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Utilization of machine-learning models to accurately predict the risk for critical COVID-19.
Assaf, Dan; Gutman, Ya'ara; Neuman, Yair; Segal, Gad; Amit, Sharon; Gefen-Halevi, Shiraz; Shilo, Noya; Epstein, Avi; Mor-Cohen, Ronit; Biber, Asaf; Rahav, Galia; Levy, Itzchak; Tirosh, Amit.
  • Assaf D; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Gutman Y; Surgery C Department, The Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.
  • Neuman Y; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Segal G; Surgery C Department, The Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.
  • Amit S; The Department of Cognitive and Brain Sciences, Ben-Gurion University of the Negev, Beer Sheva, Israel.
  • Gefen-Halevi S; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Shilo N; Corona Department and Internal Medicine "T", The Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.
  • Epstein A; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Mor-Cohen R; Clinical Microbiology Laboratory, The Chaim Sheba Medical Center, Ramat Gan, Tel Hashomer, Israel.
  • Biber A; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Rahav G; Clinical Microbiology Laboratory, The Chaim Sheba Medical Center, Ramat Gan, Tel Hashomer, Israel.
  • Levy I; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Tirosh A; Corona Intensive Care Unit, The Chaim Sheba Medical Center, Tel Hashomer, Ramat Gan, Israel.
Intern Emerg Med ; 15(8): 1435-1443, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-718479
ABSTRACT
Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Coronavirus Infections / Risk Assessment / Machine Learning Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Topics: Long Covid Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: Intern Emerg Med Journal subject: Emergency Medicine / Internal Medicine Year: 2020 Document Type: Article Affiliation country: S11739-020-02475-0

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia, Viral / Coronavirus Infections / Risk Assessment / Machine Learning Type of study: Diagnostic study / Experimental Studies / Observational study / Prognostic study Topics: Long Covid Limits: Adult / Aged / Female / Humans / Male / Middle aged Language: English Journal: Intern Emerg Med Journal subject: Emergency Medicine / Internal Medicine Year: 2020 Document Type: Article Affiliation country: S11739-020-02475-0