The prevalence of depression, anxiety, and posttraumatic stress disorder (PTSD) symptoms in intensive care unit (ICU) survivors ranges from 17% to 44%.^1-4 Psychiatric comorbidity, the presence of 2 or more psychiatric disorders, is highly prevalent in survivors of acute respiratory distress syndrome and is associated with higher mortality in postsurgical ICU survivors.^5-7 While long-term cognitive impairment in patients with ICU delirium has been associated with poor quality of life (QoL),¹ the effects of psychiatric comorbidity on QoL among similar patients are not as well understood. In this study, we examined whether psychiatric comorbidity was associated with poorer QoL in survivors of ICU delirium.

We examined subjects who participated in the Pharmacologic Management of Delirium (PMD) clinical trial. This trial examined the efficacy of a pharmacological intervention for patients who developed ICU delirium at a local tertiary-care academic hospital.⁸ Out of 62 patients who participated in the follow-up of the PMD study, 58 completed QoL interviews and validated psychiatric screens (Patient Health Questionnaire-9 [PHQ-9] for depression, the Generalized Anxiety Disorder-7 [GAD-7] questionnaire for anxiety, and the Post-Traumatic Stress Syndrome [PTSS-10] questionnaire for PTSD) at 3 months after hospital discharge. High psychiatric comorbidity was defined as having significant symptoms for all 3 conditions (depression: PHQ-9 score ≥ 10; anxiety: GAD-7 ≥ 10; and PTSD: PTSS-10 > 35). No psychiatric morbidity was defined as having no significant symptoms for all 3 conditions. Low to moderate (low-moderate) psychiatric morbidity was defined as having symptoms for 1 to 2 conditions.

Participants also completed 2 complementary QoL measures: the EuroQol 5 dimensions questionnaire 3-level (EQ-5D-3L) Index and the EuroQol 5 dimensions Visual Analog Scale (EQ-5D-VAS).⁹ The EQ-5D-3L Index asks participants to rate themselves as having (1) no problems, (2) some problems, or (3) extreme problems on the following 5 scales: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The scores are then indexed against the US population to create a continuous index scale ranging from −0.11 to 1.00. A score of 1 represents perfect health, 0 represents death, and negative values indicate a health state worse than death. The EQ-5D-VAS asks participants to draw a line on a visual scale from an anchor box to the point that represents their health state. The score ranges from 0 being the worst imaginable health state to 100 being the best imaginable health state. Demographic information, clinical characteristics, and prior history of depression, anxiety, and PTSD were obtained through PMD study records and clinical records. The Charlson Comorbidity Index, which measures chronic comorbidities, and Acute Physiology and Chronic Health Evaluation II, which estimates acute severity of illness within 24 hours of ICU admission, were calculated from patients’ available clinical information.

Fisher’s exact tests were used to compare dichotomous outcomes. Analysis of variance (ANOVA) was used to compare continuous outcomes across the 3 psychiatric groups. Analysis of covariance (ANCOVA) was used to determine whether psychiatric comorbidity in survivors of ICU delirium was associated with QoL measures. Models were adjusted for the following covariates: age, gender, Charlson Comorbidity Index, discharged to home, prior history of depression, and prior history of anxiety. To assess the relationship of psychiatric comorbidity with QoL, we chose the 2 continuous QoL measures as the outcome. Because we were interested in the effect of psychiatric burden on QoL, we used ANCOVA with QoL as the dependent variable and psychiatric burden as an independent variable. Pairwise comparisons were then performed when overall differences were significant (P < 0.05). We performed 2 separate sensitivity analyses. The first analysis looked solely at the subgroup of patients from the medical intensive care unit. We also recalculated the EQ-5D-3L index excluding the anxiety/depression item.

Nearly one-third of patients (18/58) had high psychiatric burden. The table looks at the demographic and clinical characteristics of patients with high psychiatric comorbidity versus those of low-moderate psychiatric comorbidity and those with no psychiatric morbidity. Patient groups did not differ significantly in terms of demographics. For clinical characteristics, patients with high psychiatric comorbidity were more likely than patients with low-moderate psychiatric comorbidity to have a prior history of depression (P < 0.05).

Patients with high psychiatric comorbidity were more likely to have a poorer QoL when compared with patients with low-moderate psychiatric comorbidity and to those with no morbidity as measured by a lower EQ-5D-3L Index (no, 0.69 ± 0.25; low-moderate, 0.70 ± 0.19; high, 0.48 ± 0.24; P = 0.006) and EQ-5D-VAS (no, 67.0 ± 20.7; low-moderate, 76.6 ± 20.0; high, 50.8 ± 22.4; P = 0.004). After adjusting for covariates, patients with high psychiatric comorbidity had a poorer QoL compared with those with no morbidity or low-moderate comorbidity on the EQ-5D-3L Index (P = 0.017 for overall differences), whereas patients who had high psychiatric comorbidity had a poorer QoL compared to those with low-moderate comorbidity on the EQ-5D-VAS (P = 0.039 for overall differences; Figure). Subgroup analysis of MICU patients yielded similar results. Patients with high psychiatric burden had significantly poorer QoL as measured by the EQ-5D-3L (unadjusted P = 0.044, adjusted P = 0.003) and the EQ-5D-VAS (unadjusted P = 0.007, adjusted P = 0.021). After excluding the anxiety/depression item from the EQ-5D-3L, we observed similar differences (no, 0.71 ± 0.24; low-moderate, 0.75 ± 0.15; high, 0.58 ± 0.22; unadjusted P = 0.062; adjusted P = 0.040).

Psychiatric comorbidities in ICU survivors are common and pose a significant clinical issue. Patients with multiple psychiatric comorbidities can be more complicated to identify from a diagnostic standpoint and often require more prolonged, intensive mental health treatment when compared with patients with a single psychiatric disorder.^10,11 Our study showed that high psychiatric comorbidity in survivors of ICU delirium is associated with a decreased QoL compared with those with no psychiatric comorbidity or with low-moderate psychiatric comorbidity. This finding is consistent with previous studies in the general population that patients with multiple psychiatric comorbidities are associated with a poorer QoL compared with patients with a single psychiatric comorbidity.^10,11

There is a pressing need to better characterize psychiatric comorbidities in ICU survivors because our current evidence suggests that the prevalence of psychiatric comorbidities of ICU survivors is substantially higher than that of the general population. We found that nearly one-third of survivors of ICU delirium had comorbid depression, anxiety, and PTSD symptoms at 3 months. This is consistent with the few other studies of ICU survivors, which showed a prevalence of psychiatric comorbidity of 25% to 33%.^5,12 These rates are substantially higher than the prevalence in the general population of 6%.¹³

The high rate of psychiatric comorbidities may render it difficult to effectively treat the mental health symptoms in ICU survivors.¹⁴ Treating multiple psychiatric comorbidities may also be especially challenging in survivors of ICU delirium because they have a high prevalence of cognitive impairment. Mental health treatments for patients with psychiatric disorders and comorbid cognitive impairment are limited. Better characterization of psychiatric comorbidity in ICU survivors, particularly those with ICU delirium, is vital to the development of more effective, bundled treatments for this population with multiple comorbidities.

Standardized screenings of ICU survivors at a high risk for psychiatric disorders, such as survivors of ICU delirium, may help to identify patients with comorbid psychiatric disorder symptoms and have them referred to appropriate treatment earlier with the hope of improving their QoL sooner. Although opportunities to deliver integrated outpatient collaborative mental health and medical care for a subspecialty population are limited, one potential model of care would be to utilize a collaborative-care model in an ICU survivor clinic.¹⁵

Strengths of our study include the examination of psychiatric comorbidities in survivors of ICU delirium, who often have a poor QoL. A deeper understanding of psychiatric comorbidity and its relationship with QoL is needed to better understand how to deliver more effective treatments for these survivors. Limitations include the small sample size, a one-time measurement of psychiatric comorbidities at the 3-month follow-up based on screenings tools, and a lack of objective measures of physical functioning to determine the effects of psychiatric comorbidities on physical functioning. There may also have been differences in how patients with no psychiatric comorbidity responded to the EQ-5D-VAS as a result of premorbid differences (eg, they were healthier prior to their ICU stay and perceived their survivor status more negatively). This may explain why we did not see a statistically significant difference between no psychiatric comorbidity and high psychiatric comorbidity groups on the EQ-5D-VAS. Nevertheless, we did see a difference between the low-moderate psychiatric comorbidity group on EQ-5D-VAS and differences between the no comorbidity and low-moderate comorbidity groups versus the high comorbidity group on the EQ-5D-3L. Finally, data about psychiatric history and QoL prior to ICU hospitalization were limited. Therefore, truly determining incidence versus prevalence of post-ICU comorbidities and whether psychiatric symptoms and its effects on QoL were due to ICU hospitalization or to premorbid psychiatric symptoms is difficult.

Our study demonstrated that in survivors of ICU delirium, higher comorbidity of psychiatric symptoms was associated with poorer QoL. Future studies will need to confirm these findings. We will also need to identify potentially reversible risk factors for psychiatric comorbidity and poorer QoL and develop treatments to effectively target the mental health symptoms of survivors of ICU delirium.

Disclosure

Grant support: The PMD trial is funded through the National Institutes of Health grant R01AG054205-02. SW is supported by NIA 2P30AG010133. AP is supported by CMS 1 L1 CMS331444-02-00, Indiana CTSI, and NIA R01AG054205-02. SG is supported by NIA 2P30AG010133, NIA 5R01AG045350, and NIA R01AG054205-02. SK is supported by NHBLI 5T32HL091816-07. MB is supported by NIA R01 AG040220-05, AHRQ P30 HS024384-02, CMS 1 L1 CMS331444-02-00, NIA R01 AG030618-05A1 and NIA R01AG054205-02. BK is supported by NIA K23-AG043476 and NHLBI R01HL131730. The funding agency had no role in the development of the study design, collection, analysis, interpretation of data, manuscript development, or the decision to submit the manuscript for publication. Conflicts of interest include MB, SG, and AP being funded by NIA R01AG054205-02 for the PMD study.

The prevalence of depression, anxiety, and posttraumatic stress disorder (PTSD) symptoms in intensive care unit (ICU) survivors ranges from 17% to 44%.^1-4 Psychiatric comorbidity, the presence of 2 or more psychiatric disorders, is highly prevalent in survivors of acute respiratory distress syndrome and is associated with higher mortality in postsurgical ICU survivors.^5-7 While long-term cognitive impairment in patients with ICU delirium has been associated with poor quality of life (QoL),¹ the effects of psychiatric comorbidity on QoL among similar patients are not as well understood. In this study, we examined whether psychiatric comorbidity was associated with poorer QoL in survivors of ICU delirium.

We examined subjects who participated in the Pharmacologic Management of Delirium (PMD) clinical trial. This trial examined the efficacy of a pharmacological intervention for patients who developed ICU delirium at a local tertiary-care academic hospital.⁸ Out of 62 patients who participated in the follow-up of the PMD study, 58 completed QoL interviews and validated psychiatric screens (Patient Health Questionnaire-9 [PHQ-9] for depression, the Generalized Anxiety Disorder-7 [GAD-7] questionnaire for anxiety, and the Post-Traumatic Stress Syndrome [PTSS-10] questionnaire for PTSD) at 3 months after hospital discharge. High psychiatric comorbidity was defined as having significant symptoms for all 3 conditions (depression: PHQ-9 score ≥ 10; anxiety: GAD-7 ≥ 10; and PTSD: PTSS-10 > 35). No psychiatric morbidity was defined as having no significant symptoms for all 3 conditions. Low to moderate (low-moderate) psychiatric morbidity was defined as having symptoms for 1 to 2 conditions.

Participants also completed 2 complementary QoL measures: the EuroQol 5 dimensions questionnaire 3-level (EQ-5D-3L) Index and the EuroQol 5 dimensions Visual Analog Scale (EQ-5D-VAS).⁹ The EQ-5D-3L Index asks participants to rate themselves as having (1) no problems, (2) some problems, or (3) extreme problems on the following 5 scales: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The scores are then indexed against the US population to create a continuous index scale ranging from −0.11 to 1.00. A score of 1 represents perfect health, 0 represents death, and negative values indicate a health state worse than death. The EQ-5D-VAS asks participants to draw a line on a visual scale from an anchor box to the point that represents their health state. The score ranges from 0 being the worst imaginable health state to 100 being the best imaginable health state. Demographic information, clinical characteristics, and prior history of depression, anxiety, and PTSD were obtained through PMD study records and clinical records. The Charlson Comorbidity Index, which measures chronic comorbidities, and Acute Physiology and Chronic Health Evaluation II, which estimates acute severity of illness within 24 hours of ICU admission, were calculated from patients’ available clinical information.

Fisher’s exact tests were used to compare dichotomous outcomes. Analysis of variance (ANOVA) was used to compare continuous outcomes across the 3 psychiatric groups. Analysis of covariance (ANCOVA) was used to determine whether psychiatric comorbidity in survivors of ICU delirium was associated with QoL measures. Models were adjusted for the following covariates: age, gender, Charlson Comorbidity Index, discharged to home, prior history of depression, and prior history of anxiety. To assess the relationship of psychiatric comorbidity with QoL, we chose the 2 continuous QoL measures as the outcome. Because we were interested in the effect of psychiatric burden on QoL, we used ANCOVA with QoL as the dependent variable and psychiatric burden as an independent variable. Pairwise comparisons were then performed when overall differences were significant (P < 0.05). We performed 2 separate sensitivity analyses. The first analysis looked solely at the subgroup of patients from the medical intensive care unit. We also recalculated the EQ-5D-3L index excluding the anxiety/depression item.

Nearly one-third of patients (18/58) had high psychiatric burden. The table looks at the demographic and clinical characteristics of patients with high psychiatric comorbidity versus those of low-moderate psychiatric comorbidity and those with no psychiatric morbidity. Patient groups did not differ significantly in terms of demographics. For clinical characteristics, patients with high psychiatric comorbidity were more likely than patients with low-moderate psychiatric comorbidity to have a prior history of depression (P < 0.05).

Patients with high psychiatric comorbidity were more likely to have a poorer QoL when compared with patients with low-moderate psychiatric comorbidity and to those with no morbidity as measured by a lower EQ-5D-3L Index (no, 0.69 ± 0.25; low-moderate, 0.70 ± 0.19; high, 0.48 ± 0.24; P = 0.006) and EQ-5D-VAS (no, 67.0 ± 20.7; low-moderate, 76.6 ± 20.0; high, 50.8 ± 22.4; P = 0.004). After adjusting for covariates, patients with high psychiatric comorbidity had a poorer QoL compared with those with no morbidity or low-moderate comorbidity on the EQ-5D-3L Index (P = 0.017 for overall differences), whereas patients who had high psychiatric comorbidity had a poorer QoL compared to those with low-moderate comorbidity on the EQ-5D-VAS (P = 0.039 for overall differences; Figure). Subgroup analysis of MICU patients yielded similar results. Patients with high psychiatric burden had significantly poorer QoL as measured by the EQ-5D-3L (unadjusted P = 0.044, adjusted P = 0.003) and the EQ-5D-VAS (unadjusted P = 0.007, adjusted P = 0.021). After excluding the anxiety/depression item from the EQ-5D-3L, we observed similar differences (no, 0.71 ± 0.24; low-moderate, 0.75 ± 0.15; high, 0.58 ± 0.22; unadjusted P = 0.062; adjusted P = 0.040).

Psychiatric comorbidities in ICU survivors are common and pose a significant clinical issue. Patients with multiple psychiatric comorbidities can be more complicated to identify from a diagnostic standpoint and often require more prolonged, intensive mental health treatment when compared with patients with a single psychiatric disorder.^10,11 Our study showed that high psychiatric comorbidity in survivors of ICU delirium is associated with a decreased QoL compared with those with no psychiatric comorbidity or with low-moderate psychiatric comorbidity. This finding is consistent with previous studies in the general population that patients with multiple psychiatric comorbidities are associated with a poorer QoL compared with patients with a single psychiatric comorbidity.^10,11

There is a pressing need to better characterize psychiatric comorbidities in ICU survivors because our current evidence suggests that the prevalence of psychiatric comorbidities of ICU survivors is substantially higher than that of the general population. We found that nearly one-third of survivors of ICU delirium had comorbid depression, anxiety, and PTSD symptoms at 3 months. This is consistent with the few other studies of ICU survivors, which showed a prevalence of psychiatric comorbidity of 25% to 33%.^5,12 These rates are substantially higher than the prevalence in the general population of 6%.¹³

The high rate of psychiatric comorbidities may render it difficult to effectively treat the mental health symptoms in ICU survivors.¹⁴ Treating multiple psychiatric comorbidities may also be especially challenging in survivors of ICU delirium because they have a high prevalence of cognitive impairment. Mental health treatments for patients with psychiatric disorders and comorbid cognitive impairment are limited. Better characterization of psychiatric comorbidity in ICU survivors, particularly those with ICU delirium, is vital to the development of more effective, bundled treatments for this population with multiple comorbidities.

Standardized screenings of ICU survivors at a high risk for psychiatric disorders, such as survivors of ICU delirium, may help to identify patients with comorbid psychiatric disorder symptoms and have them referred to appropriate treatment earlier with the hope of improving their QoL sooner. Although opportunities to deliver integrated outpatient collaborative mental health and medical care for a subspecialty population are limited, one potential model of care would be to utilize a collaborative-care model in an ICU survivor clinic.¹⁵

Strengths of our study include the examination of psychiatric comorbidities in survivors of ICU delirium, who often have a poor QoL. A deeper understanding of psychiatric comorbidity and its relationship with QoL is needed to better understand how to deliver more effective treatments for these survivors. Limitations include the small sample size, a one-time measurement of psychiatric comorbidities at the 3-month follow-up based on screenings tools, and a lack of objective measures of physical functioning to determine the effects of psychiatric comorbidities on physical functioning. There may also have been differences in how patients with no psychiatric comorbidity responded to the EQ-5D-VAS as a result of premorbid differences (eg, they were healthier prior to their ICU stay and perceived their survivor status more negatively). This may explain why we did not see a statistically significant difference between no psychiatric comorbidity and high psychiatric comorbidity groups on the EQ-5D-VAS. Nevertheless, we did see a difference between the low-moderate psychiatric comorbidity group on EQ-5D-VAS and differences between the no comorbidity and low-moderate comorbidity groups versus the high comorbidity group on the EQ-5D-3L. Finally, data about psychiatric history and QoL prior to ICU hospitalization were limited. Therefore, truly determining incidence versus prevalence of post-ICU comorbidities and whether psychiatric symptoms and its effects on QoL were due to ICU hospitalization or to premorbid psychiatric symptoms is difficult.

Our study demonstrated that in survivors of ICU delirium, higher comorbidity of psychiatric symptoms was associated with poorer QoL. Future studies will need to confirm these findings. We will also need to identify potentially reversible risk factors for psychiatric comorbidity and poorer QoL and develop treatments to effectively target the mental health symptoms of survivors of ICU delirium.

Disclosure

Grant support: The PMD trial is funded through the National Institutes of Health grant R01AG054205-02. SW is supported by NIA 2P30AG010133. AP is supported by CMS 1 L1 CMS331444-02-00, Indiana CTSI, and NIA R01AG054205-02. SG is supported by NIA 2P30AG010133, NIA 5R01AG045350, and NIA R01AG054205-02. SK is supported by NHBLI 5T32HL091816-07. MB is supported by NIA R01 AG040220-05, AHRQ P30 HS024384-02, CMS 1 L1 CMS331444-02-00, NIA R01 AG030618-05A1 and NIA R01AG054205-02. BK is supported by NIA K23-AG043476 and NHLBI R01HL131730. The funding agency had no role in the development of the study design, collection, analysis, interpretation of data, manuscript development, or the decision to submit the manuscript for publication. Conflicts of interest include MB, SG, and AP being funded by NIA R01AG054205-02 for the PMD study.

The prevalence of depression, anxiety, and posttraumatic stress disorder (PTSD) symptoms in intensive care unit (ICU) survivors ranges from 17% to 44%.^1-4 Psychiatric comorbidity, the presence of 2 or more psychiatric disorders, is highly prevalent in survivors of acute respiratory distress syndrome and is associated with higher mortality in postsurgical ICU survivors.^5-7 While long-term cognitive impairment in patients with ICU delirium has been associated with poor quality of life (QoL),¹ the effects of psychiatric comorbidity on QoL among similar patients are not as well understood. In this study, we examined whether psychiatric comorbidity was associated with poorer QoL in survivors of ICU delirium.

We examined subjects who participated in the Pharmacologic Management of Delirium (PMD) clinical trial. This trial examined the efficacy of a pharmacological intervention for patients who developed ICU delirium at a local tertiary-care academic hospital.⁸ Out of 62 patients who participated in the follow-up of the PMD study, 58 completed QoL interviews and validated psychiatric screens (Patient Health Questionnaire-9 [PHQ-9] for depression, the Generalized Anxiety Disorder-7 [GAD-7] questionnaire for anxiety, and the Post-Traumatic Stress Syndrome [PTSS-10] questionnaire for PTSD) at 3 months after hospital discharge. High psychiatric comorbidity was defined as having significant symptoms for all 3 conditions (depression: PHQ-9 score ≥ 10; anxiety: GAD-7 ≥ 10; and PTSD: PTSS-10 > 35). No psychiatric morbidity was defined as having no significant symptoms for all 3 conditions. Low to moderate (low-moderate) psychiatric morbidity was defined as having symptoms for 1 to 2 conditions.

Participants also completed 2 complementary QoL measures: the EuroQol 5 dimensions questionnaire 3-level (EQ-5D-3L) Index and the EuroQol 5 dimensions Visual Analog Scale (EQ-5D-VAS).⁹ The EQ-5D-3L Index asks participants to rate themselves as having (1) no problems, (2) some problems, or (3) extreme problems on the following 5 scales: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. The scores are then indexed against the US population to create a continuous index scale ranging from −0.11 to 1.00. A score of 1 represents perfect health, 0 represents death, and negative values indicate a health state worse than death. The EQ-5D-VAS asks participants to draw a line on a visual scale from an anchor box to the point that represents their health state. The score ranges from 0 being the worst imaginable health state to 100 being the best imaginable health state. Demographic information, clinical characteristics, and prior history of depression, anxiety, and PTSD were obtained through PMD study records and clinical records. The Charlson Comorbidity Index, which measures chronic comorbidities, and Acute Physiology and Chronic Health Evaluation II, which estimates acute severity of illness within 24 hours of ICU admission, were calculated from patients’ available clinical information.

Fisher’s exact tests were used to compare dichotomous outcomes. Analysis of variance (ANOVA) was used to compare continuous outcomes across the 3 psychiatric groups. Analysis of covariance (ANCOVA) was used to determine whether psychiatric comorbidity in survivors of ICU delirium was associated with QoL measures. Models were adjusted for the following covariates: age, gender, Charlson Comorbidity Index, discharged to home, prior history of depression, and prior history of anxiety. To assess the relationship of psychiatric comorbidity with QoL, we chose the 2 continuous QoL measures as the outcome. Because we were interested in the effect of psychiatric burden on QoL, we used ANCOVA with QoL as the dependent variable and psychiatric burden as an independent variable. Pairwise comparisons were then performed when overall differences were significant (P < 0.05). We performed 2 separate sensitivity analyses. The first analysis looked solely at the subgroup of patients from the medical intensive care unit. We also recalculated the EQ-5D-3L index excluding the anxiety/depression item.

Nearly one-third of patients (18/58) had high psychiatric burden. The table looks at the demographic and clinical characteristics of patients with high psychiatric comorbidity versus those of low-moderate psychiatric comorbidity and those with no psychiatric morbidity. Patient groups did not differ significantly in terms of demographics. For clinical characteristics, patients with high psychiatric comorbidity were more likely than patients with low-moderate psychiatric comorbidity to have a prior history of depression (P < 0.05).

Patients with high psychiatric comorbidity were more likely to have a poorer QoL when compared with patients with low-moderate psychiatric comorbidity and to those with no morbidity as measured by a lower EQ-5D-3L Index (no, 0.69 ± 0.25; low-moderate, 0.70 ± 0.19; high, 0.48 ± 0.24; P = 0.006) and EQ-5D-VAS (no, 67.0 ± 20.7; low-moderate, 76.6 ± 20.0; high, 50.8 ± 22.4; P = 0.004). After adjusting for covariates, patients with high psychiatric comorbidity had a poorer QoL compared with those with no morbidity or low-moderate comorbidity on the EQ-5D-3L Index (P = 0.017 for overall differences), whereas patients who had high psychiatric comorbidity had a poorer QoL compared to those with low-moderate comorbidity on the EQ-5D-VAS (P = 0.039 for overall differences; Figure). Subgroup analysis of MICU patients yielded similar results. Patients with high psychiatric burden had significantly poorer QoL as measured by the EQ-5D-3L (unadjusted P = 0.044, adjusted P = 0.003) and the EQ-5D-VAS (unadjusted P = 0.007, adjusted P = 0.021). After excluding the anxiety/depression item from the EQ-5D-3L, we observed similar differences (no, 0.71 ± 0.24; low-moderate, 0.75 ± 0.15; high, 0.58 ± 0.22; unadjusted P = 0.062; adjusted P = 0.040).

Psychiatric comorbidities in ICU survivors are common and pose a significant clinical issue. Patients with multiple psychiatric comorbidities can be more complicated to identify from a diagnostic standpoint and often require more prolonged, intensive mental health treatment when compared with patients with a single psychiatric disorder.^10,11 Our study showed that high psychiatric comorbidity in survivors of ICU delirium is associated with a decreased QoL compared with those with no psychiatric comorbidity or with low-moderate psychiatric comorbidity. This finding is consistent with previous studies in the general population that patients with multiple psychiatric comorbidities are associated with a poorer QoL compared with patients with a single psychiatric comorbidity.^10,11

There is a pressing need to better characterize psychiatric comorbidities in ICU survivors because our current evidence suggests that the prevalence of psychiatric comorbidities of ICU survivors is substantially higher than that of the general population. We found that nearly one-third of survivors of ICU delirium had comorbid depression, anxiety, and PTSD symptoms at 3 months. This is consistent with the few other studies of ICU survivors, which showed a prevalence of psychiatric comorbidity of 25% to 33%.^5,12 These rates are substantially higher than the prevalence in the general population of 6%.¹³

The high rate of psychiatric comorbidities may render it difficult to effectively treat the mental health symptoms in ICU survivors.¹⁴ Treating multiple psychiatric comorbidities may also be especially challenging in survivors of ICU delirium because they have a high prevalence of cognitive impairment. Mental health treatments for patients with psychiatric disorders and comorbid cognitive impairment are limited. Better characterization of psychiatric comorbidity in ICU survivors, particularly those with ICU delirium, is vital to the development of more effective, bundled treatments for this population with multiple comorbidities.

