Rehan Qayyum, MD, MHS

Chronic obstructive pulmonary disease (COPD) is recognized as the third leading cause of death nationally. Globally, it has been estimated that 10% of the population has COPD; in the United States, approximately 15 million people are affected.^1,2 The annual estimated cost of COPD management in the United States is approximately $50 billion, one-third of which is directly related to inpatient hospitalization for COPD exacerbation.^3,4,5 The 30-day readmission rate after hospitalization for acute exacerbation of COPD (AECOPD) is approximately 21% with an approximate cost of $13 billion per year.^6,7 To reduce the cost and to improve patient outcomes, the Centers for Medicare and Medicaid Services (CMS) has designed several interventions with little effect.⁸

In October 2012, the Affordable Care Act added section 1886(q) to the Social Security Act and established the Hospital Readmission Reduction Program (HRRP), an initiative to decrease hospitalization costs by penalizing hospitals with high 30-day readmission rates. Under this program, hospitals received up to 3% penalty for excess readmissions after the index hospitalization with acute myocardial infarction (AMI), heart failure (HF), and pneumonia.^9-11 Hospitals are penalized if their annual readmission rates are significantly above the average national readmission rate. In 2014, the HRRP was extended to include AECOPD for the FY 2015.

Since the implementation of readmission penalties, data have shown a significant decrease in the 30-day readmission rates for all conditions.^12,13 On the other hand, studies have suggested that, at least for some conditions, the decrease in the 30-day readmission rate is associated with higher adverse patients outcomes, including higher mortality.^14,15 However, whether a decrease in readmission rates after an AECOPD hospitalization is associated with a concomitant increase in mortality has not been examined. Therefore, our objective was to examine the association of the 30-day risk-adjusted hospital readmission rate with the 30-day risk-adjusted hospital mortality rate for patients discharged with a diagnosis of AECOPD.

Data Sources

Publicly available data from three sources were used. The all-cause 30-day risk-standardized readmission rate (RSRR) and the 30-day risk-standardized mortality rate (RSMR) of each hospital for patients with AECOPD were obtained from the Hospital Compare database; a database maintained by the CMS.^16,17 In 2014, the CMS started reporting three-year running average of 30-day mortality and readmission rate data on hospitals for AECOPD hospitalizations; the data start date was July 2010.^18-22 We examined data from the FY 2010-2013 to 2014-2017 cycles on readmission and mortality reported by the CMS; this included data before and after the implementation of penalties.

Hospital characteristics were also obtained from the CMS website. Hospital ownership was defined as government (owned by Federal or state), for-profit (owned by physicians or another proprietary), or nonprofit (owned by a nonprofit organization such as a church). A hospital was considered as a teaching hospital if it obtained graduate medical education funding from the CMS.

Data on local population characteristics according to ZIP codes were obtained from the 2010 decennial census and the American Community Survey five-year (2009-2013) data files available at the United States Census Bureau website.²³ For each ZIP code, we obtained data on the total population, percentage of African Americans in the population, median income, poverty level, and insurance status.

We used Hospital service area (HSA) information obtained from the Dartmouth Atlas of Health Care crosswalk files to link local population characteristics to hospitals. The Dartmouth Atlas defined 3,436 HSAs by assigning the ZIP codes to the hospital area where the greatest proportion of their Medicare residents was hospitalized.^24,25

Hospital Compare data and Census Bureau population data were matched to the HSAs from the Dartmouth Atlas of Healthcare data at the ZIP code level. First, the ZIP code-level data from the Census Bureau were pooled by the HSAs obtained from the Dartmouth Atlas of Healthcare, followed by matching these data by the HSAs to the Hospital Compare data. Merging data from these three sources generated a dataset that contained information about readmission and mortality rates from a particular hospital and the population characteristics of the local healthcare market or neighborhood. Our final dataset included hospitals that had readmission and mortality information available at the Hospital Compare website and were included in the crosswalk files of the Dartmouth Atlas of Healthcare.

Statistical Analysis

Data are summarized as mean and standard deviation (SD), median with interquartile range, or frequencies as appropriate. To model the dependence of observations from the same hospital over time, we used mixed linear models with random intercept and slope. A strength of this modeling approach is that it incorporates information from all hospitals even when some hospitals are missing data for some time periods. We reached our final model through stages with increasing model complexity at each stage. In the first stage, we developed an empty model without any covariates to determine the unconditional variance components so that we can partition mortality variance into between- and within-hospital components. In the second stage, we developed an unconditional growth curve model to determine the shape of time trend in mortality over time using linear and quadratic (by including squared time in the model) growth curves. In the third stage, we added baseline readmission rates (from 2010 to 2013) to the model to determine the effect of baseline readmission rate on mortality trends and also examined its interaction with time and squared time. We generated a change in the readmission rate variable by subtracting the last readmission rate from the baseline readmission rate (readmission rate in 2010-2013 − readmission rate in 2014-2017). In the fourth stage, we included this change in readmission rate into the third-stage model to examine how changes in the readmission rate affected the time trends of mortality and also examined its interaction with time and squared time. In the final model, we included the following potential confounding variables to the fourth stage model: African American percentage in the HSA, HSA median income, percentage of people living in poverty in the HSA, median age, ownership of hospital (government, for profit), teaching status (teaching vs nonteaching), and acute care hospital beds in the HSA. Within each stage, the models were compared using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), and the model with the lowest value of each was moved to the next stage of model development. All analyses were performed in Stata 14.1 for Windows (College Station, Texas).

Of the 3,685 acute care hospitals analyzed in the 2010-2013 data cycle for COPD, the 30-day RSRR was 20.7% (1.28), which decreased to 19.6% (1.11) in 2014-2017 (Table 1). During the same period, the 30-day all-cause RSMR increased from 7.8% (1.03) in 2010-2013 to 8.4% (1.11) in 2014-2017. The partitioning of variance showed that 57% of variation in the mortality rate over the study period was due to between-hospital differences.

The unconditional growth model examining the linear time trend revealed a 0.13% per year (95% CI = 0.12 to 0.14; P < .0001) increase in mortality rate over the five data cycles. When the squared time variable was added to the model to examine a quadratic trend, both time and squared trend were statistically significant (Table 2) and the AIC and BIC were lower for the quadratic model. Thus, the unconditional growth curve model suggested that the mortality trend was nonlinear and the coefficients demonstrated that not only the mortality rate increased, but the rate of change in the mortality rate was also increasing during the study period.

When we added the baseline readmission rate to the abovementioned quadratic growth model, we found an inverse association; each 1% increase in baseline readmission rate was associated with 0.03% (95% CI = −0.05 to −0.005; P = .02) decrease in mortality rate. These findings suggest that hospitals with higher baseline readmission rates also had lower mortality rates. To examine whether the effect of baseline readmission rate on mortality varied over time, we included the interaction term with time in the model and then added the interaction term with squared time. As the AIC and BIC were the lowest for the model with interaction between time and baseline readmission (and not when interaction between squared time and baseline readmission were included), we accepted this model. In this model, although there was no difference in mortality according to readmissions at baseline, each 1% increase in baseline readmission rate was associated with a smaller increase in mortality rate by 0.015% (95% CI = −0.02 to −0.01; P < .0001; Table 2 and Figure 1). These findings suggest that hospitals with higher readmission rates at baseline had a smaller increase in mortality rate during the study period than those with lower readmission rates.

