Abstract
The primary aim of this prospective study is to develop and validate a new prognostic model for predicting the risk of mortality in Emergency Department (ED) patients. The study involved 1765 patients in the development cohort and 1728 in the validation cohort. The main outcome was mortality up to 30 days after admission. Potential risk factors included clinical characteristics, vital signs, and routine haematological and biochemistry tests. The Bayesian Model Averaging method within the Cox’s regression model was used to identify independent risk factors for mortality. In the development cohort, the incidence of 30-day mortality was 9.8%, and the following factors were associated with a greater risk of mortality: male gender, increased respiratory rate and serum urea, decreased peripheral oxygen saturation and serum albumin, lower Glasgow Coma Score, and admission to intensive care unit. The area under the receiver operating characteristic curve for the model with the listed factors was 0.871 (95% CI, 0.844–0.898) in the development cohort and 0.783 (95% CI, 0.743–0.823) in the validation cohort. Calibration analysis found a close agreement between predicted and observed mortality risk. We conclude that the risk of mortality among ED patients could be accurately predicted by using common clinical signs and biochemical tests.
Highlights
Medical patients admitted to Emergency Department (ED) are highly heterogeneous in terms of disease spectrum and severity
The condition of care and disease severity among patients in developing countries are different from those in industrialized countries, and the difference calls for new prognostic models that can be applicable to ED patients in developing countries
After excluding patients who did not meet the inclusion criteria and patients who withdrew from the study, 1765 patients remained for the model development
Summary
In ED, the death of a patient is commonly preceded by a cumulative deterioration of vital signs and clinical abnormalities[2,3]. Several prognostic models, including the Rapid Emergency Medicine Score[4], Rapid Acute Physiology Score[5] and Worthing Physiological Scoring system[6], have been developed to make use of the clinical signs and abnormalities for predicting the risk of death in ED patients. The present study sought to develop and validate a clinical predictive model for predicting 30-day mortality risk in ED patients by using routinely collected clinical, physiological and vital signs. We demonstrated that there exists a series of models that have comparable predictive accuracy, and that these models can be used to identify high-risk patients in ED. The models reported here can empower medical care providers to individualise ED care and optimise ED utilisation
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