Standardized screenings of ICU survivors at a high risk for psychiatric disorders, such as survivors of ICU delirium, may help to identify patients with comorbid psychiatric disorder symptoms and have them referred to appropriate treatment earlier with the hope of improving their QoL sooner. Although opportunities to deliver integrated outpatient collaborative mental health and medical care for a subspecialty population are limited, one potential model of care would be to utilize a collaborative-care model in an ICU survivor clinic.¹⁵

Strengths of our study include the examination of psychiatric comorbidities in survivors of ICU delirium, who often have a poor QoL. A deeper understanding of psychiatric comorbidity and its relationship with QoL is needed to better understand how to deliver more effective treatments for these survivors. Limitations include the small sample size, a one-time measurement of psychiatric comorbidities at the 3-month follow-up based on screenings tools, and a lack of objective measures of physical functioning to determine the effects of psychiatric comorbidities on physical functioning. There may also have been differences in how patients with no psychiatric comorbidity responded to the EQ-5D-VAS as a result of premorbid differences (eg, they were healthier prior to their ICU stay and perceived their survivor status more negatively). This may explain why we did not see a statistically significant difference between no psychiatric comorbidity and high psychiatric comorbidity groups on the EQ-5D-VAS. Nevertheless, we did see a difference between the low-moderate psychiatric comorbidity group on EQ-5D-VAS and differences between the no comorbidity and low-moderate comorbidity groups versus the high comorbidity group on the EQ-5D-3L. Finally, data about psychiatric history and QoL prior to ICU hospitalization were limited. Therefore, truly determining incidence versus prevalence of post-ICU comorbidities and whether psychiatric symptoms and its effects on QoL were due to ICU hospitalization or to premorbid psychiatric symptoms is difficult.

Our study demonstrated that in survivors of ICU delirium, higher comorbidity of psychiatric symptoms was associated with poorer QoL. Future studies will need to confirm these findings. We will also need to identify potentially reversible risk factors for psychiatric comorbidity and poorer QoL and develop treatments to effectively target the mental health symptoms of survivors of ICU delirium.

Disclosure

Grant support: The PMD trial is funded through the National Institutes of Health grant R01AG054205-02. SW is supported by NIA 2P30AG010133. AP is supported by CMS 1 L1 CMS331444-02-00, Indiana CTSI, and NIA R01AG054205-02. SG is supported by NIA 2P30AG010133, NIA 5R01AG045350, and NIA R01AG054205-02. SK is supported by NHBLI 5T32HL091816-07. MB is supported by NIA R01 AG040220-05, AHRQ P30 HS024384-02, CMS 1 L1 CMS331444-02-00, NIA R01 AG030618-05A1 and NIA R01AG054205-02. BK is supported by NIA K23-AG043476 and NHLBI R01HL131730. The funding agency had no role in the development of the study design, collection, analysis, interpretation of data, manuscript development, or the decision to submit the manuscript for publication. Conflicts of interest include MB, SG, and AP being funded by NIA R01AG054205-02 for the PMD study.

Venous thromboembolism (VTE) is a common medical condition comprising deep vein thrombosis (DVT) and pulmonary embolism (PE). Estimates of the incidence of DVT in the United States vary between 0.5 and 1.5 cases per 1000 person-years.¹ Left untreated, roughly 50% of DVT patients progress to a PE, of whom 10% to 25% die within 3 months.²

Since the 1990s, multiple randomized controlled studies^3-5 demonstrated the safety and efficacy of outpatient treatment for selected DVT patients with low molecular weight heparin and warfarin. The United States Food and Drug Administration approved enoxaparin, a low molecular weight heparin for outpatient use in 1998,⁶ and by the end of the decade, multiple treatment guidelines for VTE acknowledged the safety of outpatient treatment of DVT with low molecular weight heparin in selected patients.^7-9 Recently, the approval of direct oral anticoagulants (DOACs) by the Food and Drug Administration allows an all-oral treatment regimen for VTE, which could further facilitate outpatient treatment of DVT.

Costs associated with treatment of VTE are enormous. For outpatient treatment, researchers differ on individual estimates of cost savings associated with outpatient DVT management, but most report a cost savings of several thousand dollars per patient treated as an outpatient compared with as an inpatient.^6,10 Given the incidence of DVT, reducing costs while maintaining a high quality of care in even a small percentage of DVT patients would result in significant healthcare cost savings as well as increased convenience for patients.

Despite high-quality evidence supporting the efficacy and safety of outpatient DVT treatment, little is known about the adoption of outpatient DVT treatment in the United States. Several studies that have been published were limited to single hospitals and were small in size^11,12 or limited to a cohort of patients already diagnosed with DVT.¹³

The purpose of this study was to report the frequency of outpatient treatment of DVT in the United States and describe patient characteristics associated with outpatient treatment. Information was gathered from The Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a contemporary cohort study of more than 30,000 patients residing in the contiguous United States with racial and geographic diversity. We hypothesized that an individual’s age, sex, race, region of residence, urban or rural residence, education level, and personal income would be associated with outpatient treatment. Results would allow the implementation of interventions to promote the appropriate use of outpatient treatment in order to reduce healthcare costs and increase patient convenience without compromising safety or efficacy of care.

Cohort Characteristics

VTE events were ascertained in the REGARDS cohort, a prospective, longitudinal cohort study investigating the causes of racial and geographic disparities in stroke and cognitive decline.¹⁴ Between 2003 and 2007, there were 30,239 participants in the contiguous United States ≥45 years old enrolled in REGARDS. By design, 55% were female, 41% were black, the mean age was 65 years, and 56% lived in the southeastern United States. Participants were recruited from a commercial list by mail and telephone contact followed by verbal consent. A telephone interview was followed by an in-home examination, including obtaining written informed consent. On study entry, many participants had comorbid conditions, including 8% with reported atrial fibrillation, 56% receiving treatment for hypertension, 22% receiving treatment for diabetes, 3.7% taking warfarin, and 14% who were actively smoking.^15,16 Participants were only excluded if they had active cancer, stated a self-reported race other than white or black, were unable to converse in English, had cognitive impairment as judged by the telephone interviewer, or were residing in or on the waiting list for a nursing home. Study methods were reviewed and approved by the institutional review boards at each study institution and have been published elsewhere.¹⁴

Event Ascertainment and Definitions

DVT event ascertainment is complete through 2011, with identification by telephone interview, review of reported hospitalizations, and review of deaths.¹⁷ Questionnaires in similar epidemiological studies have 98% specificity and >70% sensitivity for ascertaining VTE events.¹⁸ A research nurse reviewed the text and recorded each reported hospitalization through 2011. Any report of a blood clot in the legs, arms, or lungs was a potential case for physician review. Medical records were retrieved for up to 1 year before and 1 year after potential events. Retrieved records were used to help guide further record retrieval if they did not contain the primary VTE event. Primary inpatient and outpatient records including history and physical examinations, discharge summaries, imaging reports (to include limb ultrasounds, computed tomography scans, and magnetic resonance imaging), autopsies, and outpatient notes were retrieved using up to 3 attempts.¹⁹ Using all available information, characteristics of the VTE event and treatment were systematically recorded. For each potential VTE case, two of three physician reviewers abstracted medical records to validate and classify the event. If the physician reviewers disagreed, the third physician would review the case, and if VTE status remained uncertain, cases were discussed and resolved. Race was determined by participant self-report as black or white. Location of residence was defined by geocoding the addresses, and urban or rural status was defined by United States census tract data using rural-urban commuting area codes (RUCA; with rural areas being RUCA codes 4–10).²⁰ Other risk factors were obtained through surveys, telephone interviews, or in-home visits.¹⁴

Outpatient treatment was defined as receiving a DVT diagnosis in an emergency department or ambulatory clinic but not receiving an overnight hospitalization. Inpatient treatment was defined as at least 1 overnight stay in a hospital (but not in an emergency department). Only participants admitted with a primary diagnosis of DVT were included in the analysis. If someone was noted to have DVT but was admitted to the hospital for another cause, he or she was not included in the analysis and classified as a hospital-associated DVT. A provoked DVT was defined as occurring within 90 days of a major trauma, surgery, or marked immobility or was associated with active cancer or treatment for cancer (ie, chemotherapy, radiotherapy, or surgical therapy), while an unprovoked DVT was defined as having none of the above provoking factors. A distal DVT was defined as a DVT occurring in the posterior tibial, anterior tibial, peroneal, or soleus sinuses. The primary outcome was DVT treated as an outpatient only without concurrent diagnosis of PE or VTE as a complication of hospitalization (as these individuals were not eligible for outpatient treatment at the time).

Statistical Analysis

Age, sex, race, region of residence (inside or outside the southeastern United States), education, income (determined as greater or less than $20,000 per year), and urban or rural status of residence were compared between DVT patients treated as outpatients and inpatients using χ2 analysis by inpatient or outpatient treatment. Univariable and multivariable logistic regression was then used to determine the odds ratio (OR) of receiving outpatient DVT treatment by the same variables with age per 10-year increment. ORs were adjusted for age, sex, race, year of DVT diagnosis, and region of residence as appropriate. Statistical significance was defined as P < 0.05. All statistical analyses were performed by N.A.Z. and conducted with SAS version 9.3 (SAS Institute, Cary, NC). All authors had access to the primary clinical data.

Over a mean of 4.7 years follow-up, 379 VTE events occurred (incident and recurrent); 185 were diagnosed with a PE, and 53 occurred as a complication of hospitalization (and were not eligible for outpatient treatment), leaving 141 DVT events potentially eligible for outpatient treatment out of a population of 29,556 participants with available records and follow-up in the cohort (Figure).

Of 141 DVT events, 39 (28%) were treated as outpatients. Table 1 presents the characteristics of participants treated as inpatients and as outpatients. Factors significantly associated with outpatient DVT treatment were younger age, female sex, white race, residing in an urban area, having a distal DVT only, and having a higher income. In the study, DVT events were recorded between 2003 and 2011; the median year of a diagnosed DVT and treated as an outpatient was 2009, while the median year of inpatient treatment was 2008. Living in the Southeast versus the rest of the country (P = 0.13) and having a high school education or greater (P = 0.07) were marginally associated with receiving outpatient treatment. In absolute terms, 11% of people living in rural areas and 19% of black patients had outpatient DVT treatment while 33% of the urban dwellers and 32% of white patients received outpatient treatment (Table 1). At the time of cohort enrollment, 92% of participants claimed to have insurance; however, this did not differentiate between Medicare, Medicaid, and private insurance. Only 1 participant diagnosed with DVT had an estimated glomerular filtration rate <30, and this individual was admitted for treatment.

In this national, prospective, observational cohort study, only 28% of participants diagnosed with DVT were treated as outpatients versus being hospitalized. Urban area of residence, white race, female sex, and younger age were significantly associated with an increased odds of outpatient treatment. Groups that had particularly low outpatient treatment rates were rural dwellers and black participants, who had outpatient treatment rates of 11% and 19%, respectively. The odds of receiving outpatient treatment did improve over the course of the study, but in the last year of VTE assessment, outpatient treatment remained at 40%, but this was quite variable over the study years (being 8% two years prior).

The feasibility of outpatient treatment of DVTs requires a coordinated healthcare system and patient support to ensure education and appropriate anticoagulation monitoring. While not all DVTs should be treated as outpatients, differences in treatment location by sex, race, and residence point to potential healthcare disparities that increase the burden on patients and increase healthcare costs. Other studies have documented low outpatient treatment rates of DVTs (20% in 1 United States multicenter DVT registry) but have not discussed the associations of outpatient versus inpatient treatment.¹³ Outpatient treatment also appears to be underutilized in other developed countries; in the European Computerized Registry of Patients with Venous Thromboembolism, only 31% of DVT patients were treated on an outpatient basis between 2001 and 2011.²¹ To our knowledge, this is the first study to document the uptake of outpatient DVT treatment in the United States across multiple states, regions, and health systems well after the safety and efficacy of outpatient treatment of DVT was established by randomized controlled trials.^3-5

The strengths of this study are that these data are derived from a contemporary cohort with a large geographic and racial distribution in the United States and are well characterized with a mean of 4.6 years follow-up.¹⁹ We are limited by a relatively small number of DVT events that were eligible for outpatient treatment (n = 141) and so may miss modest associations. Further, while the geographic scope of the cohort is a tremendous strength of our study, we may have missed some events and did not have complete record retrieval of reported events and could not assess access to healthcare in detail. These data were recorded before the use of DOACs became common. DOACs are an effective and safe alternative to conventional anticoagulation treatment for acute DVT.²² Their use might result in increased outpatient treatment, as they are not parenteral; however, cost considerations (~$400.00 per month), especially with high-deductible insurance plans, may limit their impact on VTE treatment location.²³ This study cannot account for why the racial, sex, and urban–rural differences exist, and by extension if hospitalization rates differ due to associated comorbidities or if this represents a healthcare disparity. While it is reasonable from a healthcare perspective that younger individuals would more likely be treated as outpatients, there is no data to suggest that differences in DVT by sex, race, and residential location support decreased outpatient treatment. Due to the age of the cohort, most individuals had some form of insurance and a primary care provider. However, we were unable to assess the quality of insurance and the ease of access to their primary care providers. More research is needed to determine whether patients were hospitalized on medical grounds or because of a lack of coordinated healthcare systems to care for them as outpatients.

In conclusion, only a minority of patients who were potentially eligible for outpatient DVT treatment (28%) were treated as outpatients in this study, and there were significant racial and socioeconomic differences in who received inpatient and outpatient treatment. While outpatient treatment rates were below 40% in all groups, we identified groups with especially low likelihoods of receiving outpatient treatment. While all eligible individuals should be offered outpatient DVT treatment, these data highlight the need for specific efforts to overcome barriers to outpatient treatment in the elderly, rural areas, black patients, and men. Even modest increases in the rate of outpatient DVT treatment could result in substantial cost savings and increased patient convenience without compromising the efficacy or safety of medical care.

Acknowledgements

The authors thank the staff and participants of REGARDS for their important contributions. The executive committee of REGARDS reviewed and approved this manuscript for publication. This research project is supported by cooperative agreement U01 NS041588 from the National Institute of Neurological Disorders and Stroke, National Institutes of Health, Department of Health and Human Services. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health. Representatives of the funding agency have been involved in the review of the manuscript but not directly involved in the collection, management, analysis, or interpretation of the data. The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at http://www.regardsstudy.org. Additional funding was provided by an investigator-initiated grant in aid from the American Recovery and Reinvestment Act grant RC1HL099460 from the National Heart, Lung, and Blood Institute. Work for the manuscript was supported in part by the Lake Champlain Cancer Research Organization (Burlington, Vermont).

Disclosure

The authors have no conflicts of interest to report.

Venous thromboembolism (VTE) is a common medical condition comprising deep vein thrombosis (DVT) and pulmonary embolism (PE). Estimates of the incidence of DVT in the United States vary between 0.5 and 1.5 cases per 1000 person-years.¹ Left untreated, roughly 50% of DVT patients progress to a PE, of whom 10% to 25% die within 3 months.²

Since the 1990s, multiple randomized controlled studies^3-5 demonstrated the safety and efficacy of outpatient treatment for selected DVT patients with low molecular weight heparin and warfarin. The United States Food and Drug Administration approved enoxaparin, a low molecular weight heparin for outpatient use in 1998,⁶ and by the end of the decade, multiple treatment guidelines for VTE acknowledged the safety of outpatient treatment of DVT with low molecular weight heparin in selected patients.^7-9 Recently, the approval of direct oral anticoagulants (DOACs) by the Food and Drug Administration allows an all-oral treatment regimen for VTE, which could further facilitate outpatient treatment of DVT.

Costs associated with treatment of VTE are enormous. For outpatient treatment, researchers differ on individual estimates of cost savings associated with outpatient DVT management, but most report a cost savings of several thousand dollars per patient treated as an outpatient compared with as an inpatient.^6,10 Given the incidence of DVT, reducing costs while maintaining a high quality of care in even a small percentage of DVT patients would result in significant healthcare cost savings as well as increased convenience for patients.

Despite high-quality evidence supporting the efficacy and safety of outpatient DVT treatment, little is known about the adoption of outpatient DVT treatment in the United States. Several studies that have been published were limited to single hospitals and were small in size^11,12 or limited to a cohort of patients already diagnosed with DVT.¹³

The purpose of this study was to report the frequency of outpatient treatment of DVT in the United States and describe patient characteristics associated with outpatient treatment. Information was gathered from The Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a contemporary cohort study of more than 30,000 patients residing in the contiguous United States with racial and geographic diversity. We hypothesized that an individual’s age, sex, race, region of residence, urban or rural residence, education level, and personal income would be associated with outpatient treatment. Results would allow the implementation of interventions to promote the appropriate use of outpatient treatment in order to reduce healthcare costs and increase patient convenience without compromising safety or efficacy of care.

Cohort Characteristics

VTE events were ascertained in the REGARDS cohort, a prospective, longitudinal cohort study investigating the causes of racial and geographic disparities in stroke and cognitive decline.¹⁴ Between 2003 and 2007, there were 30,239 participants in the contiguous United States ≥45 years old enrolled in REGARDS. By design, 55% were female, 41% were black, the mean age was 65 years, and 56% lived in the southeastern United States. Participants were recruited from a commercial list by mail and telephone contact followed by verbal consent. A telephone interview was followed by an in-home examination, including obtaining written informed consent. On study entry, many participants had comorbid conditions, including 8% with reported atrial fibrillation, 56% receiving treatment for hypertension, 22% receiving treatment for diabetes, 3.7% taking warfarin, and 14% who were actively smoking.^15,16 Participants were only excluded if they had active cancer, stated a self-reported race other than white or black, were unable to converse in English, had cognitive impairment as judged by the telephone interviewer, or were residing in or on the waiting list for a nursing home. Study methods were reviewed and approved by the institutional review boards at each study institution and have been published elsewhere.¹⁴

Event Ascertainment and Definitions

DVT event ascertainment is complete through 2011, with identification by telephone interview, review of reported hospitalizations, and review of deaths.¹⁷ Questionnaires in similar epidemiological studies have 98% specificity and >70% sensitivity for ascertaining VTE events.¹⁸ A research nurse reviewed the text and recorded each reported hospitalization through 2011. Any report of a blood clot in the legs, arms, or lungs was a potential case for physician review. Medical records were retrieved for up to 1 year before and 1 year after potential events. Retrieved records were used to help guide further record retrieval if they did not contain the primary VTE event. Primary inpatient and outpatient records including history and physical examinations, discharge summaries, imaging reports (to include limb ultrasounds, computed tomography scans, and magnetic resonance imaging), autopsies, and outpatient notes were retrieved using up to 3 attempts.¹⁹ Using all available information, characteristics of the VTE event and treatment were systematically recorded. For each potential VTE case, two of three physician reviewers abstracted medical records to validate and classify the event. If the physician reviewers disagreed, the third physician would review the case, and if VTE status remained uncertain, cases were discussed and resolved. Race was determined by participant self-report as black or white. Location of residence was defined by geocoding the addresses, and urban or rural status was defined by United States census tract data using rural-urban commuting area codes (RUCA; with rural areas being RUCA codes 4–10).²⁰ Other risk factors were obtained through surveys, telephone interviews, or in-home visits.¹⁴

Outpatient treatment was defined as receiving a DVT diagnosis in an emergency department or ambulatory clinic but not receiving an overnight hospitalization. Inpatient treatment was defined as at least 1 overnight stay in a hospital (but not in an emergency department). Only participants admitted with a primary diagnosis of DVT were included in the analysis. If someone was noted to have DVT but was admitted to the hospital for another cause, he or she was not included in the analysis and classified as a hospital-associated DVT. A provoked DVT was defined as occurring within 90 days of a major trauma, surgery, or marked immobility or was associated with active cancer or treatment for cancer (ie, chemotherapy, radiotherapy, or surgical therapy), while an unprovoked DVT was defined as having none of the above provoking factors. A distal DVT was defined as a DVT occurring in the posterior tibial, anterior tibial, peroneal, or soleus sinuses. The primary outcome was DVT treated as an outpatient only without concurrent diagnosis of PE or VTE as a complication of hospitalization (as these individuals were not eligible for outpatient treatment at the time).

Statistical Analysis

Age, sex, race, region of residence (inside or outside the southeastern United States), education, income (determined as greater or less than $20,000 per year), and urban or rural status of residence were compared between DVT patients treated as outpatients and inpatients using χ2 analysis by inpatient or outpatient treatment. Univariable and multivariable logistic regression was then used to determine the odds ratio (OR) of receiving outpatient DVT treatment by the same variables with age per 10-year increment. ORs were adjusted for age, sex, race, year of DVT diagnosis, and region of residence as appropriate. Statistical significance was defined as P < 0.05. All statistical analyses were performed by N.A.Z. and conducted with SAS version 9.3 (SAS Institute, Cary, NC). All authors had access to the primary clinical data.

Over a mean of 4.7 years follow-up, 379 VTE events occurred (incident and recurrent); 185 were diagnosed with a PE, and 53 occurred as a complication of hospitalization (and were not eligible for outpatient treatment), leaving 141 DVT events potentially eligible for outpatient treatment out of a population of 29,556 participants with available records and follow-up in the cohort (Figure).

Of 141 DVT events, 39 (28%) were treated as outpatients. Table 1 presents the characteristics of participants treated as inpatients and as outpatients. Factors significantly associated with outpatient DVT treatment were younger age, female sex, white race, residing in an urban area, having a distal DVT only, and having a higher income. In the study, DVT events were recorded between 2003 and 2011; the median year of a diagnosed DVT and treated as an outpatient was 2009, while the median year of inpatient treatment was 2008. Living in the Southeast versus the rest of the country (P = 0.13) and having a high school education or greater (P = 0.07) were marginally associated with receiving outpatient treatment. In absolute terms, 11% of people living in rural areas and 19% of black patients had outpatient DVT treatment while 33% of the urban dwellers and 32% of white patients received outpatient treatment (Table 1). At the time of cohort enrollment, 92% of participants claimed to have insurance; however, this did not differentiate between Medicare, Medicaid, and private insurance. Only 1 participant diagnosed with DVT had an estimated glomerular filtration rate <30, and this individual was admitted for treatment.

In this national, prospective, observational cohort study, only 28% of participants diagnosed with DVT were treated as outpatients versus being hospitalized. Urban area of residence, white race, female sex, and younger age were significantly associated with an increased odds of outpatient treatment. Groups that had particularly low outpatient treatment rates were rural dwellers and black participants, who had outpatient treatment rates of 11% and 19%, respectively. The odds of receiving outpatient treatment did improve over the course of the study, but in the last year of VTE assessment, outpatient treatment remained at 40%, but this was quite variable over the study years (being 8% two years prior).

The feasibility of outpatient treatment of DVTs requires a coordinated healthcare system and patient support to ensure education and appropriate anticoagulation monitoring. While not all DVTs should be treated as outpatients, differences in treatment location by sex, race, and residence point to potential healthcare disparities that increase the burden on patients and increase healthcare costs. Other studies have documented low outpatient treatment rates of DVTs (20% in 1 United States multicenter DVT registry) but have not discussed the associations of outpatient versus inpatient treatment.¹³ Outpatient treatment also appears to be underutilized in other developed countries; in the European Computerized Registry of Patients with Venous Thromboembolism, only 31% of DVT patients were treated on an outpatient basis between 2001 and 2011.²¹ To our knowledge, this is the first study to document the uptake of outpatient DVT treatment in the United States across multiple states, regions, and health systems well after the safety and efficacy of outpatient treatment of DVT was established by randomized controlled trials.^3-5

The strengths of this study are that these data are derived from a contemporary cohort with a large geographic and racial distribution in the United States and are well characterized with a mean of 4.6 years follow-up.¹⁹ We are limited by a relatively small number of DVT events that were eligible for outpatient treatment (n = 141) and so may miss modest associations. Further, while the geographic scope of the cohort is a tremendous strength of our study, we may have missed some events and did not have complete record retrieval of reported events and could not assess access to healthcare in detail. These data were recorded before the use of DOACs became common. DOACs are an effective and safe alternative to conventional anticoagulation treatment for acute DVT.²² Their use might result in increased outpatient treatment, as they are not parenteral; however, cost considerations (~$400.00 per month), especially with high-deductible insurance plans, may limit their impact on VTE treatment location.²³ This study cannot account for why the racial, sex, and urban–rural differences exist, and by extension if hospitalization rates differ due to associated comorbidities or if this represents a healthcare disparity. While it is reasonable from a healthcare perspective that younger individuals would more likely be treated as outpatients, there is no data to suggest that differences in DVT by sex, race, and residential location support decreased outpatient treatment. Due to the age of the cohort, most individuals had some form of insurance and a primary care provider. However, we were unable to assess the quality of insurance and the ease of access to their primary care providers. More research is needed to determine whether patients were hospitalized on medical grounds or because of a lack of coordinated healthcare systems to care for them as outpatients.

In conclusion, only a minority of patients who were potentially eligible for outpatient DVT treatment (28%) were treated as outpatients in this study, and there were significant racial and socioeconomic differences in who received inpatient and outpatient treatment. While outpatient treatment rates were below 40% in all groups, we identified groups with especially low likelihoods of receiving outpatient treatment. While all eligible individuals should be offered outpatient DVT treatment, these data highlight the need for specific efforts to overcome barriers to outpatient treatment in the elderly, rural areas, black patients, and men. Even modest increases in the rate of outpatient DVT treatment could result in substantial cost savings and increased patient convenience without compromising the efficacy or safety of medical care.

Acknowledgements

The authors thank the staff and participants of REGARDS for their important contributions. The executive committee of REGARDS reviewed and approved this manuscript for publication. This research project is supported by cooperative agreement U01 NS041588 from the National Institute of Neurological Disorders and Stroke, National Institutes of Health, Department of Health and Human Services. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health. Representatives of the funding agency have been involved in the review of the manuscript but not directly involved in the collection, management, analysis, or interpretation of the data. The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at http://www.regardsstudy.org. Additional funding was provided by an investigator-initiated grant in aid from the American Recovery and Reinvestment Act grant RC1HL099460 from the National Heart, Lung, and Blood Institute. Work for the manuscript was supported in part by the Lake Champlain Cancer Research Organization (Burlington, Vermont).

