Multivariable prediction model for predicting deaths in severe dengue cases

Abstract

Background. Many predictive models have been developed to predict an outbreak, identify and stratify dengue but none has predicted death in severe dengue cases. To build a predictive model for deaths in severe dengue, a multicentre retrospective cohort study was conducted. Methods. Patients with severe dengue based on the World Health Organisation (WHO) 2009 classification were studied. Demographic, clinical and laboratory data were collected at diagnosis of severe dengue. Penalised regression was used for variable selection and model-building. Ten-fold cross-validation with 1000 repeats were performed for internal validation. Results. A cohort of 786 severe dengue cases, including 35 deaths, was analysed. Our model that predicts death in severe dengue cases comprises eight independent predictors: persistent diarrhoea, body mass index, respiratory rate, platelet count, aspartate transaminase, serum bicarbonate, serum lactate and serum albumin. The area under the receiver operating characteristic curve is 89·6% with a sensitivity of 99·6%, specificity of 23·6%, positive predictive value of 96·6%, negative predictive value of 71·1%, positive likelihood ratio 1·45 and negative likelihood ratio 0·01. We also found that the proportion of patients that were in the febrile phase at diagnosis of severe dengue for the overall cohort, decompensated and compensated shock were 74·3%, 73% and 75·4%, respectively. Conclusions. We developed a high-performance dengue death prediction model comprising clinical and laboratory data, and deployed an open-access web-based tool (www.saifulsafuan.com/REPROSED2017E2) for any centre to utilise for local validation. We additionally found that a large majority of patients developed severe dengue during the febrile phase. Keywords: Dengue; severe; model; predict; deaths; phase.