Optimal triage for COVID-19 patients under limited healthcare resources: Development of a parsimonious machine learning prediction model and threshold optimization using discrete-event simulation.

ABSTRACT:

Background

The coronavirus disease 2019 (COVID-19) pandemic has placed an unprecedented burden on healthcare systems.

Objective

To effectively triage COVID-19 patients within situations of limited data availability and to explore optimal thresholds to minimize mortality rates while maintaining healthcare system capacity.

Methods

A nationwide sample of 5,601 patients confirmed for COVID-19 up until April 2020 was retrospectively reviewed. XGBoost and logistic regression analysis were used to develop prediction models for the maximum clinical severity during hospitalization, classified according to the WHO Ordinal Scale for Clinical Improvement (OSCI). The recursive feature elimination technique was used to evaluate maintenance of the model performance when clinical and laboratory variables were eliminated. Using populations based on hypothetical patient influx scenarios, discrete-event simulation was performed to find an optimal threshold within limited resource environments that minimizes mortality rates.

Results

The cross-validated area under the receiver operating characteristics (AUROC) of the baseline XGBoost model that utilized all 37 variables was 0.965 for OSCI ≥ 6. Compared to the baseline model's performance, the AUROC of the feature-eliminated model that utilized 17 variables was maintained at 0.963 with statistical insignificance. Optimal thresholds were found to minimize mortality rates in a hypothetical patient influx scenario. The benefit of utilizing an optimal triage threshold was clear, reducing mortality up to 18.1%, compared to the conventional Youden Index.

Conclusions

Our adaptive triage model and its threshold optimization capability revealed that COVID-19 management can be achieved via the cooperation of both medical and healthcare management sectors for maximum treatment efficacy. The model is available online for clinical implementation.

Clinicaltrial

SUBMITTER: Kim JM 

PROVIDER: S-EPMC8565604 | biostudies-literature |

REPOSITORIES: biostudies-literature