Disclosure

The authors have no conflicts of interest to report.

Venous thromboembolism (VTE) is a common medical condition comprising deep vein thrombosis (DVT) and pulmonary embolism (PE). Estimates of the incidence of DVT in the United States vary between 0.5 and 1.5 cases per 1000 person-years.¹ Left untreated, roughly 50% of DVT patients progress to a PE, of whom 10% to 25% die within 3 months.²

Since the 1990s, multiple randomized controlled studies^3-5 demonstrated the safety and efficacy of outpatient treatment for selected DVT patients with low molecular weight heparin and warfarin. The United States Food and Drug Administration approved enoxaparin, a low molecular weight heparin for outpatient use in 1998,⁶ and by the end of the decade, multiple treatment guidelines for VTE acknowledged the safety of outpatient treatment of DVT with low molecular weight heparin in selected patients.^7-9 Recently, the approval of direct oral anticoagulants (DOACs) by the Food and Drug Administration allows an all-oral treatment regimen for VTE, which could further facilitate outpatient treatment of DVT.

Costs associated with treatment of VTE are enormous. For outpatient treatment, researchers differ on individual estimates of cost savings associated with outpatient DVT management, but most report a cost savings of several thousand dollars per patient treated as an outpatient compared with as an inpatient.^6,10 Given the incidence of DVT, reducing costs while maintaining a high quality of care in even a small percentage of DVT patients would result in significant healthcare cost savings as well as increased convenience for patients.

Despite high-quality evidence supporting the efficacy and safety of outpatient DVT treatment, little is known about the adoption of outpatient DVT treatment in the United States. Several studies that have been published were limited to single hospitals and were small in size^11,12 or limited to a cohort of patients already diagnosed with DVT.¹³

The purpose of this study was to report the frequency of outpatient treatment of DVT in the United States and describe patient characteristics associated with outpatient treatment. Information was gathered from The Reasons for Geographic and Racial Differences in Stroke (REGARDS) study, a contemporary cohort study of more than 30,000 patients residing in the contiguous United States with racial and geographic diversity. We hypothesized that an individual’s age, sex, race, region of residence, urban or rural residence, education level, and personal income would be associated with outpatient treatment. Results would allow the implementation of interventions to promote the appropriate use of outpatient treatment in order to reduce healthcare costs and increase patient convenience without compromising safety or efficacy of care.

Cohort Characteristics

VTE events were ascertained in the REGARDS cohort, a prospective, longitudinal cohort study investigating the causes of racial and geographic disparities in stroke and cognitive decline.¹⁴ Between 2003 and 2007, there were 30,239 participants in the contiguous United States ≥45 years old enrolled in REGARDS. By design, 55% were female, 41% were black, the mean age was 65 years, and 56% lived in the southeastern United States. Participants were recruited from a commercial list by mail and telephone contact followed by verbal consent. A telephone interview was followed by an in-home examination, including obtaining written informed consent. On study entry, many participants had comorbid conditions, including 8% with reported atrial fibrillation, 56% receiving treatment for hypertension, 22% receiving treatment for diabetes, 3.7% taking warfarin, and 14% who were actively smoking.^15,16 Participants were only excluded if they had active cancer, stated a self-reported race other than white or black, were unable to converse in English, had cognitive impairment as judged by the telephone interviewer, or were residing in or on the waiting list for a nursing home. Study methods were reviewed and approved by the institutional review boards at each study institution and have been published elsewhere.¹⁴

Event Ascertainment and Definitions

DVT event ascertainment is complete through 2011, with identification by telephone interview, review of reported hospitalizations, and review of deaths.¹⁷ Questionnaires in similar epidemiological studies have 98% specificity and >70% sensitivity for ascertaining VTE events.¹⁸ A research nurse reviewed the text and recorded each reported hospitalization through 2011. Any report of a blood clot in the legs, arms, or lungs was a potential case for physician review. Medical records were retrieved for up to 1 year before and 1 year after potential events. Retrieved records were used to help guide further record retrieval if they did not contain the primary VTE event. Primary inpatient and outpatient records including history and physical examinations, discharge summaries, imaging reports (to include limb ultrasounds, computed tomography scans, and magnetic resonance imaging), autopsies, and outpatient notes were retrieved using up to 3 attempts.¹⁹ Using all available information, characteristics of the VTE event and treatment were systematically recorded. For each potential VTE case, two of three physician reviewers abstracted medical records to validate and classify the event. If the physician reviewers disagreed, the third physician would review the case, and if VTE status remained uncertain, cases were discussed and resolved. Race was determined by participant self-report as black or white. Location of residence was defined by geocoding the addresses, and urban or rural status was defined by United States census tract data using rural-urban commuting area codes (RUCA; with rural areas being RUCA codes 4–10).²⁰ Other risk factors were obtained through surveys, telephone interviews, or in-home visits.¹⁴

Outpatient treatment was defined as receiving a DVT diagnosis in an emergency department or ambulatory clinic but not receiving an overnight hospitalization. Inpatient treatment was defined as at least 1 overnight stay in a hospital (but not in an emergency department). Only participants admitted with a primary diagnosis of DVT were included in the analysis. If someone was noted to have DVT but was admitted to the hospital for another cause, he or she was not included in the analysis and classified as a hospital-associated DVT. A provoked DVT was defined as occurring within 90 days of a major trauma, surgery, or marked immobility or was associated with active cancer or treatment for cancer (ie, chemotherapy, radiotherapy, or surgical therapy), while an unprovoked DVT was defined as having none of the above provoking factors. A distal DVT was defined as a DVT occurring in the posterior tibial, anterior tibial, peroneal, or soleus sinuses. The primary outcome was DVT treated as an outpatient only without concurrent diagnosis of PE or VTE as a complication of hospitalization (as these individuals were not eligible for outpatient treatment at the time).

Statistical Analysis

Age, sex, race, region of residence (inside or outside the southeastern United States), education, income (determined as greater or less than $20,000 per year), and urban or rural status of residence were compared between DVT patients treated as outpatients and inpatients using χ2 analysis by inpatient or outpatient treatment. Univariable and multivariable logistic regression was then used to determine the odds ratio (OR) of receiving outpatient DVT treatment by the same variables with age per 10-year increment. ORs were adjusted for age, sex, race, year of DVT diagnosis, and region of residence as appropriate. Statistical significance was defined as P < 0.05. All statistical analyses were performed by N.A.Z. and conducted with SAS version 9.3 (SAS Institute, Cary, NC). All authors had access to the primary clinical data.

Over a mean of 4.7 years follow-up, 379 VTE events occurred (incident and recurrent); 185 were diagnosed with a PE, and 53 occurred as a complication of hospitalization (and were not eligible for outpatient treatment), leaving 141 DVT events potentially eligible for outpatient treatment out of a population of 29,556 participants with available records and follow-up in the cohort (Figure).

Of 141 DVT events, 39 (28%) were treated as outpatients. Table 1 presents the characteristics of participants treated as inpatients and as outpatients. Factors significantly associated with outpatient DVT treatment were younger age, female sex, white race, residing in an urban area, having a distal DVT only, and having a higher income. In the study, DVT events were recorded between 2003 and 2011; the median year of a diagnosed DVT and treated as an outpatient was 2009, while the median year of inpatient treatment was 2008. Living in the Southeast versus the rest of the country (P = 0.13) and having a high school education or greater (P = 0.07) were marginally associated with receiving outpatient treatment. In absolute terms, 11% of people living in rural areas and 19% of black patients had outpatient DVT treatment while 33% of the urban dwellers and 32% of white patients received outpatient treatment (Table 1). At the time of cohort enrollment, 92% of participants claimed to have insurance; however, this did not differentiate between Medicare, Medicaid, and private insurance. Only 1 participant diagnosed with DVT had an estimated glomerular filtration rate <30, and this individual was admitted for treatment.

In this national, prospective, observational cohort study, only 28% of participants diagnosed with DVT were treated as outpatients versus being hospitalized. Urban area of residence, white race, female sex, and younger age were significantly associated with an increased odds of outpatient treatment. Groups that had particularly low outpatient treatment rates were rural dwellers and black participants, who had outpatient treatment rates of 11% and 19%, respectively. The odds of receiving outpatient treatment did improve over the course of the study, but in the last year of VTE assessment, outpatient treatment remained at 40%, but this was quite variable over the study years (being 8% two years prior).

The feasibility of outpatient treatment of DVTs requires a coordinated healthcare system and patient support to ensure education and appropriate anticoagulation monitoring. While not all DVTs should be treated as outpatients, differences in treatment location by sex, race, and residence point to potential healthcare disparities that increase the burden on patients and increase healthcare costs. Other studies have documented low outpatient treatment rates of DVTs (20% in 1 United States multicenter DVT registry) but have not discussed the associations of outpatient versus inpatient treatment.¹³ Outpatient treatment also appears to be underutilized in other developed countries; in the European Computerized Registry of Patients with Venous Thromboembolism, only 31% of DVT patients were treated on an outpatient basis between 2001 and 2011.²¹ To our knowledge, this is the first study to document the uptake of outpatient DVT treatment in the United States across multiple states, regions, and health systems well after the safety and efficacy of outpatient treatment of DVT was established by randomized controlled trials.^3-5

The strengths of this study are that these data are derived from a contemporary cohort with a large geographic and racial distribution in the United States and are well characterized with a mean of 4.6 years follow-up.¹⁹ We are limited by a relatively small number of DVT events that were eligible for outpatient treatment (n = 141) and so may miss modest associations. Further, while the geographic scope of the cohort is a tremendous strength of our study, we may have missed some events and did not have complete record retrieval of reported events and could not assess access to healthcare in detail. These data were recorded before the use of DOACs became common. DOACs are an effective and safe alternative to conventional anticoagulation treatment for acute DVT.²² Their use might result in increased outpatient treatment, as they are not parenteral; however, cost considerations (~$400.00 per month), especially with high-deductible insurance plans, may limit their impact on VTE treatment location.²³ This study cannot account for why the racial, sex, and urban–rural differences exist, and by extension if hospitalization rates differ due to associated comorbidities or if this represents a healthcare disparity. While it is reasonable from a healthcare perspective that younger individuals would more likely be treated as outpatients, there is no data to suggest that differences in DVT by sex, race, and residential location support decreased outpatient treatment. Due to the age of the cohort, most individuals had some form of insurance and a primary care provider. However, we were unable to assess the quality of insurance and the ease of access to their primary care providers. More research is needed to determine whether patients were hospitalized on medical grounds or because of a lack of coordinated healthcare systems to care for them as outpatients.

In conclusion, only a minority of patients who were potentially eligible for outpatient DVT treatment (28%) were treated as outpatients in this study, and there were significant racial and socioeconomic differences in who received inpatient and outpatient treatment. While outpatient treatment rates were below 40% in all groups, we identified groups with especially low likelihoods of receiving outpatient treatment. While all eligible individuals should be offered outpatient DVT treatment, these data highlight the need for specific efforts to overcome barriers to outpatient treatment in the elderly, rural areas, black patients, and men. Even modest increases in the rate of outpatient DVT treatment could result in substantial cost savings and increased patient convenience without compromising the efficacy or safety of medical care.

Acknowledgements

The authors thank the staff and participants of REGARDS for their important contributions. The executive committee of REGARDS reviewed and approved this manuscript for publication. This research project is supported by cooperative agreement U01 NS041588 from the National Institute of Neurological Disorders and Stroke, National Institutes of Health, Department of Health and Human Services. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute of Neurological Disorders and Stroke or the National Institutes of Health. Representatives of the funding agency have been involved in the review of the manuscript but not directly involved in the collection, management, analysis, or interpretation of the data. The authors thank the other investigators, the staff, and the participants of the REGARDS study for their valuable contributions. A full list of participating REGARDS investigators and institutions can be found at http://www.regardsstudy.org. Additional funding was provided by an investigator-initiated grant in aid from the American Recovery and Reinvestment Act grant RC1HL099460 from the National Heart, Lung, and Blood Institute. Work for the manuscript was supported in part by the Lake Champlain Cancer Research Organization (Burlington, Vermont).

Disclosure

The authors have no conflicts of interest to report.

With some areas of the country spending close to 3 times more on healthcare than others, regional variation in healthcare spending has been the focus of national attention.^1-7 Since 1973, the Dartmouth Institute has studied regional variation in healthcare utilization and spending and concluded that variation is “unwarranted” because it is driven by providers’ practice patterns rather than differences in medical need, patient preferences, or evidence-based medicine.^8-11 However, critics of the Dartmouth Institute’s findings argue that their approach does not adequately adjust for community-level income, and that higher costs in some areas reflect greater patient needs that are not reflected in illness acuity alone.^12-14

While Medicare data have made it possible to study variations in spending for the senior population, fragmentation of insurance coverage and nonstandardized data structures make studying the pediatric population more difficult. However, the Children’s Hospital Association’s (CHA) Pediatric Health Information System (PHIS) has made large-scale comparisons more feasible. To overcome challenges associated with using charges and nonuniform cost data, PHIS-derived standardized costs provide new opportunities for comparisons.^15,16 Initial analyses using PHIS data showed significant interhospital variations in costs of care,¹⁵ but they did not adjust for differences in populations and assess the drivers of variation. A more recent study that controlled for payer status, comorbidities, and illness severity found that intensive care unit (ICU) utilization varied significantly for children hospitalized for asthma, suggesting that hospital practice patterns drive differences in cost.¹⁷

This study uses PHIS data to analyze regional variations in standardized costs of care for 3 conditions for which children are hospitalized. To assess potential drivers of variation, the study investigates the effects of patient-level demographic and illness-severity variables as well as encounter-level variables on costs of care. It also estimates cost savings from reducing variation.

Data Source

This retrospective cohort study uses the PHIS database (CHA, Overland Park, KS), which includes 48 freestanding children’s hospitals located in noncompeting markets across the United States and accounts for approximately 20% of pediatric hospitalizations. PHIS includes patient demographics, International Classification of Diseases, 9th Revision (ICD-9) diagnosis and procedure codes, as well as hospital charges. In addition to total charges, PHIS reports imaging, laboratory, pharmacy, and “other” charges. The “other” category aggregates clinical, supply, room, and nursing charges (including facility fees and ancillary staff services).

Inclusion Criteria

Inpatient- and observation-status hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 PHIS hospitals from October 2014 to September 2015 were included. Two hospitals were excluded because of missing data. Hospitalizations for patients >18 years were excluded.

Hospitalizations were categorized by using All Patient Refined-Diagnosis Related Groups (APR-DRGs) version 24 (3M Health Information Systems, St. Paul, MN)¹⁸ based on the ICD-9 diagnosis and procedure codes assigned during the episode of care. Analyses included APR-DRG 141 (asthma), primary diagnosis ICD-9 codes 250.11 and 250.13 (DKA), and APR-DRG 249 (AGE). ICD-9 codes were used for DKA for increased specificity.¹⁹ These conditions were chosen to represent 3 clinical scenarios: (1) a diagnosis for which hospitals differ on whether certain aspects of care are provided in the ICU (asthma), (2) a diagnosis that frequently includes care in an ICU (DKA), and (3) a diagnosis that typically does not include ICU care (AGE).¹⁹

Study Design

To focus the analysis on variation in resource utilization across hospitals rather than variations in hospital item charges, each billed resource was assigned a standardized cost.^15,16 For each clinical transaction code (CTC), the median unit cost was calculated for each hospital. The median of the hospital medians was defined as the standardized unit cost for that CTC.

The primary outcome variable was the total standardized cost for the hospitalization adjusted for patient-level demographic and illness-severity variables. Patient demographic and illness-severity covariates included age, race, gender, ZIP code-based median annual household income (HHI), rural-urban location, distance from home ZIP code to the hospital, chronic condition indicator (CCI), and severity-of-illness (SOI). When assessing drivers of variation, encounter-level covariates were added, including length of stay (LOS) in hours, ICU utilization, and 7-day readmission (an imprecise measure to account for quality of care during the index visit). The contribution of imaging, laboratory, pharmacy, and “other” costs was also considered.

Median annual HHI for patients’ home ZIP code was obtained from 2010 US Census data. Community-level HHI, a proxy for socioeconomic status (SES),^20,21 was classified into categories based on the 2015 US federal poverty level (FPL) for a family of 4²²: HHI-1 = ≤ 1.5 × FPL; HHI-2 = 1.5 to 2 × FPL; HHI-3 = 2 to 3 × FPL; HHI-4 = ≥ 3 × FPL. Rural-urban commuting area (RUCA) codes were used to determine the rural-urban classification of the patient’s home.²³ The distance from home ZIP code to the hospital was included as an additional control for illness severity because patients traveling longer distances are often more sick and require more resources.²⁴

The Agency for Healthcare Research and Quality CCI classification system was used to identify the presence of a chronic condition.²⁵ For asthma, CCI was flagged if the patient had a chronic condition other than asthma; for DKA, CCI was flagged if the patient had a chronic condition other than DKA; and for AGE, CCI was flagged if the patient had any chronic condition.

The APR-DRG system provides a 4-level SOI score with each APR-DRG category. Patient factors, such as comorbid diagnoses, are considered in severity scores generated through 3M’s proprietary algorithms.¹⁸

For the first analysis, the 46 hospitals were categorized into 7 geographic regions based on 2010 US Census Divisions.²⁶ To overcome small hospital sample sizes, Mountain and Pacific were combined into West, and Middle Atlantic and New England were combined into North East. Because PHIS hospitals are located in noncompeting geographic regions, for the second analysis, we examined hospital-level variation (considering each hospital as its own region).

Data Analysis

To focus the analysis on “typical” patients and produce more robust estimates of central tendencies, the top and bottom 5% of hospitalizations with the most extreme standardized costs by condition were trimmed.²⁷ Standardized costs were log-transformed because of their nonnormal distribution and analyzed by using linear mixed models. Covariates were added stepwise to assess the proportion of the variance explained by each predictor. Post-hoc tests with conservative single-step stepwise mutation model corrections for multiple testing were used to compare adjusted costs. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). P values < 0.05 were considered significant. The Children’s Hospital of Philadelphia Institutional Review Board did not classify this study as human subjects research.

During the study period, there were 26,430 hospitalizations for asthma, 5056 for DKA, and 16,274 for AGE (Table 1).

Variation Across Census Regions

After adjusting for patient-level demographic and illness-severity variables, differences in adjusted total standardized costs remained between regions (P < 0.001). Although no region was an outlier compared to the overall mean for any of the conditions, regions were statistically different in pairwise comparison. The East North Central, South Atlantic, and West South Central regions had the highest adjusted total standardized costs for each of the conditions. The East South Central and West North Central regions had the lowest costs for each of the conditions. Adjusted total standardized costs were 120% higher for asthma ($1920 vs $4227), 46% higher for DKA ($7429 vs $10,881), and 150% higher for AGE ($3316 vs $8292) in the highest-cost region compared with the lowest-cost region (Table 2A).

Variation Within Census Regions

After controlling for patient-level demographic and illness-severity variables, standardized costs were different across hospitals in the same region (P < 0.001; panel A in Figure). This was true for all conditions in each region. Differences between the lowest- and highest-cost hospitals within the same region ranged from 111% to 420% for asthma, 101% to 398% for DKA, and 166% to 787% for AGE (Table 3).

Variation Across Hospitals (Each Hospital as Its Own Region)

One hospital had the highest adjusted standardized costs for all 3 conditions ($9087 for asthma, $28,564 for DKA, and $23,387 for AGE) and was outside of the 95% confidence interval compared with the overall means. The second highest-cost hospitals for asthma ($5977) and AGE ($18,780) were also outside of the 95% confidence interval. After removing these outliers, the difference between the highest- and lowest-cost hospitals was 549% for asthma ($721 vs $4678), 491% for DKA ($2738 vs $16,192), and 681% for AGE ($1317 vs $10,281; Table 2B).

Drivers of Variation Across Census Regions

Patient-level demographic and illness-severity variables explained very little of the variation in standardized costs across regions. For each of the conditions, age, race, gender, community-level HHI, RUCA, and distance from home to the hospital each accounted for <1.5% of variation, while SOI and CCI each accounted for <5%. Overall, patient-level variables explained 5.5%, 3.7%, and 6.7% of variation for asthma, DKA, and AGE.

Encounter-level variables explained a much larger percentage of the variation in costs. LOS accounted for 17.8% of the variation for asthma, 9.8% for DKA, and 8.7% for AGE. ICU utilization explained 6.9% of the variation for asthma and 12.5% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. The combination of patient-level and encounter-level variables explained 27%, 24%, and 15% of the variation for asthma, DKA, and AGE.

Drivers of Variation Across Hospitals

For each of the conditions, patient-level demographic variables each accounted for <2% of variation in costs between hospitals. SOI accounted for 4.5% of the variation for asthma and CCI accounted for 5.2% for AGE. Overall, patient-level variables explained 6.9%, 5.3%, and 7.3% of variation for asthma, DKA, and AGE.

Encounter-level variables accounted for a much larger percentage of the variation in cost. LOS explained 25.4% for asthma, 13.3% for DKA, and 14.2% for AGE. ICU utilization accounted for 13.4% for asthma and 21.9% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. Together, patient-level and encounter-level variables explained 40%, 36%, and 22% of variation for asthma, DKA, and AGE.

Imaging, Laboratory, Pharmacy, and “Other” Costs

The largest contributor to total costs adjusted for patient-level factors for all conditions was “other,” which aggregates room, nursing, clinical, and supply charges (panel B in Figure). When considering drivers of variation, this category explained >50% for each of the conditions. The next largest contributor to total costs was laboratory charges, which accounted for 15% of the variation across regions for asthma and 11% for DKA. Differences in imaging accounted for 18% of the variation for DKA and 15% for AGE. Differences in pharmacy charges accounted for <4% of the variation for each of the conditions. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 78%, and 72% of the variation across census regions for asthma, DKA, and AGE.

For the hospital-level analysis, differences in “other” remained the largest driver of cost variation. For asthma, “other” explained 61% of variation, while pharmacy, laboratory, and imaging each accounted for <8%. For DKA, differences in imaging accounted for 18% of the variation and laboratory charges accounted for 12%. For AGE, imaging accounted for 15% of the variation. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 72%, and 67% of the variation for asthma, DKA, and AGE.

Cost Savings

If all hospitals in this cohort with adjusted standardized costs above the national PHIS average achieved costs equal to the national PHIS average, estimated annual savings in adjusted standardized costs for these 3 conditions would be $69.1 million. If each hospital with adjusted costs above the average within its census region achieved costs equal to its regional average, estimated annual savings in adjusted standardized costs for these conditions would be $25.2 million.

This study reported on the regional variation in costs of care for 3 conditions treated at 46 children’s hospitals across 7 geographic regions, and it demonstrated that variations in costs of care exist in pediatrics. This study used standardized costs to compare utilization patterns across hospitals and adjusted for several patient-level demographic and illness-severity factors, and it found that differences in costs of care for children hospitalized with asthma, DKA, and AGE remained both between and within regions.

These variations are noteworthy, as hospitals strive to improve the value of healthcare. If the higher-cost hospitals in this cohort could achieve costs equal to the national PHIS averages, estimated annual savings in adjusted standardized costs for these conditions alone would equal $69.1 million. If higher-cost hospitals relative to the average in their own region reduced costs to their regional averages, annual standardized cost savings could equal $25.2 million for these conditions.

The differences observed are also significant in that they provide a foundation for exploring whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes.²⁸ If so, studying what those hospitals do to achieve outcomes more efficiently can serve as the basis for the establishment of best practices.²⁹ Standardizing best practices through protocols, pathways, and care-model redesign can reduce potentially unnecessary spending.³⁰

Our findings showed that patient-level demographic and illness-severity covariates, including community-level HHI and SOI, did not consistently explain cost differences. Instead, LOS and ICU utilization were associated with higher costs.^17,19 When considering the effect of the 4 cost components on the variation in total standardized costs between regions and between hospitals, the fact that the “other” category accounted for the largest percent of the variation is not surprising, because the cost of room occupancy and nursing services increases with longer LOS and more time in the ICU. Other individual cost components that were major drivers of variation were laboratory utilization for asthma and imaging for DKA and AGE³¹ (though they accounted for a much smaller proportion of total adjusted costs).¹⁹

To determine if these factors are modifiable, more information is needed to explain why practices differ. Many factors may contribute to varying utilization patterns, including differences in capabilities and resources (in the hospital and in the community) and patient volumes. For example, some hospitals provide continuous albuterol for status asthmaticus only in ICUs, while others provide it on regular units.³² But if certain hospitals do not have adequate resources or volumes to effectively care for certain populations outside of the ICU, their higher-value approach (considering quality and cost) may be to utilize ICU beds, even if some other hospitals care for those patients on non-ICU floors. Another possibility is that family preferences about care delivery (such as how long children stay in the hospital) may vary across regions.³³

Other evidence suggests that physician practice and spending patterns are strongly influenced by the practices of the region where they trained.³⁴ Because physicians often practice close to where they trained,^35,36 this may partially explain how regional patterns are reinforced.

Even considering all mentioned covariates, our model did not fully explain variation in standardized costs. After adding the cost components as covariates, between one-third and one-fifth of the variation remained unexplained. It is possible that this unexplained variation stemmed from unmeasured patient-level factors.

In addition, while proxies for SES, including community-level HHI, did not significantly predict differences in costs across regions, it is possible that SES affected LOS differently in different regions. Previous studies have suggested that lower SES is associated with longer LOS.³⁷ If this effect is more pronounced in certain regions (potentially because of differences in social service infrastructures), SES may be contributing to variations in cost through LOS.

Our findings were subject to limitations. First, this study only examined 3 diagnoses and did not include surgical or less common conditions. Second, while PHIS includes tertiary care, academic, and freestanding children’s hospitals, it does not include general hospitals, which is where most pediatric patients receive care.³⁸ Third, we used ZIP code-based median annual HHI to account for SES, and we used ZIP codes to determine the distance to the hospital and rural-urban location of patients’ homes. These approximations lack precision because SES and distances vary within ZIP codes.³⁹ Fourth, while adjusted standardized costs allow for comparisons between hospitals, they do not represent actual costs to patients or individual hospitals. Additionally, when determining whether variation remained after controlling for patient-level variables, we included SOI as a reflection of illness-severity at presentation. However, in practice, SOI scores may be assigned partially based on factors determined during the hospitalization.¹⁸ Finally, the use of other regional boundaries or the selection of different hospitals may yield different results.

