Predicting invasive mechanical ventilation in COVID 19 patients: A validation study

Liran Statlender, Leonid Shvartser, Shmuel Teppler, Itai Bendavid, Shiri Kushinir, Roy Azullay and Pierre Singer

PLOS ONE, 2024, vol. 19, issue 1, 1-12

Abstract: Introduction: The decision to intubate and ventilate a patient is mainly clinical. Both delaying intubation (when needed) and unnecessarily invasively ventilating (when it can be avoided) are harmful. We recently developed an algorithm predicting respiratory failure and invasive mechanical ventilation in COVID-19 patients. This is an internal validation study of this model, which also suggests a categorized “time-weighted” model. Methods: We used a dataset of COVID-19 patients who were admitted to Rabin Medical Center after the algorithm was developed. We evaluated model performance in predicting ventilation, regarding the actual endpoint of each patient. We further categorized each patient into one of four categories, based on the strength of the prediction of ventilation over time. We evaluated this categorized model performance regarding the actual endpoint of each patient. Results: 881 patients were included in the study; 96 of them were ventilated. AUC of the original algorithm is 0.87–0.94. The AUC of the categorized model is 0.95. Conclusions: A minor degradation in the algorithm accuracy was noted in the internal validation, however, its accuracy remained high. The categorized model allows accurate prediction over time, with very high negative predictive value.

Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0296386 (text/html)
https://journals.plos.org/plosone/article/file?id= ... 96386&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0296386

DOI: 10.1371/journal.pone.0296386

Access Statistics for this article

More articles in PLOS ONE from Public Library of Science
Bibliographic data for series maintained by plosone ().