Peter K. Lindenauer, MD, MSc

Ascites is the most common complication of cirrhosis leading to hospital admission.[1] Approximately 12% of hospitalized patients who present with decompensated cirrhosis and ascites have spontaneous bacterial peritonitis (SBP); half of these patients do not present with abdominal pain, fever, nausea, or vomiting.[2] Guidelines published by the American Association for the Study of Liver Diseases (AASLD) recommend paracentesis for all hospitalized patients with cirrhosis and ascites and also recommend long‐term antibiotic prophylaxis for survivors of an SBP episode.[3] Despite evidence that in‐hospital mortality is reduced in those patients who receive paracentesis in a timely manner,[4, 5] only 40% to 60% of eligible patients receive paracentesis.[4, 6, 7] We aimed to describe clinical predictors of paracentesis and use of antibiotics following an episode of SBP in patients with decompensated cirrhosis and ascites.

METHODS

We conducted a retrospective cohort study of adults admitted to a single tertiary care center between January 1, 2009 and December 31, 2009.⁷ We included patients with an International Classification of Diseases, Ninth Revision discharge code consistent with decompensated cirrhosis who met clinical criteria for decompensated cirrhosis (see Supporting Figure 1 in the online version of this article) [7] and had enough ascitic fluid to be sampled under imaging guidance. We collected presenting vital signs, laboratory data (within 24 hours of admission), evidence of infection other than SBP (eg, urinary infection, pneumonia), results of peritoneal fluid analysis (defining SBP as 250 polymorphonuclear leukocytes), and use of antibiotic therapy. Our statistical analysis calculated summary statistics as means, medians, and proportions. Furthermore, we used multiple logistic regression to examine the association between predictors and receipt of paracentesis, including age, sex, and clinical measures associated with paracentesis at P0.20 using the Fisher exact test. Alpha was set at 0.05 (2‐sided) for all comparisons.

RESULTS

We identified 193 admissions for 103 patients with decompensated cirrhosis and ascites (Table 1). Of these, 41% (80/193) received diagnostic paracentesis. Mean/standard deviation for age was 53.6/12.4 years; 71% of patients were male and 63% were English speaking. Common comorbidities included diabetes mellitus (33%), psychiatric diagnosis (29%), substance abuse (18%), and renal failure (17%). Excluding SBP, 31% of patients had another documented infection. Gastroenterology was consulted in 50% of the admissions. Fever was present in 27% of patients, elevated white blood cell (WBC) count (ie, WBC >11 k/mm³) was present in 27% of patients, International Normalized Ratio (INR) was elevated (>1.1) in 92% of patients, and 16% of patients had a platelet count of <50,000/mm³. Patients who received paracentesis were less likely to have a fever on presentation (19% vs 32%, P=0.06), low (ie, <50,000/mm³) platelet count (11% vs 19%, P=0.14), or concurrent gastrointestinal (GI) bleed (6% vs 16%, P=0.05). In a multiple logistic regression model including characteristics associated at P0.2 with paracentesis, fever, low platelet count, and concurrent GI bleeding were associated with decreased odds of receiving paracentesis (Appendix 1).

Of the patients who received paracentesis (n=80), 14% were diagnosed with SBP. Of these, 55% received prophylaxis on discharge. Among the patients who did not receive paracentesis (n=113), 38 (34%) received antibiotics for another documented infection (eg, pneumonia), and 25 patients (22%) received antibiotics with no other documented infection or evidence of variceal bleeding. Of these 25 patients who were presumed to be empirically treated for SBP (Figure 1), only 20% were prescribed prophylactic antibiotics on discharge.

Figure 1

CONCLUSION

We found that many patients with decompensated cirrhosis and ascites did not receive paracentesis when hospitalized, which is similar to previously published data.[4, 6, 7] Clinical evidence of infection, such as fever or elevated WBC count, did not increase the odds of receiving paracentesis. Many patients treated for SBP were not discharged on prophylaxis.

This study is limited by its small single‐center design. We could only use data from 1 year (2009), because study data collection was part of a quality‐improvement project that took place for that year only. We did not adjust for the number of red blood cells in the ascitic fluid samples. We were also unable to determine the timing of gastroenterology consultation (whether it was done prior to paracentesis), admission venue (floor vs intensive care), or patient history of SBP.

Despite these limitations, there are important implications. First, the decision to perform paracentesis was not associated with symptoms of infection, although some clinical factors (eg, low platelets or GI bleeding) were associated with reduced odds of receiving paracentesis. Second, a majority of patients treated for SBP did not receive prophylactic antibiotics at discharge. These findings suggest a clear opportunity to increase awareness and acceptance of AASLD guidelines among hospital medicine practitioners. Quality‐improvement efforts should focus on the education of providers, and future research should identify barriers to paracentesis at both the practitioner and system levels (eg, availability of interventional radiology). Checklists or decision support within electronic order entry systems may also help reduce the low rates of paracentesis seen in our and prior studies.[4, 6, 7]

Disclosures: Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Drs. Lagu, Ghaoui, and Brooling had full access to all of the data in the study. They take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Lagu, Ghaoui, and Brooling conceived of the study. Dr. Ghaoui acquired the data. Ms. Friderici carried out the statistical analyses. Drs. Lagu, Ghaoui, Brooling, Lindenauer, and Ms. Friderici analyzed and interpreted the data, drafted the manuscript, and critically reviewed the manuscript for important intellectual content. The authors report no conflicts of interest.

Ascites is the most common complication of cirrhosis leading to hospital admission.[1] Approximately 12% of hospitalized patients who present with decompensated cirrhosis and ascites have spontaneous bacterial peritonitis (SBP); half of these patients do not present with abdominal pain, fever, nausea, or vomiting.[2] Guidelines published by the American Association for the Study of Liver Diseases (AASLD) recommend paracentesis for all hospitalized patients with cirrhosis and ascites and also recommend long‐term antibiotic prophylaxis for survivors of an SBP episode.[3] Despite evidence that in‐hospital mortality is reduced in those patients who receive paracentesis in a timely manner,[4, 5] only 40% to 60% of eligible patients receive paracentesis.[4, 6, 7] We aimed to describe clinical predictors of paracentesis and use of antibiotics following an episode of SBP in patients with decompensated cirrhosis and ascites.

METHODS

We conducted a retrospective cohort study of adults admitted to a single tertiary care center between January 1, 2009 and December 31, 2009.⁷ We included patients with an International Classification of Diseases, Ninth Revision discharge code consistent with decompensated cirrhosis who met clinical criteria for decompensated cirrhosis (see Supporting Figure 1 in the online version of this article) [7] and had enough ascitic fluid to be sampled under imaging guidance. We collected presenting vital signs, laboratory data (within 24 hours of admission), evidence of infection other than SBP (eg, urinary infection, pneumonia), results of peritoneal fluid analysis (defining SBP as 250 polymorphonuclear leukocytes), and use of antibiotic therapy. Our statistical analysis calculated summary statistics as means, medians, and proportions. Furthermore, we used multiple logistic regression to examine the association between predictors and receipt of paracentesis, including age, sex, and clinical measures associated with paracentesis at P0.20 using the Fisher exact test. Alpha was set at 0.05 (2‐sided) for all comparisons.

RESULTS

We identified 193 admissions for 103 patients with decompensated cirrhosis and ascites (Table 1). Of these, 41% (80/193) received diagnostic paracentesis. Mean/standard deviation for age was 53.6/12.4 years; 71% of patients were male and 63% were English speaking. Common comorbidities included diabetes mellitus (33%), psychiatric diagnosis (29%), substance abuse (18%), and renal failure (17%). Excluding SBP, 31% of patients had another documented infection. Gastroenterology was consulted in 50% of the admissions. Fever was present in 27% of patients, elevated white blood cell (WBC) count (ie, WBC >11 k/mm³) was present in 27% of patients, International Normalized Ratio (INR) was elevated (>1.1) in 92% of patients, and 16% of patients had a platelet count of <50,000/mm³. Patients who received paracentesis were less likely to have a fever on presentation (19% vs 32%, P=0.06), low (ie, <50,000/mm³) platelet count (11% vs 19%, P=0.14), or concurrent gastrointestinal (GI) bleed (6% vs 16%, P=0.05). In a multiple logistic regression model including characteristics associated at P0.2 with paracentesis, fever, low platelet count, and concurrent GI bleeding were associated with decreased odds of receiving paracentesis (Appendix 1).

Of the patients who received paracentesis (n=80), 14% were diagnosed with SBP. Of these, 55% received prophylaxis on discharge. Among the patients who did not receive paracentesis (n=113), 38 (34%) received antibiotics for another documented infection (eg, pneumonia), and 25 patients (22%) received antibiotics with no other documented infection or evidence of variceal bleeding. Of these 25 patients who were presumed to be empirically treated for SBP (Figure 1), only 20% were prescribed prophylactic antibiotics on discharge.

Figure 1

CONCLUSION

We found that many patients with decompensated cirrhosis and ascites did not receive paracentesis when hospitalized, which is similar to previously published data.[4, 6, 7] Clinical evidence of infection, such as fever or elevated WBC count, did not increase the odds of receiving paracentesis. Many patients treated for SBP were not discharged on prophylaxis.

This study is limited by its small single‐center design. We could only use data from 1 year (2009), because study data collection was part of a quality‐improvement project that took place for that year only. We did not adjust for the number of red blood cells in the ascitic fluid samples. We were also unable to determine the timing of gastroenterology consultation (whether it was done prior to paracentesis), admission venue (floor vs intensive care), or patient history of SBP.