This study reveals regional variations in costs of care for 3 inpatient pediatric conditions. Future studies should explore whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes. To the extent that variation is driven by modifiable factors and lower spending does not compromise outcomes, these data may prompt reviews of care models to reduce unwarranted variation and improve the value of care delivery at local, regional, and national levels.

Disclosure

Internal funds from the CHA and The Children’s Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, or affiliations relevant to the subject matter or materials discussed in the manuscript to disclose. The authors have no potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose

With some areas of the country spending close to 3 times more on healthcare than others, regional variation in healthcare spending has been the focus of national attention.^1-7 Since 1973, the Dartmouth Institute has studied regional variation in healthcare utilization and spending and concluded that variation is “unwarranted” because it is driven by providers’ practice patterns rather than differences in medical need, patient preferences, or evidence-based medicine.^8-11 However, critics of the Dartmouth Institute’s findings argue that their approach does not adequately adjust for community-level income, and that higher costs in some areas reflect greater patient needs that are not reflected in illness acuity alone.^12-14

While Medicare data have made it possible to study variations in spending for the senior population, fragmentation of insurance coverage and nonstandardized data structures make studying the pediatric population more difficult. However, the Children’s Hospital Association’s (CHA) Pediatric Health Information System (PHIS) has made large-scale comparisons more feasible. To overcome challenges associated with using charges and nonuniform cost data, PHIS-derived standardized costs provide new opportunities for comparisons.^15,16 Initial analyses using PHIS data showed significant interhospital variations in costs of care,¹⁵ but they did not adjust for differences in populations and assess the drivers of variation. A more recent study that controlled for payer status, comorbidities, and illness severity found that intensive care unit (ICU) utilization varied significantly for children hospitalized for asthma, suggesting that hospital practice patterns drive differences in cost.¹⁷

This study uses PHIS data to analyze regional variations in standardized costs of care for 3 conditions for which children are hospitalized. To assess potential drivers of variation, the study investigates the effects of patient-level demographic and illness-severity variables as well as encounter-level variables on costs of care. It also estimates cost savings from reducing variation.

Data Source

This retrospective cohort study uses the PHIS database (CHA, Overland Park, KS), which includes 48 freestanding children’s hospitals located in noncompeting markets across the United States and accounts for approximately 20% of pediatric hospitalizations. PHIS includes patient demographics, International Classification of Diseases, 9th Revision (ICD-9) diagnosis and procedure codes, as well as hospital charges. In addition to total charges, PHIS reports imaging, laboratory, pharmacy, and “other” charges. The “other” category aggregates clinical, supply, room, and nursing charges (including facility fees and ancillary staff services).

Inclusion Criteria

Inpatient- and observation-status hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 PHIS hospitals from October 2014 to September 2015 were included. Two hospitals were excluded because of missing data. Hospitalizations for patients >18 years were excluded.

Hospitalizations were categorized by using All Patient Refined-Diagnosis Related Groups (APR-DRGs) version 24 (3M Health Information Systems, St. Paul, MN)¹⁸ based on the ICD-9 diagnosis and procedure codes assigned during the episode of care. Analyses included APR-DRG 141 (asthma), primary diagnosis ICD-9 codes 250.11 and 250.13 (DKA), and APR-DRG 249 (AGE). ICD-9 codes were used for DKA for increased specificity.¹⁹ These conditions were chosen to represent 3 clinical scenarios: (1) a diagnosis for which hospitals differ on whether certain aspects of care are provided in the ICU (asthma), (2) a diagnosis that frequently includes care in an ICU (DKA), and (3) a diagnosis that typically does not include ICU care (AGE).¹⁹

Study Design

To focus the analysis on variation in resource utilization across hospitals rather than variations in hospital item charges, each billed resource was assigned a standardized cost.^15,16 For each clinical transaction code (CTC), the median unit cost was calculated for each hospital. The median of the hospital medians was defined as the standardized unit cost for that CTC.

The primary outcome variable was the total standardized cost for the hospitalization adjusted for patient-level demographic and illness-severity variables. Patient demographic and illness-severity covariates included age, race, gender, ZIP code-based median annual household income (HHI), rural-urban location, distance from home ZIP code to the hospital, chronic condition indicator (CCI), and severity-of-illness (SOI). When assessing drivers of variation, encounter-level covariates were added, including length of stay (LOS) in hours, ICU utilization, and 7-day readmission (an imprecise measure to account for quality of care during the index visit). The contribution of imaging, laboratory, pharmacy, and “other” costs was also considered.

Median annual HHI for patients’ home ZIP code was obtained from 2010 US Census data. Community-level HHI, a proxy for socioeconomic status (SES),^20,21 was classified into categories based on the 2015 US federal poverty level (FPL) for a family of 4²²: HHI-1 = ≤ 1.5 × FPL; HHI-2 = 1.5 to 2 × FPL; HHI-3 = 2 to 3 × FPL; HHI-4 = ≥ 3 × FPL. Rural-urban commuting area (RUCA) codes were used to determine the rural-urban classification of the patient’s home.²³ The distance from home ZIP code to the hospital was included as an additional control for illness severity because patients traveling longer distances are often more sick and require more resources.²⁴

The Agency for Healthcare Research and Quality CCI classification system was used to identify the presence of a chronic condition.²⁵ For asthma, CCI was flagged if the patient had a chronic condition other than asthma; for DKA, CCI was flagged if the patient had a chronic condition other than DKA; and for AGE, CCI was flagged if the patient had any chronic condition.

The APR-DRG system provides a 4-level SOI score with each APR-DRG category. Patient factors, such as comorbid diagnoses, are considered in severity scores generated through 3M’s proprietary algorithms.¹⁸

For the first analysis, the 46 hospitals were categorized into 7 geographic regions based on 2010 US Census Divisions.²⁶ To overcome small hospital sample sizes, Mountain and Pacific were combined into West, and Middle Atlantic and New England were combined into North East. Because PHIS hospitals are located in noncompeting geographic regions, for the second analysis, we examined hospital-level variation (considering each hospital as its own region).

Data Analysis

To focus the analysis on “typical” patients and produce more robust estimates of central tendencies, the top and bottom 5% of hospitalizations with the most extreme standardized costs by condition were trimmed.²⁷ Standardized costs were log-transformed because of their nonnormal distribution and analyzed by using linear mixed models. Covariates were added stepwise to assess the proportion of the variance explained by each predictor. Post-hoc tests with conservative single-step stepwise mutation model corrections for multiple testing were used to compare adjusted costs. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). P values < 0.05 were considered significant. The Children’s Hospital of Philadelphia Institutional Review Board did not classify this study as human subjects research.

During the study period, there were 26,430 hospitalizations for asthma, 5056 for DKA, and 16,274 for AGE (Table 1).

Variation Across Census Regions

After adjusting for patient-level demographic and illness-severity variables, differences in adjusted total standardized costs remained between regions (P < 0.001). Although no region was an outlier compared to the overall mean for any of the conditions, regions were statistically different in pairwise comparison. The East North Central, South Atlantic, and West South Central regions had the highest adjusted total standardized costs for each of the conditions. The East South Central and West North Central regions had the lowest costs for each of the conditions. Adjusted total standardized costs were 120% higher for asthma ($1920 vs $4227), 46% higher for DKA ($7429 vs $10,881), and 150% higher for AGE ($3316 vs $8292) in the highest-cost region compared with the lowest-cost region (Table 2A).

Variation Within Census Regions

After controlling for patient-level demographic and illness-severity variables, standardized costs were different across hospitals in the same region (P < 0.001; panel A in Figure). This was true for all conditions in each region. Differences between the lowest- and highest-cost hospitals within the same region ranged from 111% to 420% for asthma, 101% to 398% for DKA, and 166% to 787% for AGE (Table 3).

Variation Across Hospitals (Each Hospital as Its Own Region)

One hospital had the highest adjusted standardized costs for all 3 conditions ($9087 for asthma, $28,564 for DKA, and $23,387 for AGE) and was outside of the 95% confidence interval compared with the overall means. The second highest-cost hospitals for asthma ($5977) and AGE ($18,780) were also outside of the 95% confidence interval. After removing these outliers, the difference between the highest- and lowest-cost hospitals was 549% for asthma ($721 vs $4678), 491% for DKA ($2738 vs $16,192), and 681% for AGE ($1317 vs $10,281; Table 2B).

Drivers of Variation Across Census Regions

Patient-level demographic and illness-severity variables explained very little of the variation in standardized costs across regions. For each of the conditions, age, race, gender, community-level HHI, RUCA, and distance from home to the hospital each accounted for <1.5% of variation, while SOI and CCI each accounted for <5%. Overall, patient-level variables explained 5.5%, 3.7%, and 6.7% of variation for asthma, DKA, and AGE.

Encounter-level variables explained a much larger percentage of the variation in costs. LOS accounted for 17.8% of the variation for asthma, 9.8% for DKA, and 8.7% for AGE. ICU utilization explained 6.9% of the variation for asthma and 12.5% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. The combination of patient-level and encounter-level variables explained 27%, 24%, and 15% of the variation for asthma, DKA, and AGE.

Drivers of Variation Across Hospitals

For each of the conditions, patient-level demographic variables each accounted for <2% of variation in costs between hospitals. SOI accounted for 4.5% of the variation for asthma and CCI accounted for 5.2% for AGE. Overall, patient-level variables explained 6.9%, 5.3%, and 7.3% of variation for asthma, DKA, and AGE.

Encounter-level variables accounted for a much larger percentage of the variation in cost. LOS explained 25.4% for asthma, 13.3% for DKA, and 14.2% for AGE. ICU utilization accounted for 13.4% for asthma and 21.9% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. Together, patient-level and encounter-level variables explained 40%, 36%, and 22% of variation for asthma, DKA, and AGE.

Imaging, Laboratory, Pharmacy, and “Other” Costs

The largest contributor to total costs adjusted for patient-level factors for all conditions was “other,” which aggregates room, nursing, clinical, and supply charges (panel B in Figure). When considering drivers of variation, this category explained >50% for each of the conditions. The next largest contributor to total costs was laboratory charges, which accounted for 15% of the variation across regions for asthma and 11% for DKA. Differences in imaging accounted for 18% of the variation for DKA and 15% for AGE. Differences in pharmacy charges accounted for <4% of the variation for each of the conditions. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 78%, and 72% of the variation across census regions for asthma, DKA, and AGE.

For the hospital-level analysis, differences in “other” remained the largest driver of cost variation. For asthma, “other” explained 61% of variation, while pharmacy, laboratory, and imaging each accounted for <8%. For DKA, differences in imaging accounted for 18% of the variation and laboratory charges accounted for 12%. For AGE, imaging accounted for 15% of the variation. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 72%, and 67% of the variation for asthma, DKA, and AGE.

Cost Savings

If all hospitals in this cohort with adjusted standardized costs above the national PHIS average achieved costs equal to the national PHIS average, estimated annual savings in adjusted standardized costs for these 3 conditions would be $69.1 million. If each hospital with adjusted costs above the average within its census region achieved costs equal to its regional average, estimated annual savings in adjusted standardized costs for these conditions would be $25.2 million.

This study reported on the regional variation in costs of care for 3 conditions treated at 46 children’s hospitals across 7 geographic regions, and it demonstrated that variations in costs of care exist in pediatrics. This study used standardized costs to compare utilization patterns across hospitals and adjusted for several patient-level demographic and illness-severity factors, and it found that differences in costs of care for children hospitalized with asthma, DKA, and AGE remained both between and within regions.

These variations are noteworthy, as hospitals strive to improve the value of healthcare. If the higher-cost hospitals in this cohort could achieve costs equal to the national PHIS averages, estimated annual savings in adjusted standardized costs for these conditions alone would equal $69.1 million. If higher-cost hospitals relative to the average in their own region reduced costs to their regional averages, annual standardized cost savings could equal $25.2 million for these conditions.

The differences observed are also significant in that they provide a foundation for exploring whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes.²⁸ If so, studying what those hospitals do to achieve outcomes more efficiently can serve as the basis for the establishment of best practices.²⁹ Standardizing best practices through protocols, pathways, and care-model redesign can reduce potentially unnecessary spending.³⁰

Our findings showed that patient-level demographic and illness-severity covariates, including community-level HHI and SOI, did not consistently explain cost differences. Instead, LOS and ICU utilization were associated with higher costs.^17,19 When considering the effect of the 4 cost components on the variation in total standardized costs between regions and between hospitals, the fact that the “other” category accounted for the largest percent of the variation is not surprising, because the cost of room occupancy and nursing services increases with longer LOS and more time in the ICU. Other individual cost components that were major drivers of variation were laboratory utilization for asthma and imaging for DKA and AGE³¹ (though they accounted for a much smaller proportion of total adjusted costs).¹⁹

To determine if these factors are modifiable, more information is needed to explain why practices differ. Many factors may contribute to varying utilization patterns, including differences in capabilities and resources (in the hospital and in the community) and patient volumes. For example, some hospitals provide continuous albuterol for status asthmaticus only in ICUs, while others provide it on regular units.³² But if certain hospitals do not have adequate resources or volumes to effectively care for certain populations outside of the ICU, their higher-value approach (considering quality and cost) may be to utilize ICU beds, even if some other hospitals care for those patients on non-ICU floors. Another possibility is that family preferences about care delivery (such as how long children stay in the hospital) may vary across regions.³³

Other evidence suggests that physician practice and spending patterns are strongly influenced by the practices of the region where they trained.³⁴ Because physicians often practice close to where they trained,^35,36 this may partially explain how regional patterns are reinforced.

Even considering all mentioned covariates, our model did not fully explain variation in standardized costs. After adding the cost components as covariates, between one-third and one-fifth of the variation remained unexplained. It is possible that this unexplained variation stemmed from unmeasured patient-level factors.

In addition, while proxies for SES, including community-level HHI, did not significantly predict differences in costs across regions, it is possible that SES affected LOS differently in different regions. Previous studies have suggested that lower SES is associated with longer LOS.³⁷ If this effect is more pronounced in certain regions (potentially because of differences in social service infrastructures), SES may be contributing to variations in cost through LOS.

Our findings were subject to limitations. First, this study only examined 3 diagnoses and did not include surgical or less common conditions. Second, while PHIS includes tertiary care, academic, and freestanding children’s hospitals, it does not include general hospitals, which is where most pediatric patients receive care.³⁸ Third, we used ZIP code-based median annual HHI to account for SES, and we used ZIP codes to determine the distance to the hospital and rural-urban location of patients’ homes. These approximations lack precision because SES and distances vary within ZIP codes.³⁹ Fourth, while adjusted standardized costs allow for comparisons between hospitals, they do not represent actual costs to patients or individual hospitals. Additionally, when determining whether variation remained after controlling for patient-level variables, we included SOI as a reflection of illness-severity at presentation. However, in practice, SOI scores may be assigned partially based on factors determined during the hospitalization.¹⁸ Finally, the use of other regional boundaries or the selection of different hospitals may yield different results.

This study reveals regional variations in costs of care for 3 inpatient pediatric conditions. Future studies should explore whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes. To the extent that variation is driven by modifiable factors and lower spending does not compromise outcomes, these data may prompt reviews of care models to reduce unwarranted variation and improve the value of care delivery at local, regional, and national levels.

Disclosure

Internal funds from the CHA and The Children’s Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, or affiliations relevant to the subject matter or materials discussed in the manuscript to disclose. The authors have no potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose

With some areas of the country spending close to 3 times more on healthcare than others, regional variation in healthcare spending has been the focus of national attention.^1-7 Since 1973, the Dartmouth Institute has studied regional variation in healthcare utilization and spending and concluded that variation is “unwarranted” because it is driven by providers’ practice patterns rather than differences in medical need, patient preferences, or evidence-based medicine.^8-11 However, critics of the Dartmouth Institute’s findings argue that their approach does not adequately adjust for community-level income, and that higher costs in some areas reflect greater patient needs that are not reflected in illness acuity alone.^12-14

While Medicare data have made it possible to study variations in spending for the senior population, fragmentation of insurance coverage and nonstandardized data structures make studying the pediatric population more difficult. However, the Children’s Hospital Association’s (CHA) Pediatric Health Information System (PHIS) has made large-scale comparisons more feasible. To overcome challenges associated with using charges and nonuniform cost data, PHIS-derived standardized costs provide new opportunities for comparisons.^15,16 Initial analyses using PHIS data showed significant interhospital variations in costs of care,¹⁵ but they did not adjust for differences in populations and assess the drivers of variation. A more recent study that controlled for payer status, comorbidities, and illness severity found that intensive care unit (ICU) utilization varied significantly for children hospitalized for asthma, suggesting that hospital practice patterns drive differences in cost.¹⁷

This study uses PHIS data to analyze regional variations in standardized costs of care for 3 conditions for which children are hospitalized. To assess potential drivers of variation, the study investigates the effects of patient-level demographic and illness-severity variables as well as encounter-level variables on costs of care. It also estimates cost savings from reducing variation.

Data Source

This retrospective cohort study uses the PHIS database (CHA, Overland Park, KS), which includes 48 freestanding children’s hospitals located in noncompeting markets across the United States and accounts for approximately 20% of pediatric hospitalizations. PHIS includes patient demographics, International Classification of Diseases, 9th Revision (ICD-9) diagnosis and procedure codes, as well as hospital charges. In addition to total charges, PHIS reports imaging, laboratory, pharmacy, and “other” charges. The “other” category aggregates clinical, supply, room, and nursing charges (including facility fees and ancillary staff services).

Inclusion Criteria

Inpatient- and observation-status hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 PHIS hospitals from October 2014 to September 2015 were included. Two hospitals were excluded because of missing data. Hospitalizations for patients >18 years were excluded.

Hospitalizations were categorized by using All Patient Refined-Diagnosis Related Groups (APR-DRGs) version 24 (3M Health Information Systems, St. Paul, MN)¹⁸ based on the ICD-9 diagnosis and procedure codes assigned during the episode of care. Analyses included APR-DRG 141 (asthma), primary diagnosis ICD-9 codes 250.11 and 250.13 (DKA), and APR-DRG 249 (AGE). ICD-9 codes were used for DKA for increased specificity.¹⁹ These conditions were chosen to represent 3 clinical scenarios: (1) a diagnosis for which hospitals differ on whether certain aspects of care are provided in the ICU (asthma), (2) a diagnosis that frequently includes care in an ICU (DKA), and (3) a diagnosis that typically does not include ICU care (AGE).¹⁹

Study Design

To focus the analysis on variation in resource utilization across hospitals rather than variations in hospital item charges, each billed resource was assigned a standardized cost.^15,16 For each clinical transaction code (CTC), the median unit cost was calculated for each hospital. The median of the hospital medians was defined as the standardized unit cost for that CTC.

The primary outcome variable was the total standardized cost for the hospitalization adjusted for patient-level demographic and illness-severity variables. Patient demographic and illness-severity covariates included age, race, gender, ZIP code-based median annual household income (HHI), rural-urban location, distance from home ZIP code to the hospital, chronic condition indicator (CCI), and severity-of-illness (SOI). When assessing drivers of variation, encounter-level covariates were added, including length of stay (LOS) in hours, ICU utilization, and 7-day readmission (an imprecise measure to account for quality of care during the index visit). The contribution of imaging, laboratory, pharmacy, and “other” costs was also considered.

Median annual HHI for patients’ home ZIP code was obtained from 2010 US Census data. Community-level HHI, a proxy for socioeconomic status (SES),^20,21 was classified into categories based on the 2015 US federal poverty level (FPL) for a family of 4²²: HHI-1 = ≤ 1.5 × FPL; HHI-2 = 1.5 to 2 × FPL; HHI-3 = 2 to 3 × FPL; HHI-4 = ≥ 3 × FPL. Rural-urban commuting area (RUCA) codes were used to determine the rural-urban classification of the patient’s home.²³ The distance from home ZIP code to the hospital was included as an additional control for illness severity because patients traveling longer distances are often more sick and require more resources.²⁴

The Agency for Healthcare Research and Quality CCI classification system was used to identify the presence of a chronic condition.²⁵ For asthma, CCI was flagged if the patient had a chronic condition other than asthma; for DKA, CCI was flagged if the patient had a chronic condition other than DKA; and for AGE, CCI was flagged if the patient had any chronic condition.

The APR-DRG system provides a 4-level SOI score with each APR-DRG category. Patient factors, such as comorbid diagnoses, are considered in severity scores generated through 3M’s proprietary algorithms.¹⁸

For the first analysis, the 46 hospitals were categorized into 7 geographic regions based on 2010 US Census Divisions.²⁶ To overcome small hospital sample sizes, Mountain and Pacific were combined into West, and Middle Atlantic and New England were combined into North East. Because PHIS hospitals are located in noncompeting geographic regions, for the second analysis, we examined hospital-level variation (considering each hospital as its own region).

Data Analysis

To focus the analysis on “typical” patients and produce more robust estimates of central tendencies, the top and bottom 5% of hospitalizations with the most extreme standardized costs by condition were trimmed.²⁷ Standardized costs were log-transformed because of their nonnormal distribution and analyzed by using linear mixed models. Covariates were added stepwise to assess the proportion of the variance explained by each predictor. Post-hoc tests with conservative single-step stepwise mutation model corrections for multiple testing were used to compare adjusted costs. Statistical analyses were performed using SAS version 9.3 (SAS Institute, Cary, NC). P values < 0.05 were considered significant. The Children’s Hospital of Philadelphia Institutional Review Board did not classify this study as human subjects research.

During the study period, there were 26,430 hospitalizations for asthma, 5056 for DKA, and 16,274 for AGE (Table 1).

Variation Across Census Regions

After adjusting for patient-level demographic and illness-severity variables, differences in adjusted total standardized costs remained between regions (P < 0.001). Although no region was an outlier compared to the overall mean for any of the conditions, regions were statistically different in pairwise comparison. The East North Central, South Atlantic, and West South Central regions had the highest adjusted total standardized costs for each of the conditions. The East South Central and West North Central regions had the lowest costs for each of the conditions. Adjusted total standardized costs were 120% higher for asthma ($1920 vs $4227), 46% higher for DKA ($7429 vs $10,881), and 150% higher for AGE ($3316 vs $8292) in the highest-cost region compared with the lowest-cost region (Table 2A).

Variation Within Census Regions

After controlling for patient-level demographic and illness-severity variables, standardized costs were different across hospitals in the same region (P < 0.001; panel A in Figure). This was true for all conditions in each region. Differences between the lowest- and highest-cost hospitals within the same region ranged from 111% to 420% for asthma, 101% to 398% for DKA, and 166% to 787% for AGE (Table 3).

Variation Across Hospitals (Each Hospital as Its Own Region)

One hospital had the highest adjusted standardized costs for all 3 conditions ($9087 for asthma, $28,564 for DKA, and $23,387 for AGE) and was outside of the 95% confidence interval compared with the overall means. The second highest-cost hospitals for asthma ($5977) and AGE ($18,780) were also outside of the 95% confidence interval. After removing these outliers, the difference between the highest- and lowest-cost hospitals was 549% for asthma ($721 vs $4678), 491% for DKA ($2738 vs $16,192), and 681% for AGE ($1317 vs $10,281; Table 2B).

Drivers of Variation Across Census Regions

Patient-level demographic and illness-severity variables explained very little of the variation in standardized costs across regions. For each of the conditions, age, race, gender, community-level HHI, RUCA, and distance from home to the hospital each accounted for <1.5% of variation, while SOI and CCI each accounted for <5%. Overall, patient-level variables explained 5.5%, 3.7%, and 6.7% of variation for asthma, DKA, and AGE.

Encounter-level variables explained a much larger percentage of the variation in costs. LOS accounted for 17.8% of the variation for asthma, 9.8% for DKA, and 8.7% for AGE. ICU utilization explained 6.9% of the variation for asthma and 12.5% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. The combination of patient-level and encounter-level variables explained 27%, 24%, and 15% of the variation for asthma, DKA, and AGE.

Drivers of Variation Across Hospitals

For each of the conditions, patient-level demographic variables each accounted for <2% of variation in costs between hospitals. SOI accounted for 4.5% of the variation for asthma and CCI accounted for 5.2% for AGE. Overall, patient-level variables explained 6.9%, 5.3%, and 7.3% of variation for asthma, DKA, and AGE.

Encounter-level variables accounted for a much larger percentage of the variation in cost. LOS explained 25.4% for asthma, 13.3% for DKA, and 14.2% for AGE. ICU utilization accounted for 13.4% for asthma and 21.9% for DKA; ICU use was not a major driver for AGE. Seven-day readmissions accounted for <0.5% for each of the conditions. Together, patient-level and encounter-level variables explained 40%, 36%, and 22% of variation for asthma, DKA, and AGE.

Imaging, Laboratory, Pharmacy, and “Other” Costs

The largest contributor to total costs adjusted for patient-level factors for all conditions was “other,” which aggregates room, nursing, clinical, and supply charges (panel B in Figure). When considering drivers of variation, this category explained >50% for each of the conditions. The next largest contributor to total costs was laboratory charges, which accounted for 15% of the variation across regions for asthma and 11% for DKA. Differences in imaging accounted for 18% of the variation for DKA and 15% for AGE. Differences in pharmacy charges accounted for <4% of the variation for each of the conditions. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 78%, and 72% of the variation across census regions for asthma, DKA, and AGE.

For the hospital-level analysis, differences in “other” remained the largest driver of cost variation. For asthma, “other” explained 61% of variation, while pharmacy, laboratory, and imaging each accounted for <8%. For DKA, differences in imaging accounted for 18% of the variation and laboratory charges accounted for 12%. For AGE, imaging accounted for 15% of the variation. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 72%, and 67% of the variation for asthma, DKA, and AGE.

Cost Savings

If all hospitals in this cohort with adjusted standardized costs above the national PHIS average achieved costs equal to the national PHIS average, estimated annual savings in adjusted standardized costs for these 3 conditions would be $69.1 million. If each hospital with adjusted costs above the average within its census region achieved costs equal to its regional average, estimated annual savings in adjusted standardized costs for these conditions would be $25.2 million.

