenner, MD, Eberechukwu Onukwugha, MS, PhD

BACKGROUND: Hospitalized patients frequently report poor sleep, partly due to the inpatient environment. In-hospital sound and light levels are not well described on non–intensive care unit (non-ICU) wards. Although non-ICU wards may have lower average and peak noise levels, sound level changes (SLCs), which are important in disrupting sleep, may still be a substantial problem.

OBJECTIVE: To compare ambient sound and light levels, including SLCs, in ICU and non-ICU environments.

DESIGN: Observational study.

SETTING: Tertiary-care hospital.

MEASUREMENTS: Sound measurements of 0.5 Hz were analyzed to provide average hourly sound levels, sound peaks, and SLCs greater than or equal to 17.5 decibels (dB). For light data, measurements taken at 2-minute intervals provided average and maximum light levels.

RESULTS: The ICU rooms were louder than non-ICU wards; hourly averages ranged from 56.1 plus or minus 1.3 dB to 60.3 plus or minus 1.7 dB in the ICU, 47.3 plus or minus 3.7 dB to 55.1 plus or minus 3.7 dB on the telemetry floor, and 44.6 plus or minus 2.1 dB to 53.7 plus or minus 3.6 dB on the general ward. However, SLCs greater than or equal to 17.5 dB were not statistically different (ICU, 203.9 plus or minus 28.8 times; non-ICU, 270.9 plus or minus 39.5; P = 0.11). In both ICU and non-ICU wards, average daytime light levels were less than 250 lux, and peak light levels occurred in the afternoon and early evening.

CONCLUSIONS: While quieter, non-ICU wards have as many SLCs as ICUs do, which has implications for quality improvement measurements. Efforts to further reduce average noise levels might be counterproductive. Light levels in the hospital (ICU and non-ICU) may not be optimal for maintenance of a normal circadian rhythm for most people.

Read the entire article in the Journal of Hospital Medicine.
 

Also in JHM this month

Associations of physician empathy with patient anxiety and ratings of communication in hospital admission encounters

AUTHORS: Rachel Weiss, MD, Eric Vittinghoff, PhD, MPH, Margaret C. Fang, MD, MPH, Jenica E. W. Cimino, Kristen Adams Chasteen, MD, Robert M. Arnold, MD, Andrew D. Auerbach, MD, Wendy G. Anderson, MD, MS


A concise tool for measuring care coordination from the provider’s perspective in the hospital setting

AUTHORS: Christine M. Weston, PhD, and Sehyo Yune, MD, Eric B. Bass, MD, MPH, Scott A. Berkowitz, MD, MBA, Daniel J. Brotman, MD, Amy Deutschendorf, MS, RN, ACNS-BC, Eric E. Howell, MD, Melissa B. Richardson, MBA Carol Sylvester, RN, MS, Albert W. Wu, MD, MPH


Post–intensive care unit psychiatric comorbidity and quality of life

AUTHORS: Sophia Wang, MD, and Chris Mosher, MD, Anthony J. Perkins, MS, Sujuan Gao, PhD, Sue Lasiter, RN, PhD, Sikandar Khan, MD, Malaz Boustani, MD, MPH, Babar Khan, MD, MS


An opportunity to improve Medicare’s planned readmissions measure

AUTHORS: Chad Ellimoottil, MD, MS, Roger K. Khouri Jr., MD, Apoorv Dhir, BA, Hechuan Hou, MS, David C. Miller, MD, MPH, James M. Dupree, MD, MPH


Against medical advice discharges

AUTHORS: David Alfandre, MD, MSPH, Jay Brenner, MD, Eberechukwu Onukwugha, MS, PhD

BACKGROUND: Hospitalized patients frequently report poor sleep, partly due to the inpatient environment. In-hospital sound and light levels are not well described on non–intensive care unit (non-ICU) wards. Although non-ICU wards may have lower average and peak noise levels, sound level changes (SLCs), which are important in disrupting sleep, may still be a substantial problem.

OBJECTIVE: To compare ambient sound and light levels, including SLCs, in ICU and non-ICU environments.

DESIGN: Observational study.

SETTING: Tertiary-care hospital.

MEASUREMENTS: Sound measurements of 0.5 Hz were analyzed to provide average hourly sound levels, sound peaks, and SLCs greater than or equal to 17.5 decibels (dB). For light data, measurements taken at 2-minute intervals provided average and maximum light levels.

RESULTS: The ICU rooms were louder than non-ICU wards; hourly averages ranged from 56.1 plus or minus 1.3 dB to 60.3 plus or minus 1.7 dB in the ICU, 47.3 plus or minus 3.7 dB to 55.1 plus or minus 3.7 dB on the telemetry floor, and 44.6 plus or minus 2.1 dB to 53.7 plus or minus 3.6 dB on the general ward. However, SLCs greater than or equal to 17.5 dB were not statistically different (ICU, 203.9 plus or minus 28.8 times; non-ICU, 270.9 plus or minus 39.5; P = 0.11). In both ICU and non-ICU wards, average daytime light levels were less than 250 lux, and peak light levels occurred in the afternoon and early evening.

CONCLUSIONS: While quieter, non-ICU wards have as many SLCs as ICUs do, which has implications for quality improvement measurements. Efforts to further reduce average noise levels might be counterproductive. Light levels in the hospital (ICU and non-ICU) may not be optimal for maintenance of a normal circadian rhythm for most people.

Read the entire article in the Journal of Hospital Medicine.
 

Also in JHM this month

Associations of physician empathy with patient anxiety and ratings of communication in hospital admission encounters

AUTHORS: Rachel Weiss, MD, Eric Vittinghoff, PhD, MPH, Margaret C. Fang, MD, MPH, Jenica E. W. Cimino, Kristen Adams Chasteen, MD, Robert M. Arnold, MD, Andrew D. Auerbach, MD, Wendy G. Anderson, MD, MS


A concise tool for measuring care coordination from the provider’s perspective in the hospital setting

AUTHORS: Christine M. Weston, PhD, and Sehyo Yune, MD, Eric B. Bass, MD, MPH, Scott A. Berkowitz, MD, MBA, Daniel J. Brotman, MD, Amy Deutschendorf, MS, RN, ACNS-BC, Eric E. Howell, MD, Melissa B. Richardson, MBA Carol Sylvester, RN, MS, Albert W. Wu, MD, MPH


