Abstract

BackgroundEbola virus disease (EVD) plagues low-resource and difficult-to-access settings. Machine learning prognostic models and mHealth tools could improve the understanding and use of evidence-based care guidelines in such settings. However, data incompleteness and lack of interoperability limit model generalizability. This study harmonizes diverse datasets from the 2014–16 EVD epidemic and generates several prognostic models incorporated into the novel Ebola Care Guidelines app that provides informed access to recommended evidence-based guidelines. MethodsMultivariate logistic regression was applied to investigate survival outcomes in 470 patients admitted to five Ebola treatment units in Liberia and Sierra Leone at various timepoints during 2014–16. We generated a parsimonious model (viral load, age, temperature, bleeding, jaundice, dyspnea, dysphagia, and time-to-presentation) and several fallback models for when these variables are unavailable. All were externally validated against two independent datasets and compared to further models including expert observational wellness assessments. Models were incorporated into an app highlighting the signs/symptoms with the largest contribution to prognosis. FindingsThe parsimonious model approached the predictive power of observational assessments by experienced clinicians (Area-Under-the-Curve, AUC = 0.70–0.79, accuracy = 0.64–0.74) and maintained its performance across subcohorts with different healthcare seeking behaviors. Age and viral load contributed >5-fold the weighting of other features and including them in a minimal model had a similar AUC, albeit at the cost of specificity. InterpretationClinically guided prognostic models can recapitulate clinical expertise and be useful when such expertise is unavailable. Incorporating these models into mHealth tools may facilitate their interpretation and provide informed access to comprehensive clinical guidelines. FundingHoward Hughes Medical Institute, US National Institutes of Health, Bill & Melinda Gates Foundation, International Medical Corps, UK Department for International Development, and GOAL Global.

Highlights

  • We previously introduced the use of prognostic models that can be deployed as mobile apps for the purpose of risk stratification in Ebola virus disease (EVD) [7]

  • Despite its notoriety as a deadly disease, the pathology of EVD includes a range of Prognostic models were based on data collected from 470 patients at five ETUs operated by IMC in Liberia (n = 178, 38%) and Sierra Leone (n = 292, 62%) between September 15, 2014 and September 15, 2015

  • Triage symptoms reported by over 50% of fatal Ebola patients were anorexia/loss of appetite, fever, asthenia/weakness, musculoskeletal pain, headache and diarrhea (Table 2A)

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Summary

Introduction

Machine learning prognostic models could improve understanding and personalized use of evidence-based guidelines by helping to prioritize recommended interventions according to the prognostic importance of each clinical feature present in the individual patient. Machine learning prognostic models and mHealth tools could improve the understanding and use of evidence-based care guidelines in such settings. This study harmonizes diverse datasets from the 2014–16 EVD epidemic and generates several prognostic models incorporated into the novel Ebola Care Guidelines app that provides informed access to recommended evidence-based guidelines. Interpretation: Clinically guided prognostic models can recapitulate clinical expertise and be useful when such expertise is unavailable Incorporating these models into mHealth tools may facilitate their interpretation and provide informed access to comprehensive clinical guidelines.

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