Development and Prospective Validation of a Deep Learning Algorithm for Predicting Need for Mechanical Ventilation.
Chest
; 159(6): 2264-2273, 2021 06.
Article
in English
| MEDLINE | ID: covidwho-987252
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 h in advance? STUDY DESIGN ANDMETHODS:
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 more than 30,000 ICU patients (including more than 700 patients with COVID-19) from two academic medical centers. The performance of the model with a 24-h prediction horizon at the development and validation sites was comparable (AUC, 0.895 vs 0.882, respectively), providing significant improvement over traditional clinical criteria (P < .001). Prospective validation of the algorithm among patients with COVID-19 yielded AUCs in the range of 0.918 to 0.943.INTERPRETATION:
A transparent deep learning 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 to optimize timing of tracheal intubation, to allocate resources and staff better, and to improve patient care.Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Respiration, Artificial
/
Deep Learning
/
COVID-19
/
Health Services Needs and Demand
Type of study:
Cohort study
/
Experimental Studies
/
Observational study
/
Prognostic study
Topics:
Long Covid
Limits:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Language:
English
Journal:
Chest
Year:
2021
Document Type:
Article
Affiliation country:
J.chest.2020.12.009
Similar
MEDLINE
...
LILACS
LIS