This study reported on the regional variation in costs of care for 3 conditions treated at 46 children’s hospitals across 7 geographic regions, and it demonstrated that variations in costs of care exist in pediatrics. This study used standardized costs to compare utilization patterns across hospitals and adjusted for several patient-level demographic and illness-severity factors, and it found that differences in costs of care for children hospitalized with asthma, DKA, and AGE remained both between and within regions.

These variations are noteworthy, as hospitals strive to improve the value of healthcare. If the higher-cost hospitals in this cohort could achieve costs equal to the national PHIS averages, estimated annual savings in adjusted standardized costs for these conditions alone would equal $69.1 million. If higher-cost hospitals relative to the average in their own region reduced costs to their regional averages, annual standardized cost savings could equal $25.2 million for these conditions.

The differences observed are also significant in that they provide a foundation for exploring whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes.²⁸ If so, studying what those hospitals do to achieve outcomes more efficiently can serve as the basis for the establishment of best practices.²⁹ Standardizing best practices through protocols, pathways, and care-model redesign can reduce potentially unnecessary spending.³⁰

Our findings showed that patient-level demographic and illness-severity covariates, including community-level HHI and SOI, did not consistently explain cost differences. Instead, LOS and ICU utilization were associated with higher costs.^17,19 When considering the effect of the 4 cost components on the variation in total standardized costs between regions and between hospitals, the fact that the “other” category accounted for the largest percent of the variation is not surprising, because the cost of room occupancy and nursing services increases with longer LOS and more time in the ICU. Other individual cost components that were major drivers of variation were laboratory utilization for asthma and imaging for DKA and AGE³¹ (though they accounted for a much smaller proportion of total adjusted costs).¹⁹

To determine if these factors are modifiable, more information is needed to explain why practices differ. Many factors may contribute to varying utilization patterns, including differences in capabilities and resources (in the hospital and in the community) and patient volumes. For example, some hospitals provide continuous albuterol for status asthmaticus only in ICUs, while others provide it on regular units.³² But if certain hospitals do not have adequate resources or volumes to effectively care for certain populations outside of the ICU, their higher-value approach (considering quality and cost) may be to utilize ICU beds, even if some other hospitals care for those patients on non-ICU floors. Another possibility is that family preferences about care delivery (such as how long children stay in the hospital) may vary across regions.³³

Other evidence suggests that physician practice and spending patterns are strongly influenced by the practices of the region where they trained.³⁴ Because physicians often practice close to where they trained,^35,36 this may partially explain how regional patterns are reinforced.

Even considering all mentioned covariates, our model did not fully explain variation in standardized costs. After adding the cost components as covariates, between one-third and one-fifth of the variation remained unexplained. It is possible that this unexplained variation stemmed from unmeasured patient-level factors.

In addition, while proxies for SES, including community-level HHI, did not significantly predict differences in costs across regions, it is possible that SES affected LOS differently in different regions. Previous studies have suggested that lower SES is associated with longer LOS.³⁷ If this effect is more pronounced in certain regions (potentially because of differences in social service infrastructures), SES may be contributing to variations in cost through LOS.

Our findings were subject to limitations. First, this study only examined 3 diagnoses and did not include surgical or less common conditions. Second, while PHIS includes tertiary care, academic, and freestanding children’s hospitals, it does not include general hospitals, which is where most pediatric patients receive care.³⁸ Third, we used ZIP code-based median annual HHI to account for SES, and we used ZIP codes to determine the distance to the hospital and rural-urban location of patients’ homes. These approximations lack precision because SES and distances vary within ZIP codes.³⁹ Fourth, while adjusted standardized costs allow for comparisons between hospitals, they do not represent actual costs to patients or individual hospitals. Additionally, when determining whether variation remained after controlling for patient-level variables, we included SOI as a reflection of illness-severity at presentation. However, in practice, SOI scores may be assigned partially based on factors determined during the hospitalization.¹⁸ Finally, the use of other regional boundaries or the selection of different hospitals may yield different results.

This study reveals regional variations in costs of care for 3 inpatient pediatric conditions. Future studies should explore whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes. To the extent that variation is driven by modifiable factors and lower spending does not compromise outcomes, these data may prompt reviews of care models to reduce unwarranted variation and improve the value of care delivery at local, regional, and national levels.

Disclosure

Internal funds from the CHA and The Children’s Hospital of Philadelphia supported the conduct of this work. The authors have no financial interests, relationships, or affiliations relevant to the subject matter or materials discussed in the manuscript to disclose. The authors have no potential conflicts of interest relevant to the subject matter or materials discussed in the manuscript to disclose

Care Coordination has been defined as “…the deliberate organization of patient care activities between two or more participants (including the patient) involved in a patient’s care to facilitate the appropriate delivery of healthcare services.”¹ The Institute of Medicine identified care coordination as a key strategy to improve the American healthcare system,² and evidence has been building that well-coordinated care improves patient outcomes and reduces healthcare costs associated with chronic conditions.^3-5 In 2012, Johns Hopkins Medicine was awarded a Healthcare Innovation Award by the Centers for Medicare & Medicaid Services to improve coordination of care across the continuum of care for adult patients admitted to Johns Hopkins Hospital (JHH) and Johns Hopkins Bayview Medical Center (JHBMC), and for high-risk low-income Medicare and Medicaid beneficiaries receiving ambulatory care in targeted zip codes. The purpose of this project, known as the Johns Hopkins Community Health Partnership (J-CHiP), was to improve health and healthcare and to reduce healthcare costs. The acute care component of the program consisted of a bundle of interventions focused on improving coordination of care for all patients, including a “bridge to home” discharge process, as they transitioned back to the community from inpatient admission. The bundle included the following: early screening for discharge planning to predict needed postdischarge services; discussion in daily multidisciplinary rounds about goals and priorities of the hospitalization and potential postdischarge needs; patient and family self-care management; education enhanced medication management, including the option of “medications in hand” at the time of discharge; postdischarge telephone follow-up by nurses; and, for patients identified as high-risk, a “transition guide” (a nurse who works with the patient via home visits and by phone to optimize compliance with care for 30 days postdischarge).⁶ While the primary endpoints of the J-CHiP program were to improve clinical outcomes and reduce healthcare costs, we were also interested in the impact of the program on care coordination processes in the acute care setting. This created the need for an instrument to measure healthcare professionals’ views of care coordination in their immediate work environments.

We began our search for existing measures by reviewing the Coordination Measures Atlas published in 2014.⁷ Although this report evaluates over 80 different measures of care coordination, most of them focus on the perspective of the patient and/or family members, on specific conditions, and on primary care or outpatient settings.^7,8 We were unable to identify an existing measure from the provider perspective, designed for the inpatient setting, that was both brief but comprehensive enough to cover a range of care coordination domains.⁸

Consequently, our first aim was to develop a brief, comprehensive tool to measure care coordination from the perspective of hospital inpatient staff that could be used to compare different units or types of providers, or to conduct longitudinal assessment. The second aim was to conduct a preliminary evaluation of the tool in our healthcare setting, including to assess its psychometric properties, to describe provider perceptions of care coordination after the implementation of J-CHiP, and to explore potential differences among departments, types of professionals, and between the 2 hospitals.

Development of the Care Coordination Questionnaire

The survey was developed in collaboration with leaders of the J-CHiP Acute Care Team. We met at the outset and on multiple subsequent occasions to align survey domains with the main components of the J-CHiP acute care intervention and to assure that the survey would be relevant and understandable to a variety of multidisciplinary professionals, including physicians, nurses, social workers, physical therapists, and other health professionals. Care was taken to avoid redundancy with existing evaluation efforts and to minimize respondent burden. This process helped to ensure the content validity of the items, the usefulness of the results, and the future usability of the tool.

We modeled the Care Coordination Questionnaire (CCQ) after the Safety Attitudes Questionnaire (SAQ),⁹ a widely used survey that is deployed approximately annually at JHH and JHBMC. While the SAQ focuses on healthcare provider attitudes about issues relevant to patient safety (often referred to as safety climate or safety culture), this new tool was designed to focus on healthcare professionals’ attitudes about care coordination. Similar to the way that the SAQ “elicits a snapshot of the safety climate through surveys of frontline worker perceptions,” we sought to elicit a picture of our care coordination climate through a survey of frontline hospital staff.

The CCQ was built upon the domains and approaches to care coordination described in the Agency for Healthcare Research and Quality Care Coordination Atlas.³ This report identifies 9 mechanisms for achieving care coordination, including the following: Establish Accountability or Negotiate Responsibility; Communicate; Facilitate Transitions; Assess Needs and Goals; Create a Proactive Plan of Care; Monitor, Follow Up, and Respond to Change; Support Self-Management Goals; Link to Community Resources; and Align Resources with Patient and Population Needs; as well as 5 broad approaches commonly used to improve the delivery of healthcare, including Teamwork Focused on Coordination, Healthcare Home, Care Management, Medication Management, and Health IT-Enabled Coordination.⁷ We generated at least 1 item to represent 8 of the 9 domains, as well as the broad approach described as Teamwork Focused on Coordination. After developing an initial set of items, we sought input from 3 senior leaders of the J-CHiP Acute Care Team to determine if the items covered the care coordination domains of interest, and to provide feedback on content validity. To test the interpretability of survey items and consistency across professional groups, we sent an initial version of the survey questions to at least 1 person from each of the following professional groups: hospitalist, social worker, case manager, clinical pharmacist, and nurse. We asked them to review all of our survey questions and to provide us with feedback on all aspects of the questions, such as whether they believed the questions were relevant and understandable to the members of their professional discipline, the appropriateness of the wording of the questions, and other comments. Modifications were made to the content and wording of the questions based on the feedback received. The final draft of the questionnaire was reviewed by the leadership team of the J-CHiP Acute Care Team to ensure its usefulness in providing actionable information.

The resulting 12-item questionnaire used a 5-point Likert response scale ranging from 1 = “disagree strongly” to 5 = “agree strongly,” and an additional option of “not applicable (N/A).” To help assess construct validity, a global question was added at the end of the questionnaire asking, “Overall, how would you rate the care coordination at the hospital of your primary work setting?” The response was measured on a 10-point Likert-type scale ranging from 1 = “totally uncoordinated care” to 10 = “perfectly coordinated care” (see Appendix). In addition, the questionnaire requested information about the respondents’ gender, position, and their primary unit, department, and hospital affiliation.

Data Collection Procedures

An invitation to complete an anonymous questionnaire was sent to the following inpatient care professionals: all nursing staff working on care coordination units in the departments of medicine, surgery, and neurology/neurosurgery, as well as physicians, pharmacists, acute care therapists (eg, occupational and physical therapists), and other frontline staff. All healthcare staff fitting these criteria was sent an e-mail with a request to fill out the survey online using Qualtrics^TM (Qualtrics Labs Inc., Provo, UT), as well as multiple follow-up reminders. The participants worked either at the JHH (a 1194-bed tertiary academic medical center in Baltimore, MD) or the JHBMC (a 440-bed academic community hospital located nearby). Data were collected from October 2015 through January 2016.

Analysis

Means and standard deviations were calculated by treating the responses as continuous variables. We tried 3 different methods to handle missing data: (1) without imputation, (2) imputing the mean value of each item, and (3) substituting a neutral score. Because all 3 methods produced very similar results, we treated the N/A responses as missing values without imputation for simplicity of analysis. We used STATA 13.1 (Stata Corporation, College Station, Texas) to analyze the data.

To identify subscales, we performed exploratory factor analysis on responses to the 12 specific items. Promax rotation was selected based on the simple structure. Subscale scores for each respondent were generated by computing the mean of responses to the items in the subscale. Internal consistency reliability of the subscales was estimated using Cronbach’s alpha. We calculated Pearson correlation coefficients for the items in each subscale, and examined Cronbach’s alpha deleting each item in turn. For each of the subscales identified and the global scale, we calculated the mean, standard deviation, median and interquartile range. Although distributions of scores tended to be non-normal, this was done to increase interpretability. We also calculated percent scoring at the ceiling (highest possible score).

We analyzed the data with 3 research questions in mind: Was there a difference in perceptions of care coordination between (1) staff affiliated with the 2 different hospitals, (2) staff affiliated with different clinical departments, or (3) staff with different professional roles? For comparisons based on hospital and department, and type of professional, nonparametric tests (Wilcoxon rank-sum and Kruskal-Wallis test) were used with a level of statistical significance set at 0.05. The comparison between hospitals and departments was made only among nurses to minimize the confounding effect of different distribution of professionals. We tested the distribution of “years in specialty” between hospitals and departments for this comparison using Pearson’s χ² test. The difference was not statistically significant (P = 0.167 for hospitals, and P = 0.518 for departments), so we assumed that the potential confounding effect of this variable was negligible in this analysis. The comparison of scores within each professional group used the Friedman test. Pearson’s χ² test was used to compare the baseline characteristics between 2 hospitals.

Among the 1486 acute care professionals asked to participate in the survey, 841 completed the questionnaire (response rate 56.6%). Table 1 shows the characteristics of the participants from each hospital. Table 2 summarizes the item response rates, proportion scoring at the ceiling, and weighting from the factor analysis. All items had completion rates of 99.2% or higher, with N/A responses ranging from 0% (item 2) to 3.1% (item 7). The percent scoring at the ceiling was 1.7% for the global item and ranged from 18.3% up to 63.3% for other individual items.

This study resulted in one of the first measurement tools to succinctly measure multiple aspects of care coordination in the hospital from the perspective of healthcare professionals. Given the hectic work environment of healthcare professionals, and the increasing emphasis on collecting data for evaluation and improvement, it is important to minimize respondent burden. This effort was catalyzed by a multifaceted initiative to redesign acute care delivery and promote seamless transitions of care, supported by the Center for Medicare & Medicaid Innovation. In initial testing, this questionnaire has evidence for reliability and validity. It was encouraging to find that the preliminary psychometric performance of the measure was very similar in 2 different settings of a tertiary academic hospital and a community hospital.

Our analysis of the survey data explored potential differences between the 2 hospitals, among different types of healthcare professionals and across different departments. Although we expected differences, we had no specific hypotheses about what those differences might be, and, in fact, did not observe any substantial differences. This could be taken to indicate that the intervention was uniformly and successfully implemented in both hospitals, and engaged various professionals in different departments. The ability to detect differences in care coordination at the nursing unit level could also prove to be beneficial for more precisely targeting where process improvement is needed. Further data collection and analyses should be conducted to more systematically compare units and to help identify those where practice is most advanced and those where improvements may be needed. It would also be informative to link differences in care coordination scores with patient outcomes. In addition, differences identified on specific domains between professional groups could be helpful to identify where greater efforts are needed to improve interdisciplinary practice. Sampling strategies stratified by provider type would need to be targeted to make this kind of analysis informative.

The consistently lower scores observed for patient engagement, from the perspective of care professionals in all groups, suggest that this is an area where improvement is needed. These findings are consistent with published reports on the common failure by hospitals to include patients as a member of their own care team. In addition to measuring care processes from the perspective of frontline healthcare workers, future evaluations within the healthcare system would also benefit from including data collected from the perspective of the patient and family.

This study had some limitations. First, there may be more than 4 domains of care coordination that are important and can be measured in the acute care setting from provider perspective. However, the addition of more domains should be balanced against practicality and respondent burden. It may be possible to further clarify priority domains in hospital settings as opposed to the primary care setting. Future research should be directed to find these areas and to develop a more comprehensive, yet still concise measurement instrument. Second, the tool was developed to measure the impact of a large-scale intervention, and to fit into the specific context of 2 hospitals. Therefore, it should be tested in different settings of hospital care to see how it performs. However, virtually all hospitals in the United States today are adapting to changes in both financing and healthcare delivery. A tool such as the one described in this paper could be helpful to many organizations. Third, the scoring system for the overall scale score is not weighted and therefore reflects teamwork more than other components of care coordination, which are represented by fewer items. In general, we believe that use of the subscale scores may be more informative. Alternative scoring systems might also be proposed, including item weighting based on factor scores.

For the purposes of evaluation in this specific instance, we only collected data at a single point in time, after the intervention had been deployed. Thus, we were not able to evaluate the effectiveness of the J-CHiP intervention. We also did not intend to focus too much on the differences between units, given the limited number of respondents from individual units. It would be useful to collect more data at future time points, both to test the responsiveness of the scales and to evaluate the impact of future interventions at both the hospital and unit level.

The preliminary data from this study have generated insights about gaps in current practice, such as in engaging patients in the inpatient care process. It has also increased awareness by hospital leaders about the need to achieve high reliability in the adoption of new procedures and interdisciplinary practice. This tool might be used to find areas in need of improvement, to evaluate the effect of initiatives to improve care coordination, to monitor the change over time in the perception of care coordination among healthcare professionals, and to develop better intervention strategies for coordination activities in acute care settings. Additional research is needed to provide further evidence for the reliability and validity of this measure in diverse settings.

Disclosure

 The project described was supported by Grant Number 1C1CMS331053-01-00 from the US Department of Health and Human Services, Centers for Medicare & Medicaid Services. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the US Department of Health and Human Services or any of its agencies. The research presented was conducted by the awardee. Results may or may not be consistent with or confirmed by the findings of the independent evaluation contractor.

The authors have no other disclosures.

Care Coordination has been defined as “…the deliberate organization of patient care activities between two or more participants (including the patient) involved in a patient’s care to facilitate the appropriate delivery of healthcare services.”¹ The Institute of Medicine identified care coordination as a key strategy to improve the American healthcare system,² and evidence has been building that well-coordinated care improves patient outcomes and reduces healthcare costs associated with chronic conditions.^3-5 In 2012, Johns Hopkins Medicine was awarded a Healthcare Innovation Award by the Centers for Medicare & Medicaid Services to improve coordination of care across the continuum of care for adult patients admitted to Johns Hopkins Hospital (JHH) and Johns Hopkins Bayview Medical Center (JHBMC), and for high-risk low-income Medicare and Medicaid beneficiaries receiving ambulatory care in targeted zip codes. The purpose of this project, known as the Johns Hopkins Community Health Partnership (J-CHiP), was to improve health and healthcare and to reduce healthcare costs. The acute care component of the program consisted of a bundle of interventions focused on improving coordination of care for all patients, including a “bridge to home” discharge process, as they transitioned back to the community from inpatient admission. The bundle included the following: early screening for discharge planning to predict needed postdischarge services; discussion in daily multidisciplinary rounds about goals and priorities of the hospitalization and potential postdischarge needs; patient and family self-care management; education enhanced medication management, including the option of “medications in hand” at the time of discharge; postdischarge telephone follow-up by nurses; and, for patients identified as high-risk, a “transition guide” (a nurse who works with the patient via home visits and by phone to optimize compliance with care for 30 days postdischarge).⁶ While the primary endpoints of the J-CHiP program were to improve clinical outcomes and reduce healthcare costs, we were also interested in the impact of the program on care coordination processes in the acute care setting. This created the need for an instrument to measure healthcare professionals’ views of care coordination in their immediate work environments.

We began our search for existing measures by reviewing the Coordination Measures Atlas published in 2014.⁷ Although this report evaluates over 80 different measures of care coordination, most of them focus on the perspective of the patient and/or family members, on specific conditions, and on primary care or outpatient settings.^7,8 We were unable to identify an existing measure from the provider perspective, designed for the inpatient setting, that was both brief but comprehensive enough to cover a range of care coordination domains.⁸

Consequently, our first aim was to develop a brief, comprehensive tool to measure care coordination from the perspective of hospital inpatient staff that could be used to compare different units or types of providers, or to conduct longitudinal assessment. The second aim was to conduct a preliminary evaluation of the tool in our healthcare setting, including to assess its psychometric properties, to describe provider perceptions of care coordination after the implementation of J-CHiP, and to explore potential differences among departments, types of professionals, and between the 2 hospitals.

Development of the Care Coordination Questionnaire

The survey was developed in collaboration with leaders of the J-CHiP Acute Care Team. We met at the outset and on multiple subsequent occasions to align survey domains with the main components of the J-CHiP acute care intervention and to assure that the survey would be relevant and understandable to a variety of multidisciplinary professionals, including physicians, nurses, social workers, physical therapists, and other health professionals. Care was taken to avoid redundancy with existing evaluation efforts and to minimize respondent burden. This process helped to ensure the content validity of the items, the usefulness of the results, and the future usability of the tool.

We modeled the Care Coordination Questionnaire (CCQ) after the Safety Attitudes Questionnaire (SAQ),⁹ a widely used survey that is deployed approximately annually at JHH and JHBMC. While the SAQ focuses on healthcare provider attitudes about issues relevant to patient safety (often referred to as safety climate or safety culture), this new tool was designed to focus on healthcare professionals’ attitudes about care coordination. Similar to the way that the SAQ “elicits a snapshot of the safety climate through surveys of frontline worker perceptions,” we sought to elicit a picture of our care coordination climate through a survey of frontline hospital staff.

The CCQ was built upon the domains and approaches to care coordination described in the Agency for Healthcare Research and Quality Care Coordination Atlas.³ This report identifies 9 mechanisms for achieving care coordination, including the following: Establish Accountability or Negotiate Responsibility; Communicate; Facilitate Transitions; Assess Needs and Goals; Create a Proactive Plan of Care; Monitor, Follow Up, and Respond to Change; Support Self-Management Goals; Link to Community Resources; and Align Resources with Patient and Population Needs; as well as 5 broad approaches commonly used to improve the delivery of healthcare, including Teamwork Focused on Coordination, Healthcare Home, Care Management, Medication Management, and Health IT-Enabled Coordination.⁷ We generated at least 1 item to represent 8 of the 9 domains, as well as the broad approach described as Teamwork Focused on Coordination. After developing an initial set of items, we sought input from 3 senior leaders of the J-CHiP Acute Care Team to determine if the items covered the care coordination domains of interest, and to provide feedback on content validity. To test the interpretability of survey items and consistency across professional groups, we sent an initial version of the survey questions to at least 1 person from each of the following professional groups: hospitalist, social worker, case manager, clinical pharmacist, and nurse. We asked them to review all of our survey questions and to provide us with feedback on all aspects of the questions, such as whether they believed the questions were relevant and understandable to the members of their professional discipline, the appropriateness of the wording of the questions, and other comments. Modifications were made to the content and wording of the questions based on the feedback received. The final draft of the questionnaire was reviewed by the leadership team of the J-CHiP Acute Care Team to ensure its usefulness in providing actionable information.

The resulting 12-item questionnaire used a 5-point Likert response scale ranging from 1 = “disagree strongly” to 5 = “agree strongly,” and an additional option of “not applicable (N/A).” To help assess construct validity, a global question was added at the end of the questionnaire asking, “Overall, how would you rate the care coordination at the hospital of your primary work setting?” The response was measured on a 10-point Likert-type scale ranging from 1 = “totally uncoordinated care” to 10 = “perfectly coordinated care” (see Appendix). In addition, the questionnaire requested information about the respondents’ gender, position, and their primary unit, department, and hospital affiliation.

Data Collection Procedures

An invitation to complete an anonymous questionnaire was sent to the following inpatient care professionals: all nursing staff working on care coordination units in the departments of medicine, surgery, and neurology/neurosurgery, as well as physicians, pharmacists, acute care therapists (eg, occupational and physical therapists), and other frontline staff. All healthcare staff fitting these criteria was sent an e-mail with a request to fill out the survey online using Qualtrics^TM (Qualtrics Labs Inc., Provo, UT), as well as multiple follow-up reminders. The participants worked either at the JHH (a 1194-bed tertiary academic medical center in Baltimore, MD) or the JHBMC (a 440-bed academic community hospital located nearby). Data were collected from October 2015 through January 2016.

Analysis

Means and standard deviations were calculated by treating the responses as continuous variables. We tried 3 different methods to handle missing data: (1) without imputation, (2) imputing the mean value of each item, and (3) substituting a neutral score. Because all 3 methods produced very similar results, we treated the N/A responses as missing values without imputation for simplicity of analysis. We used STATA 13.1 (Stata Corporation, College Station, Texas) to analyze the data.

To identify subscales, we performed exploratory factor analysis on responses to the 12 specific items. Promax rotation was selected based on the simple structure. Subscale scores for each respondent were generated by computing the mean of responses to the items in the subscale. Internal consistency reliability of the subscales was estimated using Cronbach’s alpha. We calculated Pearson correlation coefficients for the items in each subscale, and examined Cronbach’s alpha deleting each item in turn. For each of the subscales identified and the global scale, we calculated the mean, standard deviation, median and interquartile range. Although distributions of scores tended to be non-normal, this was done to increase interpretability. We also calculated percent scoring at the ceiling (highest possible score).

We analyzed the data with 3 research questions in mind: Was there a difference in perceptions of care coordination between (1) staff affiliated with the 2 different hospitals, (2) staff affiliated with different clinical departments, or (3) staff with different professional roles? For comparisons based on hospital and department, and type of professional, nonparametric tests (Wilcoxon rank-sum and Kruskal-Wallis test) were used with a level of statistical significance set at 0.05. The comparison between hospitals and departments was made only among nurses to minimize the confounding effect of different distribution of professionals. We tested the distribution of “years in specialty” between hospitals and departments for this comparison using Pearson’s χ² test. The difference was not statistically significant (P = 0.167 for hospitals, and P = 0.518 for departments), so we assumed that the potential confounding effect of this variable was negligible in this analysis. The comparison of scores within each professional group used the Friedman test. Pearson’s χ² test was used to compare the baseline characteristics between 2 hospitals.

Among the 1486 acute care professionals asked to participate in the survey, 841 completed the questionnaire (response rate 56.6%). Table 1 shows the characteristics of the participants from each hospital. Table 2 summarizes the item response rates, proportion scoring at the ceiling, and weighting from the factor analysis. All items had completion rates of 99.2% or higher, with N/A responses ranging from 0% (item 2) to 3.1% (item 7). The percent scoring at the ceiling was 1.7% for the global item and ranged from 18.3% up to 63.3% for other individual items.

This study resulted in one of the first measurement tools to succinctly measure multiple aspects of care coordination in the hospital from the perspective of healthcare professionals. Given the hectic work environment of healthcare professionals, and the increasing emphasis on collecting data for evaluation and improvement, it is important to minimize respondent burden. This effort was catalyzed by a multifaceted initiative to redesign acute care delivery and promote seamless transitions of care, supported by the Center for Medicare & Medicaid Innovation. In initial testing, this questionnaire has evidence for reliability and validity. It was encouraging to find that the preliminary psychometric performance of the measure was very similar in 2 different settings of a tertiary academic hospital and a community hospital.