Post–intensive care unit psychiatric comorbidity and quality of life

AUTHORS: Sophia Wang, MD, and Chris Mosher, MD, Anthony J. Perkins, MS, Sujuan Gao, PhD, Sue Lasiter, RN, PhD, Sikandar Khan, MD, Malaz Boustani, MD, MPH, Babar Khan, MD, MS


An opportunity to improve Medicare’s planned readmissions measure

AUTHORS: Chad Ellimoottil, MD, MS, Roger K. Khouri Jr., MD, Apoorv Dhir, BA, Hechuan Hou, MS, David C. Miller, MD, MPH, James M. Dupree, MD, MPH


Against medical advice discharges

AUTHORS: David Alfandre, MD, MSPH, Jay Brenner, MD, Eberechukwu Onukwugha, MS, PhD

The hospital environment fails to promote adequate sleep for acutely or critically ill patients. Intensive care units (ICUs) have received the most scrutiny, because critically ill patients suffer from severely fragmented sleep as well as a lack of deeper, more restorative sleep.^1-4 ICU survivors frequently cite sleep deprivation, contributed to by ambient noise, as a major stressor while receiving care.^5,6 Importantly, efforts to modify the ICU environment to promote sleep have been associated with reductions in delirium.^7,8 However, sleep deprivation and delirium in the hospital are not limited to ICU patients.

Sleep in the non-ICU setting is also notoriously poor, with 50%-80% of patients reporting sleep as “unsound” or otherwise subjectively poor.^9-11 Additionally, patients frequently ask for and/or receive pharmacological sleeping aids¹² despite little evidence of efficacy¹³ and increasing evidence of harm.¹⁴ Here too, efforts to improve sleep seems to attenuate risk of delirium,¹⁵ which remains a substantial problem on general wards, with incidence reported as high as 20%-30%. The reasons for poor sleep in the hospital are multifactorial, but data suggest that the inpatient environment, including noise and light levels, which are measurable and modifiable entities, contribute significantly to the problem.¹⁶

The World Health Organization (WHO) recommends that nighttime baseline noise levels do not exceed 30 decibels (dB) and that nighttime noise peaks (ie, loud noises) do not exceed 40 dB¹⁷; most studies suggest that ICU and general ward rooms are above this range on average.^10,18 Others have also demonstrated an association between loud noises and patients’ subjective perception of poor sleep.^10,19 However, when considering clinically important noise, peak and average noise levels may not be the key factor in causing arousals from sleep. Buxton and colleagues²⁰ found that noise quality affects arousal probability; for example, electronic alarms and conversational noise are more likely to cause awakenings compared with the opening or closing of doors and ice machines. Importantly, peak and average noise levels may also matter less for sleep than do sound level changes (SLCs), which are defined as the difference between background/baseline noise and peak noise. Using healthy subjects exposed to simulated ICU noise, Stanchina et al.²¹ found that SLCs >17.5 dB were more likely to cause polysomnographic arousals from sleep regardless of peak noise level. This sound pressure change of approximately 20 dB would be perceived as 4 times louder, or, as an example, would be the difference between normal conversation between 2 people (~40 dB) that is then interrupted by the start of a vacuum cleaner (~60 dB). To our knowledge, no other studies have closely examined SLCs in different hospital environments.

Ambient light also likely affects sleep quality in the hospital. The circadian rhythm system, which controls the human sleep–wake cycle as well as multiple other physiologic functions, depends on ambient light as the primary external factor for regulating the internal clock.^22,23 Insufficient and inappropriately timed light exposure can desynchronize the biological clock, thereby negatively affecting sleep quality.^24,25 Conversely, patients exposed to early-morning bright light may sleep better while in the hospital.¹⁶ In addition to sleep patterns, ambient light affects other aspects of patient care; for example, lower light levels in the hospital have recently been associated with higher levels of fatigue and mood disturbance.²⁶A growing body of data has investigated the ambient environment in the ICU, but fewer studies have focused on sound and light analysis in other inpatient areas such as the general ward and telemetry floors. We examined sound and light levels in the ICU and non-ICU environment, hypothesizing that average sound levels would be higher in the ICU than on non-ICU floors but that the number of SLCs >17.5 dB would be similar. Additionally, we expected that average light levels would be higher in the ICU than on non-ICU floors.

This was an observational study of the sound and light environment in the inpatient setting. Per our Institutional Review Board, no consent was required. Battery-operated sound-level (SDL600, Extech Instruments, Nashua, NH) and light-level (SDL400, Extech Instruments, Nashua, NH) meters were placed in 24 patient rooms in our tertiary-care adult hospital in La Jolla, CA. Recordings were obtained in randomly selected, single-patient occupied rooms that were from 3 different hospital units and included 8 general ward rooms, 8 telemetry floor rooms, and 8 ICU rooms. We recorded for approximately 24-72 hours. Depending on the geographic layout of the room, meters were placed as close to the head of the patient’s bed as possible and were generally not placed farther than 2 meters away from the patient’s head of bed; all rooms contained a window.

Sound Measurements

Sound meters measured ambient noise in dB every 2 seconds and were set for A-weighted frequency measurements. We averaged individual data points to obtain hourly averages for ICU and non-ICU rooms. For hourly sound averages, we further separated the data to compare the general ward telemetry floors (both non-ICU), the latter of which has more patient monitoring and a lower nurse-to-patient ratio compared with the general ward floor.

Data from ICU versus non-ICU rooms were analyzed for the number of sound peaks throughout the 24-hour day and for sound peak over the nighttime, defined as the number of times sound levels exceeded 65 dB, 70 dB, or 80 dB, which were averaged over 24 hours and over the nighttime (10 PM to 6 AM). We also calculated the number of average SLCs ≥17.5 dB observed over 24 hours and over the nighttime.

Light Measurements

Light meters measured luminescence in lux at a frequency of 120 seconds. We averaged individual data points to obtain hourly averages for ICU and non-ICU rooms. In addition to hourly averages, light-level data were analyzed for maximum levels throughout the day and night.

Statistical Analysis

Hourly sound-level averages between the 3 floors were evaluated using a 1-way analysis of variance (ANOVA); sound averages from the general ward and telemetry floor were also compared at each hour using a Student t test. Light-level data, sound-level peak data, as well as SLC data were also evaluated using a Student t test.

