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Machine learning-based models for prediction of in-hospital mortality in patients with dengue shock syndrome
Luan Thanh Vo1, Thien Vu2,3, Thach Ngoc Pham1
1Department of Infectious Diseases, Children's Hospital No. 2, Ho Chi Minh City 700000, Viet Nam.
Background:
Severe dengue children with critical complications have been attributed to high mortality rates, varying from approximately 1% to over 20%. To date, there is a lack of data on machine-learning-based algorithms for predicting the risk of in-hospital mortality in children with dengue shock syndrome (DSS).
Aim:
To develop machine-learning models to estimate the risk of death in hospitalized children with DSS.
Methods:
This single-center retrospective study was conducted at tertiary Children's Hospital No. 2 in Viet Nam, between 2013 and 2022. The primary outcome was the in-hospital mortality rate in children with DSS admitted to the pediatric intensive care unit (PICU). Nine significant features were predetermined for further analysis using machine learning models. An oversampling method was used to enhance the model performance. Supervised models, including logistic regression, Naïve Bayes, Random Forest (RF), K-nearest neighbors, Decision Tree and Extreme Gradient Boosting (XGBoost), were employed to develop predictive models. The Shapley Additive Explanation was used to determine the degree of contribution of the features.
Results:
In total, 1278 PICU-admitted children with complete data were included in the analysis. The median patient age was 8.1 years (interquartile range: 5.4-10.7). Thirty-nine patients (3%) died. The RF and XGboost models demonstrated the highest performance. The Shapley Addictive Explanations model revealed that the most important predictive features included younger age, female patients, presence of underlying diseases, severe transaminitis, severe bleeding, low platelet counts requiring platelet transfusion, elevated levels of international normalized ratio, blood lactate and serum creatinine, large volume of resuscitation fluid and a high vasoactive inotropic score (> 30).
Conclusion:
We developed robust machine learning-based models to estimate the risk of death in hospitalized children with DSS. The study findings are applicable to the design of management schemes to enhance survival outcomes of patients with DSS.
