ICU Admission Prediction for Patients With Kawasaki Disease or MIS-C Using Machine Learning.
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Woo JiWon, Mosier Rebecca, Mukherjee Rishima, Harahsheh Ashraf S, Jain Supriya S, Raghuveer Geetha, Sundaram Balasubramanian, Lee Simon, Portman Michael A, Dahdah Nagib, Fabi Marianna, Nowlen Todd T, Dionne Audrey, El Ganzoury Mona, Harris Tyler H, Barnes Benjamin T, Dallaire Frederic, Dancey Paul, Norozi Kambiz, Alsalehi Mahmoud, Selamet Tierney Elif Seda, Szmuszkovicz Jacqueline R, Jone Pei-Ni, Prasad Deepa, Yetman Anji T, Misra Nilanjana, Hicar Mark D, Thacker Deepika, Choueiter Nadine F, Cooke Elisa Fernandez, Mauriello Daniel, Mondal Tapas, Elias Matthew D, McHugh Kimberly E, Merves Shae A, Garrido-Garcia Luis Martin, Khoury Michael, Larios Guillermo, Chinni Bhargava, Pruthi Kaashvi, Yang Wenyu, Greenstein Joseph, Taylor Casey, Farid Pedrom, McCrindle Brian W, Manlhiot Cedric, International Kawasaki Disease Registry
Аннотация
OBJECTIVES: The aim of the study was to develop a machine learning (ML) model to predict future ICU admission for patients with KD or MIS-C to augment clinical decision-making.
METHODS: We developed a prediction model for ICU admission using 2,539 patients <18 years of age with MIS-C or KD enrolled in the International Kawasaki Disease Registry. Using discrete time-point clinical features and engineered time-series clinical features, we developed predictive snapshot and window ML models with logistic regression, XGBoost, and random forest. Performance was compared between the various iterations of the models.
RESULTS: ML models effectively predicted admission to the ICU within the next 48 hours of the time of prediction. The time-series window-XGBoost model outperformed other models with an AUROC of 0.92 and an area under the precision-recall curve of 0.86. The incorporation of engineered time-series features improved the precision and recall independent of the length of the sampling time window. Higher ferritin level, treatment with anticoagulant or unfractionated heparin, higher C-reactive protein level, and lower platelet count were identified as the most predictive features for positive ICU prediction.
CONCLUSIONS: ML algorithms can effectively predict ICU admission for pediatric patients with MIS-C or KD. These models may prompt physicians to pre-emptively implement supportive measures, possibly mitigating the risk of clinical deterioration.