Despite these limitations, there are important implications. First, the decision to perform paracentesis was not associated with symptoms of infection, although some clinical factors (eg, low platelets or GI bleeding) were associated with reduced odds of receiving paracentesis. Second, a majority of patients treated for SBP did not receive prophylactic antibiotics at discharge. These findings suggest a clear opportunity to increase awareness and acceptance of AASLD guidelines among hospital medicine practitioners. Quality‐improvement efforts should focus on the education of providers, and future research should identify barriers to paracentesis at both the practitioner and system levels (eg, availability of interventional radiology). Checklists or decision support within electronic order entry systems may also help reduce the low rates of paracentesis seen in our and prior studies.[4, 6, 7]

Disclosures: Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Drs. Lagu, Ghaoui, and Brooling had full access to all of the data in the study. They take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Lagu, Ghaoui, and Brooling conceived of the study. Dr. Ghaoui acquired the data. Ms. Friderici carried out the statistical analyses. Drs. Lagu, Ghaoui, Brooling, Lindenauer, and Ms. Friderici analyzed and interpreted the data, drafted the manuscript, and critically reviewed the manuscript for important intellectual content. The authors report no conflicts of interest.

Ascites is the most common complication of cirrhosis leading to hospital admission.[1] Approximately 12% of hospitalized patients who present with decompensated cirrhosis and ascites have spontaneous bacterial peritonitis (SBP); half of these patients do not present with abdominal pain, fever, nausea, or vomiting.[2] Guidelines published by the American Association for the Study of Liver Diseases (AASLD) recommend paracentesis for all hospitalized patients with cirrhosis and ascites and also recommend long‐term antibiotic prophylaxis for survivors of an SBP episode.[3] Despite evidence that in‐hospital mortality is reduced in those patients who receive paracentesis in a timely manner,[4, 5] only 40% to 60% of eligible patients receive paracentesis.[4, 6, 7] We aimed to describe clinical predictors of paracentesis and use of antibiotics following an episode of SBP in patients with decompensated cirrhosis and ascites.

METHODS

We conducted a retrospective cohort study of adults admitted to a single tertiary care center between January 1, 2009 and December 31, 2009.⁷ We included patients with an International Classification of Diseases, Ninth Revision discharge code consistent with decompensated cirrhosis who met clinical criteria for decompensated cirrhosis (see Supporting Figure 1 in the online version of this article) [7] and had enough ascitic fluid to be sampled under imaging guidance. We collected presenting vital signs, laboratory data (within 24 hours of admission), evidence of infection other than SBP (eg, urinary infection, pneumonia), results of peritoneal fluid analysis (defining SBP as 250 polymorphonuclear leukocytes), and use of antibiotic therapy. Our statistical analysis calculated summary statistics as means, medians, and proportions. Furthermore, we used multiple logistic regression to examine the association between predictors and receipt of paracentesis, including age, sex, and clinical measures associated with paracentesis at P0.20 using the Fisher exact test. Alpha was set at 0.05 (2‐sided) for all comparisons.

RESULTS

We identified 193 admissions for 103 patients with decompensated cirrhosis and ascites (Table 1). Of these, 41% (80/193) received diagnostic paracentesis. Mean/standard deviation for age was 53.6/12.4 years; 71% of patients were male and 63% were English speaking. Common comorbidities included diabetes mellitus (33%), psychiatric diagnosis (29%), substance abuse (18%), and renal failure (17%). Excluding SBP, 31% of patients had another documented infection. Gastroenterology was consulted in 50% of the admissions. Fever was present in 27% of patients, elevated white blood cell (WBC) count (ie, WBC >11 k/mm³) was present in 27% of patients, International Normalized Ratio (INR) was elevated (>1.1) in 92% of patients, and 16% of patients had a platelet count of <50,000/mm³. Patients who received paracentesis were less likely to have a fever on presentation (19% vs 32%, P=0.06), low (ie, <50,000/mm³) platelet count (11% vs 19%, P=0.14), or concurrent gastrointestinal (GI) bleed (6% vs 16%, P=0.05). In a multiple logistic regression model including characteristics associated at P0.2 with paracentesis, fever, low platelet count, and concurrent GI bleeding were associated with decreased odds of receiving paracentesis (Appendix 1).

Of the patients who received paracentesis (n=80), 14% were diagnosed with SBP. Of these, 55% received prophylaxis on discharge. Among the patients who did not receive paracentesis (n=113), 38 (34%) received antibiotics for another documented infection (eg, pneumonia), and 25 patients (22%) received antibiotics with no other documented infection or evidence of variceal bleeding. Of these 25 patients who were presumed to be empirically treated for SBP (Figure 1), only 20% were prescribed prophylactic antibiotics on discharge.

Figure 1

CONCLUSION

We found that many patients with decompensated cirrhosis and ascites did not receive paracentesis when hospitalized, which is similar to previously published data.[4, 6, 7] Clinical evidence of infection, such as fever or elevated WBC count, did not increase the odds of receiving paracentesis. Many patients treated for SBP were not discharged on prophylaxis.

This study is limited by its small single‐center design. We could only use data from 1 year (2009), because study data collection was part of a quality‐improvement project that took place for that year only. We did not adjust for the number of red blood cells in the ascitic fluid samples. We were also unable to determine the timing of gastroenterology consultation (whether it was done prior to paracentesis), admission venue (floor vs intensive care), or patient history of SBP.

Despite these limitations, there are important implications. First, the decision to perform paracentesis was not associated with symptoms of infection, although some clinical factors (eg, low platelets or GI bleeding) were associated with reduced odds of receiving paracentesis. Second, a majority of patients treated for SBP did not receive prophylactic antibiotics at discharge. These findings suggest a clear opportunity to increase awareness and acceptance of AASLD guidelines among hospital medicine practitioners. Quality‐improvement efforts should focus on the education of providers, and future research should identify barriers to paracentesis at both the practitioner and system levels (eg, availability of interventional radiology). Checklists or decision support within electronic order entry systems may also help reduce the low rates of paracentesis seen in our and prior studies.[4, 6, 7]

Disclosures: Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Drs. Lagu, Ghaoui, and Brooling had full access to all of the data in the study. They take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Lagu, Ghaoui, and Brooling conceived of the study. Dr. Ghaoui acquired the data. Ms. Friderici carried out the statistical analyses. Drs. Lagu, Ghaoui, Brooling, Lindenauer, and Ms. Friderici analyzed and interpreted the data, drafted the manuscript, and critically reviewed the manuscript for important intellectual content. The authors report no conflicts of interest.

Antipsychotic (AP) medications are often used in the hospitalized geriatric population for the treatment of delirium.[1] Because of adverse events associated with APs, efforts have been made to reduce their use in hospitalized elders,[2] but it is not clear if these recommendations have been widely adopted. We studied the use of APs in a cohort of hospitalized elders to better understand why APs are started and how often they are continued on discharge.

METHODS

We conducted a retrospective cohort study of patients aged 65 years or older admitted to a tertiary care hospital between October 1, 2012 and September 31, 2013. Using Stata's (StataCorp., College Station, TX) sample command,[3] we included a subset of randomly selected inpatients who received more than 1 dose of oral APs (determined using the electronic medication administration summary). We excluded patients admitted under observation status or to the psychiatric service, those who were on APs prior to admission, and those who only received prochloperazine for nausea. Using prior literature to identify terms frequently used to describe delirium (Figure 1), we created an algorithm and a chart abstraction form (see Supporting Information, Appendix 1, in the online version of this article).[4] We tested these instruments in a preliminary chart review involving 30 patients. Disagreements were discussed with coauthors and resolved through consensus, resulting in some algorithm changes (eg, excluding a large number of patients who received only 1 dose of haloperidol postoperatively, because we hypothesized that this use could be a prophylactic measure).[5] Two investigators extracted the remaining charts independently. We used descriptive statistics and performed cross‐tabulations on the selected variables.

Figure 1

RESULTS

Of 12,817 geriatric hospitalizations during the study period, 1120 (9%) were treated with antipsychotics. We randomly selected 300 of these for extraction: 54% were male, and 67% were admitted to the medical service (Table 1). The inpatient mortality rate was 10% (30/300). The most frequent indication for AP use was delirium (83%, 249/300). Only 35% of delirious patients received a formal assessment with the Confusion Assessment Method (CAM). The most commonly used atypical antipsychotic was quetiapine (86%); 55% received more than 1 antipsychotic medication during hospitalization, and 48% (143/297) of patients were continued on APs at discharge (excluding 3 patients transferred to other acute care hospitals).

Approximately 45% (134/300) had documented or suspected dementia, and 30% (89/300) were physically restrained during the hospital stay. Consultations with geriatrics were obtained in 40% (120/300) of the cases and with psychiatry in 10% (29/300) of the cases. Neurology is rarely consulted for delirium in our institution; thus, we did not collect data on those referrals. Electrocardiography (ECG) (recommended for patients at high cardiac risk[6]) was performed in 88% (265/300) of patients prior to AP administration and 52% (157/300) after. The corrected QT interval exceeded 500 ms in 15% (41/265) of patients prior to AP administration and 25% (39/157) after. Although few patients (12%) were admitted from nursing facilities, 66% (199/300) were eventually discharged to skilled nursing facilities (SNFs) or rehabilitation facilities; most of these patients (117/199, 59%) received AP treatment, compared to 38% of patients discharged to home (26/68).

DISCUSSION

In a cohort of hospitalized elders, we found that 9% were treated with APs. Most received APs for perceived delirium; in‐hospital ECG monitoring was suboptimal. Half of the patients started on APs remained on them at discharge; those discharged to SNFs were more likely to receive ongoing AP treatment.