As efforts to decrease readmission rates continue as a part of the HRRP implementation by the CMS, our study shows that among hospitals that discharged patients with AECOPD during 2010-2017, the all-cause 30-day RSRR was decreased, whereas the all-cause 30-day RSMR was increased. Of particular concern is that the rate of increase in mortality also increased. We also found that hospitals with higher readmission rates in 2010-2013 had a lower rate of increase in mortality than hospitals with lower readmission rates. In addition, hospitals that had a larger decrease in readmission rates during the study period had a larger rate of increase in mortality than hospitals with a smaller decrease in readmission rates. Our findings were robust to potential confounders such as hospital characteristics and local population characteristics in which hospitals operate.

Our study findings raise the question whether the implementation of the HRRP resulted in unintentional patient harm by forcing hospitals to make changes that may affect overall patient care. This question is particularly important as other studies on hospitalized patients with HF have found similar results.^13,14 On the other hand, a similar association between readmission and mortality rates has not been observed in patients with pneumonia or AMI.¹⁴ Several possible explanations can be given for the observed discrepancy between the diseases and their effect on the relationship between readmission rate and mortality rate. Both COPD and HF are chronic diseases and characterized by exacerbations, whereas AMI and pneumonia are episodic diseases that are treatable. As the number of patients hospitalized with AECOPD and HF is much larger, hospitals may have a greater focus on reducing the 30-day readmission rates and may attempt to game the process, such as by delaying admissions through the emergency department within the 30-day period or by admitting patients for observation. In fact, a study found a 3% reduction in the within-hospital readmission rate with a concurrent 0.8% increase in observation unit use since the implementation of the HRRP.²⁶ Such approaches to patient care may lead to adverse outcomes.

It is possible that readmissions and mortality act as competing risks and hence hospitals with higher mortality rates are left with fewer patients and thus have fewer readmissions, whereas those with lower mortality rates have more patients and a higher readmission rate.²⁷ Such studies are not possible with hospital-level data, and patient-level studies will be required to examine this competing risk hypothesis. Our study results provide some support to the competing risk hypothesis (hospitals with lower baseline readmission rates had a steeper increase in mortality); however, it is not possible to draw any conclusions due to the high risk of ecological fallacy bias.

This study has important potential implications for healthcare policy, public health, and research. We found that an important national intervention aimed at decreasing readmission rates and improving the quality of care for patients with AECOPD may be associated with higher mortality rates in these patients. There may be a need to redefine measures for determining the performance of an institution. Our study supports research into the underlying mechanisms resulting in an inverse association between readmissions and mortality. In particular, health policy researchers may need to examine how incentives and penalties affect the allocation of resources within hospitals.

This study has several strengths and some potential weaknesses. We used a national dataset to examine readmission and mortality rates that include the majority of hospitals in the United States. We also included data from the local population for each hospital, thus allowing us to examine hospital performance within the context of its target population. One potential limitation is that we used hospital-level data and not patient-level data; however, the readmission penalties are designed for hospitals, which justifies our use of hospital-level data. Furthermore, data were not available for shorter time intervals; data from shorter time intervals may be associated with greater variability. Being an observational study, it is difficult to establish a causal relationship; the longitudinal nature of the study does establish temporality, an important factor in establishing causality.

In conclusion, we found that although the readmission rates decreased, there was an increase in the mortality rate within the 30 days of discharge from the hospital in patients with AECOPD. The rate of increase in mortality was higher in hospitals with lower readmission rates than in hospitals with higher readmission rates. Further research for determining the mechanism responsible for this association is needed. Future health policy interventions may need to consider the potential for adverse outcomes.

Chronic obstructive pulmonary disease (COPD) is recognized as the third leading cause of death nationally. Globally, it has been estimated that 10% of the population has COPD; in the United States, approximately 15 million people are affected.^1,2 The annual estimated cost of COPD management in the United States is approximately $50 billion, one-third of which is directly related to inpatient hospitalization for COPD exacerbation.^3,4,5 The 30-day readmission rate after hospitalization for acute exacerbation of COPD (AECOPD) is approximately 21% with an approximate cost of $13 billion per year.^6,7 To reduce the cost and to improve patient outcomes, the Centers for Medicare and Medicaid Services (CMS) has designed several interventions with little effect.⁸

In October 2012, the Affordable Care Act added section 1886(q) to the Social Security Act and established the Hospital Readmission Reduction Program (HRRP), an initiative to decrease hospitalization costs by penalizing hospitals with high 30-day readmission rates. Under this program, hospitals received up to 3% penalty for excess readmissions after the index hospitalization with acute myocardial infarction (AMI), heart failure (HF), and pneumonia.^9-11 Hospitals are penalized if their annual readmission rates are significantly above the average national readmission rate. In 2014, the HRRP was extended to include AECOPD for the FY 2015.

Since the implementation of readmission penalties, data have shown a significant decrease in the 30-day readmission rates for all conditions.^12,13 On the other hand, studies have suggested that, at least for some conditions, the decrease in the 30-day readmission rate is associated with higher adverse patients outcomes, including higher mortality.^14,15 However, whether a decrease in readmission rates after an AECOPD hospitalization is associated with a concomitant increase in mortality has not been examined. Therefore, our objective was to examine the association of the 30-day risk-adjusted hospital readmission rate with the 30-day risk-adjusted hospital mortality rate for patients discharged with a diagnosis of AECOPD.

Data Sources

Publicly available data from three sources were used. The all-cause 30-day risk-standardized readmission rate (RSRR) and the 30-day risk-standardized mortality rate (RSMR) of each hospital for patients with AECOPD were obtained from the Hospital Compare database; a database maintained by the CMS.^16,17 In 2014, the CMS started reporting three-year running average of 30-day mortality and readmission rate data on hospitals for AECOPD hospitalizations; the data start date was July 2010.^18-22 We examined data from the FY 2010-2013 to 2014-2017 cycles on readmission and mortality reported by the CMS; this included data before and after the implementation of penalties.

Hospital characteristics were also obtained from the CMS website. Hospital ownership was defined as government (owned by Federal or state), for-profit (owned by physicians or another proprietary), or nonprofit (owned by a nonprofit organization such as a church). A hospital was considered as a teaching hospital if it obtained graduate medical education funding from the CMS.

Data on local population characteristics according to ZIP codes were obtained from the 2010 decennial census and the American Community Survey five-year (2009-2013) data files available at the United States Census Bureau website.²³ For each ZIP code, we obtained data on the total population, percentage of African Americans in the population, median income, poverty level, and insurance status.

We used Hospital service area (HSA) information obtained from the Dartmouth Atlas of Health Care crosswalk files to link local population characteristics to hospitals. The Dartmouth Atlas defined 3,436 HSAs by assigning the ZIP codes to the hospital area where the greatest proportion of their Medicare residents was hospitalized.^24,25

Hospital Compare data and Census Bureau population data were matched to the HSAs from the Dartmouth Atlas of Healthcare data at the ZIP code level. First, the ZIP code-level data from the Census Bureau were pooled by the HSAs obtained from the Dartmouth Atlas of Healthcare, followed by matching these data by the HSAs to the Hospital Compare data. Merging data from these three sources generated a dataset that contained information about readmission and mortality rates from a particular hospital and the population characteristics of the local healthcare market or neighborhood. Our final dataset included hospitals that had readmission and mortality information available at the Hospital Compare website and were included in the crosswalk files of the Dartmouth Atlas of Healthcare.

