Laboratory values improve predictions of hospital mortality

Objective. To compare the precision of risk adjustment in the measurement o f mortality rates using: (i) data in hospitals' electronic discharge abstra cts, including data elements that distinguish between comorbidities and com plications; (ii) these data plus laboratory values; and (iii) these data pl us laboratory values and other clinical data abstracted from medical record s. Design. Retrospective cohort study. Setting. Twenty-two acute care hospitals in St Louis, Missouri, USA. Study participants. Patients hospitalized in 1995 with acute myocardial inf arction, 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 ad mission (administrative models); administrative data and laboratory values (laboratory models); and administrative data, laboratory values, and abstra cted clinical information (clinical models). All data were abstracted from medical records. Results. Administrative models (average area under receiver operating chara cteristic curve = 0.834) did not predict death as well as did clinical mode ls (average area under receiver operating characteristic curve = 0.875). Ad ding laboratory values to administrative data improved predictions of death (average area under receiver operating characteristic curve = 0.860). Addi ng laboratory data to administrative data improved its average correlation of patient-level predicted values with those of the clinical model from r=0 .86 to r=0.95 and improved the average correlation of hospital-level predic ted values with those of the clinical model from r=0.94 for the administrat ive 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 electronica lly) were combined with administrative data that included only those second ary diagnoses present on admission (i.e. comorbidities). Additional clinica l data contribute little more to predictive power.