Our study is limited by its retrospective, single‐center design, a lack of inter‐rater reliability measurement (although our training process was designed to standardize extraction methods), and the infrequent use of formal CAM assessment. Additionally, we were unable to determine how frequently APs were initiated in the intensive care unit. Any retrospective study is limited by the difficulty of distinguishing delirium from the behavioral and psychiatric symptoms of dementia, but we identified delirium using standard terms described in previous literature.

Our study also has a number of important implications. Because of a reported association between the use of APs and risk of death in the postacute setting,[7] national provider organizations have called for a reduction in AP initiation in hospitalized elders.[2] However, this study indicates that APs continue to be prescribed for delirium, which may be attributed to the lack of behavioral modification options in most hospitals, such as acute care for elders (ACE) units and hospital elder life programs (HELP).[8, 9] Our findings suggest that this problem would be further amplified in hospitals that lack access to geriatrics expertise.

Without alternative behavioral options, patients are at risk for prolonged delirium, which is associated with significant suffering and subsequent risk of further cognitive impairment and death.[10] Although evidence for the efficacy of APs in the treatment of delirium is limited and inconclusive, no better pharmacologic options exist. Hospitals that wish to reduce use of APs should therefore consider investing in environmental interventions (eg, ACE units, HELP), which lower the incidence of delirium and could, in turn, decrease the prescription and continuation of antipsychotics.[8, 9]

Antipsychotic (AP) medications are often used in the hospitalized geriatric population for the treatment of delirium.[1] Because of adverse events associated with APs, efforts have been made to reduce their use in hospitalized elders,[2] but it is not clear if these recommendations have been widely adopted. We studied the use of APs in a cohort of hospitalized elders to better understand why APs are started and how often they are continued on discharge.

METHODS

We conducted a retrospective cohort study of patients aged 65 years or older admitted to a tertiary care hospital between October 1, 2012 and September 31, 2013. Using Stata's (StataCorp., College Station, TX) sample command,[3] we included a subset of randomly selected inpatients who received more than 1 dose of oral APs (determined using the electronic medication administration summary). We excluded patients admitted under observation status or to the psychiatric service, those who were on APs prior to admission, and those who only received prochloperazine for nausea. Using prior literature to identify terms frequently used to describe delirium (Figure 1), we created an algorithm and a chart abstraction form (see Supporting Information, Appendix 1, in the online version of this article).[4] We tested these instruments in a preliminary chart review involving 30 patients. Disagreements were discussed with coauthors and resolved through consensus, resulting in some algorithm changes (eg, excluding a large number of patients who received only 1 dose of haloperidol postoperatively, because we hypothesized that this use could be a prophylactic measure).[5] Two investigators extracted the remaining charts independently. We used descriptive statistics and performed cross‐tabulations on the selected variables.

Figure 1

RESULTS

Of 12,817 geriatric hospitalizations during the study period, 1120 (9%) were treated with antipsychotics. We randomly selected 300 of these for extraction: 54% were male, and 67% were admitted to the medical service (Table 1). The inpatient mortality rate was 10% (30/300). The most frequent indication for AP use was delirium (83%, 249/300). Only 35% of delirious patients received a formal assessment with the Confusion Assessment Method (CAM). The most commonly used atypical antipsychotic was quetiapine (86%); 55% received more than 1 antipsychotic medication during hospitalization, and 48% (143/297) of patients were continued on APs at discharge (excluding 3 patients transferred to other acute care hospitals).

Approximately 45% (134/300) had documented or suspected dementia, and 30% (89/300) were physically restrained during the hospital stay. Consultations with geriatrics were obtained in 40% (120/300) of the cases and with psychiatry in 10% (29/300) of the cases. Neurology is rarely consulted for delirium in our institution; thus, we did not collect data on those referrals. Electrocardiography (ECG) (recommended for patients at high cardiac risk[6]) was performed in 88% (265/300) of patients prior to AP administration and 52% (157/300) after. The corrected QT interval exceeded 500 ms in 15% (41/265) of patients prior to AP administration and 25% (39/157) after. Although few patients (12%) were admitted from nursing facilities, 66% (199/300) were eventually discharged to skilled nursing facilities (SNFs) or rehabilitation facilities; most of these patients (117/199, 59%) received AP treatment, compared to 38% of patients discharged to home (26/68).

DISCUSSION

In a cohort of hospitalized elders, we found that 9% were treated with APs. Most received APs for perceived delirium; in‐hospital ECG monitoring was suboptimal. Half of the patients started on APs remained on them at discharge; those discharged to SNFs were more likely to receive ongoing AP treatment.

Our study is limited by its retrospective, single‐center design, a lack of inter‐rater reliability measurement (although our training process was designed to standardize extraction methods), and the infrequent use of formal CAM assessment. Additionally, we were unable to determine how frequently APs were initiated in the intensive care unit. Any retrospective study is limited by the difficulty of distinguishing delirium from the behavioral and psychiatric symptoms of dementia, but we identified delirium using standard terms described in previous literature.

Our study also has a number of important implications. Because of a reported association between the use of APs and risk of death in the postacute setting,[7] national provider organizations have called for a reduction in AP initiation in hospitalized elders.[2] However, this study indicates that APs continue to be prescribed for delirium, which may be attributed to the lack of behavioral modification options in most hospitals, such as acute care for elders (ACE) units and hospital elder life programs (HELP).[8, 9] Our findings suggest that this problem would be further amplified in hospitals that lack access to geriatrics expertise.

Without alternative behavioral options, patients are at risk for prolonged delirium, which is associated with significant suffering and subsequent risk of further cognitive impairment and death.[10] Although evidence for the efficacy of APs in the treatment of delirium is limited and inconclusive, no better pharmacologic options exist. Hospitals that wish to reduce use of APs should therefore consider investing in environmental interventions (eg, ACE units, HELP), which lower the incidence of delirium and could, in turn, decrease the prescription and continuation of antipsychotics.[8, 9]

Antipsychotic (AP) medications are often used in the hospitalized geriatric population for the treatment of delirium.[1] Because of adverse events associated with APs, efforts have been made to reduce their use in hospitalized elders,[2] but it is not clear if these recommendations have been widely adopted. We studied the use of APs in a cohort of hospitalized elders to better understand why APs are started and how often they are continued on discharge.

METHODS

We conducted a retrospective cohort study of patients aged 65 years or older admitted to a tertiary care hospital between October 1, 2012 and September 31, 2013. Using Stata's (StataCorp., College Station, TX) sample command,[3] we included a subset of randomly selected inpatients who received more than 1 dose of oral APs (determined using the electronic medication administration summary). We excluded patients admitted under observation status or to the psychiatric service, those who were on APs prior to admission, and those who only received prochloperazine for nausea. Using prior literature to identify terms frequently used to describe delirium (Figure 1), we created an algorithm and a chart abstraction form (see Supporting Information, Appendix 1, in the online version of this article).[4] We tested these instruments in a preliminary chart review involving 30 patients. Disagreements were discussed with coauthors and resolved through consensus, resulting in some algorithm changes (eg, excluding a large number of patients who received only 1 dose of haloperidol postoperatively, because we hypothesized that this use could be a prophylactic measure).[5] Two investigators extracted the remaining charts independently. We used descriptive statistics and performed cross‐tabulations on the selected variables.

Figure 1

RESULTS

Of 12,817 geriatric hospitalizations during the study period, 1120 (9%) were treated with antipsychotics. We randomly selected 300 of these for extraction: 54% were male, and 67% were admitted to the medical service (Table 1). The inpatient mortality rate was 10% (30/300). The most frequent indication for AP use was delirium (83%, 249/300). Only 35% of delirious patients received a formal assessment with the Confusion Assessment Method (CAM). The most commonly used atypical antipsychotic was quetiapine (86%); 55% received more than 1 antipsychotic medication during hospitalization, and 48% (143/297) of patients were continued on APs at discharge (excluding 3 patients transferred to other acute care hospitals).

Approximately 45% (134/300) had documented or suspected dementia, and 30% (89/300) were physically restrained during the hospital stay. Consultations with geriatrics were obtained in 40% (120/300) of the cases and with psychiatry in 10% (29/300) of the cases. Neurology is rarely consulted for delirium in our institution; thus, we did not collect data on those referrals. Electrocardiography (ECG) (recommended for patients at high cardiac risk[6]) was performed in 88% (265/300) of patients prior to AP administration and 52% (157/300) after. The corrected QT interval exceeded 500 ms in 15% (41/265) of patients prior to AP administration and 25% (39/157) after. Although few patients (12%) were admitted from nursing facilities, 66% (199/300) were eventually discharged to skilled nursing facilities (SNFs) or rehabilitation facilities; most of these patients (117/199, 59%) received AP treatment, compared to 38% of patients discharged to home (26/68).

DISCUSSION

In a cohort of hospitalized elders, we found that 9% were treated with APs. Most received APs for perceived delirium; in‐hospital ECG monitoring was suboptimal. Half of the patients started on APs remained on them at discharge; those discharged to SNFs were more likely to receive ongoing AP treatment.

Our study is limited by its retrospective, single‐center design, a lack of inter‐rater reliability measurement (although our training process was designed to standardize extraction methods), and the infrequent use of formal CAM assessment. Additionally, we were unable to determine how frequently APs were initiated in the intensive care unit. Any retrospective study is limited by the difficulty of distinguishing delirium from the behavioral and psychiatric symptoms of dementia, but we identified delirium using standard terms described in previous literature.

