Laboratory values improve predictions of hospital mortality

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Abstract
Objective.To compare the precision of risk adjustment in the measurement of mortality rates using: (i) data in hospitals' electronic discharge abstracts, including data elements that distinguish between comorbidities and complications; (ii) these data plus laboratory values; and (iii) these data plus laboratory values and other clinical data abstracted from medical records. Design.Retrospective cohort study. Setting.Twenty-two acute care hospitals in St Louis, Missouri, USA. Study participants.Patients hospitalized in 1995 with acute myocardial infarction, congestive heart failure, or pneumonia (n=5966). Main outcome measures.Each patient's probability of death calculated using: administrative data that designated all secondary diagnoses present on admission (administrative models); administrative data and laboratory values (laboratory models); and administrative data, laboratory values, and abstracted clinical information (clinical models). All data were abstracted from medical records. Results.Administrative models (average area under receiver operating characteristic curve=0.834) did not predict death as well as did clinical models (average area under receiver operating characteristics curve=0.875). Adding laboratory values to administrative data improved predictions of death (average area under receiver operating characteristic curve=0.860). Adding laboratory data to administrative data improved its average correlation of hospital-level predicted values with those of the clinical model from r=0.94 for the administrative model to r=0.98 for the laboratory model. Conclusions.In the conditions studied, predictions of inpatient mortality improved noticeably when laboratory values (sometimes available electronically) were combined with administrative data that included only those secondary diagnoses present on admission (i.e. comorbidities). Additional clinical data contribute little more to predictive power. Keywords:clinical outcomes, hospital mortality, laboratory data, predictive power, quality measurement, risk adjustment

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