Development and Prospective Validation of a Deep Learning Algorithm for Predicting Need for Mechanical Ventilation

Chest. 2020 Dec 17;S0012-3692(20)35454-4. doi: 10.1016/j.chest.2020.12.009. Online ahead of print.

Abstract

Background: Objective and early identification of hospitalized patients, and particularly those with novel coronavirus disease 2019 (COVID-19), who may require mechanical ventilation (MV) may aid in delivering timely treatment.

Research question: Can a transparent deep learning (DL) model predict the need for MV in hospitalized patients and those with COVID-19 up to 24 hours in advance?

Study design and methods: We trained and externally validated a transparent DL algorithm to predict the future need for MV in hospitalized patients, including those with COVID-19, using commonly available data in electronic health records. Additionally, commonly used clinical criteria (heart rate, oxygen saturation, respiratory rate, FiO2 and pH) were used to assess future need for MV. Performance of the algorithm was evaluated using the area under receiver operating characteristic curve (AUC), sensitivity, specificity and positive predictive value.

Results: We obtained data from over 30,000 ICU patients (including over 700 patients with COVID-19) from two academic medical centers. The performance of the model with a 24-hour prediction horizon at the development and validation sites was comparable (AUC of 0.895 versus 0.882, respectively), providing significant improvement over traditional clinical criteria (p<0.001). Prospective validation of the algorithm among patients with COVID-19 yielded AUCs in the range 0.918-0.943.

Interpretation: A transparent DL algorithm improves on traditional clinical criteria to predict the need for MV in hospitalized patients, including in those with COVID-19. Such an algorithm may help clinicians optimize timing of tracheal intubation, better allocate resources and staff, and improve patient care.

Keywords: Artificial Intelligence; Artificial Respiration; Coronavirus; Deep Learning; Lung.