Our study also has a number of important implications. Because of a reported association between the use of APs and risk of death in the postacute setting,[7] national provider organizations have called for a reduction in AP initiation in hospitalized elders.[2] However, this study indicates that APs continue to be prescribed for delirium, which may be attributed to the lack of behavioral modification options in most hospitals, such as acute care for elders (ACE) units and hospital elder life programs (HELP).[8, 9] Our findings suggest that this problem would be further amplified in hospitals that lack access to geriatrics expertise.

Without alternative behavioral options, patients are at risk for prolonged delirium, which is associated with significant suffering and subsequent risk of further cognitive impairment and death.[10] Although evidence for the efficacy of APs in the treatment of delirium is limited and inconclusive, no better pharmacologic options exist. Hospitals that wish to reduce use of APs should therefore consider investing in environmental interventions (eg, ACE units, HELP), which lower the incidence of delirium and could, in turn, decrease the prescription and continuation of antipsychotics.[8, 9]

There are approximately 3 million cases of Clostridium difficile infection (CDI) per year in the United States.[1, 2, 3, 4] Of these, 10% result in a hospitalization or occur as a consequence of the exposures and treatments associated with hospitalization.[1, 2, 3, 4] Some patients with CDI experience mild diarrhea that is responsive to therapy, but other patients experience severe, life‐threatening disease that is refractory to treatment, leading to pseudomembranous colitis, toxic megacolon, and sepsis with a 60‐day mortality rate that exceeds 12%.[5, 6, 7, 8, 9, 10, 11, 12, 13, 14]

Hospital‐onset CDI (HOCDI), defined as C difficile‐associated diarrhea and related symptoms with onset more than 48 hours after admission to a healthcare facility,[15] represents a unique marriage of CDI risk factors.[5] A vulnerable patient is introduced into an environment that contains both exposure to C difficile (through other patients or healthcare workers) and treatment with antibacterial agents that may diminish normal flora. Consequently, CDI is common among hospitalized patients.[16, 17, 18] A particularly important group for understanding the burden of disease is patients who initially present to the hospital with sepsis and subsequently develop HOCDI. Sepsis patients are often critically ill and are universally treated with antibiotics.

Determining the incremental cost and mortality risk attributable to HOCDI is methodologically challenging. Because HOCDI is associated with presenting severity, the sickest patients are also the most likely to contract the disease. HOCDI is also associated with time of exposure or length of stay (LOS). Because LOS is a risk factor, comparing LOS between those with and without HOCDI will overestimate the impact if the time to diagnosis is not taken into account.[16, 17, 19, 20] We aimed to examine the impact of HOCDI in hospitalized patients with sepsis using a large, multihospital database with statistical methods that took presenting severity and time to diagnosis into account.

METHODS

RESULTS

We identified 486,943 adult sepsis admissions to a Premier hospital between July 1, 2004 and December 31, 2010. After applying all exclusion criteria, we had a final sample of 218,915 admissions with sepsis (from 400 hospitals) at risk for HOCDI (Figure 1). Of these, 2368 (1.08%) met criteria for diagnosis of CDI after hospital day 2 and were matched to controls using index date and propensity score.

Figure 1

Distribution by LOS, Index Day, and Risk for Mortality

The unadjusted and unmatched LOS was longer for cases than controls (19 days vs 8 days, Table 2) (see Supporting Figure 1 in the online version of this article). Approximately 90% of the patients had an index day of 14 or less (Figure 2). Among patients both with and without CDI, the unadjusted mortality risk increased as the index day (and thus the total LOS) increased.

Table 2.Comparison of Length of Stay, Mortality, and Costs for Propensity‐Matched Patients With and Without HOCDI

Outcome	HOCDI	No HOCDI	Difference (95% CI)	P
NOTE: Abbreviations: CI, confidence interval; HOCDI, hospital‐onset Clostridium difficile infection; RR, relative risk.
Length of stay, d
Raw results	19.2	8.3	8.4 (8.48.5)	<0.01
Raw results for survivors only	18.6	8.0	10.6 (10.311.0)	<0.01
Matched results	19.2	14.2	5.1(4.45.7)	<0.01
Matched results for survivors only	18.6	13.6	5.1 (4.45.8)	<0.01
Mortality, %
Raw results	24.0	10.1	13.9 (12.615.1), RR=2.4 (2.22.5)	<0.01
Matched results	24.0	15.4	8.6 (6.410.9), RR=1.6 (1.41.8)	<0.01
Costs, US$
Raw results median costs [interquartile range]	$26,187 [$15,117$46,273]	$9,988 [$6,296$17,351]	$16,190 ($15,826$16,555)	<0.01
Raw results for survivors only [interquartile range]	$24,038 [$14,169$41,654]	$9,429 [$6,070$15,875]	$14,620 ($14,246$14,996)	<0.01
Matched results [interquartile range]	$26,187 [$15,117$46,273]	$19,160 [$12,392$33,777]	$5,308 ($4,521$6,108)
Matched results for survivors only [interquartile range]	$24,038 [$14,169$41,654]	$17,811 [$11,614$29,298]	$4,916 ($4,088$5,768)	<0.01

Figure 2

Adjusted Results

Compared to patients without disease, HOCDI patients had an increased unadjusted mortality (24% vs 10%, P<0.001). This translates into a relative risk of 2.4 (95% confidence interval [CI]: 2.2, 2.5). In the matched cohort, the difference in the mortality rates was attenuated, but still significantly higher in the HOCDI patients (24% versus 15%, P<0.001, an absolute difference of 9%; 95% CI: 6.410.8). The adjusted relative risk of mortality for HOCDI was 1.6 (95% CI: 1.41.8; Table 2). After matching, patients with CDI had a LOS of 19.2 days versus 14.2 days in matched controls (difference of 5.1 days; 95% CI: 4.45.7; P<0.001). When the LOS analysis was limited to survivors only, this difference of 5 days remained (P<0.001). In an analysis limited to survivors only, the difference in median costs between cases and controls was $4916 (95% CI: $4088$5768; P<0.001). In a secondary analysis examining heterogeneity between HOCDI and outcomes across clinical subgroups, the absolute difference in mortality and costs between cases and controls varied across demographics, comorbidity, and ICU admission, but the relative risks were similar (Figure 3) (see Supporting Figure 3 in the online version of this article).

Figure 3

DISCUSSION

In this large cohort of patients with sepsis, we found that approximately 1 in 100 patients with sepsis developed HOCDI. Even after matching with controls based on the date of symptom onset and propensity score, patients who developed HOCDI were more than 1.6 times more likely to die in the hospital. HOCDI also added 5 days to the average hospitalization for patients with sepsis and increased median costs by approximately $5000. These findings suggest that a hospital that prevents 1 case of HOCDI per month in sepsis patients could avoid 1 death and 60 inpatient days annually, achieving an approximate yearly savings of $60,000.

Until now, the incremental cost and mortality attributable to HOCDI in sepsis patients have been poorly understood. Attributing outcomes can be methodologically challenging because patients who are at greatest risk for poor outcomes are the most likely to contract the disease and are at risk for longer periods of time. Therefore, it is necessary to take into account differences in severity of illness and time at risk between diseased and nondiseased populations and to ensure that outcomes attributed to the disease occur after disease onset.[28, 32] The majority of prior studies examining the impact of CDI on hospitalized patients have been limited by a lack of adequate matching to controls, small sample size, or failure to take into account time to infection.[16, 17, 19, 20]

A few studies have taken into account severity, time to infection, or both in estimating the impact of HOCDI. Using a time‐dependent Cox model that accounted for time to infection, Micek et al. found no difference in mortality but a longer LOS in mechanically ventilated patients (not limited to sepsis) with CDI.[33] However, their study was conducted at only 3 centers, did not take into account severity at the time of diagnosis, and did not clearly distinguish between community‐onset CDI and HOCDI. Oake et al. and Forster et al. examined the impact of CDI on patients hospitalized in a 2‐hospital health system in Canada.[12, 13] Using the baseline mortality estimate in a Cox multivariate proportional hazards regression model that accounted for the time‐varying nature of CDI, they found that HOCDI increased absolute risk of death by approximately 10%. Also, notably similar to our study were their findings that HOCDI occurred in approximately 1 in 100 patients and that the attributable median increase in LOS due to hospital‐onset CDI was 6 days. Although methodologically rigorous, these 2 small studies did not assess the impact of CDI on costs of care, were not focused on sepsis patients or even patients who received antibiotics, and also did not clearly distinguish between community‐onset CDI and HOCDI.

Our study therefore has important strengths. It is the first to examine the impact of HOCDI, including costs, on the outcomes of patients hospitalized with sepsis. The fact that we took into account both time to diagnosis and severity at the time of diagnosis (by using an index date for both cases and controls and determining severity on that date) prevented us from overestimating the impact of HOCDI on outcomes. The large differences in outcomes we observed in unadjusted and unmatched data were tempered after multivariate adjustment (eg, difference in LOS from 10.6 days to 5.1 additional days, costs from $14,620 to $4916 additional costs after adjustment). Our patient sample was derived from a large, multihospital database that contains actual hospital costs as derived from internal accounting systems. The fact that our study used data from hundreds of hospitals means that our estimates of cost, LOS, and mortality may be more generalizable than the work of Micek et al., Oake et al., and Forster et al.

