Using electronic health record collected clinical variables to predict medical intensive care unit mortality

BACKGROUND: Clinical decision support systems are used to help predict patient stability and mortality in the Intensive Care Unit (ICU). Accurate patient information can assist clinicians with patient management and in allocating finite resources. However, systems currently in common use have limited predictive value in the clinical setting. The increasing availability of Electronic Health Records (EHR) provides an opportunity to use medical information for more accurate patient stability and mortality prediction in the ICU. OBJECTIVE: Develop and evaluate an algorithm which more accurately predicts patient mortality in the ICU, using the correlations between widely available clinical variables from the EHR. Each clinical variable produces a subscore, and combinations of two or three discretized clinical variables also produce subscores. A combination of weighted subscores produces the overall score. We validated the performance of this algorithm in a retrospective study on the MIMIC III medical ICU dataset. RESULTS: maintains a specificity of 81% with a diagnostic odds ratio of 16.26. CONCLUSIONS: provides an improvement in the specificity and sensitivity of patient mortality prediction over existing prediction methods.