Key result
A simple predictive model using age, Charlson comorbidity score, and Laboratory-Based Acute Physiology Score accurately predicted 30-day mortality with a validation c-statistic of 0.8484.
Why the study?
Can a simple predictive model using age, Charlson comorbidity score, and Laboratory-Based Acute Physiology Score accurately predict 30-day mortality among medical inpatients?
Observational (n=147,991)
Yes
Can a simple predictive model using age, Charlson comorbidity score, and Laboratory-Based Acute Physiology Score accurately predict 30-day mortality among medical inpatients?
A simple predictive model using age, Charlson comorbidity score, and Laboratory-Based Acute Physiology Score accurately predicts 30-day mortality among medical inpatients.
Answers are grounded in this paper and cite their sources.
May aid risk stratification in medical inpatients; leaves open prospective validation before routine adoption.
BACKGROUND: Risk adjustment for mortality is increasingly important in an era when hospitals and health care systems are being compared with respect to health outcomes and quality. A powerful predictive model has been developed to risk-adjust for 30-day mortality among inpatients, but it is complex and not widely used. OBJECTIVE: To develop and validate a simpler model, with predictive power similar to more complex models. RESEARCH DESIGN: This was a retrospective split-validation study. In a derivation cohort, a predictive model for 30-day mortality was developed using logistic regression with the Charlson comorbidity score, Laboratory-Based Acute Physiology Score, and age as the predictor variables. In the validation cohort, the performance and calibration of the model to predict 30-day mortality was examined. SUBJECTS: All admissions to the medical service of 2 urban university-based teaching hospitals located in Bronx, New York, between July 1, 2002, and April 30, 2008. MEASURES: All-cause mortality was taken from the social security death registry. Predictor variables were constructed from demographic characteristics, laboratory and billing data extracted from a clinical data repository. RESULTS: The study sample included 147 991 admissions and overall 30-day mortality was 5.4%. The model had excellent discrimination, with a c-statistics of 0.8585 in the derivation cohort and 0.8484 in the validation cohort. The model accurately predicts 30-day mortality in all risk deciles. CONCLUSIONS: This simple and powerful predictive model can be used by hospitals and health care systems as a risk-adjustment tool for quality and research purposes.
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Tremblay et al. (2016) conducted an observational in Medical service admissions (n=147,991). Simple predictive model (Charlson comorbidity score, Laboratory-Based Acute Physiology Score, and age) was evaluated on 30-day all-cause mortality (c-statistic 0.8484). A simple predictive model using age, Charlson comorbidity score, and Laboratory-Based Acute Physiology Score accurately predicted 30-day mortality with a validation c-statistic of 0.8484.
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