Sound Measurements

Examples of the raw data distribution for individual sound recordings in an ICU and non-ICU room are shown in Figure 1A and 1B. Sound-level analysis with specific average values and significance levels between ICU and non-ICU rooms (with non-ICU rooms further divided between telemetry and general ward floors for purposes of hourly averages) are shown in Table 1. The average hourly values in all 3 locations were always above the 30-35 dB level (nighttime and daytime, respectively) recommended by the WHO (Figure 1C). A 1-way ANOVA analysis revealed significant differences between the 3 floors at all time points except for 10 AM. An analysis of the means at each time point between the telemetry floor and the general ward floor showed that the telemetry floor had significantly higher sound averages compared with the general ward floor at 10 PM, 11 PM, and 12 AM. Sound levels dropped during the nighttime on both non-ICU wards but remained fairly constant throughout the day and night in the ICU.

Light Measurements

Examples of light levels over a 24-hour period in an ICU and non-ICU room are shown in Figure 2A and 2B, respectively. Maximum average light levels (reported here as average value ± standard deviation to demonstrate variability within the data) in the ICU were 169.7 ± 127.1 lux and occurred at 1 PM, while maximum average light levels in the non-ICU rooms were 213.5 ± 341.6 lux and occurred at 5 PM (Figure 2C). Average light levels in the morning hours remained low and ranged from 15.9 ± 12.7 lux to 38.9 ± 43.4 lux in the ICU and from 22.3 ± 17.5 lux to 100.7 ± 92.0 lux on the non-ICU floors. The maximum measured level from any of the recordings was 2530 lux and occurred in a general ward room in the 5 PM hour. Overall, light averages remained low, but this particular room had light levels that were significantly higher than the others. A t test analysis of the hourly averages revealed only 1 time point of significant difference between the 2 floors; at 7 AM, the general ward floor had a higher lux level of 49.9 ± 27.5 versus 19.2 ± 10.7 in the ICU (P = 0.038). Otherwise, there were no differences between light levels in ICU rooms versus non-ICU rooms. Evaluation of the data revealed a substantial amount of variability in light levels throughout the daytime hours. Light levels during the nighttime remained low and were not significantly different between the 2 groups.

To our knowledge, this is the first study to directly compare the ICU and non-ICU environment for its potential impact on sleep and circadian alignment. Our study adds to the literature with several novel findings. First, average sound levels on non-ICU wards are lower than in the ICU. Second, although quieter on average, SLCs >17.5 dB occurred an equivalent number of times for both the ICU and non-ICU wards. Third, average daytime light levels in both the ICU and non-ICU environment are low. Lastly, peak light levels for both ICU and non-ICU wards occur later in the day instead of in the morning. All of the above have potential impact for optimizing the ward environment to better aid in sleep for patients.

Sound-Level Findings

Data on sound levels for non-ICU floors are limited but mostly consistent with our findings; sound averages in our study ranged from 44.6 to 55.1 dB in non-ICU rooms, while others report averages ranging from 48 dB¹⁹ to 63.5 dB,¹⁰ although the latter measurement includes rooms occupied with 4 to 6 patients, which we expect would increase the noise levels. Others report average noise levels in the ICU similar to our values, which ranged from 56.1 to 60.3 dB.^18,27 Here, we show that average and peak sound levels on non-ICU wards are consistently lower than in the ICU. However, sound levels on the general ward and telemetry floors still remain quite high and potentially disruptive to patients, with average nighttime sound levels reaching the range of light outdoor traffic. The sleep environment could play an even larger role in sleep quality for non-ICU patients, as they do not typically receive sedation (though pharmacological sleeping-aid use is quite high, despite the risks)²⁸ and thus may be more sensitive to environmental factors that impact sleep.

Average and peak sound levels contribute to the ambient noise experienced by patients but may not be the source of sleep disruptions. Using polysomnography in healthy subjects exposed to recordings of ICU noise, Stanchina et al.²¹ showed that SLCs from baseline and not peak sound levels determined whether a subject was aroused from sleep by sound. Accordingly, they also found that increasing baseline sound levels by using white noise reduced the number of arousals that subjects experienced. To our knowledge, other studies have not quantified and compared SLCs in the ICU and non-ICU environments. Our data show that patients on non-ICU floors experience at least the same number of SLCs, and thereby the same potential for arousals from sleep, when compared with ICU patients. The higher baseline level of noise in the ICU likely explains the relatively lower number of SLCs when compared with the non-ICU floors. Although decreasing overall noise to promote sleep in the hospital seems like the obvious solution, the treatment for noise pollution in the hospital may actually be more background noise, not less.

Recent studies support the clinical implications of our findings. First, decreasing overall noise levels is difficult to accomplish.²⁹ Second, recent studies utilized white noise in different hospital settings with some success in improving patients’ subjective sleep quality, although more studies using objective data measurements are needed to further understand the impact of white noise on sleep in hospitalized patients.^30,31 Third, efforts at reducing interruptions—which likely will decrease the number of SLCs—such as clustering nursing care or reducing intermittent alarms may be more beneficial in improving sleep than efforts at decreasing average sound levels. For example, Bartick et al. reduced the number of patient interruptions at night by eliminating routine vital signs and clustering medication administration. Although they included other interventions as well, we note that this approach likely reduced SLCs and was associated with a reduction in the use of sedative medications.³² Ultimately, our data show that a focus on reducing SLCs will be one necessary component of a multipronged solution to improving inpatient sleep.³³

Light-Level Findings

Because of its effect on circadian rhythms, the daily light-dark cycle has a powerful impact on human physiology and behavior, which includes sleep.³⁴ Little is understood about how light affects sleep and other circadian-related functions in general ward patients, as it is not commonly measured. Our findings suggest that patients admitted to the hospital are exposed to light levels and patterns that may not optimally promote wake and sleep. Encouragingly, we did not find excessive average light levels during the nighttime in either ICU or non-ICU environment of our hospital, although others have described intrusive nighttime light in the hospital setting.^35,36 Even short bursts of low or moderate light during the nighttime can cause circadian phase delay,³⁷ and efforts to maintain darkness in patient rooms at night should continue.