Our analysis of the survey data explored potential differences between the 2 hospitals, among different types of healthcare professionals and across different departments. Although we expected differences, we had no specific hypotheses about what those differences might be, and, in fact, did not observe any substantial differences. This could be taken to indicate that the intervention was uniformly and successfully implemented in both hospitals, and engaged various professionals in different departments. The ability to detect differences in care coordination at the nursing unit level could also prove to be beneficial for more precisely targeting where process improvement is needed. Further data collection and analyses should be conducted to more systematically compare units and to help identify those where practice is most advanced and those where improvements may be needed. It would also be informative to link differences in care coordination scores with patient outcomes. In addition, differences identified on specific domains between professional groups could be helpful to identify where greater efforts are needed to improve interdisciplinary practice. Sampling strategies stratified by provider type would need to be targeted to make this kind of analysis informative.

The consistently lower scores observed for patient engagement, from the perspective of care professionals in all groups, suggest that this is an area where improvement is needed. These findings are consistent with published reports on the common failure by hospitals to include patients as a member of their own care team. In addition to measuring care processes from the perspective of frontline healthcare workers, future evaluations within the healthcare system would also benefit from including data collected from the perspective of the patient and family.

This study had some limitations. First, there may be more than 4 domains of care coordination that are important and can be measured in the acute care setting from provider perspective. However, the addition of more domains should be balanced against practicality and respondent burden. It may be possible to further clarify priority domains in hospital settings as opposed to the primary care setting. Future research should be directed to find these areas and to develop a more comprehensive, yet still concise measurement instrument. Second, the tool was developed to measure the impact of a large-scale intervention, and to fit into the specific context of 2 hospitals. Therefore, it should be tested in different settings of hospital care to see how it performs. However, virtually all hospitals in the United States today are adapting to changes in both financing and healthcare delivery. A tool such as the one described in this paper could be helpful to many organizations. Third, the scoring system for the overall scale score is not weighted and therefore reflects teamwork more than other components of care coordination, which are represented by fewer items. In general, we believe that use of the subscale scores may be more informative. Alternative scoring systems might also be proposed, including item weighting based on factor scores.

For the purposes of evaluation in this specific instance, we only collected data at a single point in time, after the intervention had been deployed. Thus, we were not able to evaluate the effectiveness of the J-CHiP intervention. We also did not intend to focus too much on the differences between units, given the limited number of respondents from individual units. It would be useful to collect more data at future time points, both to test the responsiveness of the scales and to evaluate the impact of future interventions at both the hospital and unit level.

The preliminary data from this study have generated insights about gaps in current practice, such as in engaging patients in the inpatient care process. It has also increased awareness by hospital leaders about the need to achieve high reliability in the adoption of new procedures and interdisciplinary practice. This tool might be used to find areas in need of improvement, to evaluate the effect of initiatives to improve care coordination, to monitor the change over time in the perception of care coordination among healthcare professionals, and to develop better intervention strategies for coordination activities in acute care settings. Additional research is needed to provide further evidence for the reliability and validity of this measure in diverse settings.

Disclosure

 The project described was supported by Grant Number 1C1CMS331053-01-00 from the US Department of Health and Human Services, Centers for Medicare & Medicaid Services. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the US Department of Health and Human Services or any of its agencies. The research presented was conducted by the awardee. Results may or may not be consistent with or confirmed by the findings of the independent evaluation contractor.

The authors have no other disclosures.

Care Coordination has been defined as “…the deliberate organization of patient care activities between two or more participants (including the patient) involved in a patient’s care to facilitate the appropriate delivery of healthcare services.”¹ The Institute of Medicine identified care coordination as a key strategy to improve the American healthcare system,² and evidence has been building that well-coordinated care improves patient outcomes and reduces healthcare costs associated with chronic conditions.^3-5 In 2012, Johns Hopkins Medicine was awarded a Healthcare Innovation Award by the Centers for Medicare & Medicaid Services to improve coordination of care across the continuum of care for adult patients admitted to Johns Hopkins Hospital (JHH) and Johns Hopkins Bayview Medical Center (JHBMC), and for high-risk low-income Medicare and Medicaid beneficiaries receiving ambulatory care in targeted zip codes. The purpose of this project, known as the Johns Hopkins Community Health Partnership (J-CHiP), was to improve health and healthcare and to reduce healthcare costs. The acute care component of the program consisted of a bundle of interventions focused on improving coordination of care for all patients, including a “bridge to home” discharge process, as they transitioned back to the community from inpatient admission. The bundle included the following: early screening for discharge planning to predict needed postdischarge services; discussion in daily multidisciplinary rounds about goals and priorities of the hospitalization and potential postdischarge needs; patient and family self-care management; education enhanced medication management, including the option of “medications in hand” at the time of discharge; postdischarge telephone follow-up by nurses; and, for patients identified as high-risk, a “transition guide” (a nurse who works with the patient via home visits and by phone to optimize compliance with care for 30 days postdischarge).⁶ While the primary endpoints of the J-CHiP program were to improve clinical outcomes and reduce healthcare costs, we were also interested in the impact of the program on care coordination processes in the acute care setting. This created the need for an instrument to measure healthcare professionals’ views of care coordination in their immediate work environments.

We began our search for existing measures by reviewing the Coordination Measures Atlas published in 2014.⁷ Although this report evaluates over 80 different measures of care coordination, most of them focus on the perspective of the patient and/or family members, on specific conditions, and on primary care or outpatient settings.^7,8 We were unable to identify an existing measure from the provider perspective, designed for the inpatient setting, that was both brief but comprehensive enough to cover a range of care coordination domains.⁸

Consequently, our first aim was to develop a brief, comprehensive tool to measure care coordination from the perspective of hospital inpatient staff that could be used to compare different units or types of providers, or to conduct longitudinal assessment. The second aim was to conduct a preliminary evaluation of the tool in our healthcare setting, including to assess its psychometric properties, to describe provider perceptions of care coordination after the implementation of J-CHiP, and to explore potential differences among departments, types of professionals, and between the 2 hospitals.

Development of the Care Coordination Questionnaire

The survey was developed in collaboration with leaders of the J-CHiP Acute Care Team. We met at the outset and on multiple subsequent occasions to align survey domains with the main components of the J-CHiP acute care intervention and to assure that the survey would be relevant and understandable to a variety of multidisciplinary professionals, including physicians, nurses, social workers, physical therapists, and other health professionals. Care was taken to avoid redundancy with existing evaluation efforts and to minimize respondent burden. This process helped to ensure the content validity of the items, the usefulness of the results, and the future usability of the tool.

We modeled the Care Coordination Questionnaire (CCQ) after the Safety Attitudes Questionnaire (SAQ),⁹ a widely used survey that is deployed approximately annually at JHH and JHBMC. While the SAQ focuses on healthcare provider attitudes about issues relevant to patient safety (often referred to as safety climate or safety culture), this new tool was designed to focus on healthcare professionals’ attitudes about care coordination. Similar to the way that the SAQ “elicits a snapshot of the safety climate through surveys of frontline worker perceptions,” we sought to elicit a picture of our care coordination climate through a survey of frontline hospital staff.

The CCQ was built upon the domains and approaches to care coordination described in the Agency for Healthcare Research and Quality Care Coordination Atlas.³ This report identifies 9 mechanisms for achieving care coordination, including the following: Establish Accountability or Negotiate Responsibility; Communicate; Facilitate Transitions; Assess Needs and Goals; Create a Proactive Plan of Care; Monitor, Follow Up, and Respond to Change; Support Self-Management Goals; Link to Community Resources; and Align Resources with Patient and Population Needs; as well as 5 broad approaches commonly used to improve the delivery of healthcare, including Teamwork Focused on Coordination, Healthcare Home, Care Management, Medication Management, and Health IT-Enabled Coordination.⁷ We generated at least 1 item to represent 8 of the 9 domains, as well as the broad approach described as Teamwork Focused on Coordination. After developing an initial set of items, we sought input from 3 senior leaders of the J-CHiP Acute Care Team to determine if the items covered the care coordination domains of interest, and to provide feedback on content validity. To test the interpretability of survey items and consistency across professional groups, we sent an initial version of the survey questions to at least 1 person from each of the following professional groups: hospitalist, social worker, case manager, clinical pharmacist, and nurse. We asked them to review all of our survey questions and to provide us with feedback on all aspects of the questions, such as whether they believed the questions were relevant and understandable to the members of their professional discipline, the appropriateness of the wording of the questions, and other comments. Modifications were made to the content and wording of the questions based on the feedback received. The final draft of the questionnaire was reviewed by the leadership team of the J-CHiP Acute Care Team to ensure its usefulness in providing actionable information.

The resulting 12-item questionnaire used a 5-point Likert response scale ranging from 1 = “disagree strongly” to 5 = “agree strongly,” and an additional option of “not applicable (N/A).” To help assess construct validity, a global question was added at the end of the questionnaire asking, “Overall, how would you rate the care coordination at the hospital of your primary work setting?” The response was measured on a 10-point Likert-type scale ranging from 1 = “totally uncoordinated care” to 10 = “perfectly coordinated care” (see Appendix). In addition, the questionnaire requested information about the respondents’ gender, position, and their primary unit, department, and hospital affiliation.

Data Collection Procedures

An invitation to complete an anonymous questionnaire was sent to the following inpatient care professionals: all nursing staff working on care coordination units in the departments of medicine, surgery, and neurology/neurosurgery, as well as physicians, pharmacists, acute care therapists (eg, occupational and physical therapists), and other frontline staff. All healthcare staff fitting these criteria was sent an e-mail with a request to fill out the survey online using Qualtrics^TM (Qualtrics Labs Inc., Provo, UT), as well as multiple follow-up reminders. The participants worked either at the JHH (a 1194-bed tertiary academic medical center in Baltimore, MD) or the JHBMC (a 440-bed academic community hospital located nearby). Data were collected from October 2015 through January 2016.

Analysis

Means and standard deviations were calculated by treating the responses as continuous variables. We tried 3 different methods to handle missing data: (1) without imputation, (2) imputing the mean value of each item, and (3) substituting a neutral score. Because all 3 methods produced very similar results, we treated the N/A responses as missing values without imputation for simplicity of analysis. We used STATA 13.1 (Stata Corporation, College Station, Texas) to analyze the data.

To identify subscales, we performed exploratory factor analysis on responses to the 12 specific items. Promax rotation was selected based on the simple structure. Subscale scores for each respondent were generated by computing the mean of responses to the items in the subscale. Internal consistency reliability of the subscales was estimated using Cronbach’s alpha. We calculated Pearson correlation coefficients for the items in each subscale, and examined Cronbach’s alpha deleting each item in turn. For each of the subscales identified and the global scale, we calculated the mean, standard deviation, median and interquartile range. Although distributions of scores tended to be non-normal, this was done to increase interpretability. We also calculated percent scoring at the ceiling (highest possible score).

We analyzed the data with 3 research questions in mind: Was there a difference in perceptions of care coordination between (1) staff affiliated with the 2 different hospitals, (2) staff affiliated with different clinical departments, or (3) staff with different professional roles? For comparisons based on hospital and department, and type of professional, nonparametric tests (Wilcoxon rank-sum and Kruskal-Wallis test) were used with a level of statistical significance set at 0.05. The comparison between hospitals and departments was made only among nurses to minimize the confounding effect of different distribution of professionals. We tested the distribution of “years in specialty” between hospitals and departments for this comparison using Pearson’s χ² test. The difference was not statistically significant (P = 0.167 for hospitals, and P = 0.518 for departments), so we assumed that the potential confounding effect of this variable was negligible in this analysis. The comparison of scores within each professional group used the Friedman test. Pearson’s χ² test was used to compare the baseline characteristics between 2 hospitals.

Among the 1486 acute care professionals asked to participate in the survey, 841 completed the questionnaire (response rate 56.6%). Table 1 shows the characteristics of the participants from each hospital. Table 2 summarizes the item response rates, proportion scoring at the ceiling, and weighting from the factor analysis. All items had completion rates of 99.2% or higher, with N/A responses ranging from 0% (item 2) to 3.1% (item 7). The percent scoring at the ceiling was 1.7% for the global item and ranged from 18.3% up to 63.3% for other individual items.

This study resulted in one of the first measurement tools to succinctly measure multiple aspects of care coordination in the hospital from the perspective of healthcare professionals. Given the hectic work environment of healthcare professionals, and the increasing emphasis on collecting data for evaluation and improvement, it is important to minimize respondent burden. This effort was catalyzed by a multifaceted initiative to redesign acute care delivery and promote seamless transitions of care, supported by the Center for Medicare & Medicaid Innovation. In initial testing, this questionnaire has evidence for reliability and validity. It was encouraging to find that the preliminary psychometric performance of the measure was very similar in 2 different settings of a tertiary academic hospital and a community hospital.

Our analysis of the survey data explored potential differences between the 2 hospitals, among different types of healthcare professionals and across different departments. Although we expected differences, we had no specific hypotheses about what those differences might be, and, in fact, did not observe any substantial differences. This could be taken to indicate that the intervention was uniformly and successfully implemented in both hospitals, and engaged various professionals in different departments. The ability to detect differences in care coordination at the nursing unit level could also prove to be beneficial for more precisely targeting where process improvement is needed. Further data collection and analyses should be conducted to more systematically compare units and to help identify those where practice is most advanced and those where improvements may be needed. It would also be informative to link differences in care coordination scores with patient outcomes. In addition, differences identified on specific domains between professional groups could be helpful to identify where greater efforts are needed to improve interdisciplinary practice. Sampling strategies stratified by provider type would need to be targeted to make this kind of analysis informative.

The consistently lower scores observed for patient engagement, from the perspective of care professionals in all groups, suggest that this is an area where improvement is needed. These findings are consistent with published reports on the common failure by hospitals to include patients as a member of their own care team. In addition to measuring care processes from the perspective of frontline healthcare workers, future evaluations within the healthcare system would also benefit from including data collected from the perspective of the patient and family.

This study had some limitations. First, there may be more than 4 domains of care coordination that are important and can be measured in the acute care setting from provider perspective. However, the addition of more domains should be balanced against practicality and respondent burden. It may be possible to further clarify priority domains in hospital settings as opposed to the primary care setting. Future research should be directed to find these areas and to develop a more comprehensive, yet still concise measurement instrument. Second, the tool was developed to measure the impact of a large-scale intervention, and to fit into the specific context of 2 hospitals. Therefore, it should be tested in different settings of hospital care to see how it performs. However, virtually all hospitals in the United States today are adapting to changes in both financing and healthcare delivery. A tool such as the one described in this paper could be helpful to many organizations. Third, the scoring system for the overall scale score is not weighted and therefore reflects teamwork more than other components of care coordination, which are represented by fewer items. In general, we believe that use of the subscale scores may be more informative. Alternative scoring systems might also be proposed, including item weighting based on factor scores.

For the purposes of evaluation in this specific instance, we only collected data at a single point in time, after the intervention had been deployed. Thus, we were not able to evaluate the effectiveness of the J-CHiP intervention. We also did not intend to focus too much on the differences between units, given the limited number of respondents from individual units. It would be useful to collect more data at future time points, both to test the responsiveness of the scales and to evaluate the impact of future interventions at both the hospital and unit level.

The preliminary data from this study have generated insights about gaps in current practice, such as in engaging patients in the inpatient care process. It has also increased awareness by hospital leaders about the need to achieve high reliability in the adoption of new procedures and interdisciplinary practice. This tool might be used to find areas in need of improvement, to evaluate the effect of initiatives to improve care coordination, to monitor the change over time in the perception of care coordination among healthcare professionals, and to develop better intervention strategies for coordination activities in acute care settings. Additional research is needed to provide further evidence for the reliability and validity of this measure in diverse settings.

Disclosure

 The project described was supported by Grant Number 1C1CMS331053-01-00 from the US Department of Health and Human Services, Centers for Medicare & Medicaid Services. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the US Department of Health and Human Services or any of its agencies. The research presented was conducted by the awardee. Results may or may not be consistent with or confirmed by the findings of the independent evaluation contractor.

The authors have no other disclosures.

Admission to a hospital can be a stressful event,^1,2 and patients report having many concerns at the time of hospital admission.³ Over the last 20 years, the United States has widely adopted the hospitalist model of inpatient care. Although this model has clear benefits, it also has the potential to contribute to patient stress, as hospitalized patients generally lack preexisting relationships with their inpatient physicians.^4,5 In this changing hospital environment, defining and promoting effective medical communication has become an essential goal of both individual practitioners and medical centers.

Successful communication and strong therapeutic relationships with physicians support patients’ coping with illness-associated stress^6,7 as well as promote adherence to medical treatment plans.⁸ Empathy serves as an important building block of patient-centered communication and encourages a strong therapeutic alliance.⁹ Studies from primary care, oncology, and intensive care unit (ICU) settings indicate that physician empathy is associated with decreased emotional distress,^10,11 improved ratings of communication,¹² and even better medical outcomes.¹³

Prior work has shown that hospitalists, like other clinicians, underutilize empathy as a tool in their daily interactions with patients.^14-16 Our prior qualitative analysis of audio-recorded hospitalist-patient admission encounters indicated that how hospitalists respond to patient expressions of negative emotion influences relationships with patients and alignment around care plans.¹⁷ To determine whether empathic communication is associated with patient-reported outcomes in the hospitalist model, we quantitatively analyzed coded admission encounters and survey data to examine the association between hospitalists’ responses to patient expressions of negative emotion (anxiety, sadness, and anger) and patient anxiety and ratings of communication. Given the often-limited time hospitalists have to complete admission encounters, we also examined the association between response to emotion and encounter length.

We analyzed data collected as part of an observational study of hospitalist-patient communication during hospital admission encounters¹⁴ to assess the association between the way physicians responded to patient expressions of negative emotion and patient anxiety, ratings of communication in the encounter, and encounter length. We collected data between August 2008 and March 2009 on the general medical service at 2 urban hospitals that are part of an academic medical center. Participants were attending hospitalists (not physician trainees), and patients admitted under participating hospitalists’ care who were able to communicate verbally in English and provide informed consent for the study. The institutional review board at the University of California, San Francisco approved the study; physician and patient participants provided written informed consent.

Enrollment and data collection has been described previously.¹⁷ Our cohort for this analysis included 76 patients of 27 physicians who completed encounter audio recordings and pre- and postencounter surveys. Following enrollment, patients completed a preencounter survey to collect demographic information and to measure their baseline anxiety via the State Anxiety Scale (STAI-S), which assesses transient anxious mood using 20 items answered on a 4-point scale for a final score range of 20 to 80.^10,18,19 We timed and audio-recorded admission encounters. Encounter recordings were obtained solely from patient interactions with attending hospitalists and did not take into account the time patients may have spent with other physicians, including trainees. After the encounter, patients completed postencounter surveys, which included the STAI-S and patients’ ratings of communication during the encounter. To rate communication, patients responded to 7 items on a 0- to 10-point scale that were derived from previous work (Table 1)^12,20,21; the anchors were “not at all” and “completely.” To identify patients with serious illness, which we used as a covariate in regression models, we asked physicians on a postencounter survey whether or not they “would be surprised by this patient’s death or admission to the ICU in the next year.”²²

We analyzed data from admission encounters with 76 patients (consent rate 63%) and 27 hospitalists (consent rate 91%). Their characteristics are shown in Table 3. Median encounter length was 19 minutes (mean 21 minutes, range 3-68). Patients expressed negative emotion in 190 instances across all encounters; median number of expressions per encounter was 1 (range 0-14). Hospitalists responded empathically to 32% (n = 61) of the patient expressions, neutrally to 43% (n = 81), and nonempathically to 25% (n = 48).

The STAI-S was normally distributed. The mean preencounter STAI-S score was 39 (standard deviation [SD] 8.9). Mean postencounter STAI-S score was 38 (SD 10.7). Mean change in anxiety over the course of the encounter, calculated as the postencounter minus preencounter mean was −1.2 (SD 7.6). Table 1 shows summary statistics for the patient ratings of communication items. All items were rated highly. Across the items, between 51% and 78% of patients rated the highest score of 10.

Across the range of frequencies of emotional expressions per encounter in our data set (0-14 expressions), each additional empathic hospitalist response was associated with a 1.65-point decrease in the STAI-S (95% confidence interval [CI], 0.48-2.82). We did not find significant associations between changes in the STAI-S and the number of neutral hospitalist responses (−0.65 per response; 95% CI, −1.67-0.37) or nonempathic hospitalist responses (0.61 per response; 95% CI, −0.88-2.10).

Our study set out to measure how hospitalists responded to expressions of negative emotion during admission encounters with patients and how those responses correlated with patient anxiety, ratings of communication, and encounter length. We found that empathic responses were associated with diminishing patient anxiety after the visit, as well as with better ratings of several domains of hospitalist communication. Moreover, nonempathic responses to negative emotion were associated with more strongly negative ratings of hospitalist communication. Finally, while clinicians may worry that encouraging patients to speak further about emotion will result in excessive visit lengths, we did not find a statistical association between empathic responses and encounter duration. To our knowledge, this is the first study to indicate an association between empathy and patient anxiety and communication ratings within the hospitalist model, which is rapidly becoming the predominant model for providing inpatient care in the United States.^4,5

As in oncologic care, anxiety is an emotion commonly confronted by clinicians meeting admitted medical patients for the first time. Studies show that not only do patient anxiety levels remain high throughout a hospital course, patients who experience higher levels of anxiety tend to stay longer in the hospital.^1,2,27-30 But unlike oncologic care or other therapy provided in an outpatient setting, the hospitalist model does not facilitate “continuity” of care, or the ability to care for the same patients over a long period of time. This reality of inpatient care makes rapid, effective rapport-building critical to establishing strong physician-patient relationships. In this setting, a simple communication tool that is potentially able to reduce inpatients’ anxiety could have a meaningful impact on hospitalist-provided care and patient outcomes.

In terms of the magnitude of the effect of empathic responses, the clinical significance of a 1.65-point decrease in the STAI-S anxiety score is not precisely clear. A prior study that examined the effect of music therapy on anxiety levels in patients with cancer found an average anxiety reduction of approximately 9.5 units on the STAIS-S scale after sensitivity analysis, suggesting a rather large meaningful effect size.³¹ Given we found a reduction of 1.65 points for each empathic response, however, with a range of 0-14 negative emotions expressed over a median 19-minute encounter, there is opportunity for hospitalists to achieve a clinically significant decrease in patient anxiety during an admission encounter. The potential to reduce anxiety is extended further when we consider that the impact of an empathic response may apply not just to the admission encounter alone but also to numerous other patient-clinician interactions over the course of a hospitalization.

A healthy body of communication research supports the associations we found in our study between empathy and patient ratings of communication and physicians. Families in ICU conferences rate communication more positively when physicians express empathy,¹² and a number of studies indicate an association between empathy and patient satisfaction in outpatient settings.⁸ Given the associations we found with negative ratings on the items in our study, promoting empathic responses to expressions of emotion and, more importantly, stressing avoidance of nonempathic responses may be relevant efforts in working to improve patient satisfaction scores on surveys reporting “top box” percentages, such as Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). More notably, evidence indicates that empathy has positive impacts beyond satisfaction surveys, such as adherence, better diagnostic and clinical outcomes, and strengthening of patient enablement.⁸Not all hospitalist responses to emotion were associated with patient ratings across the 7 communication items we assessed. For example, we did not find an association between how physicians responded to patient expressions of negative emotion and patient perception that enough time was spent in the visit or the degree to which talking with the doctor met a patient’s overall needs. It follows logically, and other research supports, that empathy would influence patient ratings of physician caring and trust,³² whereas other communication factors we were unable to measure (eg, physician body language, tone, and use of jargon and patient health literacy and primary language) may have a more significant association with patient ratings of the other items we assessed.

In considering the clinical application of our results, it is important to note that communication skills, including responding empathically to patient expressions of negative emotion, can be imparted through training in the same way as abdominal examination or electrocardiogram interpretation skills.^33-35 However, training of hospitalists in communication skills requires time and some financial investment on the part of the physician, their hospital or group, or, ideally, both. Effective training methods, like those for other skill acquisition, involve learner-centered teaching and practicing skills with role-play and feedback.³⁶ Given the importance of a learner-centered approach, learning would likely be better received and more effective if it was tailored to the specific needs and patient scenarios commonly encountered by hospitalist physicians. As these programs are developed, it will be important to assess the impact of any training on the patient-reported outcomes we assessed in this observational study, along with clinical outcomes.

Our study has several limitations. First, we were only able to evaluate whether hospitalists verbally responded to patient emotion and were thus not able to account for nonverbal empathy such as facial expressions, body language, or voice tone. Second, given our patient consent rate of 63%, patients who agreed to participate in the study may have had different opinions than those who declined to participate. Also, hospitalists and patients may have behaved differently as a result of being audio recorded. We only included patients who spoke English, and our patient population was predominately non-Hispanic white. Patients who spoke other languages or came from other cultural backgrounds may have had different responses. Third, we did not use a single validated scale for patient ratings of communication, and multiple analyses increase our risk of finding statistically significant associations by chance. The skewing of the communication rating items toward high scores may also have led to our results being driven by outliers, although the model we chose for analysis does penalize for this. Furthermore, our sample size was small, leading to wide CIs and potential for lack of statistical associations due to insufficient power. Our findings warrant replication in larger studies. Fourth, the setting of our study in an academic center may affect generalizability. Finally, the age of our data (collected between 2008 and 2009) is also a limitation. Given a recent focus on communication and patient experience since the initiation of HCAHPS feedback, a similar analysis of empathy and communication methods now may result in different outcomes.

In conclusion, our results suggest that enhancing hospitalists’ empathic responses to patient expressions of negative emotion could decrease patient anxiety and improve patients’ perceptions of (and thus possibly their relationships with) hospitalists, without sacrificing efficiency. Future work should focus on tailoring and implementing communication skills training programs for hospitalists and evaluating the impact of training on patient outcomes.

Acknowledgments

The authors extend their sincere thanks to the patients and physicians who participated in this study. Dr. Anderson was funded by the National Palliative Care Research Center and the University of California, San Francisco Clinical and Translational Science Institute Career Development Program, National Institutes of Health (NIH) grant number 5 KL2 RR024130-04. Project costs were funded by a grant from the University of California, San Francisco Academic Senate.