Statistical Analysis

Data are summarized as mean and standard deviation (SD), median with interquartile range, or frequencies as appropriate. To model the dependence of observations from the same hospital over time, we used mixed linear models with random intercept and slope. A strength of this modeling approach is that it incorporates information from all hospitals even when some hospitals are missing data for some time periods. We reached our final model through stages with increasing model complexity at each stage. In the first stage, we developed an empty model without any covariates to determine the unconditional variance components so that we can partition mortality variance into between- and within-hospital components. In the second stage, we developed an unconditional growth curve model to determine the shape of time trend in mortality over time using linear and quadratic (by including squared time in the model) growth curves. In the third stage, we added baseline readmission rates (from 2010 to 2013) to the model to determine the effect of baseline readmission rate on mortality trends and also examined its interaction with time and squared time. We generated a change in the readmission rate variable by subtracting the last readmission rate from the baseline readmission rate (readmission rate in 2010-2013 − readmission rate in 2014-2017). In the fourth stage, we included this change in readmission rate into the third-stage model to examine how changes in the readmission rate affected the time trends of mortality and also examined its interaction with time and squared time. In the final model, we included the following potential confounding variables to the fourth stage model: African American percentage in the HSA, HSA median income, percentage of people living in poverty in the HSA, median age, ownership of hospital (government, for profit), teaching status (teaching vs nonteaching), and acute care hospital beds in the HSA. Within each stage, the models were compared using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), and the model with the lowest value of each was moved to the next stage of model development. All analyses were performed in Stata 14.1 for Windows (College Station, Texas).

Of the 3,685 acute care hospitals analyzed in the 2010-2013 data cycle for COPD, the 30-day RSRR was 20.7% (1.28), which decreased to 19.6% (1.11) in 2014-2017 (Table 1). During the same period, the 30-day all-cause RSMR increased from 7.8% (1.03) in 2010-2013 to 8.4% (1.11) in 2014-2017. The partitioning of variance showed that 57% of variation in the mortality rate over the study period was due to between-hospital differences.

The unconditional growth model examining the linear time trend revealed a 0.13% per year (95% CI = 0.12 to 0.14; P < .0001) increase in mortality rate over the five data cycles. When the squared time variable was added to the model to examine a quadratic trend, both time and squared trend were statistically significant (Table 2) and the AIC and BIC were lower for the quadratic model. Thus, the unconditional growth curve model suggested that the mortality trend was nonlinear and the coefficients demonstrated that not only the mortality rate increased, but the rate of change in the mortality rate was also increasing during the study period.

When we added the baseline readmission rate to the abovementioned quadratic growth model, we found an inverse association; each 1% increase in baseline readmission rate was associated with 0.03% (95% CI = −0.05 to −0.005; P = .02) decrease in mortality rate. These findings suggest that hospitals with higher baseline readmission rates also had lower mortality rates. To examine whether the effect of baseline readmission rate on mortality varied over time, we included the interaction term with time in the model and then added the interaction term with squared time. As the AIC and BIC were the lowest for the model with interaction between time and baseline readmission (and not when interaction between squared time and baseline readmission were included), we accepted this model. In this model, although there was no difference in mortality according to readmissions at baseline, each 1% increase in baseline readmission rate was associated with a smaller increase in mortality rate by 0.015% (95% CI = −0.02 to −0.01; P < .0001; Table 2 and Figure 1). These findings suggest that hospitals with higher readmission rates at baseline had a smaller increase in mortality rate during the study period than those with lower readmission rates.

As efforts to decrease readmission rates continue as a part of the HRRP implementation by the CMS, our study shows that among hospitals that discharged patients with AECOPD during 2010-2017, the all-cause 30-day RSRR was decreased, whereas the all-cause 30-day RSMR was increased. Of particular concern is that the rate of increase in mortality also increased. We also found that hospitals with higher readmission rates in 2010-2013 had a lower rate of increase in mortality than hospitals with lower readmission rates. In addition, hospitals that had a larger decrease in readmission rates during the study period had a larger rate of increase in mortality than hospitals with a smaller decrease in readmission rates. Our findings were robust to potential confounders such as hospital characteristics and local population characteristics in which hospitals operate.

Our study findings raise the question whether the implementation of the HRRP resulted in unintentional patient harm by forcing hospitals to make changes that may affect overall patient care. This question is particularly important as other studies on hospitalized patients with HF have found similar results.^13,14 On the other hand, a similar association between readmission and mortality rates has not been observed in patients with pneumonia or AMI.¹⁴ Several possible explanations can be given for the observed discrepancy between the diseases and their effect on the relationship between readmission rate and mortality rate. Both COPD and HF are chronic diseases and characterized by exacerbations, whereas AMI and pneumonia are episodic diseases that are treatable. As the number of patients hospitalized with AECOPD and HF is much larger, hospitals may have a greater focus on reducing the 30-day readmission rates and may attempt to game the process, such as by delaying admissions through the emergency department within the 30-day period or by admitting patients for observation. In fact, a study found a 3% reduction in the within-hospital readmission rate with a concurrent 0.8% increase in observation unit use since the implementation of the HRRP.²⁶ Such approaches to patient care may lead to adverse outcomes.

It is possible that readmissions and mortality act as competing risks and hence hospitals with higher mortality rates are left with fewer patients and thus have fewer readmissions, whereas those with lower mortality rates have more patients and a higher readmission rate.²⁷ Such studies are not possible with hospital-level data, and patient-level studies will be required to examine this competing risk hypothesis. Our study results provide some support to the competing risk hypothesis (hospitals with lower baseline readmission rates had a steeper increase in mortality); however, it is not possible to draw any conclusions due to the high risk of ecological fallacy bias.

This study has important potential implications for healthcare policy, public health, and research. We found that an important national intervention aimed at decreasing readmission rates and improving the quality of care for patients with AECOPD may be associated with higher mortality rates in these patients. There may be a need to redefine measures for determining the performance of an institution. Our study supports research into the underlying mechanisms resulting in an inverse association between readmissions and mortality. In particular, health policy researchers may need to examine how incentives and penalties affect the allocation of resources within hospitals.

This study has several strengths and some potential weaknesses. We used a national dataset to examine readmission and mortality rates that include the majority of hospitals in the United States. We also included data from the local population for each hospital, thus allowing us to examine hospital performance within the context of its target population. One potential limitation is that we used hospital-level data and not patient-level data; however, the readmission penalties are designed for hospitals, which justifies our use of hospital-level data. Furthermore, data were not available for shorter time intervals; data from shorter time intervals may be associated with greater variability. Being an observational study, it is difficult to establish a causal relationship; the longitudinal nature of the study does establish temporality, an important factor in establishing causality.

In conclusion, we found that although the readmission rates decreased, there was an increase in the mortality rate within the 30 days of discharge from the hospital in patients with AECOPD. The rate of increase in mortality was higher in hospitals with lower readmission rates than in hospitals with higher readmission rates. Further research for determining the mechanism responsible for this association is needed. Future health policy interventions may need to consider the potential for adverse outcomes.

