Theses (Master In Health Informatics & Analytics)

Although various predictive models exist for dengue, only four models exist for predicting mortality in severe dengue cases. However, none of the existing models consider the timing of events during model development. To address this gap, a multicentre retrospective cohort study was conducted to create a predictive model for deaths in severe dengue. The study aimed to provide a more comprehensive and effective approach to predicting mortality rates in such cases. The study focused on patients diagnosed with severe dengue based on the classification provided by WHO 2009 and gathered a total of 18 predictor variables comprising demographic, clinical, and laboratory data. We used LASSO for variable selection and model-building and conducted a ten-fold cross-validation for internal validation. The analysis was conducted on a cohort of 786 severe dengue cases, out of which 35 (4%) resulted in fatalities. Furthermore, 575 out of 786 severe dengue patients (73.2%) were diagnosed with severe dengue during the febrile phase. LASSO model identified Body Mass Index (BMI), vomiting, third space fluid accumulation, respiratory rate, white cell count, platelet count, AST, serum albumin, bicarbonate, and lactate at diagnosis of severe dengue as the predictor variables. The LASSO model's accuracy was 0.96 (95%CI: 0.91 to 0.98), with low sensitivity of 0.29 (95%CI: 0.15 – 0.46), high specificity of 1.00 (95%CI: 0.99 – 1.00). We have developed a high-performance dengue mortality prediction model based on timing of the event, clinical and laboratory data. We will deploy an open-access web tool for local validation and to stratify severe dengue patients. However, further investigations are required as 2/3 of the study population were diagnosed with severe dengue fever during the febrile phase, as this contradicts the current guideline.