Abstract
Methods The study was completed as a prospective observational cohort study at a regional teaching hospital. The discriminatory power (ability to discriminate between survivors and non-survivors) of the models was estimated using the area under the receiver-operating characteristics curve (AUROC). Values above 0.8 represent good discriminatory power. Calibration (accuracy of the prediction) was assessed using Hosmer-Lemeshow c goodness of fit test with a p-value above 0.05 indicating acceptable calibration. The blood tests used for calculation were drawn upon admission, and always within 2 hours of arrival at the department. As this was an observational study, only analyses ordered by the treating physician were included.
Highlights
We sought to externally validate the ability of two existing risk stratification tools based primarily on biochemical analyses to predict in-hospital mortality
The discriminatory power of the models was estimated using the area under the receiver-operating characteristics curve (AUROC)
When using the risk stratification system introduced by Prytherch et al we were able to include 2,671 patients (87.6 %)
Summary
We sought to externally validate the ability of two existing risk stratification tools based primarily on biochemical analyses to predict in-hospital mortality. Can biochemical analyses risk stratify acutely admitted medical patients: an external validation of two existing systems From 4th Danish Emergency Medicine Conference Roskilde, Denmark. Background We sought to externally validate the ability of two existing risk stratification tools based primarily on biochemical analyses to predict in-hospital mortality. Methods The study was completed as a prospective observational cohort study at a regional teaching hospital.
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