This work also has important implications. First, hospital administrators, clinicians, and researchers can use our results to evaluate the cost‐effectiveness of HOCDI prevention measures (eg, hand hygiene programs, antibiotic stewardship). By quantifying the cost per case in sepsis patients, we allow administrators and researchers to compare the incremental costs of HOCDI prevention programs to the dollars and lives saved due to prevention efforts. Second, we found that our propensity model identified patients whose risk varied greatly. This suggests that an opportunity exists to identify subgroups of patients that are at highest risk. Identifying high‐risk subgroups will allow for targeted risk reduction interventions and the opportunity to reduce transmission (eg, by placing high‐risk patients in a private room). Finally, we have reaffirmed that time to diagnosis and presenting severity need to be rigorously addressed prior to making estimates of the impact of CDI burden and other hospital‐acquired conditions and injuries.

There are limitations to this study as well. We did not have access to microbiological data. However, we required a diagnosis code of CDI, evidence of testing, and treatment after the date of testing to confirm a diagnosis. We also adopted detailed exclusion criteria to ensure that CDI that was not present on admission and that controls did not have CDI. These stringent inclusion and exclusion criteria strengthened the internal validity of our estimates of disease impact. We used administrative claims data, which limited our ability to adjust for severity. However, the detailed nature of the database allowed us to use treatments, such as vasopressors and antibiotics, to identify cases; treatments were also used as a validated indicator of severity,[26] which may have helped to reduce some of this potential bias. Although our propensity model included many predictors of CDI, such as use of proton pump inhibitors and factors associated with mortality, not every confounder was completely balanced after propensity matching, although the statistical differences may have been related to our large sample size and therefore might not be clinically significant. We also may have failed to include all possible predictors of CDI in the propensity model.

In a large, diverse cohort of hospitalized patients with sepsis, we found that HOCDI lengthened hospital stay by approximately 5 days, increased risk of in‐hospital mortality by 9%, and increased hospital cost by approximately $5000 per patient. These findings highlight the importance of identifying effective prevention measures and of determining the patient populations at greatest risk for HOCDI.

Disclosures: The study was conducted with funding from the Division of Critical Care and the Center for Quality of Care Research at Baystate Medical Center. Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Dr. Stefan is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114631. Drs. Lagu and Lindenauer had full access to all of the data in the study; they take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Lagu, Lindenauer, Steingrub, Higgins, Stefan, Haessler, and Rothberg conceived of the study. Dr. Lindenauer acquired the data. Drs. Lagu, Lindenauer, Rothberg, Steingrub, Nathanson, Stefan, Haessler, Higgins, and Mr. Hannon analyzed and interpreted the data. Dr. Lagu drafted the manuscript. Drs. Lagu, Lindenauer, Rothberg, Steingrub, Nathanson, Stefan, Haessler, Higgins, and Mr. Hannon critically reviewed the manuscript for important intellectual content. Dr. Nathanson carried out the statistical analyses. Dr. Nathanson, through his company OptiStatim LLC, was paid by the investigators with funding from the Department of Medicine at Baystate Medical Center to assist in conducting the statistical analyses in this study. The authors report no further conflicts of interest.

There are approximately 3 million cases of Clostridium difficile infection (CDI) per year in the United States.[1, 2, 3, 4] Of these, 10% result in a hospitalization or occur as a consequence of the exposures and treatments associated with hospitalization.[1, 2, 3, 4] Some patients with CDI experience mild diarrhea that is responsive to therapy, but other patients experience severe, life‐threatening disease that is refractory to treatment, leading to pseudomembranous colitis, toxic megacolon, and sepsis with a 60‐day mortality rate that exceeds 12%.[5, 6, 7, 8, 9, 10, 11, 12, 13, 14]

Hospital‐onset CDI (HOCDI), defined as C difficile‐associated diarrhea and related symptoms with onset more than 48 hours after admission to a healthcare facility,[15] represents a unique marriage of CDI risk factors.[5] A vulnerable patient is introduced into an environment that contains both exposure to C difficile (through other patients or healthcare workers) and treatment with antibacterial agents that may diminish normal flora. Consequently, CDI is common among hospitalized patients.[16, 17, 18] A particularly important group for understanding the burden of disease is patients who initially present to the hospital with sepsis and subsequently develop HOCDI. Sepsis patients are often critically ill and are universally treated with antibiotics.

Determining the incremental cost and mortality risk attributable to HOCDI is methodologically challenging. Because HOCDI is associated with presenting severity, the sickest patients are also the most likely to contract the disease. HOCDI is also associated with time of exposure or length of stay (LOS). Because LOS is a risk factor, comparing LOS between those with and without HOCDI will overestimate the impact if the time to diagnosis is not taken into account.[16, 17, 19, 20] We aimed to examine the impact of HOCDI in hospitalized patients with sepsis using a large, multihospital database with statistical methods that took presenting severity and time to diagnosis into account.

METHODS

RESULTS

We identified 486,943 adult sepsis admissions to a Premier hospital between July 1, 2004 and December 31, 2010. After applying all exclusion criteria, we had a final sample of 218,915 admissions with sepsis (from 400 hospitals) at risk for HOCDI (Figure 1). Of these, 2368 (1.08%) met criteria for diagnosis of CDI after hospital day 2 and were matched to controls using index date and propensity score.

Figure 1

Distribution by LOS, Index Day, and Risk for Mortality

The unadjusted and unmatched LOS was longer for cases than controls (19 days vs 8 days, Table 2) (see Supporting Figure 1 in the online version of this article). Approximately 90% of the patients had an index day of 14 or less (Figure 2). Among patients both with and without CDI, the unadjusted mortality risk increased as the index day (and thus the total LOS) increased.

Table 2.Comparison of Length of Stay, Mortality, and Costs for Propensity‐Matched Patients With and Without HOCDI

Outcome	HOCDI	No HOCDI	Difference (95% CI)	P
NOTE: Abbreviations: CI, confidence interval; HOCDI, hospital‐onset Clostridium difficile infection; RR, relative risk.
Length of stay, d
Raw results	19.2	8.3	8.4 (8.48.5)	<0.01
Raw results for survivors only	18.6	8.0	10.6 (10.311.0)	<0.01
Matched results	19.2	14.2	5.1(4.45.7)	<0.01
Matched results for survivors only	18.6	13.6	5.1 (4.45.8)	<0.01
Mortality, %
Raw results	24.0	10.1	13.9 (12.615.1), RR=2.4 (2.22.5)	<0.01
Matched results	24.0	15.4	8.6 (6.410.9), RR=1.6 (1.41.8)	<0.01
Costs, US$
Raw results median costs [interquartile range]	$26,187 [$15,117$46,273]	$9,988 [$6,296$17,351]	$16,190 ($15,826$16,555)	<0.01
Raw results for survivors only [interquartile range]	$24,038 [$14,169$41,654]	$9,429 [$6,070$15,875]	$14,620 ($14,246$14,996)	<0.01
Matched results [interquartile range]	$26,187 [$15,117$46,273]	$19,160 [$12,392$33,777]	$5,308 ($4,521$6,108)
Matched results for survivors only [interquartile range]	$24,038 [$14,169$41,654]	$17,811 [$11,614$29,298]	$4,916 ($4,088$5,768)	<0.01

Figure 2

Adjusted Results

Compared to patients without disease, HOCDI patients had an increased unadjusted mortality (24% vs 10%, P<0.001). This translates into a relative risk of 2.4 (95% confidence interval [CI]: 2.2, 2.5). In the matched cohort, the difference in the mortality rates was attenuated, but still significantly higher in the HOCDI patients (24% versus 15%, P<0.001, an absolute difference of 9%; 95% CI: 6.410.8). The adjusted relative risk of mortality for HOCDI was 1.6 (95% CI: 1.41.8; Table 2). After matching, patients with CDI had a LOS of 19.2 days versus 14.2 days in matched controls (difference of 5.1 days; 95% CI: 4.45.7; P<0.001). When the LOS analysis was limited to survivors only, this difference of 5 days remained (P<0.001). In an analysis limited to survivors only, the difference in median costs between cases and controls was $4916 (95% CI: $4088$5768; P<0.001). In a secondary analysis examining heterogeneity between HOCDI and outcomes across clinical subgroups, the absolute difference in mortality and costs between cases and controls varied across demographics, comorbidity, and ICU admission, but the relative risks were similar (Figure 3) (see Supporting Figure 3 in the online version of this article).

Figure 3

DISCUSSION

In this large cohort of patients with sepsis, we found that approximately 1 in 100 patients with sepsis developed HOCDI. Even after matching with controls based on the date of symptom onset and propensity score, patients who developed HOCDI were more than 1.6 times more likely to die in the hospital. HOCDI also added 5 days to the average hospitalization for patients with sepsis and increased median costs by approximately $5000. These findings suggest that a hospital that prevents 1 case of HOCDI per month in sepsis patients could avoid 1 death and 60 inpatient days annually, achieving an approximate yearly savings of $60,000.

