Which hospitals have significantly better or worse than expected mortality rates for acute myocardial infarction patients? Improved risk adjustment with present-at-admission diagnoses.

BACKGROUND: Public reports that compare hospital mortality rates for patients with acute myocardial infarction are commonly used strategies for improving the quality of care delivered to these patients. Fair comparisons of hospital mortality rates require thorough adjustments for differences among patients in baseline mortality risk. This study examines the effect on hospital mortality rate comparisons of improved risk adjustment methods using diagnoses reported as present-at-admission. METHODS AND RESULTS: Logistic regression models and related methods originally used by California to compare hospital mortality rates for patients with acute myocardial infarction are replicated. These results are contrasted with results obtained for the same hospitals by patient-level mortality risk adjustment models using present-at-admission diagnoses, using 3 statistical methods of identifying hospitals with higher or lower than expected mortality: indirect standardization, adjusted odds ratios, and hierarchical models. Models using present-at-admission diagnoses identified substantially fewer hospitals as outliers than did California model A for each of the 3 statistical methods considered. CONCLUSIONS: Large improvements in statistical performance can be achieved with the use of present-at-admission diagnoses to characterize baseline mortality risk. These improvements are important because models with better statistical performance identify different hospitals as having better or worse than expected mortality.

Present-at-admission diagnoses improved mortality risk adjustment among acute myocardial infarction patients.

OBJECTIVE: Hospital mortality outcomes for acute myocardial infarction (AMI) patients are a focus of quality improvement programs conducted by government agencies. AMI mortality risk-adjustment models using administrative data typically adjust for baseline differences in mortality risk with a limited set of common and definite comorbidities. In this study, we present an AMI mortality risk-adjustment model that adjusts for comorbid disease and for AMI severity using information from secondary diagnoses reported as present at admission for California hospital patients. STUDY DESIGN AND SETTING: AMI patients were selected from California hospital administrative data for 1996 through 1999 according to criteria used by the California Hospital Outcomes Project Report on Heart Attack Outcomes, a state-mandated public report that compares hospital mortality outcomes. We compared results for the new model to two mortality risk-adjustment models used to assess hospital AMI mortality outcomes by the state of California, and to two other models used in prior research. RESULTS: The model using present-at-admission diagnoses obtained substantially better discrimination between predicted survival and inpatient death than the other models we considered. CONCLUSION: AMI mortality risk-adjustment methods can be meaningfully improved using present-at-admission diagnoses to identify comorbid disease and conditions related closely to AMI.