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- Title
Multi-horizon predictive models for guiding extracorporeal resource allocation in critically ill COVID-19 patients.
- Authors
Xue, Bing; Shah, Neel; Yang, Hanqing; Kannampallil, Thomas; Payne, Philip Richard Orrin; Lu, Chenyang; Said, Ahmed Sameh
- Abstract
Objective Extracorporeal membrane oxygenation (ECMO) resource allocation tools are currently lacking. We developed machine learning (ML) models for predicting COVID-19 patients at risk of receiving ECMO to guide patient triage and resource allocation. Material and Methods We included COVID-19 patients admitted to intensive care units for >24 h from March 2020 to October 2021, divided into training and testing development and testing-only holdout cohorts. We developed ECMO deployment timely prediction model ForecastECMO using Gradient Boosting Tree (GBT), with pre-ECMO prediction horizons from 0 to 48 h, compared to PaO2/FiO2 ratio, Sequential Organ Failure Assessment score, PREdiction of Survival on ECMO Therapy score, logistic regression, and 30 pre-selected clinical variables GBT Clinical GBT models, with area under the receiver operator curve (AUROC) and precision recall curve (AUPRC) metrics. Results ECMO prevalence was 2.89% and 1.73% in development and holdout cohorts. ForecastECMO had the best performance in both cohorts. At the 18-h prediction horizon, a potentially clinically actionable pre-ECMO window, ForecastECMO , had the highest AUROC (0.94 and 0.95) and AUPRC (0.54 and 0.37) in development and holdout cohorts in identifying ECMO patients without data 18 h prior to ECMO. Discussion and Conclusions We developed a multi-horizon model, ForecastECMO , with high performance in identifying patients receiving ECMO at various prediction horizons. This model has potential to be used as early alert tool to guide ECMO resource allocation for COVID-19 patients. Future prospective multicenter validation would provide evidence for generalizability and real-world application of such models to improve patient outcomes.
- Subjects
COVID-19; RESOURCE allocation; MACHINE learning; PREDICTION models; CRITICALLY ill; INTENSIVE care patients
- Publication
Journal of the American Medical Informatics Association, 2023, Vol 30, Issue 4, p656
- ISSN
1067-5027
- Publication type
Article
- DOI
10.1093/jamia/ocac256