Our measurements show that average daytime light levels did not exceed 250 lux, which corresponds to low, office-level lighting, while the brightest average light levels occurred in the afternoon for both environments. These levels are consistent with other reports^26,35,36 as is the light-level variability noted throughout the day (which is not unexpected given room positioning, patient preference, curtains, etc). The level and amount of daytime light needed to maintain circadian rhythms in humans is still unknown.³⁸ Brighter light is generally more effective at influencing the circadian pacemaker in a dose-dependent manner.³⁹ Although entrainment (synchronization of the body’s biological rhythm with environmental cues such as ambient light) of the human circadian rhythm has been shown with low light levels (eg, <100 lux), these studies included healthy volunteers in a carefully controlled, constant, routine environment.²³ How these data apply to acutely ill subjects in the hospital environment is not clear. We note that low to moderate levels of light (50-1000 lux) are less effective for entrainment of the circadian rhythm in older people (age >65 years, the majority of our admissions) compared with younger people. Thus, older, hospitalized patients may require greater light levels for regulation of the sleep-wake cycle.⁴⁰ These data are important when designing interventions to improve light for and maintain circadian rhythms in hospitalized patients. For example, Simons et al. found that dynamic light-application therapy, which achieved a maximum average lux level of <800 lux, did not reduce rates of delirium in critically ill patients (mean age ~65). One interpretation of these results, though there are many others, is that the light levels achieved were not high enough to influence circadian timing in hospitalized, mostly elderly patients. The physiological impact of light on the circadian rhythm in hospitalized patients still remains to be measured.

Our study does have a few limitations. We did not assess sound quality, which is another determinant of arousal potential.²⁰ Also, a shorter measurement interval might be useful in determining sharper sound increases. It may also be important to consider A- versus C-weighted measurements of sound levels, as A-weighted measurements usually reflect higher-frequency sound while C-weighted measurements usually reflect low-frequency noise¹⁸; we obtained only A-weighted measurements in our study. However, A-weighted measurements are generally considered more reflective of what the human ear considers noise and are used more standardly than C-weighted measurements.

Regarding light measurements, we recorded from rooms facing different cardinal directions and during different times of the year, which likely contributed to some of the variability in the daytime light levels on both floors. Additionally, light levels were not measured directly at the patient’s eye level. However, given that overhead fluorescent lighting was the primary source of lighting, it is doubtful that we substantially underestimated optic-nerve light levels. In the future, it may also be important to measure the different wavelengths of lights, as blue light may have a greater impact on sleep than other wavelengths.⁴¹ Although our findings align with others’, we note that this was a single-center study, which could limit the generalizability of our findings given inter-hospital variations in patient volume, interior layout and structure, and geographic location.

Overall, our study suggests that the light and sound environment for sleep in the inpatient setting, including both the ICU and non-ICU wards, has multiple areas for improvement. Our data also suggest specific directions for future clinical efforts at improvement. For example, efforts to decrease average sound levels may worsen sleep fragmentation. Similarly, more light during the day may be more helpful than further attempts to limit light during the night.

Disclosure

This research was funded in part by a NIH/NCATS flagship Clinical and Translational Science Award Grant (5KL2TR001112). None of the authors report any conflict of interest, financial or otherwise, in the preparation of this article.

The hospital environment fails to promote adequate sleep for acutely or critically ill patients. Intensive care units (ICUs) have received the most scrutiny, because critically ill patients suffer from severely fragmented sleep as well as a lack of deeper, more restorative sleep.^1-4 ICU survivors frequently cite sleep deprivation, contributed to by ambient noise, as a major stressor while receiving care.^5,6 Importantly, efforts to modify the ICU environment to promote sleep have been associated with reductions in delirium.^7,8 However, sleep deprivation and delirium in the hospital are not limited to ICU patients.

Sleep in the non-ICU setting is also notoriously poor, with 50%-80% of patients reporting sleep as “unsound” or otherwise subjectively poor.^9-11 Additionally, patients frequently ask for and/or receive pharmacological sleeping aids¹² despite little evidence of efficacy¹³ and increasing evidence of harm.¹⁴ Here too, efforts to improve sleep seems to attenuate risk of delirium,¹⁵ which remains a substantial problem on general wards, with incidence reported as high as 20%-30%. The reasons for poor sleep in the hospital are multifactorial, but data suggest that the inpatient environment, including noise and light levels, which are measurable and modifiable entities, contribute significantly to the problem.¹⁶

The World Health Organization (WHO) recommends that nighttime baseline noise levels do not exceed 30 decibels (dB) and that nighttime noise peaks (ie, loud noises) do not exceed 40 dB¹⁷; most studies suggest that ICU and general ward rooms are above this range on average.^10,18 Others have also demonstrated an association between loud noises and patients’ subjective perception of poor sleep.^10,19 However, when considering clinically important noise, peak and average noise levels may not be the key factor in causing arousals from sleep. Buxton and colleagues²⁰ found that noise quality affects arousal probability; for example, electronic alarms and conversational noise are more likely to cause awakenings compared with the opening or closing of doors and ice machines. Importantly, peak and average noise levels may also matter less for sleep than do sound level changes (SLCs), which are defined as the difference between background/baseline noise and peak noise. Using healthy subjects exposed to simulated ICU noise, Stanchina et al.²¹ found that SLCs >17.5 dB were more likely to cause polysomnographic arousals from sleep regardless of peak noise level. This sound pressure change of approximately 20 dB would be perceived as 4 times louder, or, as an example, would be the difference between normal conversation between 2 people (~40 dB) that is then interrupted by the start of a vacuum cleaner (~60 dB). To our knowledge, no other studies have closely examined SLCs in different hospital environments.

Ambient light also likely affects sleep quality in the hospital. The circadian rhythm system, which controls the human sleep–wake cycle as well as multiple other physiologic functions, depends on ambient light as the primary external factor for regulating the internal clock.^22,23 Insufficient and inappropriately timed light exposure can desynchronize the biological clock, thereby negatively affecting sleep quality.^24,25 Conversely, patients exposed to early-morning bright light may sleep better while in the hospital.¹⁶ In addition to sleep patterns, ambient light affects other aspects of patient care; for example, lower light levels in the hospital have recently been associated with higher levels of fatigue and mood disturbance.²⁶A growing body of data has investigated the ambient environment in the ICU, but fewer studies have focused on sound and light analysis in other inpatient areas such as the general ward and telemetry floors. We examined sound and light levels in the ICU and non-ICU environment, hypothesizing that average sound levels would be higher in the ICU than on non-ICU floors but that the number of SLCs >17.5 dB would be similar. Additionally, we expected that average light levels would be higher in the ICU than on non-ICU floors.

This was an observational study of the sound and light environment in the inpatient setting. Per our Institutional Review Board, no consent was required. Battery-operated sound-level (SDL600, Extech Instruments, Nashua, NH) and light-level (SDL400, Extech Instruments, Nashua, NH) meters were placed in 24 patient rooms in our tertiary-care adult hospital in La Jolla, CA. Recordings were obtained in randomly selected, single-patient occupied rooms that were from 3 different hospital units and included 8 general ward rooms, 8 telemetry floor rooms, and 8 ICU rooms. We recorded for approximately 24-72 hours. Depending on the geographic layout of the room, meters were placed as close to the head of the patient’s bed as possible and were generally not placed farther than 2 meters away from the patient’s head of bed; all rooms contained a window.