Chronic obstructive pulmonary disease (COPD) is recognized as the third leading cause of death nationally. Globally, it has been estimated that 10% of the population has COPD; in the United States, approximately 15 million people are affected.^1,2 The annual estimated cost of COPD management in the United States is approximately $50 billion, one-third of which is directly related to inpatient hospitalization for COPD exacerbation.^3,4,5 The 30-day readmission rate after hospitalization for acute exacerbation of COPD (AECOPD) is approximately 21% with an approximate cost of $13 billion per year.^6,7 To reduce the cost and to improve patient outcomes, the Centers for Medicare and Medicaid Services (CMS) has designed several interventions with little effect.⁸

In October 2012, the Affordable Care Act added section 1886(q) to the Social Security Act and established the Hospital Readmission Reduction Program (HRRP), an initiative to decrease hospitalization costs by penalizing hospitals with high 30-day readmission rates. Under this program, hospitals received up to 3% penalty for excess readmissions after the index hospitalization with acute myocardial infarction (AMI), heart failure (HF), and pneumonia.^9-11 Hospitals are penalized if their annual readmission rates are significantly above the average national readmission rate. In 2014, the HRRP was extended to include AECOPD for the FY 2015.

Since the implementation of readmission penalties, data have shown a significant decrease in the 30-day readmission rates for all conditions.^12,13 On the other hand, studies have suggested that, at least for some conditions, the decrease in the 30-day readmission rate is associated with higher adverse patients outcomes, including higher mortality.^14,15 However, whether a decrease in readmission rates after an AECOPD hospitalization is associated with a concomitant increase in mortality has not been examined. Therefore, our objective was to examine the association of the 30-day risk-adjusted hospital readmission rate with the 30-day risk-adjusted hospital mortality rate for patients discharged with a diagnosis of AECOPD.

Data Sources

Publicly available data from three sources were used. The all-cause 30-day risk-standardized readmission rate (RSRR) and the 30-day risk-standardized mortality rate (RSMR) of each hospital for patients with AECOPD were obtained from the Hospital Compare database; a database maintained by the CMS.^16,17 In 2014, the CMS started reporting three-year running average of 30-day mortality and readmission rate data on hospitals for AECOPD hospitalizations; the data start date was July 2010.^18-22 We examined data from the FY 2010-2013 to 2014-2017 cycles on readmission and mortality reported by the CMS; this included data before and after the implementation of penalties.

Hospital characteristics were also obtained from the CMS website. Hospital ownership was defined as government (owned by Federal or state), for-profit (owned by physicians or another proprietary), or nonprofit (owned by a nonprofit organization such as a church). A hospital was considered as a teaching hospital if it obtained graduate medical education funding from the CMS.

Data on local population characteristics according to ZIP codes were obtained from the 2010 decennial census and the American Community Survey five-year (2009-2013) data files available at the United States Census Bureau website.²³ For each ZIP code, we obtained data on the total population, percentage of African Americans in the population, median income, poverty level, and insurance status.

We used Hospital service area (HSA) information obtained from the Dartmouth Atlas of Health Care crosswalk files to link local population characteristics to hospitals. The Dartmouth Atlas defined 3,436 HSAs by assigning the ZIP codes to the hospital area where the greatest proportion of their Medicare residents was hospitalized.^24,25

Hospital Compare data and Census Bureau population data were matched to the HSAs from the Dartmouth Atlas of Healthcare data at the ZIP code level. First, the ZIP code-level data from the Census Bureau were pooled by the HSAs obtained from the Dartmouth Atlas of Healthcare, followed by matching these data by the HSAs to the Hospital Compare data. Merging data from these three sources generated a dataset that contained information about readmission and mortality rates from a particular hospital and the population characteristics of the local healthcare market or neighborhood. Our final dataset included hospitals that had readmission and mortality information available at the Hospital Compare website and were included in the crosswalk files of the Dartmouth Atlas of Healthcare.

Statistical Analysis

Data are summarized as mean and standard deviation (SD), median with interquartile range, or frequencies as appropriate. To model the dependence of observations from the same hospital over time, we used mixed linear models with random intercept and slope. A strength of this modeling approach is that it incorporates information from all hospitals even when some hospitals are missing data for some time periods. We reached our final model through stages with increasing model complexity at each stage. In the first stage, we developed an empty model without any covariates to determine the unconditional variance components so that we can partition mortality variance into between- and within-hospital components. In the second stage, we developed an unconditional growth curve model to determine the shape of time trend in mortality over time using linear and quadratic (by including squared time in the model) growth curves. In the third stage, we added baseline readmission rates (from 2010 to 2013) to the model to determine the effect of baseline readmission rate on mortality trends and also examined its interaction with time and squared time. We generated a change in the readmission rate variable by subtracting the last readmission rate from the baseline readmission rate (readmission rate in 2010-2013 − readmission rate in 2014-2017). In the fourth stage, we included this change in readmission rate into the third-stage model to examine how changes in the readmission rate affected the time trends of mortality and also examined its interaction with time and squared time. In the final model, we included the following potential confounding variables to the fourth stage model: African American percentage in the HSA, HSA median income, percentage of people living in poverty in the HSA, median age, ownership of hospital (government, for profit), teaching status (teaching vs nonteaching), and acute care hospital beds in the HSA. Within each stage, the models were compared using the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), and the model with the lowest value of each was moved to the next stage of model development. All analyses were performed in Stata 14.1 for Windows (College Station, Texas).

Of the 3,685 acute care hospitals analyzed in the 2010-2013 data cycle for COPD, the 30-day RSRR was 20.7% (1.28), which decreased to 19.6% (1.11) in 2014-2017 (Table 1). During the same period, the 30-day all-cause RSMR increased from 7.8% (1.03) in 2010-2013 to 8.4% (1.11) in 2014-2017. The partitioning of variance showed that 57% of variation in the mortality rate over the study period was due to between-hospital differences.

The unconditional growth model examining the linear time trend revealed a 0.13% per year (95% CI = 0.12 to 0.14; P < .0001) increase in mortality rate over the five data cycles. When the squared time variable was added to the model to examine a quadratic trend, both time and squared trend were statistically significant (Table 2) and the AIC and BIC were lower for the quadratic model. Thus, the unconditional growth curve model suggested that the mortality trend was nonlinear and the coefficients demonstrated that not only the mortality rate increased, but the rate of change in the mortality rate was also increasing during the study period.

When we added the baseline readmission rate to the abovementioned quadratic growth model, we found an inverse association; each 1% increase in baseline readmission rate was associated with 0.03% (95% CI = −0.05 to −0.005; P = .02) decrease in mortality rate. These findings suggest that hospitals with higher baseline readmission rates also had lower mortality rates. To examine whether the effect of baseline readmission rate on mortality varied over time, we included the interaction term with time in the model and then added the interaction term with squared time. As the AIC and BIC were the lowest for the model with interaction between time and baseline readmission (and not when interaction between squared time and baseline readmission were included), we accepted this model. In this model, although there was no difference in mortality according to readmissions at baseline, each 1% increase in baseline readmission rate was associated with a smaller increase in mortality rate by 0.015% (95% CI = −0.02 to −0.01; P < .0001; Table 2 and Figure 1). These findings suggest that hospitals with higher readmission rates at baseline had a smaller increase in mortality rate during the study period than those with lower readmission rates.