Disclosure

 All coauthors have seen and agree with the contents of this manuscript. This submission is not under review by any other publication. Wendy Anderson received funding for this project from the National Palliative Care Research Center, University of California San Francisco Clinical and Translational Science Institute (NIH grant number 5KL2RR024130-04), and the University of San Francisco Academic Senate [From Section 2 of Author Disclosure Form]. Andy Auerbach has a Patient-Centered Outcomes Research Institute research grant in development [From Section 3 of the Author Disclosure Form].

Admission to a hospital can be a stressful event,^1,2 and patients report having many concerns at the time of hospital admission.³ Over the last 20 years, the United States has widely adopted the hospitalist model of inpatient care. Although this model has clear benefits, it also has the potential to contribute to patient stress, as hospitalized patients generally lack preexisting relationships with their inpatient physicians.^4,5 In this changing hospital environment, defining and promoting effective medical communication has become an essential goal of both individual practitioners and medical centers.

Successful communication and strong therapeutic relationships with physicians support patients’ coping with illness-associated stress^6,7 as well as promote adherence to medical treatment plans.⁸ Empathy serves as an important building block of patient-centered communication and encourages a strong therapeutic alliance.⁹ Studies from primary care, oncology, and intensive care unit (ICU) settings indicate that physician empathy is associated with decreased emotional distress,^10,11 improved ratings of communication,¹² and even better medical outcomes.¹³

Prior work has shown that hospitalists, like other clinicians, underutilize empathy as a tool in their daily interactions with patients.^14-16 Our prior qualitative analysis of audio-recorded hospitalist-patient admission encounters indicated that how hospitalists respond to patient expressions of negative emotion influences relationships with patients and alignment around care plans.¹⁷ To determine whether empathic communication is associated with patient-reported outcomes in the hospitalist model, we quantitatively analyzed coded admission encounters and survey data to examine the association between hospitalists’ responses to patient expressions of negative emotion (anxiety, sadness, and anger) and patient anxiety and ratings of communication. Given the often-limited time hospitalists have to complete admission encounters, we also examined the association between response to emotion and encounter length.

We analyzed data collected as part of an observational study of hospitalist-patient communication during hospital admission encounters¹⁴ to assess the association between the way physicians responded to patient expressions of negative emotion and patient anxiety, ratings of communication in the encounter, and encounter length. We collected data between August 2008 and March 2009 on the general medical service at 2 urban hospitals that are part of an academic medical center. Participants were attending hospitalists (not physician trainees), and patients admitted under participating hospitalists’ care who were able to communicate verbally in English and provide informed consent for the study. The institutional review board at the University of California, San Francisco approved the study; physician and patient participants provided written informed consent.

Enrollment and data collection has been described previously.¹⁷ Our cohort for this analysis included 76 patients of 27 physicians who completed encounter audio recordings and pre- and postencounter surveys. Following enrollment, patients completed a preencounter survey to collect demographic information and to measure their baseline anxiety via the State Anxiety Scale (STAI-S), which assesses transient anxious mood using 20 items answered on a 4-point scale for a final score range of 20 to 80.^10,18,19 We timed and audio-recorded admission encounters. Encounter recordings were obtained solely from patient interactions with attending hospitalists and did not take into account the time patients may have spent with other physicians, including trainees. After the encounter, patients completed postencounter surveys, which included the STAI-S and patients’ ratings of communication during the encounter. To rate communication, patients responded to 7 items on a 0- to 10-point scale that were derived from previous work (Table 1)^12,20,21; the anchors were “not at all” and “completely.” To identify patients with serious illness, which we used as a covariate in regression models, we asked physicians on a postencounter survey whether or not they “would be surprised by this patient’s death or admission to the ICU in the next year.”²²

We analyzed data from admission encounters with 76 patients (consent rate 63%) and 27 hospitalists (consent rate 91%). Their characteristics are shown in Table 3. Median encounter length was 19 minutes (mean 21 minutes, range 3-68). Patients expressed negative emotion in 190 instances across all encounters; median number of expressions per encounter was 1 (range 0-14). Hospitalists responded empathically to 32% (n = 61) of the patient expressions, neutrally to 43% (n = 81), and nonempathically to 25% (n = 48).

The STAI-S was normally distributed. The mean preencounter STAI-S score was 39 (standard deviation [SD] 8.9). Mean postencounter STAI-S score was 38 (SD 10.7). Mean change in anxiety over the course of the encounter, calculated as the postencounter minus preencounter mean was −1.2 (SD 7.6). Table 1 shows summary statistics for the patient ratings of communication items. All items were rated highly. Across the items, between 51% and 78% of patients rated the highest score of 10.

Across the range of frequencies of emotional expressions per encounter in our data set (0-14 expressions), each additional empathic hospitalist response was associated with a 1.65-point decrease in the STAI-S (95% confidence interval [CI], 0.48-2.82). We did not find significant associations between changes in the STAI-S and the number of neutral hospitalist responses (−0.65 per response; 95% CI, −1.67-0.37) or nonempathic hospitalist responses (0.61 per response; 95% CI, −0.88-2.10).

Our study set out to measure how hospitalists responded to expressions of negative emotion during admission encounters with patients and how those responses correlated with patient anxiety, ratings of communication, and encounter length. We found that empathic responses were associated with diminishing patient anxiety after the visit, as well as with better ratings of several domains of hospitalist communication. Moreover, nonempathic responses to negative emotion were associated with more strongly negative ratings of hospitalist communication. Finally, while clinicians may worry that encouraging patients to speak further about emotion will result in excessive visit lengths, we did not find a statistical association between empathic responses and encounter duration. To our knowledge, this is the first study to indicate an association between empathy and patient anxiety and communication ratings within the hospitalist model, which is rapidly becoming the predominant model for providing inpatient care in the United States.^4,5

As in oncologic care, anxiety is an emotion commonly confronted by clinicians meeting admitted medical patients for the first time. Studies show that not only do patient anxiety levels remain high throughout a hospital course, patients who experience higher levels of anxiety tend to stay longer in the hospital.^1,2,27-30 But unlike oncologic care or other therapy provided in an outpatient setting, the hospitalist model does not facilitate “continuity” of care, or the ability to care for the same patients over a long period of time. This reality of inpatient care makes rapid, effective rapport-building critical to establishing strong physician-patient relationships. In this setting, a simple communication tool that is potentially able to reduce inpatients’ anxiety could have a meaningful impact on hospitalist-provided care and patient outcomes.

In terms of the magnitude of the effect of empathic responses, the clinical significance of a 1.65-point decrease in the STAI-S anxiety score is not precisely clear. A prior study that examined the effect of music therapy on anxiety levels in patients with cancer found an average anxiety reduction of approximately 9.5 units on the STAIS-S scale after sensitivity analysis, suggesting a rather large meaningful effect size.³¹ Given we found a reduction of 1.65 points for each empathic response, however, with a range of 0-14 negative emotions expressed over a median 19-minute encounter, there is opportunity for hospitalists to achieve a clinically significant decrease in patient anxiety during an admission encounter. The potential to reduce anxiety is extended further when we consider that the impact of an empathic response may apply not just to the admission encounter alone but also to numerous other patient-clinician interactions over the course of a hospitalization.

A healthy body of communication research supports the associations we found in our study between empathy and patient ratings of communication and physicians. Families in ICU conferences rate communication more positively when physicians express empathy,¹² and a number of studies indicate an association between empathy and patient satisfaction in outpatient settings.⁸ Given the associations we found with negative ratings on the items in our study, promoting empathic responses to expressions of emotion and, more importantly, stressing avoidance of nonempathic responses may be relevant efforts in working to improve patient satisfaction scores on surveys reporting “top box” percentages, such as Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). More notably, evidence indicates that empathy has positive impacts beyond satisfaction surveys, such as adherence, better diagnostic and clinical outcomes, and strengthening of patient enablement.⁸Not all hospitalist responses to emotion were associated with patient ratings across the 7 communication items we assessed. For example, we did not find an association between how physicians responded to patient expressions of negative emotion and patient perception that enough time was spent in the visit or the degree to which talking with the doctor met a patient’s overall needs. It follows logically, and other research supports, that empathy would influence patient ratings of physician caring and trust,³² whereas other communication factors we were unable to measure (eg, physician body language, tone, and use of jargon and patient health literacy and primary language) may have a more significant association with patient ratings of the other items we assessed.

In considering the clinical application of our results, it is important to note that communication skills, including responding empathically to patient expressions of negative emotion, can be imparted through training in the same way as abdominal examination or electrocardiogram interpretation skills.^33-35 However, training of hospitalists in communication skills requires time and some financial investment on the part of the physician, their hospital or group, or, ideally, both. Effective training methods, like those for other skill acquisition, involve learner-centered teaching and practicing skills with role-play and feedback.³⁶ Given the importance of a learner-centered approach, learning would likely be better received and more effective if it was tailored to the specific needs and patient scenarios commonly encountered by hospitalist physicians. As these programs are developed, it will be important to assess the impact of any training on the patient-reported outcomes we assessed in this observational study, along with clinical outcomes.

Our study has several limitations. First, we were only able to evaluate whether hospitalists verbally responded to patient emotion and were thus not able to account for nonverbal empathy such as facial expressions, body language, or voice tone. Second, given our patient consent rate of 63%, patients who agreed to participate in the study may have had different opinions than those who declined to participate. Also, hospitalists and patients may have behaved differently as a result of being audio recorded. We only included patients who spoke English, and our patient population was predominately non-Hispanic white. Patients who spoke other languages or came from other cultural backgrounds may have had different responses. Third, we did not use a single validated scale for patient ratings of communication, and multiple analyses increase our risk of finding statistically significant associations by chance. The skewing of the communication rating items toward high scores may also have led to our results being driven by outliers, although the model we chose for analysis does penalize for this. Furthermore, our sample size was small, leading to wide CIs and potential for lack of statistical associations due to insufficient power. Our findings warrant replication in larger studies. Fourth, the setting of our study in an academic center may affect generalizability. Finally, the age of our data (collected between 2008 and 2009) is also a limitation. Given a recent focus on communication and patient experience since the initiation of HCAHPS feedback, a similar analysis of empathy and communication methods now may result in different outcomes.

In conclusion, our results suggest that enhancing hospitalists’ empathic responses to patient expressions of negative emotion could decrease patient anxiety and improve patients’ perceptions of (and thus possibly their relationships with) hospitalists, without sacrificing efficiency. Future work should focus on tailoring and implementing communication skills training programs for hospitalists and evaluating the impact of training on patient outcomes.

Acknowledgments

The authors extend their sincere thanks to the patients and physicians who participated in this study. Dr. Anderson was funded by the National Palliative Care Research Center and the University of California, San Francisco Clinical and Translational Science Institute Career Development Program, National Institutes of Health (NIH) grant number 5 KL2 RR024130-04. Project costs were funded by a grant from the University of California, San Francisco Academic Senate.

Disclosure

 All coauthors have seen and agree with the contents of this manuscript. This submission is not under review by any other publication. Wendy Anderson received funding for this project from the National Palliative Care Research Center, University of California San Francisco Clinical and Translational Science Institute (NIH grant number 5KL2RR024130-04), and the University of San Francisco Academic Senate [From Section 2 of Author Disclosure Form]. Andy Auerbach has a Patient-Centered Outcomes Research Institute research grant in development [From Section 3 of the Author Disclosure Form].

Admission to a hospital can be a stressful event,^1,2 and patients report having many concerns at the time of hospital admission.³ Over the last 20 years, the United States has widely adopted the hospitalist model of inpatient care. Although this model has clear benefits, it also has the potential to contribute to patient stress, as hospitalized patients generally lack preexisting relationships with their inpatient physicians.^4,5 In this changing hospital environment, defining and promoting effective medical communication has become an essential goal of both individual practitioners and medical centers.

Successful communication and strong therapeutic relationships with physicians support patients’ coping with illness-associated stress^6,7 as well as promote adherence to medical treatment plans.⁸ Empathy serves as an important building block of patient-centered communication and encourages a strong therapeutic alliance.⁹ Studies from primary care, oncology, and intensive care unit (ICU) settings indicate that physician empathy is associated with decreased emotional distress,^10,11 improved ratings of communication,¹² and even better medical outcomes.¹³

Prior work has shown that hospitalists, like other clinicians, underutilize empathy as a tool in their daily interactions with patients.^14-16 Our prior qualitative analysis of audio-recorded hospitalist-patient admission encounters indicated that how hospitalists respond to patient expressions of negative emotion influences relationships with patients and alignment around care plans.¹⁷ To determine whether empathic communication is associated with patient-reported outcomes in the hospitalist model, we quantitatively analyzed coded admission encounters and survey data to examine the association between hospitalists’ responses to patient expressions of negative emotion (anxiety, sadness, and anger) and patient anxiety and ratings of communication. Given the often-limited time hospitalists have to complete admission encounters, we also examined the association between response to emotion and encounter length.

We analyzed data collected as part of an observational study of hospitalist-patient communication during hospital admission encounters¹⁴ to assess the association between the way physicians responded to patient expressions of negative emotion and patient anxiety, ratings of communication in the encounter, and encounter length. We collected data between August 2008 and March 2009 on the general medical service at 2 urban hospitals that are part of an academic medical center. Participants were attending hospitalists (not physician trainees), and patients admitted under participating hospitalists’ care who were able to communicate verbally in English and provide informed consent for the study. The institutional review board at the University of California, San Francisco approved the study; physician and patient participants provided written informed consent.

Enrollment and data collection has been described previously.¹⁷ Our cohort for this analysis included 76 patients of 27 physicians who completed encounter audio recordings and pre- and postencounter surveys. Following enrollment, patients completed a preencounter survey to collect demographic information and to measure their baseline anxiety via the State Anxiety Scale (STAI-S), which assesses transient anxious mood using 20 items answered on a 4-point scale for a final score range of 20 to 80.^10,18,19 We timed and audio-recorded admission encounters. Encounter recordings were obtained solely from patient interactions with attending hospitalists and did not take into account the time patients may have spent with other physicians, including trainees. After the encounter, patients completed postencounter surveys, which included the STAI-S and patients’ ratings of communication during the encounter. To rate communication, patients responded to 7 items on a 0- to 10-point scale that were derived from previous work (Table 1)^12,20,21; the anchors were “not at all” and “completely.” To identify patients with serious illness, which we used as a covariate in regression models, we asked physicians on a postencounter survey whether or not they “would be surprised by this patient’s death or admission to the ICU in the next year.”²²

We analyzed data from admission encounters with 76 patients (consent rate 63%) and 27 hospitalists (consent rate 91%). Their characteristics are shown in Table 3. Median encounter length was 19 minutes (mean 21 minutes, range 3-68). Patients expressed negative emotion in 190 instances across all encounters; median number of expressions per encounter was 1 (range 0-14). Hospitalists responded empathically to 32% (n = 61) of the patient expressions, neutrally to 43% (n = 81), and nonempathically to 25% (n = 48).

The STAI-S was normally distributed. The mean preencounter STAI-S score was 39 (standard deviation [SD] 8.9). Mean postencounter STAI-S score was 38 (SD 10.7). Mean change in anxiety over the course of the encounter, calculated as the postencounter minus preencounter mean was −1.2 (SD 7.6). Table 1 shows summary statistics for the patient ratings of communication items. All items were rated highly. Across the items, between 51% and 78% of patients rated the highest score of 10.

Across the range of frequencies of emotional expressions per encounter in our data set (0-14 expressions), each additional empathic hospitalist response was associated with a 1.65-point decrease in the STAI-S (95% confidence interval [CI], 0.48-2.82). We did not find significant associations between changes in the STAI-S and the number of neutral hospitalist responses (−0.65 per response; 95% CI, −1.67-0.37) or nonempathic hospitalist responses (0.61 per response; 95% CI, −0.88-2.10).

Our study set out to measure how hospitalists responded to expressions of negative emotion during admission encounters with patients and how those responses correlated with patient anxiety, ratings of communication, and encounter length. We found that empathic responses were associated with diminishing patient anxiety after the visit, as well as with better ratings of several domains of hospitalist communication. Moreover, nonempathic responses to negative emotion were associated with more strongly negative ratings of hospitalist communication. Finally, while clinicians may worry that encouraging patients to speak further about emotion will result in excessive visit lengths, we did not find a statistical association between empathic responses and encounter duration. To our knowledge, this is the first study to indicate an association between empathy and patient anxiety and communication ratings within the hospitalist model, which is rapidly becoming the predominant model for providing inpatient care in the United States.^4,5

As in oncologic care, anxiety is an emotion commonly confronted by clinicians meeting admitted medical patients for the first time. Studies show that not only do patient anxiety levels remain high throughout a hospital course, patients who experience higher levels of anxiety tend to stay longer in the hospital.^1,2,27-30 But unlike oncologic care or other therapy provided in an outpatient setting, the hospitalist model does not facilitate “continuity” of care, or the ability to care for the same patients over a long period of time. This reality of inpatient care makes rapid, effective rapport-building critical to establishing strong physician-patient relationships. In this setting, a simple communication tool that is potentially able to reduce inpatients’ anxiety could have a meaningful impact on hospitalist-provided care and patient outcomes.

In terms of the magnitude of the effect of empathic responses, the clinical significance of a 1.65-point decrease in the STAI-S anxiety score is not precisely clear. A prior study that examined the effect of music therapy on anxiety levels in patients with cancer found an average anxiety reduction of approximately 9.5 units on the STAIS-S scale after sensitivity analysis, suggesting a rather large meaningful effect size.³¹ Given we found a reduction of 1.65 points for each empathic response, however, with a range of 0-14 negative emotions expressed over a median 19-minute encounter, there is opportunity for hospitalists to achieve a clinically significant decrease in patient anxiety during an admission encounter. The potential to reduce anxiety is extended further when we consider that the impact of an empathic response may apply not just to the admission encounter alone but also to numerous other patient-clinician interactions over the course of a hospitalization.

A healthy body of communication research supports the associations we found in our study between empathy and patient ratings of communication and physicians. Families in ICU conferences rate communication more positively when physicians express empathy,¹² and a number of studies indicate an association between empathy and patient satisfaction in outpatient settings.⁸ Given the associations we found with negative ratings on the items in our study, promoting empathic responses to expressions of emotion and, more importantly, stressing avoidance of nonempathic responses may be relevant efforts in working to improve patient satisfaction scores on surveys reporting “top box” percentages, such as Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). More notably, evidence indicates that empathy has positive impacts beyond satisfaction surveys, such as adherence, better diagnostic and clinical outcomes, and strengthening of patient enablement.⁸Not all hospitalist responses to emotion were associated with patient ratings across the 7 communication items we assessed. For example, we did not find an association between how physicians responded to patient expressions of negative emotion and patient perception that enough time was spent in the visit or the degree to which talking with the doctor met a patient’s overall needs. It follows logically, and other research supports, that empathy would influence patient ratings of physician caring and trust,³² whereas other communication factors we were unable to measure (eg, physician body language, tone, and use of jargon and patient health literacy and primary language) may have a more significant association with patient ratings of the other items we assessed.

In considering the clinical application of our results, it is important to note that communication skills, including responding empathically to patient expressions of negative emotion, can be imparted through training in the same way as abdominal examination or electrocardiogram interpretation skills.^33-35 However, training of hospitalists in communication skills requires time and some financial investment on the part of the physician, their hospital or group, or, ideally, both. Effective training methods, like those for other skill acquisition, involve learner-centered teaching and practicing skills with role-play and feedback.³⁶ Given the importance of a learner-centered approach, learning would likely be better received and more effective if it was tailored to the specific needs and patient scenarios commonly encountered by hospitalist physicians. As these programs are developed, it will be important to assess the impact of any training on the patient-reported outcomes we assessed in this observational study, along with clinical outcomes.

Our study has several limitations. First, we were only able to evaluate whether hospitalists verbally responded to patient emotion and were thus not able to account for nonverbal empathy such as facial expressions, body language, or voice tone. Second, given our patient consent rate of 63%, patients who agreed to participate in the study may have had different opinions than those who declined to participate. Also, hospitalists and patients may have behaved differently as a result of being audio recorded. We only included patients who spoke English, and our patient population was predominately non-Hispanic white. Patients who spoke other languages or came from other cultural backgrounds may have had different responses. Third, we did not use a single validated scale for patient ratings of communication, and multiple analyses increase our risk of finding statistically significant associations by chance. The skewing of the communication rating items toward high scores may also have led to our results being driven by outliers, although the model we chose for analysis does penalize for this. Furthermore, our sample size was small, leading to wide CIs and potential for lack of statistical associations due to insufficient power. Our findings warrant replication in larger studies. Fourth, the setting of our study in an academic center may affect generalizability. Finally, the age of our data (collected between 2008 and 2009) is also a limitation. Given a recent focus on communication and patient experience since the initiation of HCAHPS feedback, a similar analysis of empathy and communication methods now may result in different outcomes.

In conclusion, our results suggest that enhancing hospitalists’ empathic responses to patient expressions of negative emotion could decrease patient anxiety and improve patients’ perceptions of (and thus possibly their relationships with) hospitalists, without sacrificing efficiency. Future work should focus on tailoring and implementing communication skills training programs for hospitalists and evaluating the impact of training on patient outcomes.

Acknowledgments

The authors extend their sincere thanks to the patients and physicians who participated in this study. Dr. Anderson was funded by the National Palliative Care Research Center and the University of California, San Francisco Clinical and Translational Science Institute Career Development Program, National Institutes of Health (NIH) grant number 5 KL2 RR024130-04. Project costs were funded by a grant from the University of California, San Francisco Academic Senate.

Disclosure

 All coauthors have seen and agree with the contents of this manuscript. This submission is not under review by any other publication. Wendy Anderson received funding for this project from the National Palliative Care Research Center, University of California San Francisco Clinical and Translational Science Institute (NIH grant number 5KL2RR024130-04), and the University of San Francisco Academic Senate [From Section 2 of Author Disclosure Form]. Andy Auerbach has a Patient-Centered Outcomes Research Institute research grant in development [From Section 3 of the Author Disclosure Form].

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.

A treatment for cancer is finding a new purpose in treating another life-threatening condition. The FDA expanded approval of Ibrutinib for treatment of adults with chronic graft versus host disease (cGVHD) after ≥ 1 treatments have failed. Ibrutinib was previously approved for certain indications in treating chronic lymphocytic leukemia, Waldenström macroglobulinemia, and marginal zone lymphoma.

An estimated 30% to 70% of patients who receive hematopoietic stem cell transplantation for blood or bone marrow cancer develop cGVHD.

Ibrutinib , a kinase inhibitor, was tested in a single-arm trial of 42 patients with cGVHD. Most had mouth ulcers and skin rashes; > 50% had ≥ 2 organs affected. Their symptoms had persisted despite standard treatment with corticosteroids. In the study, cGVHD symptoms improved in 67%. For nearly half (48%), the improvements lasted for 5 months or longer.

Common adverse effects include fatigue, bruising, diarrhea, and thrombocytopenia. Serious adverse effects include severe bleeding, infections, and cytopenia.

A treatment for cancer is finding a new purpose in treating another life-threatening condition. The FDA expanded approval of Ibrutinib for treatment of adults with chronic graft versus host disease (cGVHD) after ≥ 1 treatments have failed. Ibrutinib was previously approved for certain indications in treating chronic lymphocytic leukemia, Waldenström macroglobulinemia, and marginal zone lymphoma.

An estimated 30% to 70% of patients who receive hematopoietic stem cell transplantation for blood or bone marrow cancer develop cGVHD.

Ibrutinib , a kinase inhibitor, was tested in a single-arm trial of 42 patients with cGVHD. Most had mouth ulcers and skin rashes; > 50% had ≥ 2 organs affected. Their symptoms had persisted despite standard treatment with corticosteroids. In the study, cGVHD symptoms improved in 67%. For nearly half (48%), the improvements lasted for 5 months or longer.

Common adverse effects include fatigue, bruising, diarrhea, and thrombocytopenia. Serious adverse effects include severe bleeding, infections, and cytopenia.

A treatment for cancer is finding a new purpose in treating another life-threatening condition. The FDA expanded approval of Ibrutinib for treatment of adults with chronic graft versus host disease (cGVHD) after ≥ 1 treatments have failed. Ibrutinib was previously approved for certain indications in treating chronic lymphocytic leukemia, Waldenström macroglobulinemia, and marginal zone lymphoma.

An estimated 30% to 70% of patients who receive hematopoietic stem cell transplantation for blood or bone marrow cancer develop cGVHD.

Ibrutinib , a kinase inhibitor, was tested in a single-arm trial of 42 patients with cGVHD. Most had mouth ulcers and skin rashes; > 50% had ≥ 2 organs affected. Their symptoms had persisted despite standard treatment with corticosteroids. In the study, cGVHD symptoms improved in 67%. For nearly half (48%), the improvements lasted for 5 months or longer.

Common adverse effects include fatigue, bruising, diarrhea, and thrombocytopenia. Serious adverse effects include severe bleeding, infections, and cytopenia.

Photo by Rhoda Baer

A new study suggests cancer patients may be open to using marijuana, but healthcare providers may be falling short in educating patients on marijuana use.

This single-center study included more than 900 cancer patients in a US state with legalized medicinal and recreational marijuana.

More than 90% of the patients surveyed said they were interested in learning more about marijuana use in the context of cancer, and nearly three-quarters of the patients wanted their cancer care providers to supply information on the topic.

However, less than 15% of patients received such information from providers. Instead, patients learned about marijuana use from sources such as the Internet or other patients.

“Cancer patients desire but are not receiving information from their cancer doctors about marijuana use during their treatment, so many of them are seeking information from alternate, non-scientific sources,” said Steven Pergam, MD, of the Fred Hutchinson Cancer Research Center in Seattle, Washington.

Dr Pergam and his colleagues reported this finding in the journal Cancer.

Eight US states and the District of Columbia have legalized recreational marijuana, and more than half of states have passed laws allowing for medical marijuana in some form. Marijuana is purported to alleviate symptoms related to cancer treatment, but patterns of use among cancer patients are not well known.

To investigate, Dr Pergam and his colleagues surveyed 926 patients at the Seattle Cancer Center Alliance. The patients’ median age was 58, 52% were male, and 59% had at least a college degree. Thirty-four percent of patients had hematologic malignancies.