Until now, the incremental cost and mortality attributable to HOCDI in sepsis patients have been poorly understood. Attributing outcomes can be methodologically challenging because patients who are at greatest risk for poor outcomes are the most likely to contract the disease and are at risk for longer periods of time. Therefore, it is necessary to take into account differences in severity of illness and time at risk between diseased and nondiseased populations and to ensure that outcomes attributed to the disease occur after disease onset.[28, 32] The majority of prior studies examining the impact of CDI on hospitalized patients have been limited by a lack of adequate matching to controls, small sample size, or failure to take into account time to infection.[16, 17, 19, 20]

A few studies have taken into account severity, time to infection, or both in estimating the impact of HOCDI. Using a time‐dependent Cox model that accounted for time to infection, Micek et al. found no difference in mortality but a longer LOS in mechanically ventilated patients (not limited to sepsis) with CDI.[33] However, their study was conducted at only 3 centers, did not take into account severity at the time of diagnosis, and did not clearly distinguish between community‐onset CDI and HOCDI. Oake et al. and Forster et al. examined the impact of CDI on patients hospitalized in a 2‐hospital health system in Canada.[12, 13] Using the baseline mortality estimate in a Cox multivariate proportional hazards regression model that accounted for the time‐varying nature of CDI, they found that HOCDI increased absolute risk of death by approximately 10%. Also, notably similar to our study were their findings that HOCDI occurred in approximately 1 in 100 patients and that the attributable median increase in LOS due to hospital‐onset CDI was 6 days. Although methodologically rigorous, these 2 small studies did not assess the impact of CDI on costs of care, were not focused on sepsis patients or even patients who received antibiotics, and also did not clearly distinguish between community‐onset CDI and HOCDI.

Our study therefore has important strengths. It is the first to examine the impact of HOCDI, including costs, on the outcomes of patients hospitalized with sepsis. The fact that we took into account both time to diagnosis and severity at the time of diagnosis (by using an index date for both cases and controls and determining severity on that date) prevented us from overestimating the impact of HOCDI on outcomes. The large differences in outcomes we observed in unadjusted and unmatched data were tempered after multivariate adjustment (eg, difference in LOS from 10.6 days to 5.1 additional days, costs from $14,620 to $4916 additional costs after adjustment). Our patient sample was derived from a large, multihospital database that contains actual hospital costs as derived from internal accounting systems. The fact that our study used data from hundreds of hospitals means that our estimates of cost, LOS, and mortality may be more generalizable than the work of Micek et al., Oake et al., and Forster et al.

This work also has important implications. First, hospital administrators, clinicians, and researchers can use our results to evaluate the cost‐effectiveness of HOCDI prevention measures (eg, hand hygiene programs, antibiotic stewardship). By quantifying the cost per case in sepsis patients, we allow administrators and researchers to compare the incremental costs of HOCDI prevention programs to the dollars and lives saved due to prevention efforts. Second, we found that our propensity model identified patients whose risk varied greatly. This suggests that an opportunity exists to identify subgroups of patients that are at highest risk. Identifying high‐risk subgroups will allow for targeted risk reduction interventions and the opportunity to reduce transmission (eg, by placing high‐risk patients in a private room). Finally, we have reaffirmed that time to diagnosis and presenting severity need to be rigorously addressed prior to making estimates of the impact of CDI burden and other hospital‐acquired conditions and injuries.

There are limitations to this study as well. We did not have access to microbiological data. However, we required a diagnosis code of CDI, evidence of testing, and treatment after the date of testing to confirm a diagnosis. We also adopted detailed exclusion criteria to ensure that CDI that was not present on admission and that controls did not have CDI. These stringent inclusion and exclusion criteria strengthened the internal validity of our estimates of disease impact. We used administrative claims data, which limited our ability to adjust for severity. However, the detailed nature of the database allowed us to use treatments, such as vasopressors and antibiotics, to identify cases; treatments were also used as a validated indicator of severity,[26] which may have helped to reduce some of this potential bias. Although our propensity model included many predictors of CDI, such as use of proton pump inhibitors and factors associated with mortality, not every confounder was completely balanced after propensity matching, although the statistical differences may have been related to our large sample size and therefore might not be clinically significant. We also may have failed to include all possible predictors of CDI in the propensity model.

In a large, diverse cohort of hospitalized patients with sepsis, we found that HOCDI lengthened hospital stay by approximately 5 days, increased risk of in‐hospital mortality by 9%, and increased hospital cost by approximately $5000 per patient. These findings highlight the importance of identifying effective prevention measures and of determining the patient populations at greatest risk for HOCDI.

Disclosures: The study was conducted with funding from the Division of Critical Care and the Center for Quality of Care Research at Baystate Medical Center. Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Dr. Stefan is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114631. Drs. Lagu and Lindenauer had full access to all of the data in the study; they take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Lagu, Lindenauer, Steingrub, Higgins, Stefan, Haessler, and Rothberg conceived of the study. Dr. Lindenauer acquired the data. Drs. Lagu, Lindenauer, Rothberg, Steingrub, Nathanson, Stefan, Haessler, Higgins, and Mr. Hannon analyzed and interpreted the data. Dr. Lagu drafted the manuscript. Drs. Lagu, Lindenauer, Rothberg, Steingrub, Nathanson, Stefan, Haessler, Higgins, and Mr. Hannon critically reviewed the manuscript for important intellectual content. Dr. Nathanson carried out the statistical analyses. Dr. Nathanson, through his company OptiStatim LLC, was paid by the investigators with funding from the Department of Medicine at Baystate Medical Center to assist in conducting the statistical analyses in this study. The authors report no further conflicts of interest.

There are approximately 3 million cases of Clostridium difficile infection (CDI) per year in the United States.[1, 2, 3, 4] Of these, 10% result in a hospitalization or occur as a consequence of the exposures and treatments associated with hospitalization.[1, 2, 3, 4] Some patients with CDI experience mild diarrhea that is responsive to therapy, but other patients experience severe, life‐threatening disease that is refractory to treatment, leading to pseudomembranous colitis, toxic megacolon, and sepsis with a 60‐day mortality rate that exceeds 12%.[5, 6, 7, 8, 9, 10, 11, 12, 13, 14]

Hospital‐onset CDI (HOCDI), defined as C difficile‐associated diarrhea and related symptoms with onset more than 48 hours after admission to a healthcare facility,[15] represents a unique marriage of CDI risk factors.[5] A vulnerable patient is introduced into an environment that contains both exposure to C difficile (through other patients or healthcare workers) and treatment with antibacterial agents that may diminish normal flora. Consequently, CDI is common among hospitalized patients.[16, 17, 18] A particularly important group for understanding the burden of disease is patients who initially present to the hospital with sepsis and subsequently develop HOCDI. Sepsis patients are often critically ill and are universally treated with antibiotics.

Determining the incremental cost and mortality risk attributable to HOCDI is methodologically challenging. Because HOCDI is associated with presenting severity, the sickest patients are also the most likely to contract the disease. HOCDI is also associated with time of exposure or length of stay (LOS). Because LOS is a risk factor, comparing LOS between those with and without HOCDI will overestimate the impact if the time to diagnosis is not taken into account.[16, 17, 19, 20] We aimed to examine the impact of HOCDI in hospitalized patients with sepsis using a large, multihospital database with statistical methods that took presenting severity and time to diagnosis into account.

METHODS

RESULTS

We identified 486,943 adult sepsis admissions to a Premier hospital between July 1, 2004 and December 31, 2010. After applying all exclusion criteria, we had a final sample of 218,915 admissions with sepsis (from 400 hospitals) at risk for HOCDI (Figure 1). Of these, 2368 (1.08%) met criteria for diagnosis of CDI after hospital day 2 and were matched to controls using index date and propensity score.

Figure 1

Distribution by LOS, Index Day, and Risk for Mortality

The unadjusted and unmatched LOS was longer for cases than controls (19 days vs 8 days, Table 2) (see Supporting Figure 1 in the online version of this article). Approximately 90% of the patients had an index day of 14 or less (Figure 2). Among patients both with and without CDI, the unadjusted mortality risk increased as the index day (and thus the total LOS) increased.

Table 2.Comparison of Length of Stay, Mortality, and Costs for Propensity‐Matched Patients With and Without HOCDI

Outcome	HOCDI	No HOCDI	Difference (95% CI)	P
NOTE: Abbreviations: CI, confidence interval; HOCDI, hospital‐onset Clostridium difficile infection; RR, relative risk.
Length of stay, d
Raw results	19.2	8.3	8.4 (8.48.5)	<0.01
Raw results for survivors only	18.6	8.0	10.6 (10.311.0)	<0.01
Matched results	19.2	14.2	5.1(4.45.7)	<0.01
Matched results for survivors only	18.6	13.6	5.1 (4.45.8)	<0.01
Mortality, %
Raw results	24.0	10.1	13.9 (12.615.1), RR=2.4 (2.22.5)	<0.01
Matched results	24.0	15.4	8.6 (6.410.9), RR=1.6 (1.41.8)	<0.01
Costs, US$
Raw results median costs [interquartile range]	$26,187 [$15,117$46,273]	$9,988 [$6,296$17,351]	$16,190 ($15,826$16,555)	<0.01
Raw results for survivors only [interquartile range]	$24,038 [$14,169$41,654]	$9,429 [$6,070$15,875]	$14,620 ($14,246$14,996)	<0.01
Matched results [interquartile range]	$26,187 [$15,117$46,273]	$19,160 [$12,392$33,777]	$5,308 ($4,521$6,108)
Matched results for survivors only [interquartile range]	$24,038 [$14,169$41,654]	$17,811 [$11,614$29,298]	$4,916 ($4,088$5,768)	<0.01

Figure 2

Adjusted Results

Compared to patients without disease, HOCDI patients had an increased unadjusted mortality (24% vs 10%, P<0.001). This translates into a relative risk of 2.4 (95% confidence interval [CI]: 2.2, 2.5). In the matched cohort, the difference in the mortality rates was attenuated, but still significantly higher in the HOCDI patients (24% versus 15%, P<0.001, an absolute difference of 9%; 95% CI: 6.410.8). The adjusted relative risk of mortality for HOCDI was 1.6 (95% CI: 1.41.8; Table 2). After matching, patients with CDI had a LOS of 19.2 days versus 14.2 days in matched controls (difference of 5.1 days; 95% CI: 4.45.7; P<0.001). When the LOS analysis was limited to survivors only, this difference of 5 days remained (P<0.001). In an analysis limited to survivors only, the difference in median costs between cases and controls was $4916 (95% CI: $4088$5768; P<0.001). In a secondary analysis examining heterogeneity between HOCDI and outcomes across clinical subgroups, the absolute difference in mortality and costs between cases and controls varied across demographics, comorbidity, and ICU admission, but the relative risks were similar (Figure 3) (see Supporting Figure 3 in the online version of this article).