As efforts to decrease readmission rates continue as a part of the HRRP implementation by the CMS, our study shows that among hospitals that discharged patients with AECOPD during 2010-2017, the all-cause 30-day RSRR was decreased, whereas the all-cause 30-day RSMR was increased. Of particular concern is that the rate of increase in mortality also increased. We also found that hospitals with higher readmission rates in 2010-2013 had a lower rate of increase in mortality than hospitals with lower readmission rates. In addition, hospitals that had a larger decrease in readmission rates during the study period had a larger rate of increase in mortality than hospitals with a smaller decrease in readmission rates. Our findings were robust to potential confounders such as hospital characteristics and local population characteristics in which hospitals operate.

Our study findings raise the question whether the implementation of the HRRP resulted in unintentional patient harm by forcing hospitals to make changes that may affect overall patient care. This question is particularly important as other studies on hospitalized patients with HF have found similar results.^13,14 On the other hand, a similar association between readmission and mortality rates has not been observed in patients with pneumonia or AMI.¹⁴ Several possible explanations can be given for the observed discrepancy between the diseases and their effect on the relationship between readmission rate and mortality rate. Both COPD and HF are chronic diseases and characterized by exacerbations, whereas AMI and pneumonia are episodic diseases that are treatable. As the number of patients hospitalized with AECOPD and HF is much larger, hospitals may have a greater focus on reducing the 30-day readmission rates and may attempt to game the process, such as by delaying admissions through the emergency department within the 30-day period or by admitting patients for observation. In fact, a study found a 3% reduction in the within-hospital readmission rate with a concurrent 0.8% increase in observation unit use since the implementation of the HRRP.²⁶ Such approaches to patient care may lead to adverse outcomes.

It is possible that readmissions and mortality act as competing risks and hence hospitals with higher mortality rates are left with fewer patients and thus have fewer readmissions, whereas those with lower mortality rates have more patients and a higher readmission rate.²⁷ Such studies are not possible with hospital-level data, and patient-level studies will be required to examine this competing risk hypothesis. Our study results provide some support to the competing risk hypothesis (hospitals with lower baseline readmission rates had a steeper increase in mortality); however, it is not possible to draw any conclusions due to the high risk of ecological fallacy bias.

This study has important potential implications for healthcare policy, public health, and research. We found that an important national intervention aimed at decreasing readmission rates and improving the quality of care for patients with AECOPD may be associated with higher mortality rates in these patients. There may be a need to redefine measures for determining the performance of an institution. Our study supports research into the underlying mechanisms resulting in an inverse association between readmissions and mortality. In particular, health policy researchers may need to examine how incentives and penalties affect the allocation of resources within hospitals.

This study has several strengths and some potential weaknesses. We used a national dataset to examine readmission and mortality rates that include the majority of hospitals in the United States. We also included data from the local population for each hospital, thus allowing us to examine hospital performance within the context of its target population. One potential limitation is that we used hospital-level data and not patient-level data; however, the readmission penalties are designed for hospitals, which justifies our use of hospital-level data. Furthermore, data were not available for shorter time intervals; data from shorter time intervals may be associated with greater variability. Being an observational study, it is difficult to establish a causal relationship; the longitudinal nature of the study does establish temporality, an important factor in establishing causality.

In conclusion, we found that although the readmission rates decreased, there was an increase in the mortality rate within the 30 days of discharge from the hospital in patients with AECOPD. The rate of increase in mortality was higher in hospitals with lower readmission rates than in hospitals with higher readmission rates. Further research for determining the mechanism responsible for this association is needed. Future health policy interventions may need to consider the potential for adverse outcomes.

Clinical guidelines are periodically released by medical societies with the overarching goal of improving deliverable medical care by standardizing disease management according to best available published literature and by reducing healthcare expenditure associated with unnecessary and superfluous testing.¹ Unfortunately, nonadherence to guidelines is common in clinical practice² and contributes to the rising cost of healthcare.³ Health resource utilization is particularly relevant in management of cirrhosis, a condition with an annual healthcare expenditure of $13 billion.⁴ Hepatic encephalopathy (HE), the most common complication of cirrhosis, is characterized by altered sensorium and is the leading indication for hospitalization among cirrhotics. The joint guidelines of the European Association for the Study of the Liver (EASL) and the American Association for the Study of Liver Diseases (AASLD) for diagnostic workup for HE recommend identification and treatment of potential precipitants.⁵ The guidelines also recommend against checking serum ammonia levels, which have not been shown to correlate with diagnosis or severity of HE.^6-8 Currently, limited data are available on practice patterns regarding guideline adherence and unnecessary serum ammonia testing for initial evaluation of HE in hospitals. To overcome this gap in knowledge, we conducted the present study to provide granular details regarding the diagnostic workup for hospitalized patients with HE.

This study adopted a retrospective design and recruited patients admitted to the Virginia Commonwealth University Medical Center between July 1, 2016 and July 1, 2017. The institutional review board approved the study, and the manuscript was reviewed and approved by all authors prior to submission. All chart reviews were performed by hepatologists with access to patients’ electronic medical record (EMR).

Patient Population

Patients were identified from the EMR system by using ICD-9 and ICD-10 codes for cirrhosis, hepatic encephalopathy, and altered mental status. All consecutive admissions with these diagnosis codes were considered for inclusion. Adult patients with cirrhosis resulting from any etiology of chronic liver diseases with primary reason for admission of HE were included. If patients were readmitted for HE during the study period, then only the data from index HE admission was included in the analysis and data from subsequent admissions were excluded. The other exclusion criteria included non-HE causes of confusion, acute liver failure, and those admitted with a preformulated plan (eg, direct hepatology clinic admission or outside hospital transfer). Patients who developed HE during their hospitalization where HE was not the indication for admission were also excluded. Finally, all patients admitted under the direct care of hepatology were excluded.

Diagnostic Workup

The recommendations of the AASLD and the EASL for workup for HE include obtaining detailed history and physical examination supplemented by diagnostic evaluation for potential HE precipitants including infections, electrolyte disturbances, dehydration, renal failure, glycemic disturbances, and toxin ingestion (eg, alcohol, illicit drugs).⁵ Based on the guideline recommendation, this study defined a “complete workup” as including all of the following elements: infection evaluation (blood culture, urinalysis/urine culture, chest radiograph, diagnostic paracentesis in the presence of ascites), electrolyte/renal evaluation (serum sodium, potassium, creatinine, and glucose), and toxin evaluation (urine drug screening). Any HE admission that was missing elements from the aforementioned battery of tests was defined as “incomplete workup.” In patients admitted with decompensated cirrhosis, serum ammonia testing was considered inappropriate unless there was a nuanced explanation supporting its use documented within the EMR. The frequency and specialty of the physician ordering serum ammonia level tests were determined. The financial burden of unnecessary ammonia testing was estimated by assigning a laboratory charge ($258) for each patient.

Statistical Analysis

Continuous and categorical variables are reported as means (± standard deviation), median (interquartile range or IQR), or proportion (%) as appropriate. Across-group differences were compared using Student t-test for normally distributed continuous variables and Mann-Whitney U test for skewed data. Fisher’s exact test was used to compare proportion. HE evaluations were quantified by the number of patients with complete workup and by the number of patients with missing components of the workup. A nominal P value of less than .05 was considered statistically significant. All statistical analyses were performed using SPSS Statistics version 24.0 (IBM Corporation, Armonk, New York).