Sound Measurements

Sound meters measured ambient noise in dB every 2 seconds and were set for A-weighted frequency measurements. We averaged individual data points to obtain hourly averages for ICU and non-ICU rooms. For hourly sound averages, we further separated the data to compare the general ward telemetry floors (both non-ICU), the latter of which has more patient monitoring and a lower nurse-to-patient ratio compared with the general ward floor.

Data from ICU versus non-ICU rooms were analyzed for the number of sound peaks throughout the 24-hour day and for sound peak over the nighttime, defined as the number of times sound levels exceeded 65 dB, 70 dB, or 80 dB, which were averaged over 24 hours and over the nighttime (10 PM to 6 AM). We also calculated the number of average SLCs ≥17.5 dB observed over 24 hours and over the nighttime.

Light Measurements

Light meters measured luminescence in lux at a frequency of 120 seconds. We averaged individual data points to obtain hourly averages for ICU and non-ICU rooms. In addition to hourly averages, light-level data were analyzed for maximum levels throughout the day and night.

Statistical Analysis

Hourly sound-level averages between the 3 floors were evaluated using a 1-way analysis of variance (ANOVA); sound averages from the general ward and telemetry floor were also compared at each hour using a Student t test. Light-level data, sound-level peak data, as well as SLC data were also evaluated using a Student t test.

Sound Measurements

Examples of the raw data distribution for individual sound recordings in an ICU and non-ICU room are shown in Figure 1A and 1B. Sound-level analysis with specific average values and significance levels between ICU and non-ICU rooms (with non-ICU rooms further divided between telemetry and general ward floors for purposes of hourly averages) are shown in Table 1. The average hourly values in all 3 locations were always above the 30-35 dB level (nighttime and daytime, respectively) recommended by the WHO (Figure 1C). A 1-way ANOVA analysis revealed significant differences between the 3 floors at all time points except for 10 AM. An analysis of the means at each time point between the telemetry floor and the general ward floor showed that the telemetry floor had significantly higher sound averages compared with the general ward floor at 10 PM, 11 PM, and 12 AM. Sound levels dropped during the nighttime on both non-ICU wards but remained fairly constant throughout the day and night in the ICU.

Light Measurements

Examples of light levels over a 24-hour period in an ICU and non-ICU room are shown in Figure 2A and 2B, respectively. Maximum average light levels (reported here as average value ± standard deviation to demonstrate variability within the data) in the ICU were 169.7 ± 127.1 lux and occurred at 1 PM, while maximum average light levels in the non-ICU rooms were 213.5 ± 341.6 lux and occurred at 5 PM (Figure 2C). Average light levels in the morning hours remained low and ranged from 15.9 ± 12.7 lux to 38.9 ± 43.4 lux in the ICU and from 22.3 ± 17.5 lux to 100.7 ± 92.0 lux on the non-ICU floors. The maximum measured level from any of the recordings was 2530 lux and occurred in a general ward room in the 5 PM hour. Overall, light averages remained low, but this particular room had light levels that were significantly higher than the others. A t test analysis of the hourly averages revealed only 1 time point of significant difference between the 2 floors; at 7 AM, the general ward floor had a higher lux level of 49.9 ± 27.5 versus 19.2 ± 10.7 in the ICU (P = 0.038). Otherwise, there were no differences between light levels in ICU rooms versus non-ICU rooms. Evaluation of the data revealed a substantial amount of variability in light levels throughout the daytime hours. Light levels during the nighttime remained low and were not significantly different between the 2 groups.

To our knowledge, this is the first study to directly compare the ICU and non-ICU environment for its potential impact on sleep and circadian alignment. Our study adds to the literature with several novel findings. First, average sound levels on non-ICU wards are lower than in the ICU. Second, although quieter on average, SLCs >17.5 dB occurred an equivalent number of times for both the ICU and non-ICU wards. Third, average daytime light levels in both the ICU and non-ICU environment are low. Lastly, peak light levels for both ICU and non-ICU wards occur later in the day instead of in the morning. All of the above have potential impact for optimizing the ward environment to better aid in sleep for patients.

Sound-Level Findings

Data on sound levels for non-ICU floors are limited but mostly consistent with our findings; sound averages in our study ranged from 44.6 to 55.1 dB in non-ICU rooms, while others report averages ranging from 48 dB¹⁹ to 63.5 dB,¹⁰ although the latter measurement includes rooms occupied with 4 to 6 patients, which we expect would increase the noise levels. Others report average noise levels in the ICU similar to our values, which ranged from 56.1 to 60.3 dB.^18,27 Here, we show that average and peak sound levels on non-ICU wards are consistently lower than in the ICU. However, sound levels on the general ward and telemetry floors still remain quite high and potentially disruptive to patients, with average nighttime sound levels reaching the range of light outdoor traffic. The sleep environment could play an even larger role in sleep quality for non-ICU patients, as they do not typically receive sedation (though pharmacological sleeping-aid use is quite high, despite the risks)²⁸ and thus may be more sensitive to environmental factors that impact sleep.

Average and peak sound levels contribute to the ambient noise experienced by patients but may not be the source of sleep disruptions. Using polysomnography in healthy subjects exposed to recordings of ICU noise, Stanchina et al.²¹ showed that SLCs from baseline and not peak sound levels determined whether a subject was aroused from sleep by sound. Accordingly, they also found that increasing baseline sound levels by using white noise reduced the number of arousals that subjects experienced. To our knowledge, other studies have not quantified and compared SLCs in the ICU and non-ICU environments. Our data show that patients on non-ICU floors experience at least the same number of SLCs, and thereby the same potential for arousals from sleep, when compared with ICU patients. The higher baseline level of noise in the ICU likely explains the relatively lower number of SLCs when compared with the non-ICU floors. Although decreasing overall noise to promote sleep in the hospital seems like the obvious solution, the treatment for noise pollution in the hospital may actually be more background noise, not less.