Figure 3

DISCUSSION

In this large cohort of patients with sepsis, we found that approximately 1 in 100 patients with sepsis developed HOCDI. Even after matching with controls based on the date of symptom onset and propensity score, patients who developed HOCDI were more than 1.6 times more likely to die in the hospital. HOCDI also added 5 days to the average hospitalization for patients with sepsis and increased median costs by approximately $5000. These findings suggest that a hospital that prevents 1 case of HOCDI per month in sepsis patients could avoid 1 death and 60 inpatient days annually, achieving an approximate yearly savings of $60,000.

Until now, the incremental cost and mortality attributable to HOCDI in sepsis patients have been poorly understood. Attributing outcomes can be methodologically challenging because patients who are at greatest risk for poor outcomes are the most likely to contract the disease and are at risk for longer periods of time. Therefore, it is necessary to take into account differences in severity of illness and time at risk between diseased and nondiseased populations and to ensure that outcomes attributed to the disease occur after disease onset.[28, 32] The majority of prior studies examining the impact of CDI on hospitalized patients have been limited by a lack of adequate matching to controls, small sample size, or failure to take into account time to infection.[16, 17, 19, 20]

A few studies have taken into account severity, time to infection, or both in estimating the impact of HOCDI. Using a time‐dependent Cox model that accounted for time to infection, Micek et al. found no difference in mortality but a longer LOS in mechanically ventilated patients (not limited to sepsis) with CDI.[33] However, their study was conducted at only 3 centers, did not take into account severity at the time of diagnosis, and did not clearly distinguish between community‐onset CDI and HOCDI. Oake et al. and Forster et al. examined the impact of CDI on patients hospitalized in a 2‐hospital health system in Canada.[12, 13] Using the baseline mortality estimate in a Cox multivariate proportional hazards regression model that accounted for the time‐varying nature of CDI, they found that HOCDI increased absolute risk of death by approximately 10%. Also, notably similar to our study were their findings that HOCDI occurred in approximately 1 in 100 patients and that the attributable median increase in LOS due to hospital‐onset CDI was 6 days. Although methodologically rigorous, these 2 small studies did not assess the impact of CDI on costs of care, were not focused on sepsis patients or even patients who received antibiotics, and also did not clearly distinguish between community‐onset CDI and HOCDI.

Our study therefore has important strengths. It is the first to examine the impact of HOCDI, including costs, on the outcomes of patients hospitalized with sepsis. The fact that we took into account both time to diagnosis and severity at the time of diagnosis (by using an index date for both cases and controls and determining severity on that date) prevented us from overestimating the impact of HOCDI on outcomes. The large differences in outcomes we observed in unadjusted and unmatched data were tempered after multivariate adjustment (eg, difference in LOS from 10.6 days to 5.1 additional days, costs from $14,620 to $4916 additional costs after adjustment). Our patient sample was derived from a large, multihospital database that contains actual hospital costs as derived from internal accounting systems. The fact that our study used data from hundreds of hospitals means that our estimates of cost, LOS, and mortality may be more generalizable than the work of Micek et al., Oake et al., and Forster et al.

This work also has important implications. First, hospital administrators, clinicians, and researchers can use our results to evaluate the cost‐effectiveness of HOCDI prevention measures (eg, hand hygiene programs, antibiotic stewardship). By quantifying the cost per case in sepsis patients, we allow administrators and researchers to compare the incremental costs of HOCDI prevention programs to the dollars and lives saved due to prevention efforts. Second, we found that our propensity model identified patients whose risk varied greatly. This suggests that an opportunity exists to identify subgroups of patients that are at highest risk. Identifying high‐risk subgroups will allow for targeted risk reduction interventions and the opportunity to reduce transmission (eg, by placing high‐risk patients in a private room). Finally, we have reaffirmed that time to diagnosis and presenting severity need to be rigorously addressed prior to making estimates of the impact of CDI burden and other hospital‐acquired conditions and injuries.

There are limitations to this study as well. We did not have access to microbiological data. However, we required a diagnosis code of CDI, evidence of testing, and treatment after the date of testing to confirm a diagnosis. We also adopted detailed exclusion criteria to ensure that CDI that was not present on admission and that controls did not have CDI. These stringent inclusion and exclusion criteria strengthened the internal validity of our estimates of disease impact. We used administrative claims data, which limited our ability to adjust for severity. However, the detailed nature of the database allowed us to use treatments, such as vasopressors and antibiotics, to identify cases; treatments were also used as a validated indicator of severity,[26] which may have helped to reduce some of this potential bias. Although our propensity model included many predictors of CDI, such as use of proton pump inhibitors and factors associated with mortality, not every confounder was completely balanced after propensity matching, although the statistical differences may have been related to our large sample size and therefore might not be clinically significant. We also may have failed to include all possible predictors of CDI in the propensity model.

In a large, diverse cohort of hospitalized patients with sepsis, we found that HOCDI lengthened hospital stay by approximately 5 days, increased risk of in‐hospital mortality by 9%, and increased hospital cost by approximately $5000 per patient. These findings highlight the importance of identifying effective prevention measures and of determining the patient populations at greatest risk for HOCDI.

Disclosures: The study was conducted with funding from the Division of Critical Care and the Center for Quality of Care Research at Baystate Medical Center. Dr. Lagu is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114745. Dr. Stefan is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under award number K01HL114631. Drs. Lagu and Lindenauer had full access to all of the data in the study; they take responsibility for the integrity of the data and the accuracy of the data analysis. Drs. Lagu, Lindenauer, Steingrub, Higgins, Stefan, Haessler, and Rothberg conceived of the study. Dr. Lindenauer acquired the data. Drs. Lagu, Lindenauer, Rothberg, Steingrub, Nathanson, Stefan, Haessler, Higgins, and Mr. Hannon analyzed and interpreted the data. Dr. Lagu drafted the manuscript. Drs. Lagu, Lindenauer, Rothberg, Steingrub, Nathanson, Stefan, Haessler, Higgins, and Mr. Hannon critically reviewed the manuscript for important intellectual content. Dr. Nathanson carried out the statistical analyses. Dr. Nathanson, through his company OptiStatim LLC, was paid by the investigators with funding from the Department of Medicine at Baystate Medical Center to assist in conducting the statistical analyses in this study. The authors report no further conflicts of interest.

Chronic obstructive pulmonary disease (COPD) affects as many as 24 million individuals in the United States, is responsible for more than 700,000 annual hospital admissions, and is currently the nation's third leading cause of death, accounting for nearly $49.9 billion in medical spending in 2010.[1, 2] Reported in‐hospital mortality rates for patients hospitalized for exacerbations of COPD range from 2% to 5%.[3, 4, 5, 6, 7] Information about 30‐day mortality rates following hospitalization for COPD is more limited; however, international studies suggest that rates range from 3% to 9%,[8, 9] and 90‐day mortality rates exceed 15%.[10]

Despite this significant clinical and economic impact, there have been no large‐scale, sustained efforts to measure the quality or outcomes of hospital care for patients with COPD in the United States. What little is known about the treatment of patients with COPD suggests widespread opportunities to increase adherence to guideline‐recommended therapies, to reduce the use of ineffective treatments and tests, and to address variation in care across institutions.[5, 11, 12]

Public reporting of hospital performance is a key strategy for improving the quality and safety of hospital care, both in the United States and internationally.[13] Since 2007, the Centers for Medicare and Medicaid Services (CMS) has reported hospital mortality rates on the Hospital Compare Web site, and COPD is 1 of the conditions highlighted in the Affordable Care Act for future consideration.[14] Such initiatives rely on validated, risk‐adjusted performance measures for comparisons across institutions and to enable outcomes to be tracked over time. We present the development, validation, and results of a model intended for public reporting of risk‐standardized mortality rates for patients hospitalized with exacerbations of COPD that has been endorsed by the National Quality Forum.[15]

METHODS

RESULTS

Model Derivation

After exclusions were applied, the development sample included 150,035 admissions in 2008 at 4537 US hospitals (Figure 1). Factors that were most strongly associated with the risk of mortality included metastatic cancer (odds ratio [OR] 2.34), protein calorie malnutrition (OR 2.18), nonmetastatic cancers of the lung and upper digestive tract, (OR 1.80) cardiorespiratory failure and shock (OR 1.60), and congestive heart failure (OR 1.34) (Table 2).

Figure 1

Hospital Risk‐Standardized Mortality Rates

The mean unadjusted hospital 30‐day mortality rate was 8.6% and ranged from 0% to 100% (Figure 2a). Risk‐standardized mortality rates varied across hospitals (Figure 2b). The mean risk‐standardized mortality rate was 8.6% and ranged from 5.9% to 13.5%. The odds of mortality at a hospital 1 standard deviation above average was 1.20 times that of a hospital 1 standard deviation below average.

