Identifying admitted patients at risk of dying: A prospective observational validation of four biochemical scoring systems

Abstract

Objectives: Risk assessment is an important part of emergency patient care. Risk assessment tools based on biochemical data have the advantage that calculation can be automated and results can be easily provided. However, to be used clinically, existing tools have to be validated by independent researchers. This study involved an independent external validation of four risk stratification systems predicting death that rely primarily on biochemical variables. Design: Prospective observational study. Setting: The medical admission unit at a regional teaching hospital in Denmark. Participants: Of 5894 adult (age 15 or above) acutely admitted medical patients, 205 (3.5%) died during admission and 46 died (0.8%) within one calendar day. Interventions: None. Main outcome measures: The main outcome measure was the ability to identify patients at an increased risk of dying (discriminatory power) as area under the receiver-operating characteristic curve (AUROC) and the accuracy of the predicted probability (calibration) using the Hosmer-Lemeshow goodness-offit test. The endpoint was all-cause mortality, defined in accordance with the original manuscripts. Results: Using the original coefficients, all four systems were excellent at identifying patients at increased risk (discriminatory power, AUROC ?0.80). The accuracy was poor (we could assess calibration for two systems, which failed). After recalculation of the coefficients, two systems had improved discriminatory power and two remained unchanged. Calibration failed for one system in the validation cohort. Conclusions: Four biochemical risk stratification systems can risk-stratify the acutely admitted medical patients for mortality with excellent discriminatory power. We could improve the models for use in our setting by recalculating the risk coefficient for the chosen variables.