Cohort Characteristics

The baseline cohort demographics are listed in the Table. Of the 145 patients identified using diagnostic codes for cirrhosis, 78 subjects met the study criteria. The most common exclusion criteria included non-HE etiology of altered mental status (n = 37) and patients with readmissions for HE during the study period (n = 30). The mean age of the study cohort was 59.3 years, and the most common etiology of cirrhosis was hepatitis C (n = 41), alcohol induced (n = 14), and nonalcoholic steatohepatitis (n = 13).

Initial Diagnostic Evaluation

The major precipitants of HE in the study cohort were ineffective lactulose dosing (n = 43), infections (n = 25), and electrolyte disturbances/renal injury (n = 6). At the time of admission, 53 patients were on therapy for HE. Only 17 (22%) patients had complete diagnostic workup within 24 hours of hospital admission. The individual components of the complete workup are shown in the Figure. Notably, 23 (30%) patients were missing blood cultures, 16 (21%) were missing urinalysis, 15 (20%) were missing chest radiograph, and 34 (44%) were missing urine drug screening. Of the 34 patients with ascites on admission, only eight (23%) had diagnostic paracentesis performed on admission to rule out spontaneous bacterial peritonitis.

Serum Ammonia Testing

Serum ammonia testing was performed on 74 patients (94.9%), and no patient met the criteria for appropriate testing. Forty patients already had a known diagnosis of HE prior to index admission. Furthermore, 10 (14%) patients had serum ammonia testing repeated after admission without documentation in the EMR to justify repeat testing. Emergency Department (ED) physicians ordered ammonia testing in 57 cases (77%), internists ordered the testing in 11 cases (15%), and intensivists ordered the testing in two cases (3%). The patient’s charges for serum ammonia testing at the time of admission and for repeat testing were $19,092 and $2,580, respectively.

This study utilized HE in patients with decompensated cirrhosis as a framework to analyze adherence to societal guidelines. The adherence rate to AALSD/EASL recommended inpatient evaluation of HE is surprisingly low, and most patients are missing key essential elements of the diagnostic work up. While the diagnostic tests that are ordered as part of a panel are completed universally (renal function, electrolytes, and glucose testing), individual testing is less inclined to be ordered (blood cultures, urine culture/urinalysis, CXR, UDS) and procedural testing, such as diagnostic paracentesis, is often missed. This last finding is in line with published literature showing that 40% of patients admitted with ascites or HE did not have diagnostic paracentesis during hospital admission despite 24% reduction of inhospital mortality among patients undergoing the procedure.⁹

Although serum ammonia testing is not endorsed by the AASLD/EASL guidelines for HE,⁵ it is ordered nearly universally. The cost of an individual test is relatively low, but the cumulative cost of serum ammonia testing can be substantial because HE is the most common indication for hospitalization among patients with cirrhosis.⁴ Initiatives, such as the Choosing Wisely^® campaign, encourage high-value and evidence-based care by limiting excessive and unnecessary diagnostic testing.¹⁰ The Canadian Choosing Wisely campaign specifically includes avoidance of serum ammonia testing for diagnosis of HE to provide high-value care in hepatology.¹¹

Although the exact reasons for nonadherence to recommended HE evaluations are unclear, a potential method to mitigate excessive testing is to utilize the EMR and ordering system.³ EMR-based strategies can curb unnecessary testing in inpatient settings.¹² The use of HE order sets, the inclusion of clinical decision support systems, and the restriction of access to specialized testing can be readily incorporated into the EMR to encourage adherence to guideline-based care while limiting unnecessary testing.

This study should be interpreted in the context of study limitations. Given the retrospective design of the study, salient factors in decisions behind diagnostic testing cannot be assessed. Future studies should utilize mixed-model methodology to elucidate reasons behind these decisions. The present study used a strict definition of complete workup including all the mentioned elements of the diagnostic workup for HE; however, in clinical practice, providers could be justified in not ordering certain tests if the specific clinical scenario does not lead to its use (eg, chest X-ray deferred in a patient with clear lung exam, no symptoms, or hypoxia). Similarly, UDS was included as a required element for a complete workup. While it may be ordered in a case-by-case basis to screen for illicit drug abuse, UDS is also a critical element of the workup to screen for opioid use as a precipitant of HE. Finally, considering the strict study entry criteria, we excluded repeated admissions for HE during the study period and therefore likely underestimate the cost burden of serum ammonia testing.

In conclusion, valuable guideline-based diagnostic testing is often missing in patients admitted for HE while serum ammonia testing is nearly universally ordered. These findings underscore the importance of implementing educational strategies, such as the Choosing Wisely^® campaign, and EMR-based clinical decision support systems to improve health resource utilization in patients with cirrhosis and HE.

Disclosures

The authors have nothing to disclose.

Clinical guidelines are periodically released by medical societies with the overarching goal of improving deliverable medical care by standardizing disease management according to best available published literature and by reducing healthcare expenditure associated with unnecessary and superfluous testing.¹ Unfortunately, nonadherence to guidelines is common in clinical practice² and contributes to the rising cost of healthcare.³ Health resource utilization is particularly relevant in management of cirrhosis, a condition with an annual healthcare expenditure of $13 billion.⁴ Hepatic encephalopathy (HE), the most common complication of cirrhosis, is characterized by altered sensorium and is the leading indication for hospitalization among cirrhotics. The joint guidelines of the European Association for the Study of the Liver (EASL) and the American Association for the Study of Liver Diseases (AASLD) for diagnostic workup for HE recommend identification and treatment of potential precipitants.⁵ The guidelines also recommend against checking serum ammonia levels, which have not been shown to correlate with diagnosis or severity of HE.^6-8 Currently, limited data are available on practice patterns regarding guideline adherence and unnecessary serum ammonia testing for initial evaluation of HE in hospitals. To overcome this gap in knowledge, we conducted the present study to provide granular details regarding the diagnostic workup for hospitalized patients with HE.

This study adopted a retrospective design and recruited patients admitted to the Virginia Commonwealth University Medical Center between July 1, 2016 and July 1, 2017. The institutional review board approved the study, and the manuscript was reviewed and approved by all authors prior to submission. All chart reviews were performed by hepatologists with access to patients’ electronic medical record (EMR).

Patient Population

Patients were identified from the EMR system by using ICD-9 and ICD-10 codes for cirrhosis, hepatic encephalopathy, and altered mental status. All consecutive admissions with these diagnosis codes were considered for inclusion. Adult patients with cirrhosis resulting from any etiology of chronic liver diseases with primary reason for admission of HE were included. If patients were readmitted for HE during the study period, then only the data from index HE admission was included in the analysis and data from subsequent admissions were excluded. The other exclusion criteria included non-HE causes of confusion, acute liver failure, and those admitted with a preformulated plan (eg, direct hepatology clinic admission or outside hospital transfer). Patients who developed HE during their hospitalization where HE was not the indication for admission were also excluded. Finally, all patients admitted under the direct care of hepatology were excluded.