Results

Sixty-six percent of patients said they had used marijuana in the past, 24% used in the last year, and 21% used in the last month.

A random analysis of patient urine samples showed that 14% of patients had evidence of recent marijuana use, similar to the 18% of users who reported at least weekly marijuana use.

When compared to patients who never used marijuana and those who previously used marijuana but quit, patients currently using marijuana said they were more likely to do so because the drug had been legalized. Women were more likely than men to use because of legalization.

Current marijuana users were younger, had less education, and were less likely to have undergone hematopoietic stem cell transplant. There was no difference in marijuana use according to a patient’s cancer type.

Most patients said they used marijuana to relieve physical symptoms (75%) and neuropsychiatric symptoms (63%), though some also used it recreationally (35%).

In addition, 26% of current marijuana users said they believed the drug was helping to treat their cancer. And 5% of these users said this was their only reason for marijuana use.

Most patients (92%) said they wanted to learn more about marijuana and cancer. However, the level of interest varied with age, with younger patients expressing the most interest.

Seventy-four percent of patients said they would prefer to get information on marijuana use from their cancer team, but less than 15% received such information from their cancer physician or nurse.

Patients said they received information on marijuana and cancer from friends and family, newspaper and magazine articles, websites and blogs, or another cancer patient.

More than a third of patients said they had not received any information on marijuana and cancer.

“We hope that this study helps to open up the door for more studies aimed at evaluating the risks and benefits of marijuana in this population,” Dr Pergam said. “This is important because if we do not educate our patients about marijuana, they will continue to get their information elsewhere.”

Photo by Rhoda Baer

A new study suggests cancer patients may be open to using marijuana, but healthcare providers may be falling short in educating patients on marijuana use.

This single-center study included more than 900 cancer patients in a US state with legalized medicinal and recreational marijuana.

More than 90% of the patients surveyed said they were interested in learning more about marijuana use in the context of cancer, and nearly three-quarters of the patients wanted their cancer care providers to supply information on the topic.

However, less than 15% of patients received such information from providers. Instead, patients learned about marijuana use from sources such as the Internet or other patients.

“Cancer patients desire but are not receiving information from their cancer doctors about marijuana use during their treatment, so many of them are seeking information from alternate, non-scientific sources,” said Steven Pergam, MD, of the Fred Hutchinson Cancer Research Center in Seattle, Washington.

Dr Pergam and his colleagues reported this finding in the journal Cancer.

Eight US states and the District of Columbia have legalized recreational marijuana, and more than half of states have passed laws allowing for medical marijuana in some form. Marijuana is purported to alleviate symptoms related to cancer treatment, but patterns of use among cancer patients are not well known.

To investigate, Dr Pergam and his colleagues surveyed 926 patients at the Seattle Cancer Center Alliance. The patients’ median age was 58, 52% were male, and 59% had at least a college degree. Thirty-four percent of patients had hematologic malignancies.

Results

Sixty-six percent of patients said they had used marijuana in the past, 24% used in the last year, and 21% used in the last month.

A random analysis of patient urine samples showed that 14% of patients had evidence of recent marijuana use, similar to the 18% of users who reported at least weekly marijuana use.

When compared to patients who never used marijuana and those who previously used marijuana but quit, patients currently using marijuana said they were more likely to do so because the drug had been legalized. Women were more likely than men to use because of legalization.

Current marijuana users were younger, had less education, and were less likely to have undergone hematopoietic stem cell transplant. There was no difference in marijuana use according to a patient’s cancer type.

Most patients said they used marijuana to relieve physical symptoms (75%) and neuropsychiatric symptoms (63%), though some also used it recreationally (35%).

In addition, 26% of current marijuana users said they believed the drug was helping to treat their cancer. And 5% of these users said this was their only reason for marijuana use.

Most patients (92%) said they wanted to learn more about marijuana and cancer. However, the level of interest varied with age, with younger patients expressing the most interest.

Seventy-four percent of patients said they would prefer to get information on marijuana use from their cancer team, but less than 15% received such information from their cancer physician or nurse.

Patients said they received information on marijuana and cancer from friends and family, newspaper and magazine articles, websites and blogs, or another cancer patient.

More than a third of patients said they had not received any information on marijuana and cancer.

“We hope that this study helps to open up the door for more studies aimed at evaluating the risks and benefits of marijuana in this population,” Dr Pergam said. “This is important because if we do not educate our patients about marijuana, they will continue to get their information elsewhere.”

Photo by Rhoda Baer

A new study suggests cancer patients may be open to using marijuana, but healthcare providers may be falling short in educating patients on marijuana use.

This single-center study included more than 900 cancer patients in a US state with legalized medicinal and recreational marijuana.

More than 90% of the patients surveyed said they were interested in learning more about marijuana use in the context of cancer, and nearly three-quarters of the patients wanted their cancer care providers to supply information on the topic.

However, less than 15% of patients received such information from providers. Instead, patients learned about marijuana use from sources such as the Internet or other patients.

“Cancer patients desire but are not receiving information from their cancer doctors about marijuana use during their treatment, so many of them are seeking information from alternate, non-scientific sources,” said Steven Pergam, MD, of the Fred Hutchinson Cancer Research Center in Seattle, Washington.

Dr Pergam and his colleagues reported this finding in the journal Cancer.

Eight US states and the District of Columbia have legalized recreational marijuana, and more than half of states have passed laws allowing for medical marijuana in some form. Marijuana is purported to alleviate symptoms related to cancer treatment, but patterns of use among cancer patients are not well known.

To investigate, Dr Pergam and his colleagues surveyed 926 patients at the Seattle Cancer Center Alliance. The patients’ median age was 58, 52% were male, and 59% had at least a college degree. Thirty-four percent of patients had hematologic malignancies.

Results

Sixty-six percent of patients said they had used marijuana in the past, 24% used in the last year, and 21% used in the last month.

A random analysis of patient urine samples showed that 14% of patients had evidence of recent marijuana use, similar to the 18% of users who reported at least weekly marijuana use.

When compared to patients who never used marijuana and those who previously used marijuana but quit, patients currently using marijuana said they were more likely to do so because the drug had been legalized. Women were more likely than men to use because of legalization.

Current marijuana users were younger, had less education, and were less likely to have undergone hematopoietic stem cell transplant. There was no difference in marijuana use according to a patient’s cancer type.

Most patients said they used marijuana to relieve physical symptoms (75%) and neuropsychiatric symptoms (63%), though some also used it recreationally (35%).

In addition, 26% of current marijuana users said they believed the drug was helping to treat their cancer. And 5% of these users said this was their only reason for marijuana use.

Most patients (92%) said they wanted to learn more about marijuana and cancer. However, the level of interest varied with age, with younger patients expressing the most interest.

Seventy-four percent of patients said they would prefer to get information on marijuana use from their cancer team, but less than 15% received such information from their cancer physician or nurse.

Patients said they received information on marijuana and cancer from friends and family, newspaper and magazine articles, websites and blogs, or another cancer patient.

More than a third of patients said they had not received any information on marijuana and cancer.

“We hope that this study helps to open up the door for more studies aimed at evaluating the risks and benefits of marijuana in this population,” Dr Pergam said. “This is important because if we do not educate our patients about marijuana, they will continue to get their information elsewhere.”

Micrograph showing MF

Results of the SIMPLIFY-1 study revealed how 2 JAK inhibitors—momelotinib and ruxolitinib—compared to one another in myelofibrosis (MF) patients who were previously JAK-inhibitor-naïve.

Momelotinib proved noninferior to ruxolitinib when it came to spleen reduction but not symptom response.

On the other hand, momelotinib was more effective than ruxolitinib in reducing transfusion dependence.

The overall incidence of adverse events (AEs) was similar between the treatment arms.

However, patients receiving momelotinib were more likely to experience AEs leading to treatment discontinuation.

Ruben A. Mesa, MD, of Mayo Clinic Cancer Center in Phoenix, Arizona, and his colleagues reported these results in the Journal of Clinical Oncology. The study was sponsored by Gilead Sciences.

SIMPLIFY-1 was a phase 3, double-blind, active-controlled study. It enrolled 432 patients with symptomatic intermediate-1-risk MF, intermediate-2-risk MF, or high-risk MF.

These JAK-inhibitor-naïve patients were randomized to receive 24 weeks of treatment with momelotinib (n=215, 200 mg once daily) or ruxolitinib (n=217, 20 mg twice a day or per label). After that, all patients could receive open-label momelotinib.

The researchers said baseline characteristics were similar between the treatment arms. In the overall cohort, most patients were white (82.6%), male (56.5%), 65 or older (57.2%), and had primary MF (56.5%).

In all, 376 patients completed 24 weeks of treatment—175 in the momelotinib arm and 201 in the ruxolitinib arm. And 368 patients proceeded to the open-label phase of the study—171 from the momelotinib arm and 197 from the ruxolitinib arm (who switched to momelotinib).

Efficacy

The primary efficacy endpoint was spleen response rate at 24 weeks, which was defined as the proportion of patients achieving at least a 35% reduction in spleen volume.

This endpoint was achieved by a similar proportion of patients in both treatment arms—26.5% in the momelotinib arm and 29% in the ruxolitinib arm. This met the criteria for noninferiority (P=0.011).

On the other hand, noninferiority was not met for total symptom score, which was the proportion of patients achieving at least a 50% reduction in MF symptoms. This endpoint was achieved by 28.4% in the momelotinib arm and 42.2% in the ruxolitinib arm (P=0.98 for noninferiority).

A greater proportion of patients were transfusion-independent at week 24 in the momelotinib arm than the ruxolitinib arm—66.5% and 49.3%, respectively (nominal P<0.001).

The median rate of red blood cell transfusion was 0 units per month in the momelotinib arm and 0.4 units per month in the ruxolitinib arm (nominal P<0.001).

Safety

Most patients had at least 1 AE—92.1% in the momelotinib arm and 95.4% in the ruxolitinib arm. Grade 3 or higher AEs occurred in 35.5% and 43.5%, respectively.

Serious AEs occurred in 22.9% and 18.1%, respectively. AEs leading to treatment discontinuation occurred in 13.1% and 5.6%, respectively.

Treatment-emergent AEs occurring in at least 10% of patients in either treatment arm (momelotinib and ruxolitinib, respectively) were thrombocytopenia (18.7% and 29.2%), diarrhea (17.8% and 19.9%), headache (17.3% and 19.9%), dizziness (15.9% and 11.6%), nausea (15.9% and 3.7%), fatigue (14.5% and 12.0%), anemia (13.6% and 38%), abdominal pain (10.3% and 11.1%), and peripheral neuropathy (10.3% and 4.6%).

The most common grade 3/4 AEs in the momelotinib arm were thrombocytopenia (7.0%), anemia (5.6%), diarrhea (2.8%), hypertension (2.8%), and neutropenia (2.8%).

The most common grade 3/4 AEs in the ruxolitinib arm were anemia (23.1%), neutropenia (4.6%), thrombocytopenia (4.6%), and hypertension (4.2%).

There were 7 deaths in the momelotinib arm. The causes of death were listed as enteritis, mesenteric vein thrombosis, death, sudden death, sepsis, renal failure, and aortic dissection.

There were 7 deaths in the ruxolitinib arm as well. The causes of death were melena, sepsis, pneumonia, head injury, acute myeloid leukemia, recurrent mantle cell lymphoma, and coma.

Transformation to acute myeloid leukemia occurred in 1 patient in the momelotinib arm (grade 4) and 2 patients in the ruxolitinib arm (grade 3 and grade 5).

Micrograph showing MF

Results of the SIMPLIFY-1 study revealed how 2 JAK inhibitors—momelotinib and ruxolitinib—compared to one another in myelofibrosis (MF) patients who were previously JAK-inhibitor-naïve.

Momelotinib proved noninferior to ruxolitinib when it came to spleen reduction but not symptom response.

On the other hand, momelotinib was more effective than ruxolitinib in reducing transfusion dependence.

The overall incidence of adverse events (AEs) was similar between the treatment arms.

However, patients receiving momelotinib were more likely to experience AEs leading to treatment discontinuation.

Ruben A. Mesa, MD, of Mayo Clinic Cancer Center in Phoenix, Arizona, and his colleagues reported these results in the Journal of Clinical Oncology. The study was sponsored by Gilead Sciences.

SIMPLIFY-1 was a phase 3, double-blind, active-controlled study. It enrolled 432 patients with symptomatic intermediate-1-risk MF, intermediate-2-risk MF, or high-risk MF.

These JAK-inhibitor-naïve patients were randomized to receive 24 weeks of treatment with momelotinib (n=215, 200 mg once daily) or ruxolitinib (n=217, 20 mg twice a day or per label). After that, all patients could receive open-label momelotinib.

The researchers said baseline characteristics were similar between the treatment arms. In the overall cohort, most patients were white (82.6%), male (56.5%), 65 or older (57.2%), and had primary MF (56.5%).

In all, 376 patients completed 24 weeks of treatment—175 in the momelotinib arm and 201 in the ruxolitinib arm. And 368 patients proceeded to the open-label phase of the study—171 from the momelotinib arm and 197 from the ruxolitinib arm (who switched to momelotinib).

Efficacy

The primary efficacy endpoint was spleen response rate at 24 weeks, which was defined as the proportion of patients achieving at least a 35% reduction in spleen volume.

This endpoint was achieved by a similar proportion of patients in both treatment arms—26.5% in the momelotinib arm and 29% in the ruxolitinib arm. This met the criteria for noninferiority (P=0.011).

On the other hand, noninferiority was not met for total symptom score, which was the proportion of patients achieving at least a 50% reduction in MF symptoms. This endpoint was achieved by 28.4% in the momelotinib arm and 42.2% in the ruxolitinib arm (P=0.98 for noninferiority).

A greater proportion of patients were transfusion-independent at week 24 in the momelotinib arm than the ruxolitinib arm—66.5% and 49.3%, respectively (nominal P<0.001).

The median rate of red blood cell transfusion was 0 units per month in the momelotinib arm and 0.4 units per month in the ruxolitinib arm (nominal P<0.001).

Safety

Most patients had at least 1 AE—92.1% in the momelotinib arm and 95.4% in the ruxolitinib arm. Grade 3 or higher AEs occurred in 35.5% and 43.5%, respectively.

Serious AEs occurred in 22.9% and 18.1%, respectively. AEs leading to treatment discontinuation occurred in 13.1% and 5.6%, respectively.

Treatment-emergent AEs occurring in at least 10% of patients in either treatment arm (momelotinib and ruxolitinib, respectively) were thrombocytopenia (18.7% and 29.2%), diarrhea (17.8% and 19.9%), headache (17.3% and 19.9%), dizziness (15.9% and 11.6%), nausea (15.9% and 3.7%), fatigue (14.5% and 12.0%), anemia (13.6% and 38%), abdominal pain (10.3% and 11.1%), and peripheral neuropathy (10.3% and 4.6%).

The most common grade 3/4 AEs in the momelotinib arm were thrombocytopenia (7.0%), anemia (5.6%), diarrhea (2.8%), hypertension (2.8%), and neutropenia (2.8%).

The most common grade 3/4 AEs in the ruxolitinib arm were anemia (23.1%), neutropenia (4.6%), thrombocytopenia (4.6%), and hypertension (4.2%).

There were 7 deaths in the momelotinib arm. The causes of death were listed as enteritis, mesenteric vein thrombosis, death, sudden death, sepsis, renal failure, and aortic dissection.

There were 7 deaths in the ruxolitinib arm as well. The causes of death were melena, sepsis, pneumonia, head injury, acute myeloid leukemia, recurrent mantle cell lymphoma, and coma.

Transformation to acute myeloid leukemia occurred in 1 patient in the momelotinib arm (grade 4) and 2 patients in the ruxolitinib arm (grade 3 and grade 5).

Micrograph showing MF

Results of the SIMPLIFY-1 study revealed how 2 JAK inhibitors—momelotinib and ruxolitinib—compared to one another in myelofibrosis (MF) patients who were previously JAK-inhibitor-naïve.

Momelotinib proved noninferior to ruxolitinib when it came to spleen reduction but not symptom response.

On the other hand, momelotinib was more effective than ruxolitinib in reducing transfusion dependence.

The overall incidence of adverse events (AEs) was similar between the treatment arms.

However, patients receiving momelotinib were more likely to experience AEs leading to treatment discontinuation.

Ruben A. Mesa, MD, of Mayo Clinic Cancer Center in Phoenix, Arizona, and his colleagues reported these results in the Journal of Clinical Oncology. The study was sponsored by Gilead Sciences.

SIMPLIFY-1 was a phase 3, double-blind, active-controlled study. It enrolled 432 patients with symptomatic intermediate-1-risk MF, intermediate-2-risk MF, or high-risk MF.

These JAK-inhibitor-naïve patients were randomized to receive 24 weeks of treatment with momelotinib (n=215, 200 mg once daily) or ruxolitinib (n=217, 20 mg twice a day or per label). After that, all patients could receive open-label momelotinib.

The researchers said baseline characteristics were similar between the treatment arms. In the overall cohort, most patients were white (82.6%), male (56.5%), 65 or older (57.2%), and had primary MF (56.5%).

In all, 376 patients completed 24 weeks of treatment—175 in the momelotinib arm and 201 in the ruxolitinib arm. And 368 patients proceeded to the open-label phase of the study—171 from the momelotinib arm and 197 from the ruxolitinib arm (who switched to momelotinib).

Efficacy

The primary efficacy endpoint was spleen response rate at 24 weeks, which was defined as the proportion of patients achieving at least a 35% reduction in spleen volume.

This endpoint was achieved by a similar proportion of patients in both treatment arms—26.5% in the momelotinib arm and 29% in the ruxolitinib arm. This met the criteria for noninferiority (P=0.011).

On the other hand, noninferiority was not met for total symptom score, which was the proportion of patients achieving at least a 50% reduction in MF symptoms. This endpoint was achieved by 28.4% in the momelotinib arm and 42.2% in the ruxolitinib arm (P=0.98 for noninferiority).

A greater proportion of patients were transfusion-independent at week 24 in the momelotinib arm than the ruxolitinib arm—66.5% and 49.3%, respectively (nominal P<0.001).

The median rate of red blood cell transfusion was 0 units per month in the momelotinib arm and 0.4 units per month in the ruxolitinib arm (nominal P<0.001).

Safety

Most patients had at least 1 AE—92.1% in the momelotinib arm and 95.4% in the ruxolitinib arm. Grade 3 or higher AEs occurred in 35.5% and 43.5%, respectively.

Serious AEs occurred in 22.9% and 18.1%, respectively. AEs leading to treatment discontinuation occurred in 13.1% and 5.6%, respectively.

Treatment-emergent AEs occurring in at least 10% of patients in either treatment arm (momelotinib and ruxolitinib, respectively) were thrombocytopenia (18.7% and 29.2%), diarrhea (17.8% and 19.9%), headache (17.3% and 19.9%), dizziness (15.9% and 11.6%), nausea (15.9% and 3.7%), fatigue (14.5% and 12.0%), anemia (13.6% and 38%), abdominal pain (10.3% and 11.1%), and peripheral neuropathy (10.3% and 4.6%).

The most common grade 3/4 AEs in the momelotinib arm were thrombocytopenia (7.0%), anemia (5.6%), diarrhea (2.8%), hypertension (2.8%), and neutropenia (2.8%).

The most common grade 3/4 AEs in the ruxolitinib arm were anemia (23.1%), neutropenia (4.6%), thrombocytopenia (4.6%), and hypertension (4.2%).

There were 7 deaths in the momelotinib arm. The causes of death were listed as enteritis, mesenteric vein thrombosis, death, sudden death, sepsis, renal failure, and aortic dissection.

There were 7 deaths in the ruxolitinib arm as well. The causes of death were melena, sepsis, pneumonia, head injury, acute myeloid leukemia, recurrent mantle cell lymphoma, and coma.

Transformation to acute myeloid leukemia occurred in 1 patient in the momelotinib arm (grade 4) and 2 patients in the ruxolitinib arm (grade 3 and grade 5).

of Medical Research

Carbohydrates on the surface of malaria parasites play a critical role in the parasites’ ability to infect mosquito and human hosts, according to research published in Nature Communications.

Researchers found that Plasmodium falciparum “tags” its proteins with carbohydrates in order to stabilize and transport them.

And this process is crucial to completing the parasite’s life cycle.

“Malaria parasites have a complex life cycle that involves constant shape-shifting to evade detection and infect humans and, subsequently, mosquitoes,” said study author Justin Boddey, PhD, of The Walter and Eliza Hall Institute of Medical Research in Parkville, Victoria, Australia.

“We found that the parasite’s ability to ‘tag’ key proteins with carbohydrates is important for 2 stages of the malaria life cycle. It is critical for the earliest stages of human infection, when the parasite migrates through the body and invades in the liver, and later, when it is transmitted back to the mosquito from an infected human, enabling the parasite to be spread between people.”

“Interfering with the parasite’s ability to attach these carbohydrates to its proteins hinders liver infection and transmission to the mosquito and weakens the parasite to the point that it cannot survive in the host.”

Dr Boddey and his colleagues said this research suggests steps that may improve the efficacy of the malaria vaccine RTS,S/AS01 (Mosquirix).

“The protein used in the RTS,S vaccine mimics one of the proteins we’ve been studying on the surface of the malaria parasite that is readily recognized by the immune system,” said study author Ethan D. Goddard-Borger, PhD, of The Walter and Eliza Hall Institute of Medical Research.

“It was hoped that the vaccine would generate a good antibody response that protected against the parasite. However, it has, unfortunately, not been as effective at evoking protective immunity as hoped.”

“With this study, we’ve shown that the parasite protein is tagged with carbohydrates, making it slightly different to the vaccine, so the antibodies produced may not be optimal for recognizing target parasites.”

Dr Goddard-Borger said there were many documented cases where attaching carbohydrates to a protein improved its efficacy as a vaccine.

“It may be that a version of RTS,S with added carbohydrates will perform better than the current vaccine,” he said. “Now that we know how important these carbohydrates are to the parasite, we can be confident that the malaria parasite cannot ‘escape’ vaccination pressure by doing away with its carbohydrates.”

of Medical Research

Carbohydrates on the surface of malaria parasites play a critical role in the parasites’ ability to infect mosquito and human hosts, according to research published in Nature Communications.

Researchers found that Plasmodium falciparum “tags” its proteins with carbohydrates in order to stabilize and transport them.

And this process is crucial to completing the parasite’s life cycle.

“Malaria parasites have a complex life cycle that involves constant shape-shifting to evade detection and infect humans and, subsequently, mosquitoes,” said study author Justin Boddey, PhD, of The Walter and Eliza Hall Institute of Medical Research in Parkville, Victoria, Australia.

“We found that the parasite’s ability to ‘tag’ key proteins with carbohydrates is important for 2 stages of the malaria life cycle. It is critical for the earliest stages of human infection, when the parasite migrates through the body and invades in the liver, and later, when it is transmitted back to the mosquito from an infected human, enabling the parasite to be spread between people.”

“Interfering with the parasite’s ability to attach these carbohydrates to its proteins hinders liver infection and transmission to the mosquito and weakens the parasite to the point that it cannot survive in the host.”

Dr Boddey and his colleagues said this research suggests steps that may improve the efficacy of the malaria vaccine RTS,S/AS01 (Mosquirix).

“The protein used in the RTS,S vaccine mimics one of the proteins we’ve been studying on the surface of the malaria parasite that is readily recognized by the immune system,” said study author Ethan D. Goddard-Borger, PhD, of The Walter and Eliza Hall Institute of Medical Research.

“It was hoped that the vaccine would generate a good antibody response that protected against the parasite. However, it has, unfortunately, not been as effective at evoking protective immunity as hoped.”

“With this study, we’ve shown that the parasite protein is tagged with carbohydrates, making it slightly different to the vaccine, so the antibodies produced may not be optimal for recognizing target parasites.”

Dr Goddard-Borger said there were many documented cases where attaching carbohydrates to a protein improved its efficacy as a vaccine.

“It may be that a version of RTS,S with added carbohydrates will perform better than the current vaccine,” he said. “Now that we know how important these carbohydrates are to the parasite, we can be confident that the malaria parasite cannot ‘escape’ vaccination pressure by doing away with its carbohydrates.”

of Medical Research

Carbohydrates on the surface of malaria parasites play a critical role in the parasites’ ability to infect mosquito and human hosts, according to research published in Nature Communications.

Researchers found that Plasmodium falciparum “tags” its proteins with carbohydrates in order to stabilize and transport them.

And this process is crucial to completing the parasite’s life cycle.

“Malaria parasites have a complex life cycle that involves constant shape-shifting to evade detection and infect humans and, subsequently, mosquitoes,” said study author Justin Boddey, PhD, of The Walter and Eliza Hall Institute of Medical Research in Parkville, Victoria, Australia.

“We found that the parasite’s ability to ‘tag’ key proteins with carbohydrates is important for 2 stages of the malaria life cycle. It is critical for the earliest stages of human infection, when the parasite migrates through the body and invades in the liver, and later, when it is transmitted back to the mosquito from an infected human, enabling the parasite to be spread between people.”

“Interfering with the parasite’s ability to attach these carbohydrates to its proteins hinders liver infection and transmission to the mosquito and weakens the parasite to the point that it cannot survive in the host.”

Dr Boddey and his colleagues said this research suggests steps that may improve the efficacy of the malaria vaccine RTS,S/AS01 (Mosquirix).

“The protein used in the RTS,S vaccine mimics one of the proteins we’ve been studying on the surface of the malaria parasite that is readily recognized by the immune system,” said study author Ethan D. Goddard-Borger, PhD, of The Walter and Eliza Hall Institute of Medical Research.

“It was hoped that the vaccine would generate a good antibody response that protected against the parasite. However, it has, unfortunately, not been as effective at evoking protective immunity as hoped.”

“With this study, we’ve shown that the parasite protein is tagged with carbohydrates, making it slightly different to the vaccine, so the antibodies produced may not be optimal for recognizing target parasites.”

Dr Goddard-Borger said there were many documented cases where attaching carbohydrates to a protein improved its efficacy as a vaccine.

“It may be that a version of RTS,S with added carbohydrates will perform better than the current vaccine,” he said. “Now that we know how important these carbohydrates are to the parasite, we can be confident that the malaria parasite cannot ‘escape’ vaccination pressure by doing away with its carbohydrates.”