Recent studies support the clinical implications of our findings. First, decreasing overall noise levels is difficult to accomplish.²⁹ Second, recent studies utilized white noise in different hospital settings with some success in improving patients’ subjective sleep quality, although more studies using objective data measurements are needed to further understand the impact of white noise on sleep in hospitalized patients.^30,31 Third, efforts at reducing interruptions—which likely will decrease the number of SLCs—such as clustering nursing care or reducing intermittent alarms may be more beneficial in improving sleep than efforts at decreasing average sound levels. For example, Bartick et al. reduced the number of patient interruptions at night by eliminating routine vital signs and clustering medication administration. Although they included other interventions as well, we note that this approach likely reduced SLCs and was associated with a reduction in the use of sedative medications.³² Ultimately, our data show that a focus on reducing SLCs will be one necessary component of a multipronged solution to improving inpatient sleep.³³

Light-Level Findings

Because of its effect on circadian rhythms, the daily light-dark cycle has a powerful impact on human physiology and behavior, which includes sleep.³⁴ Little is understood about how light affects sleep and other circadian-related functions in general ward patients, as it is not commonly measured. Our findings suggest that patients admitted to the hospital are exposed to light levels and patterns that may not optimally promote wake and sleep. Encouragingly, we did not find excessive average light levels during the nighttime in either ICU or non-ICU environment of our hospital, although others have described intrusive nighttime light in the hospital setting.^35,36 Even short bursts of low or moderate light during the nighttime can cause circadian phase delay,³⁷ and efforts to maintain darkness in patient rooms at night should continue.

Our measurements show that average daytime light levels did not exceed 250 lux, which corresponds to low, office-level lighting, while the brightest average light levels occurred in the afternoon for both environments. These levels are consistent with other reports^26,35,36 as is the light-level variability noted throughout the day (which is not unexpected given room positioning, patient preference, curtains, etc). The level and amount of daytime light needed to maintain circadian rhythms in humans is still unknown.³⁸ Brighter light is generally more effective at influencing the circadian pacemaker in a dose-dependent manner.³⁹ Although entrainment (synchronization of the body’s biological rhythm with environmental cues such as ambient light) of the human circadian rhythm has been shown with low light levels (eg, <100 lux), these studies included healthy volunteers in a carefully controlled, constant, routine environment.²³ How these data apply to acutely ill subjects in the hospital environment is not clear. We note that low to moderate levels of light (50-1000 lux) are less effective for entrainment of the circadian rhythm in older people (age >65 years, the majority of our admissions) compared with younger people. Thus, older, hospitalized patients may require greater light levels for regulation of the sleep-wake cycle.⁴⁰ These data are important when designing interventions to improve light for and maintain circadian rhythms in hospitalized patients. For example, Simons et al. found that dynamic light-application therapy, which achieved a maximum average lux level of <800 lux, did not reduce rates of delirium in critically ill patients (mean age ~65). One interpretation of these results, though there are many others, is that the light levels achieved were not high enough to influence circadian timing in hospitalized, mostly elderly patients. The physiological impact of light on the circadian rhythm in hospitalized patients still remains to be measured.

Our study does have a few limitations. We did not assess sound quality, which is another determinant of arousal potential.²⁰ Also, a shorter measurement interval might be useful in determining sharper sound increases. It may also be important to consider A- versus C-weighted measurements of sound levels, as A-weighted measurements usually reflect higher-frequency sound while C-weighted measurements usually reflect low-frequency noise¹⁸; we obtained only A-weighted measurements in our study. However, A-weighted measurements are generally considered more reflective of what the human ear considers noise and are used more standardly than C-weighted measurements.

Regarding light measurements, we recorded from rooms facing different cardinal directions and during different times of the year, which likely contributed to some of the variability in the daytime light levels on both floors. Additionally, light levels were not measured directly at the patient’s eye level. However, given that overhead fluorescent lighting was the primary source of lighting, it is doubtful that we substantially underestimated optic-nerve light levels. In the future, it may also be important to measure the different wavelengths of lights, as blue light may have a greater impact on sleep than other wavelengths.⁴¹ Although our findings align with others’, we note that this was a single-center study, which could limit the generalizability of our findings given inter-hospital variations in patient volume, interior layout and structure, and geographic location.

Overall, our study suggests that the light and sound environment for sleep in the inpatient setting, including both the ICU and non-ICU wards, has multiple areas for improvement. Our data also suggest specific directions for future clinical efforts at improvement. For example, efforts to decrease average sound levels may worsen sleep fragmentation. Similarly, more light during the day may be more helpful than further attempts to limit light during the night.

Disclosure

This research was funded in part by a NIH/NCATS flagship Clinical and Translational Science Award Grant (5KL2TR001112). None of the authors report any conflict of interest, financial or otherwise, in the preparation of this article.

The hospital environment fails to promote adequate sleep for acutely or critically ill patients. Intensive care units (ICUs) have received the most scrutiny, because critically ill patients suffer from severely fragmented sleep as well as a lack of deeper, more restorative sleep.^1-4 ICU survivors frequently cite sleep deprivation, contributed to by ambient noise, as a major stressor while receiving care.^5,6 Importantly, efforts to modify the ICU environment to promote sleep have been associated with reductions in delirium.^7,8 However, sleep deprivation and delirium in the hospital are not limited to ICU patients.

Sleep in the non-ICU setting is also notoriously poor, with 50%-80% of patients reporting sleep as “unsound” or otherwise subjectively poor.^9-11 Additionally, patients frequently ask for and/or receive pharmacological sleeping aids¹² despite little evidence of efficacy¹³ and increasing evidence of harm.¹⁴ Here too, efforts to improve sleep seems to attenuate risk of delirium,¹⁵ which remains a substantial problem on general wards, with incidence reported as high as 20%-30%. The reasons for poor sleep in the hospital are multifactorial, but data suggest that the inpatient environment, including noise and light levels, which are measurable and modifiable entities, contribute significantly to the problem.¹⁶

The World Health Organization (WHO) recommends that nighttime baseline noise levels do not exceed 30 decibels (dB) and that nighttime noise peaks (ie, loud noises) do not exceed 40 dB¹⁷; most studies suggest that ICU and general ward rooms are above this range on average.^10,18 Others have also demonstrated an association between loud noises and patients’ subjective perception of poor sleep.^10,19 However, when considering clinically important noise, peak and average noise levels may not be the key factor in causing arousals from sleep. Buxton and colleagues²⁰ found that noise quality affects arousal probability; for example, electronic alarms and conversational noise are more likely to cause awakenings compared with the opening or closing of doors and ice machines. Importantly, peak and average noise levels may also matter less for sleep than do sound level changes (SLCs), which are defined as the difference between background/baseline noise and peak noise. Using healthy subjects exposed to simulated ICU noise, Stanchina et al.²¹ found that SLCs >17.5 dB were more likely to cause polysomnographic arousals from sleep regardless of peak noise level. This sound pressure change of approximately 20 dB would be perceived as 4 times louder, or, as an example, would be the difference between normal conversation between 2 people (~40 dB) that is then interrupted by the start of a vacuum cleaner (~60 dB). To our knowledge, no other studies have closely examined SLCs in different hospital environments.