Figure 2

DISCUSSION

We present a hospital‐level risk‐standardized mortality measure for patients admitted with COPD based on administrative claims data that are intended for public reporting and that have achieved endorsement by the National Quality Forum, a voluntary consensus standards‐setting organization. Across more than 4500 US hospitals, the mean 30‐day risk‐standardized mortality rate in 2008 was 8.6%, and we observed considerable variation across institutions, despite adjustment for case mix, suggesting that improvement by lower‐performing institutions may be an achievable goal.

Although improving the delivery of evidence‐based care processes and outcomes of patients with acute myocardial infarction, heart failure, and pneumonia has been the focus of national quality improvement efforts for more than a decade, COPD has largely been overlooked.[23] Within this context, this analysis represents the first attempt to systematically measure, at the hospital level, 30‐day all‐cause mortality for patients admitted to US hospitals for exacerbation of COPD. The model we have developed and validated is intended to be used to compare the performance of hospitals while controlling for differences in the pretreatment risk of mortality of patients and accounting for the clustering of patients within hospitals, and will facilitate surveillance of hospital‐level risk‐adjusted outcomes over time.

In contrast to process‐based measures of quality, such as the percentage of patients with pneumonia who receive appropriate antibiotic therapy, performance measures based on patient outcomes provide a more comprehensive view of care and are more consistent with patients' goals.[24] Additionally, it is well established that hospital performance on individual and composite process measures explains only a small amount of the observed variation in patient outcomes between institutions.[25] In this regard, outcome measures incorporate important, but difficult to measure aspects of care, such as diagnostic accuracy and timing, communication and teamwork, the recognition and response to complications, care coordination at the time of transfers between levels of care, and care settings. Nevertheless, when used for making inferences about the quality of hospital care, individual measures such as the risk‐standardized hospital mortality rate should be interpreted in the context of other performance measures, including readmission, patient experience, and costs of care.

A number of prior investigators have described the outcomes of care for patients hospitalized with exacerbations of COPD, including identifying risk factors for mortality. Patil et al. carried out an analysis of the 1996 Nationwide Inpatient Sample and described an overall in‐hospital mortality rate of 2.5% among patients with COPD, and reported that a multivariable model containing sociodemographic characteristics about the patient and comorbidities had an area under the ROC curve of 0.70.[3] In contrast, this hospital‐level measure includes patients with a principal diagnosis of respiratory failure and focuses on 30‐day rather than inpatient mortality, accounting for the nearly 3‐fold higher mortality rate we observed. In a more recent study that used clinical from a large multistate database, Tabak et al. developed a prediction model for inpatient mortality for patients with COPD that contained only 4 factors: age, blood urea nitrogen, mental status, and pulse, and achieved an area under the ROC curve of 0.72.[4] The simplicity of such a model and its reliance on clinical measurements makes it particularly well suited for bedside application by clinicians, but less valuable for large‐scale public reporting programs that rely on administrative data. In the only other study identified that focused on the assessment of hospital mortality rates, Agabiti et al. analyzed the outcomes of 12,756 patients hospitalized for exacerbations of COPD, using similar ICD‐9‐CM diagnostic criteria as in this study, at 21 hospitals in Rome, Italy.[26] They reported an average crude 30‐day mortality rate of 3.8% among a group of 5 benchmark hospitals and an average mortality of 7.5% (range, 5.2%17.2%) among the remaining institutions.

To put the variation we observed in mortality rates into a broader context, the relative difference in the risk‐standardized hospital mortality rates across the 10th to 90th percentiles of hospital performance was 25% for acute myocardial infarction and 39% for heart failure, whereas rates varied 30% for COPD, from 7.6% to 9.9%.[27] Model discrimination in COPD (C statistic, 0.72) was also similar to that reported for models used for public reporting of hospital mortality in acute myocardial infarction (C statistic, 0.71) and pneumonia (C statistic, 0.72).

This study has a number of important strengths. First, the model was developed from a large sample of recent Medicare claims, achieved good discrimination, and was validated in samples not limited to Medicare beneficiaries. Second, by including patients with a principal diagnosis of COPD, as well as those with a principal diagnosis of acute respiratory failure when accompanied by a secondary diagnosis of COPD with acute exacerbation, this model can be used to assess hospital performance across the full spectrum of disease severity. This broad set of ICD‐9‐CM codes used to define the cohort also ensures that efforts to measure hospital performance will be less influenced by differences in documentation and coding practices across hospitals relating to the diagnosis or sequencing of acute respiratory failure diagnoses. Moreover, the inclusion of patients with respiratory failure is important because these patients have the greatest risk of mortality, and are those in whom efforts to improve the quality and safety of care may have the greatest impact. Third, rather than relying solely on information documented during the index admission, we used ambulatory and inpatient claims from the full year prior to the index admission to identify comorbidities and to distinguish them from potential complications of care. Finally, we did not include factors such as hospital characteristics (eg, number of beds, teaching status) in the model. Although they might have improved overall predictive ability, the goal of the hospital mortality measure is to enable comparisons of mortality rates among hospitals while controlling for differences in patient characteristics. To the extent that factors such as size or teaching status might be independently associated with hospital outcomes, it would be inappropriate to adjust away their effects, because mortality risk should not be influenced by hospital characteristics other than through their effects on quality.

These results should be viewed in light of several limitations. First, we used ICD‐9‐CM codes derived from claims files to define the patient populations included in the measure rather than collecting clinical or physiologic information prospectively or through manual review of medical records, such as the forced expiratory volume in 1 second or whether the patient required long‐term oxygen therapy. Nevertheless, we included a broad set of potential diagnosis codes to capture the full spectrum of COPD exacerbations and to minimize differences in coding across hospitals. Second, because the risk‐adjustment included diagnoses coded in the year prior to the index admission, it is potentially subject to bias due to regional differences in medical care utilization that are not driven by underlying differences in patient illness.[28] Third, using administrative claims data, we observed some paradoxical associations in the model that are difficult to explain on clinical grounds, such as a protective effect of substance and alcohol abuse or prior episodes of respiratory failure. Fourth, although we excluded patients from the analysis who were enrolled in hospice prior to, or on the day of, the index admission, we did not exclude those who choose to withdraw support, transition to comfort measures only, or enrolled in hospice care during a hospitalization. We do not seek to penalize hospitals for being sensitive to the preferences of patients at the end of life. At the same time, it is equally important that the measure is capable of detecting the outcomes of suboptimal care that may in some instances lead a patient or their family to withdraw support or choose hospice. Finally, we did not have the opportunity to validate the model against a clinical registry of patients with COPD, because such data do not currently exist. Nevertheless, the use of claims as a surrogate for chart data for risk adjustment has been validated for several conditions, including acute myocardial infarction, heart failure, and pneumonia.[29, 30]

CONCLUSIONS

Risk‐standardized 30‐day mortality rates for Medicare beneficiaries with COPD vary across hospitals in the US. Calculating and reporting hospital outcomes using validated performance measures may catalyze quality improvement activities and lead to better outcomes. Additional research would be helpful to confirm that hospitals with lower mortality rates achieve care that meets the goals of patients and their families better than at hospitals with higher mortality rates.

Disclosures

Peter K. Lindenauer, MD, MSc, is the guarantor of this article, taking responsibility for the integrity of the work as a whole, from inception to published article, and takes responsibility for the content of the manuscript, including the data and data analysis. All authors have made substantial contributions to the conception and design, or acquisition of data, or analysis and interpretation of data; have drafted the submitted article or revised it critically for important intellectual content; and have provided final approval of the version to be published. Preparation of this manuscript was completed under Contract Number: HHSM‐5002008‐0025I/HHSM‐500‐T0001, Modification No. 000007, Option Year 2 Measure Instrument Development and Support (MIDS). Sponsors did not contribute to the development of the research or manuscript. Dr. Au reports being an unpaid research consultant for Bosch Inc. He receives research funding from the NIH, Department of Veterans Affairs, AHRQ, and Gilead Sciences. The views of the this manuscript represent the authors and do not necessarily represent those of the Department of Veterans Affairs. Drs. Drye and Bernheim report receiving contract funding from CMS to develop and maintain quality measures.

Chronic obstructive pulmonary disease (COPD) affects as many as 24 million individuals in the United States, is responsible for more than 700,000 annual hospital admissions, and is currently the nation's third leading cause of death, accounting for nearly $49.9 billion in medical spending in 2010.[1, 2] Reported in‐hospital mortality rates for patients hospitalized for exacerbations of COPD range from 2% to 5%.[3, 4, 5, 6, 7] Information about 30‐day mortality rates following hospitalization for COPD is more limited; however, international studies suggest that rates range from 3% to 9%,[8, 9] and 90‐day mortality rates exceed 15%.[10]

Despite this significant clinical and economic impact, there have been no large‐scale, sustained efforts to measure the quality or outcomes of hospital care for patients with COPD in the United States. What little is known about the treatment of patients with COPD suggests widespread opportunities to increase adherence to guideline‐recommended therapies, to reduce the use of ineffective treatments and tests, and to address variation in care across institutions.[5, 11, 12]

Public reporting of hospital performance is a key strategy for improving the quality and safety of hospital care, both in the United States and internationally.[13] Since 2007, the Centers for Medicare and Medicaid Services (CMS) has reported hospital mortality rates on the Hospital Compare Web site, and COPD is 1 of the conditions highlighted in the Affordable Care Act for future consideration.[14] Such initiatives rely on validated, risk‐adjusted performance measures for comparisons across institutions and to enable outcomes to be tracked over time. We present the development, validation, and results of a model intended for public reporting of risk‐standardized mortality rates for patients hospitalized with exacerbations of COPD that has been endorsed by the National Quality Forum.[15]

METHODS