Diagnostic Workup

The recommendations of the AASLD and the EASL for workup for HE include obtaining detailed history and physical examination supplemented by diagnostic evaluation for potential HE precipitants including infections, electrolyte disturbances, dehydration, renal failure, glycemic disturbances, and toxin ingestion (eg, alcohol, illicit drugs).⁵ Based on the guideline recommendation, this study defined a “complete workup” as including all of the following elements: infection evaluation (blood culture, urinalysis/urine culture, chest radiograph, diagnostic paracentesis in the presence of ascites), electrolyte/renal evaluation (serum sodium, potassium, creatinine, and glucose), and toxin evaluation (urine drug screening). Any HE admission that was missing elements from the aforementioned battery of tests was defined as “incomplete workup.” In patients admitted with decompensated cirrhosis, serum ammonia testing was considered inappropriate unless there was a nuanced explanation supporting its use documented within the EMR. The frequency and specialty of the physician ordering serum ammonia level tests were determined. The financial burden of unnecessary ammonia testing was estimated by assigning a laboratory charge ($258) for each patient.

Statistical Analysis

Continuous and categorical variables are reported as means (± standard deviation), median (interquartile range or IQR), or proportion (%) as appropriate. Across-group differences were compared using Student t-test for normally distributed continuous variables and Mann-Whitney U test for skewed data. Fisher’s exact test was used to compare proportion. HE evaluations were quantified by the number of patients with complete workup and by the number of patients with missing components of the workup. A nominal P value of less than .05 was considered statistically significant. All statistical analyses were performed using SPSS Statistics version 24.0 (IBM Corporation, Armonk, New York).

Cohort Characteristics

The baseline cohort demographics are listed in the Table. Of the 145 patients identified using diagnostic codes for cirrhosis, 78 subjects met the study criteria. The most common exclusion criteria included non-HE etiology of altered mental status (n = 37) and patients with readmissions for HE during the study period (n = 30). The mean age of the study cohort was 59.3 years, and the most common etiology of cirrhosis was hepatitis C (n = 41), alcohol induced (n = 14), and nonalcoholic steatohepatitis (n = 13).

Initial Diagnostic Evaluation

The major precipitants of HE in the study cohort were ineffective lactulose dosing (n = 43), infections (n = 25), and electrolyte disturbances/renal injury (n = 6). At the time of admission, 53 patients were on therapy for HE. Only 17 (22%) patients had complete diagnostic workup within 24 hours of hospital admission. The individual components of the complete workup are shown in the Figure. Notably, 23 (30%) patients were missing blood cultures, 16 (21%) were missing urinalysis, 15 (20%) were missing chest radiograph, and 34 (44%) were missing urine drug screening. Of the 34 patients with ascites on admission, only eight (23%) had diagnostic paracentesis performed on admission to rule out spontaneous bacterial peritonitis.

Serum Ammonia Testing

Serum ammonia testing was performed on 74 patients (94.9%), and no patient met the criteria for appropriate testing. Forty patients already had a known diagnosis of HE prior to index admission. Furthermore, 10 (14%) patients had serum ammonia testing repeated after admission without documentation in the EMR to justify repeat testing. Emergency Department (ED) physicians ordered ammonia testing in 57 cases (77%), internists ordered the testing in 11 cases (15%), and intensivists ordered the testing in two cases (3%). The patient’s charges for serum ammonia testing at the time of admission and for repeat testing were $19,092 and $2,580, respectively.

This study utilized HE in patients with decompensated cirrhosis as a framework to analyze adherence to societal guidelines. The adherence rate to AALSD/EASL recommended inpatient evaluation of HE is surprisingly low, and most patients are missing key essential elements of the diagnostic work up. While the diagnostic tests that are ordered as part of a panel are completed universally (renal function, electrolytes, and glucose testing), individual testing is less inclined to be ordered (blood cultures, urine culture/urinalysis, CXR, UDS) and procedural testing, such as diagnostic paracentesis, is often missed. This last finding is in line with published literature showing that 40% of patients admitted with ascites or HE did not have diagnostic paracentesis during hospital admission despite 24% reduction of inhospital mortality among patients undergoing the procedure.⁹

Although serum ammonia testing is not endorsed by the AASLD/EASL guidelines for HE,⁵ it is ordered nearly universally. The cost of an individual test is relatively low, but the cumulative cost of serum ammonia testing can be substantial because HE is the most common indication for hospitalization among patients with cirrhosis.⁴ Initiatives, such as the Choosing Wisely^® campaign, encourage high-value and evidence-based care by limiting excessive and unnecessary diagnostic testing.¹⁰ The Canadian Choosing Wisely campaign specifically includes avoidance of serum ammonia testing for diagnosis of HE to provide high-value care in hepatology.¹¹

Although the exact reasons for nonadherence to recommended HE evaluations are unclear, a potential method to mitigate excessive testing is to utilize the EMR and ordering system.³ EMR-based strategies can curb unnecessary testing in inpatient settings.¹² The use of HE order sets, the inclusion of clinical decision support systems, and the restriction of access to specialized testing can be readily incorporated into the EMR to encourage adherence to guideline-based care while limiting unnecessary testing.

This study should be interpreted in the context of study limitations. Given the retrospective design of the study, salient factors in decisions behind diagnostic testing cannot be assessed. Future studies should utilize mixed-model methodology to elucidate reasons behind these decisions. The present study used a strict definition of complete workup including all the mentioned elements of the diagnostic workup for HE; however, in clinical practice, providers could be justified in not ordering certain tests if the specific clinical scenario does not lead to its use (eg, chest X-ray deferred in a patient with clear lung exam, no symptoms, or hypoxia). Similarly, UDS was included as a required element for a complete workup. While it may be ordered in a case-by-case basis to screen for illicit drug abuse, UDS is also a critical element of the workup to screen for opioid use as a precipitant of HE. Finally, considering the strict study entry criteria, we excluded repeated admissions for HE during the study period and therefore likely underestimate the cost burden of serum ammonia testing.

In conclusion, valuable guideline-based diagnostic testing is often missing in patients admitted for HE while serum ammonia testing is nearly universally ordered. These findings underscore the importance of implementing educational strategies, such as the Choosing Wisely^® campaign, and EMR-based clinical decision support systems to improve health resource utilization in patients with cirrhosis and HE.

Disclosures

The authors have nothing to disclose.

Clinical guidelines are periodically released by medical societies with the overarching goal of improving deliverable medical care by standardizing disease management according to best available published literature and by reducing healthcare expenditure associated with unnecessary and superfluous testing.¹ Unfortunately, nonadherence to guidelines is common in clinical practice² and contributes to the rising cost of healthcare.³ Health resource utilization is particularly relevant in management of cirrhosis, a condition with an annual healthcare expenditure of $13 billion.⁴ Hepatic encephalopathy (HE), the most common complication of cirrhosis, is characterized by altered sensorium and is the leading indication for hospitalization among cirrhotics. The joint guidelines of the European Association for the Study of the Liver (EASL) and the American Association for the Study of Liver Diseases (AASLD) for diagnostic workup for HE recommend identification and treatment of potential precipitants.⁵ The guidelines also recommend against checking serum ammonia levels, which have not been shown to correlate with diagnosis or severity of HE.^6-8 Currently, limited data are available on practice patterns regarding guideline adherence and unnecessary serum ammonia testing for initial evaluation of HE in hospitals. To overcome this gap in knowledge, we conducted the present study to provide granular details regarding the diagnostic workup for hospitalized patients with HE.

