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.


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.
Stukenborg
George J
GJ
Department of Public Health Sciences, University of Virginia School of Medicine, Multistory Building, Room 3181, PO Box 800438, Charlottesville, VA 22908-0821, USA. gstukenborg@virginia.edu
Wagner
Douglas P
DP
Harrell
Frank E
FE
Oliver
M Norman
MN
Heim
Steven W
SW
Price
Amy L
AL
Han
Caroline Kim
CK
Wolf
Andrew M D
AM
Connors
Alfred F
AF
D000289:Admitting Department, Hospital / Q000706:statistics & numerical data; D002140:California / Q000453:epidemiology; D017052:Hospital Mortality; D006801:Humans; D016015:Logistic Models; D015233:Models, Statistical; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000401:mortality*; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data; D020379:Risk Adjustment / Q000379:methods* / Q000706:statistics & numerical data*; D012307:Risk Factors