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(Circulation. 2007;116:2960-2968.)
© 2007 American Heart Association, Inc.

Health Services and Outcomes Research

Which Hospitals Have Significantly Better or Worse Than Expected Mortality Rates for Acute Myocardial Infarction Patients?

Improved Risk Adjustment With Present-at-Admission Diagnoses

George J. Stukenborg, PhD; Douglas P. Wagner, PhD; Frank E. Harrell, Jr, PhD; M. Norman Oliver, MD; Steven W. Heim, MD; Amy L. Price, MD; Caroline Kim Han, MD; Andrew M. D. Wolf, MD; Alfred F. Connors, Jr, MD

From the University of Virginia School of Medicine, Charlottesville (G.J.S., D.P.W., M.N.O., S.W.H., C.K.H., A.M.D.W.); Vanderbilt University School of Medicine, Nashville, Tenn (F.E.H.); Eastern Virginia Medical School, Norfolk (A.L.P.); and Case Western Reserve University, Cleveland, Ohio (A.F.C.).

Correspondence to George J. Stukenborg, PhD, Department of Public Health Sciences, University of Virginia School of Medicine, Multistory Building (Hospital West Complex), Room 3181, PO Box 800438, Charlottesville, VA 22908-0821. E-mail gstukenborg{at}virginia.edu

Received April 30, 2007; accepted September 21, 2007.

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.

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