Ambient light also likely affects sleep quality in the hospital. The circadian rhythm system, which controls the human sleep–wake cycle as well as multiple other physiologic functions, depends on ambient light as the primary external factor for regulating the internal clock.^22,23 Insufficient and inappropriately timed light exposure can desynchronize the biological clock, thereby negatively affecting sleep quality.^24,25 Conversely, patients exposed to early-morning bright light may sleep better while in the hospital.¹⁶ In addition to sleep patterns, ambient light affects other aspects of patient care; for example, lower light levels in the hospital have recently been associated with higher levels of fatigue and mood disturbance.²⁶A growing body of data has investigated the ambient environment in the ICU, but fewer studies have focused on sound and light analysis in other inpatient areas such as the general ward and telemetry floors. We examined sound and light levels in the ICU and non-ICU environment, hypothesizing that average sound levels would be higher in the ICU than on non-ICU floors but that the number of SLCs >17.5 dB would be similar. Additionally, we expected that average light levels would be higher in the ICU than on non-ICU floors.

This was an observational study of the sound and light environment in the inpatient setting. Per our Institutional Review Board, no consent was required. Battery-operated sound-level (SDL600, Extech Instruments, Nashua, NH) and light-level (SDL400, Extech Instruments, Nashua, NH) meters were placed in 24 patient rooms in our tertiary-care adult hospital in La Jolla, CA. Recordings were obtained in randomly selected, single-patient occupied rooms that were from 3 different hospital units and included 8 general ward rooms, 8 telemetry floor rooms, and 8 ICU rooms. We recorded for approximately 24-72 hours. Depending on the geographic layout of the room, meters were placed as close to the head of the patient’s bed as possible and were generally not placed farther than 2 meters away from the patient’s head of bed; all rooms contained a window.

Sound Measurements

Sound meters measured ambient noise in dB every 2 seconds and were set for A-weighted frequency measurements. We averaged individual data points to obtain hourly averages for ICU and non-ICU rooms. For hourly sound averages, we further separated the data to compare the general ward telemetry floors (both non-ICU), the latter of which has more patient monitoring and a lower nurse-to-patient ratio compared with the general ward floor.

Data from ICU versus non-ICU rooms were analyzed for the number of sound peaks throughout the 24-hour day and for sound peak over the nighttime, defined as the number of times sound levels exceeded 65 dB, 70 dB, or 80 dB, which were averaged over 24 hours and over the nighttime (10 PM to 6 AM). We also calculated the number of average SLCs ≥17.5 dB observed over 24 hours and over the nighttime.

Light Measurements

Light meters measured luminescence in lux at a frequency of 120 seconds. We averaged individual data points to obtain hourly averages for ICU and non-ICU rooms. In addition to hourly averages, light-level data were analyzed for maximum levels throughout the day and night.

Statistical Analysis

Hourly sound-level averages between the 3 floors were evaluated using a 1-way analysis of variance (ANOVA); sound averages from the general ward and telemetry floor were also compared at each hour using a Student t test. Light-level data, sound-level peak data, as well as SLC data were also evaluated using a Student t test.

Sound Measurements

Examples of the raw data distribution for individual sound recordings in an ICU and non-ICU room are shown in Figure 1A and 1B. Sound-level analysis with specific average values and significance levels between ICU and non-ICU rooms (with non-ICU rooms further divided between telemetry and general ward floors for purposes of hourly averages) are shown in Table 1. The average hourly values in all 3 locations were always above the 30-35 dB level (nighttime and daytime, respectively) recommended by the WHO (Figure 1C). A 1-way ANOVA analysis revealed significant differences between the 3 floors at all time points except for 10 AM. An analysis of the means at each time point between the telemetry floor and the general ward floor showed that the telemetry floor had significantly higher sound averages compared with the general ward floor at 10 PM, 11 PM, and 12 AM. Sound levels dropped during the nighttime on both non-ICU wards but remained fairly constant throughout the day and night in the ICU.

Light Measurements

Examples of light levels over a 24-hour period in an ICU and non-ICU room are shown in Figure 2A and 2B, respectively. Maximum average light levels (reported here as average value ± standard deviation to demonstrate variability within the data) in the ICU were 169.7 ± 127.1 lux and occurred at 1 PM, while maximum average light levels in the non-ICU rooms were 213.5 ± 341.6 lux and occurred at 5 PM (Figure 2C). Average light levels in the morning hours remained low and ranged from 15.9 ± 12.7 lux to 38.9 ± 43.4 lux in the ICU and from 22.3 ± 17.5 lux to 100.7 ± 92.0 lux on the non-ICU floors. The maximum measured level from any of the recordings was 2530 lux and occurred in a general ward room in the 5 PM hour. Overall, light averages remained low, but this particular room had light levels that were significantly higher than the others. A t test analysis of the hourly averages revealed only 1 time point of significant difference between the 2 floors; at 7 AM, the general ward floor had a higher lux level of 49.9 ± 27.5 versus 19.2 ± 10.7 in the ICU (P = 0.038). Otherwise, there were no differences between light levels in ICU rooms versus non-ICU rooms. Evaluation of the data revealed a substantial amount of variability in light levels throughout the daytime hours. Light levels during the nighttime remained low and were not significantly different between the 2 groups.

To our knowledge, this is the first study to directly compare the ICU and non-ICU environment for its potential impact on sleep and circadian alignment. Our study adds to the literature with several novel findings. First, average sound levels on non-ICU wards are lower than in the ICU. Second, although quieter on average, SLCs >17.5 dB occurred an equivalent number of times for both the ICU and non-ICU wards. Third, average daytime light levels in both the ICU and non-ICU environment are low. Lastly, peak light levels for both ICU and non-ICU wards occur later in the day instead of in the morning. All of the above have potential impact for optimizing the ward environment to better aid in sleep for patients.