This study adopted a retrospective design and recruited patients admitted to the Virginia Commonwealth University Medical Center between July 1, 2016 and July 1, 2017. The institutional review board approved the study, and the manuscript was reviewed and approved by all authors prior to submission. All chart reviews were performed by hepatologists with access to patients’ electronic medical record (EMR).

Patient Population

Patients were identified from the EMR system by using ICD-9 and ICD-10 codes for cirrhosis, hepatic encephalopathy, and altered mental status. All consecutive admissions with these diagnosis codes were considered for inclusion. Adult patients with cirrhosis resulting from any etiology of chronic liver diseases with primary reason for admission of HE were included. If patients were readmitted for HE during the study period, then only the data from index HE admission was included in the analysis and data from subsequent admissions were excluded. The other exclusion criteria included non-HE causes of confusion, acute liver failure, and those admitted with a preformulated plan (eg, direct hepatology clinic admission or outside hospital transfer). Patients who developed HE during their hospitalization where HE was not the indication for admission were also excluded. Finally, all patients admitted under the direct care of hepatology were excluded.

Diagnostic Workup

The recommendations of the AASLD and the EASL for workup for HE include obtaining detailed history and physical examination supplemented by diagnostic evaluation for potential HE precipitants including infections, electrolyte disturbances, dehydration, renal failure, glycemic disturbances, and toxin ingestion (eg, alcohol, illicit drugs).⁵ Based on the guideline recommendation, this study defined a “complete workup” as including all of the following elements: infection evaluation (blood culture, urinalysis/urine culture, chest radiograph, diagnostic paracentesis in the presence of ascites), electrolyte/renal evaluation (serum sodium, potassium, creatinine, and glucose), and toxin evaluation (urine drug screening). Any HE admission that was missing elements from the aforementioned battery of tests was defined as “incomplete workup.” In patients admitted with decompensated cirrhosis, serum ammonia testing was considered inappropriate unless there was a nuanced explanation supporting its use documented within the EMR. The frequency and specialty of the physician ordering serum ammonia level tests were determined. The financial burden of unnecessary ammonia testing was estimated by assigning a laboratory charge ($258) for each patient.

Statistical Analysis

Continuous and categorical variables are reported as means (± standard deviation), median (interquartile range or IQR), or proportion (%) as appropriate. Across-group differences were compared using Student t-test for normally distributed continuous variables and Mann-Whitney U test for skewed data. Fisher’s exact test was used to compare proportion. HE evaluations were quantified by the number of patients with complete workup and by the number of patients with missing components of the workup. A nominal P value of less than .05 was considered statistically significant. All statistical analyses were performed using SPSS Statistics version 24.0 (IBM Corporation, Armonk, New York).

Cohort Characteristics

The baseline cohort demographics are listed in the Table. Of the 145 patients identified using diagnostic codes for cirrhosis, 78 subjects met the study criteria. The most common exclusion criteria included non-HE etiology of altered mental status (n = 37) and patients with readmissions for HE during the study period (n = 30). The mean age of the study cohort was 59.3 years, and the most common etiology of cirrhosis was hepatitis C (n = 41), alcohol induced (n = 14), and nonalcoholic steatohepatitis (n = 13).

Initial Diagnostic Evaluation

The major precipitants of HE in the study cohort were ineffective lactulose dosing (n = 43), infections (n = 25), and electrolyte disturbances/renal injury (n = 6). At the time of admission, 53 patients were on therapy for HE. Only 17 (22%) patients had complete diagnostic workup within 24 hours of hospital admission. The individual components of the complete workup are shown in the Figure. Notably, 23 (30%) patients were missing blood cultures, 16 (21%) were missing urinalysis, 15 (20%) were missing chest radiograph, and 34 (44%) were missing urine drug screening. Of the 34 patients with ascites on admission, only eight (23%) had diagnostic paracentesis performed on admission to rule out spontaneous bacterial peritonitis.

Serum Ammonia Testing

Serum ammonia testing was performed on 74 patients (94.9%), and no patient met the criteria for appropriate testing. Forty patients already had a known diagnosis of HE prior to index admission. Furthermore, 10 (14%) patients had serum ammonia testing repeated after admission without documentation in the EMR to justify repeat testing. Emergency Department (ED) physicians ordered ammonia testing in 57 cases (77%), internists ordered the testing in 11 cases (15%), and intensivists ordered the testing in two cases (3%). The patient’s charges for serum ammonia testing at the time of admission and for repeat testing were $19,092 and $2,580, respectively.

This study utilized HE in patients with decompensated cirrhosis as a framework to analyze adherence to societal guidelines. The adherence rate to AALSD/EASL recommended inpatient evaluation of HE is surprisingly low, and most patients are missing key essential elements of the diagnostic work up. While the diagnostic tests that are ordered as part of a panel are completed universally (renal function, electrolytes, and glucose testing), individual testing is less inclined to be ordered (blood cultures, urine culture/urinalysis, CXR, UDS) and procedural testing, such as diagnostic paracentesis, is often missed. This last finding is in line with published literature showing that 40% of patients admitted with ascites or HE did not have diagnostic paracentesis during hospital admission despite 24% reduction of inhospital mortality among patients undergoing the procedure.⁹

Although serum ammonia testing is not endorsed by the AASLD/EASL guidelines for HE,⁵ it is ordered nearly universally. The cost of an individual test is relatively low, but the cumulative cost of serum ammonia testing can be substantial because HE is the most common indication for hospitalization among patients with cirrhosis.⁴ Initiatives, such as the Choosing Wisely^® campaign, encourage high-value and evidence-based care by limiting excessive and unnecessary diagnostic testing.¹⁰ The Canadian Choosing Wisely campaign specifically includes avoidance of serum ammonia testing for diagnosis of HE to provide high-value care in hepatology.¹¹

Although the exact reasons for nonadherence to recommended HE evaluations are unclear, a potential method to mitigate excessive testing is to utilize the EMR and ordering system.³ EMR-based strategies can curb unnecessary testing in inpatient settings.¹² The use of HE order sets, the inclusion of clinical decision support systems, and the restriction of access to specialized testing can be readily incorporated into the EMR to encourage adherence to guideline-based care while limiting unnecessary testing.

This study should be interpreted in the context of study limitations. Given the retrospective design of the study, salient factors in decisions behind diagnostic testing cannot be assessed. Future studies should utilize mixed-model methodology to elucidate reasons behind these decisions. The present study used a strict definition of complete workup including all the mentioned elements of the diagnostic workup for HE; however, in clinical practice, providers could be justified in not ordering certain tests if the specific clinical scenario does not lead to its use (eg, chest X-ray deferred in a patient with clear lung exam, no symptoms, or hypoxia). Similarly, UDS was included as a required element for a complete workup. While it may be ordered in a case-by-case basis to screen for illicit drug abuse, UDS is also a critical element of the workup to screen for opioid use as a precipitant of HE. Finally, considering the strict study entry criteria, we excluded repeated admissions for HE during the study period and therefore likely underestimate the cost burden of serum ammonia testing.

In conclusion, valuable guideline-based diagnostic testing is often missing in patients admitted for HE while serum ammonia testing is nearly universally ordered. These findings underscore the importance of implementing educational strategies, such as the Choosing Wisely^® campaign, and EMR-based clinical decision support systems to improve health resource utilization in patients with cirrhosis and HE.

Disclosures

The authors have nothing to disclose.