The Diagnosis of Dengue in Patients Presenting With Acute Febrile Illness Using Supervised Machine Learning and Impact of Seasonality

Abstract

BackgroundSymptomatic dengue infection can result in a life-threatening shock syndrome and timely diagnosis is essential. Point-of-care tests for non-structural protein 1 and IgM are used widely but performance can be limited. We developed a supervised machine learning model to predict whether patients with acute febrile illnesses had a diagnosis of dengue or other febrile illnesses (OFI). The impact of seasonality on model performance over time was examined.MethodsWe analysed data from a prospective observational clinical study in Vietnam. Enrolled patients presented with an acute febrile illness of <72 h duration. A gradient boosting model (XGBoost) was used to predict final diagnosis using age, sex, haematocrit, platelet, white cell, and lymphocyte count collected on enrolment. Data was randomly split 80/20% into a training and hold-out set, respectively, with the latter not used in model development. Cross-validation and hold out set testing was used, with performance over time evaluated through a rolling window approach.ResultsWe included 8,100 patients recruited between 16th October 2010 and 10th December 2014. In total 2,240 (27.7%) patients were diagnosed with dengue infection. The optimised model from training data had an overall median area under the receiver operator curve (AUROC) of 0.86 (interquartile range 0.84–0.86), specificity of 0.92, sensitivity of 0.56, positive predictive value of 0.73, negative predictive value (NPV) of 0.84, and Brier score of 0.13 in predicting the final diagnosis, with similar performances in hold-out set testing (AUROC of 0.86). Model performances varied significantly over time as a function of seasonality and other factors. Incorporation of a dynamic threshold which continuously learns from recent cases resulted in a more consistent performance throughout the year (NPV >90%).ConclusionSupervised machine learning models are able to discriminate between dengue and OFI diagnoses in patients presenting with an early undifferentiated febrile illness. These models could be of clinical utility in supporting healthcare decision-making and provide passive surveillance across dengue endemic regions. Effects of seasonality and changing disease prevalence must however be taken into account—this is of significant importance given unpredictable effects of human-induced climate change and the impact on health.

Integration of population-level data sources into an individual-level clinical prediction model for dengue virus test positivity

“…Our results differ from those found in many logistic regression models and align with more intricate models for DENV diagnosis. Models using deep neural networks ( 47 ), random forest ( 48 ), and gradient boosting (XGBoost) ( 49 ) noted that age was the best clinical discriminative predictor. These models did not include cough or nausea as variables for assessment.…”

Mapping patient pathways and understanding clinical decision-making in dengue management to inform the development of digital health tools

“…Much of clinical management in dengue is affected by seasonality. A dramatic increase in symptomatic dengue cases during the wet season results in a reorganisation of decision-making and allocation of hospital services [ 30 ]. Utilising knowledge of external factors including local epidemiology, disease outbreaks and bed capacity to inform clinical decision-making is particularly important.…”

Cryptic transmission and re-emergence of Cosmopolitan genotype of Dengue Virus Serotype 2 within Ho Chi Minh City and Southern Vietnam

“…35 This poses further challenges for an effective dengue surveillance system, as successfully predicting transmission hotspots is difficult and planning resource allocation to conduct surveillance in such locations in advance becomes challenging. 36 Moreover, limited resources for viral genomic sequencing, in addition to the diversion of these limited resources from DENV to SARS-CoV-2 surveillance during the COVID-19 pandemic, may have also exacerbated these difficulties. As a result, addressing these multiple challenges is crucial to improving DENV surveillance in HCMC.…”