Sound-Level Findings

Data on sound levels for non-ICU floors are limited but mostly consistent with our findings; sound averages in our study ranged from 44.6 to 55.1 dB in non-ICU rooms, while others report averages ranging from 48 dB¹⁹ to 63.5 dB,¹⁰ although the latter measurement includes rooms occupied with 4 to 6 patients, which we expect would increase the noise levels. Others report average noise levels in the ICU similar to our values, which ranged from 56.1 to 60.3 dB.^18,27 Here, we show that average and peak sound levels on non-ICU wards are consistently lower than in the ICU. However, sound levels on the general ward and telemetry floors still remain quite high and potentially disruptive to patients, with average nighttime sound levels reaching the range of light outdoor traffic. The sleep environment could play an even larger role in sleep quality for non-ICU patients, as they do not typically receive sedation (though pharmacological sleeping-aid use is quite high, despite the risks)²⁸ and thus may be more sensitive to environmental factors that impact sleep.

Average and peak sound levels contribute to the ambient noise experienced by patients but may not be the source of sleep disruptions. Using polysomnography in healthy subjects exposed to recordings of ICU noise, Stanchina et al.²¹ showed that SLCs from baseline and not peak sound levels determined whether a subject was aroused from sleep by sound. Accordingly, they also found that increasing baseline sound levels by using white noise reduced the number of arousals that subjects experienced. To our knowledge, other studies have not quantified and compared SLCs in the ICU and non-ICU environments. Our data show that patients on non-ICU floors experience at least the same number of SLCs, and thereby the same potential for arousals from sleep, when compared with ICU patients. The higher baseline level of noise in the ICU likely explains the relatively lower number of SLCs when compared with the non-ICU floors. Although decreasing overall noise to promote sleep in the hospital seems like the obvious solution, the treatment for noise pollution in the hospital may actually be more background noise, not less.

Recent studies support the clinical implications of our findings. First, decreasing overall noise levels is difficult to accomplish.²⁹ Second, recent studies utilized white noise in different hospital settings with some success in improving patients’ subjective sleep quality, although more studies using objective data measurements are needed to further understand the impact of white noise on sleep in hospitalized patients.^30,31 Third, efforts at reducing interruptions—which likely will decrease the number of SLCs—such as clustering nursing care or reducing intermittent alarms may be more beneficial in improving sleep than efforts at decreasing average sound levels. For example, Bartick et al. reduced the number of patient interruptions at night by eliminating routine vital signs and clustering medication administration. Although they included other interventions as well, we note that this approach likely reduced SLCs and was associated with a reduction in the use of sedative medications.³² Ultimately, our data show that a focus on reducing SLCs will be one necessary component of a multipronged solution to improving inpatient sleep.³³

Light-Level Findings

Because of its effect on circadian rhythms, the daily light-dark cycle has a powerful impact on human physiology and behavior, which includes sleep.³⁴ Little is understood about how light affects sleep and other circadian-related functions in general ward patients, as it is not commonly measured. Our findings suggest that patients admitted to the hospital are exposed to light levels and patterns that may not optimally promote wake and sleep. Encouragingly, we did not find excessive average light levels during the nighttime in either ICU or non-ICU environment of our hospital, although others have described intrusive nighttime light in the hospital setting.^35,36 Even short bursts of low or moderate light during the nighttime can cause circadian phase delay,³⁷ and efforts to maintain darkness in patient rooms at night should continue.

Our measurements show that average daytime light levels did not exceed 250 lux, which corresponds to low, office-level lighting, while the brightest average light levels occurred in the afternoon for both environments. These levels are consistent with other reports^26,35,36 as is the light-level variability noted throughout the day (which is not unexpected given room positioning, patient preference, curtains, etc). The level and amount of daytime light needed to maintain circadian rhythms in humans is still unknown.³⁸ Brighter light is generally more effective at influencing the circadian pacemaker in a dose-dependent manner.³⁹ Although entrainment (synchronization of the body’s biological rhythm with environmental cues such as ambient light) of the human circadian rhythm has been shown with low light levels (eg, <100 lux), these studies included healthy volunteers in a carefully controlled, constant, routine environment.²³ How these data apply to acutely ill subjects in the hospital environment is not clear. We note that low to moderate levels of light (50-1000 lux) are less effective for entrainment of the circadian rhythm in older people (age >65 years, the majority of our admissions) compared with younger people. Thus, older, hospitalized patients may require greater light levels for regulation of the sleep-wake cycle.⁴⁰ These data are important when designing interventions to improve light for and maintain circadian rhythms in hospitalized patients. For example, Simons et al. found that dynamic light-application therapy, which achieved a maximum average lux level of <800 lux, did not reduce rates of delirium in critically ill patients (mean age ~65). One interpretation of these results, though there are many others, is that the light levels achieved were not high enough to influence circadian timing in hospitalized, mostly elderly patients. The physiological impact of light on the circadian rhythm in hospitalized patients still remains to be measured.

Our study does have a few limitations. We did not assess sound quality, which is another determinant of arousal potential.²⁰ Also, a shorter measurement interval might be useful in determining sharper sound increases. It may also be important to consider A- versus C-weighted measurements of sound levels, as A-weighted measurements usually reflect higher-frequency sound while C-weighted measurements usually reflect low-frequency noise¹⁸; we obtained only A-weighted measurements in our study. However, A-weighted measurements are generally considered more reflective of what the human ear considers noise and are used more standardly than C-weighted measurements.

Regarding light measurements, we recorded from rooms facing different cardinal directions and during different times of the year, which likely contributed to some of the variability in the daytime light levels on both floors. Additionally, light levels were not measured directly at the patient’s eye level. However, given that overhead fluorescent lighting was the primary source of lighting, it is doubtful that we substantially underestimated optic-nerve light levels. In the future, it may also be important to measure the different wavelengths of lights, as blue light may have a greater impact on sleep than other wavelengths.⁴¹ Although our findings align with others’, we note that this was a single-center study, which could limit the generalizability of our findings given inter-hospital variations in patient volume, interior layout and structure, and geographic location.

Overall, our study suggests that the light and sound environment for sleep in the inpatient setting, including both the ICU and non-ICU wards, has multiple areas for improvement. Our data also suggest specific directions for future clinical efforts at improvement. For example, efforts to decrease average sound levels may worsen sleep fragmentation. Similarly, more light during the day may be more helpful than further attempts to limit light during the night.

Disclosure

This research was funded in part by a NIH/NCATS flagship Clinical and Translational Science Award Grant (5KL2TR001112). None of the authors report any conflict of interest, financial or otherwise, in the preparation of this article.