# Use of machine learning models to predict mechanical ventilation, ECMO, and mortality in COVID-19

## TL;DR

This study uses machine learning to predict mechanical ventilation duration, ECMO use, and mortality in severe COVID-19 patients, aiming to improve care and resource allocation.

## Contribution

The novel contribution is the development of hierarchical machine learning models that integrate demographic and time-series clinical data for accurate prediction of critical outcomes in COVID-19.

## Key findings

- Hierarchical ML models achieved high AUROC scores (0.873 for MV duration, 0.902 for ECMO use, 0.774 for mortality).
- The models used vital signs, lab results, and demographics to predict outcomes with strong AUPRC scores (up to 0.999 for ECMO).
- The study included over 10,000 patients, showing the models' potential for real-world clinical application.

## Abstract

Patients with severe COVID-19 may require MV or ECMO. Predicting who will require interventions and the duration of those interventions are challenging due to the diverse responses among patients and the dynamic nature of the disease. As such, there is a need for better prediction of the duration and outcomes of MV use in patients, to improve patient care and aid with MV and ECMO allocation. Here we develop and examine the performance of ML models to predict MV duration, ECMO, and mortality for patients with COVID-19.

In this retrospective prognostic study, hierarchical machine-learning models were developed to predict MV duration and outcome prediction from demographic data and time-series data consisting of vital signs and laboratory results. We train our models on 10,378 patients with positive severe acute respiratory syndrome-related coronavirus (SARS-CoV-2) virus testing from Emory’s COVID CRADLE Dataset who sought treatment at Emory University Hospital between February 28, 2020, to January 24, 2022. Analysis was conducted between January 10, 2022, and April 5, 2024. The main outcomes and measures were the AUROC, AUPRC and the F-score for MV duration, need for ECMO, and mortality prediction.

Data from 10,378 patients with COVID-19 (median [IQR] age, 60 [48–72] years; 5,281 [50.89%] women) were included. Overall MV class distributions for 0 days, 1–4 days, 5–9 days, 10–14 days, 15–19 days, 20–24 days, 25–29 days, and ≥30 days of MV were 8,141 (78.44%), 812 (7.82%), 325 (3.13%), 241 (2.32%), 153 (1.47%), 97 (0.93%), 87 (0.84%), and 522 (5.03%), respectively. Overall ECMO use and mortality rates were 15 (0.14%) and 1,114 (10.73%), respectively. On MV duration, ECMO use, and mortality outcomes, the highest-performing model reached weighted average AUROC scores of 0.873, 0.902, and 0.774, and the highest-performing model reached weighted average AUPRC scores of 0.790, 0.999, and 0.893.

Hierarchical ML models trained on vital signs, laboratory results, and demographic data show promise for the prediction of MV duration, ECMO use, and mortality in COVID-19 patients.