Predicting in‐hospital mortality in patients with cirrhosis: Results differ across risk adjustment methods

Risk‐adjusted health outcomes are often used to measure the quality of hospital care, yet the optimal approach in patients with liver disease is unclear. We sought to determine whether assessments of illness severity, defined as risk for in‐hospital mortality, vary across methods in patients with cirrhosis. We identified 258,731 patients with cirrhosis hospitalized in the Nationwide Inpatient Sample between 2002 and 2005. The performance of four common risk adjustment methods (the Charlson/Deyo and Elixhauser comorbidity algorithms, Disease Staging, and All Patient Refined Diagnosis Related Groups [APR‐DRGs]) for predicting in‐hospital mortality was determined using the c‐statistic. Subgroup analyses were conducted according to a primary versus secondary diagnosis of cirrhosis and in homogeneous patient subgroups (hepatic encephalopathy, hepatocellular carcinoma, congestive heart failure, pneumonia, hip fracture, and cholelithiasis). Patients were also ranked according to the probability of death as predicted by each method, and rankings were compared across methods. Predicted mortality according to the risk adjustment methods agreed for only 55%–67% of patients. Similarly, performance of the methods for predicting in‐hospital mortality varied significantly. Overall, the c‐statistics (95% confidence interval) for the Charlson/Deyo and Elixhauser algorithms, Disease Staging, and APR‐DRGs were 0.683 (0.680–0.687), 0.749 (0.746–0.752), 0.832 (0.829–0.834), and 0.875 (0.873–0.878), respectively. Results were robust across diagnostic subgroups, but performance was lower in patients with a primary versus secondary diagnosis of cirrhosis. Conclusion: Mortality analyses in patients with cirrhosis require sensitivity to the method of risk adjustment. Because different methods often produce divergent severity rankings, analyses of provider‐specific outcomes may be biased depending on the method used. (HEPATOLOGY 2008.)

[1]  Alfred F. Connors,et al.  Comparison of the Performance of Two Comorbidity Measures, With and Without Information From Prior Hospitalizations , 2001, Medical care.

[2]  L. Iezzoni,et al.  Predicting in-hospital deaths from coronary artery bypass graft surgery. Do different severity measures give different predictions? , 1998, Medical care.

[3]  L. Iezzoni,et al.  Do severity measures explain differences in length of hospital stay? The case of hip fracture. , 1996, Health services research.

[4]  D. McClish,et al.  Results of Report Cards for Patients with Congestive Heart Failure Depend on the Method Used To Adjust for Severity , 2000, Annals of Internal Medicine.

[5]  M Pine,et al.  Predictions of Hospital Mortality Rates: A Comparison of Data Sources , 1997, Annals of Internal Medicine.

[6]  A. Donabedian,et al.  The quality of care. How can it be assessed? , 1988, JAMA.

[7]  J. Allison Risk adjustment for measuring health care outcomes , 1996 .

[8]  L I Iezzoni,et al.  Predicting Who Dies Depends on How Severity Is Measured: Implications for Evaluating Patient Outcomes , 1995, Annals of Internal Medicine.

[9]  D. K. Williams,et al.  Assessing hospital-associated deaths from discharge data. The role of length of stay and comorbidities. , 1988, JAMA.

[10]  C. Steiner,et al.  Comorbidity measures for use with administrative data. , 1998, Medical care.

[11]  Hude Quan,et al.  Assessing accuracy of diagnosis-type indicators for flagging complications in administrative data. , 2004, Journal of clinical epidemiology.

[12]  P. Loy International Classification of Diseases--9th revision. , 1978, Medical record and health care information journal.

[13]  L. Iezzoni Assessing Quality Using Administrative Data , 1997, Annals of Internal Medicine.

[14]  H. El‐Serag,et al.  GI Epidemiology: databases for epidemiological studies , 2007, Alimentary pharmacology & therapeutics.

[15]  R H Brook,et al.  The public release of performance data: what do we expect to gain? A review of the evidence. , 2000, JAMA.

[16]  R. Deyo,et al.  Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. , 1992, Journal of clinical epidemiology.

[17]  D W Simborg,et al.  DRG creep: a new hospital-acquired disease. , 1981, The New England journal of medicine.

[18]  L I Iezzoni,et al.  Judging hospitals by severity-adjusted mortality rates: the influence of the severity-adjustment method. , 1996, American journal of public health.

[19]  E. Bass,et al.  Hospital experience and outcomes for esophageal variceal bleeding. , 2003, International journal for quality in health care : journal of the International Society for Quality in Health Care.

[20]  P. Thuluvath,et al.  Racial disparities in the management of hospitalized patients with cirrhosis and complications of portal hypertension: A national study , 2007, Hepatology.

[21]  H. El‐Serag,et al.  Improved survival after variceal hemorrhage over an 11-year period in the Department of Veterans Affairs , 2000, American Journal of Gastroenterology.

[22]  S. Desharnais Current Uses of Large Data Sets to Assess the Quality of Providers: Construction of Risk-Adjusted Indexes of Hospital Performance , 1990, International Journal of Technology Assessment in Health Care.

[23]  Hude Quan,et al.  Comparison of the Elixhauser and Charlson/Deyo Methods of Comorbidity Measurement in Administrative Data , 2004, Medical care.

[24]  L. Iezzoni,et al.  How severity measures rate hospitalized patients , 1996, Journal of General Internal Medicine.

[25]  N. Ascher United Network for Organ Sharing center-specific data: our report card. , 1996, Liver transplantation and surgery : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society.

[26]  D. Hoaglin,et al.  Enhancement of claims data to improve risk adjustment of hospital mortality. , 2007, JAMA.

[27]  Hollister Avenue 2000 HCUP NATIONWIDE INPATIENT SAMPLE (NIS) COMPARISON REPORT , 2003 .

[28]  L I Iezzoni,et al.  Predicting In-hospital Mortality for Stroke Patients , 1996, Medical decision making : an international journal of the Society for Medical Decision Making.

[29]  Vijay A. Singh,et al.  The Effects of Preexisting Medical Comorbidities on Mortality and Length of Hospital Stay in Acute Burn Injury: Evidence From a National Sample of 31,338 Adult Patients , 2007, Annals of surgery.

[30]  R. Brant,et al.  Risk adjustment using administrative data: impact of a diagnosis-type indicator. , 2001, Journal of general internal medicine.

[31]  P. Romano,et al.  Do Well-Publicized Risk-Adjusted Outcomes Reports Affect Hospital Volume? , 2004, Medical care.

[32]  D. Bates,et al.  Paying more fairly for Medicare capitated care. , 1998, The New England journal of medicine.

[33]  S. Schneeweiss,et al.  Mortality following liver resection in US medicare patients: Does the presence of a liver transplant program affect outcome? , 2005, Journal of surgical oncology.

[34]  Injury rates by industry 1970, BLS Report 406, Department of Labor, Bureau of Labor Statistics. , 1972, IMS, Industrial medicine and surgery.

[35]  E. DeLong,et al.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. , 1988, Biometrics.

[36]  G. Kaplan,et al.  Relationship between hospital volume and outcomes of esophageal variceal bleeding in the United States. , 2008, Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association.

[37]  P. Katz,et al.  The relationship between hospital volume and outcomes of hepatic resection for hepatocellular carcinoma. , 1999, Archives of surgery.

[38]  The effect of the volume of procedures at transplantation centers on mortality after liver transplantation. , 1999 .