tetano
Editor, Senior Moderator
PLoS One
. 2024 Apr 25;19(4):e0290221.
doi: 10.1371/journal.pone.0290221. eCollection 2024. Predicting clinical outcomes of SARS-CoV-2 infection during the Omicron wave using machine learning
Steven Cogill[SUP] 1 2 [/SUP], Shriram Nallamshetty[SUP] 1 [/SUP], Natalie Fullenkamp[SUP] 1 [/SUP], Kent Heberer[SUP] 1 2 [/SUP], Julie Lynch[SUP] 3 4 [/SUP], Kyung Min Lee[SUP] 3 [/SUP], Mihaela Aslan[SUP] 5 6 [/SUP], Mei-Chiung Shih[SUP] 1 7 [/SUP], Jennifer S Lee[SUP] 1 2 8 [/SUP]
Affiliations
The Omicron SARS-CoV-2 variant continues to strain healthcare systems. Developing tools that facilitate the identification of patients at highest risk of adverse outcomes is a priority. The study objectives are to develop population-scale predictive models that: 1) identify predictors of adverse outcomes with Omicron surge SARS-CoV-2 infections, and 2) predict the impact of prioritized vaccination of high-risk groups for said outcome. We prepared a retrospective longitudinal observational study of a national cohort of 172,814 patients in the U.S. Veteran Health Administration who tested positive for SARS-CoV-2 from January 15 to August 15, 2022. We utilized sociodemographic characteristics, comorbidities, and vaccination status, at time of testing positive for SARS-CoV-2 to predict hospitalization, escalation of care (high-flow oxygen, mechanical ventilation, vasopressor use, dialysis, or extracorporeal membrane oxygenation), and death within 30 days. Machine learning models demonstrated that advanced age, high comorbidity burden, lower body mass index, unvaccinated status, and oral anticoagulant use were the important predictors of hospitalization and escalation of care. Similar factors predicted death. However, anticoagulant use did not predict mortality risk. The all-cause death model showed the highest discrimination (Area Under the Curve (AUC) = 0.903, 95% Confidence Interval (CI): 0.895, 0.911) followed by hospitalization (AUC = 0.822, CI: 0.818, 0.826), then escalation of care (AUC = 0.793, CI: 0.784, 0.805). Assuming a vaccine efficacy range of 70.8 to 78.7%, our simulations projected that targeted prevention in the highest risk group may have reduced 30-day hospitalization and death in more than 2 of 5 unvaccinated patients.
. 2024 Apr 25;19(4):e0290221.
doi: 10.1371/journal.pone.0290221. eCollection 2024. Predicting clinical outcomes of SARS-CoV-2 infection during the Omicron wave using machine learning
Steven Cogill[SUP] 1 2 [/SUP], Shriram Nallamshetty[SUP] 1 [/SUP], Natalie Fullenkamp[SUP] 1 [/SUP], Kent Heberer[SUP] 1 2 [/SUP], Julie Lynch[SUP] 3 4 [/SUP], Kyung Min Lee[SUP] 3 [/SUP], Mihaela Aslan[SUP] 5 6 [/SUP], Mei-Chiung Shih[SUP] 1 7 [/SUP], Jennifer S Lee[SUP] 1 2 8 [/SUP]
Affiliations
- PMID: 38662748
- DOI: 10.1371/journal.pone.0290221
The Omicron SARS-CoV-2 variant continues to strain healthcare systems. Developing tools that facilitate the identification of patients at highest risk of adverse outcomes is a priority. The study objectives are to develop population-scale predictive models that: 1) identify predictors of adverse outcomes with Omicron surge SARS-CoV-2 infections, and 2) predict the impact of prioritized vaccination of high-risk groups for said outcome. We prepared a retrospective longitudinal observational study of a national cohort of 172,814 patients in the U.S. Veteran Health Administration who tested positive for SARS-CoV-2 from January 15 to August 15, 2022. We utilized sociodemographic characteristics, comorbidities, and vaccination status, at time of testing positive for SARS-CoV-2 to predict hospitalization, escalation of care (high-flow oxygen, mechanical ventilation, vasopressor use, dialysis, or extracorporeal membrane oxygenation), and death within 30 days. Machine learning models demonstrated that advanced age, high comorbidity burden, lower body mass index, unvaccinated status, and oral anticoagulant use were the important predictors of hospitalization and escalation of care. Similar factors predicted death. However, anticoagulant use did not predict mortality risk. The all-cause death model showed the highest discrimination (Area Under the Curve (AUC) = 0.903, 95% Confidence Interval (CI): 0.895, 0.911) followed by hospitalization (AUC = 0.822, CI: 0.818, 0.826), then escalation of care (AUC = 0.793, CI: 0.784, 0.805). Assuming a vaccine efficacy range of 70.8 to 78.7%, our simulations projected that targeted prevention in the highest risk group may have reduced 30-day hospitalization and death in more than 2 of 5 unvaccinated patients.