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Outcome prediction during an ICU surge using a purely data-driven approach: A supervised machine learning case-study in critically ill patients from COVID-19 Lombardy outbreak.
Greco, Massimiliano; Angelotti, Giovanni; Caruso, Pier Francesco; Zanella, Alberto; Stomeo, Niccolò; Costantini, Elena; Protti, Alessandro; Pesenti, Antonio; Grasselli, Giacomo; Cecconi, Maurizio.
  • Greco M; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy.
  • Angelotti G; IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy.
  • Caruso PF; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy. Electronic address: pierfrancesco.caruso@humanitas.it.
  • Zanella A; Dipartimento di Anestesia, Rianimazione ed Emergenza-Urgenza, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
  • Stomeo N; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy.
  • Costantini E; IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy.
  • Protti A; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy.
  • Pesenti A; Dipartimento di Anestesia, Rianimazione ed Emergenza-Urgenza, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
  • Grasselli G; Dipartimento di Anestesia, Rianimazione ed Emergenza-Urgenza, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy; Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy.
  • Cecconi M; Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20072 Pieve Emanuele, Milan, Italy; IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Rozzano, Milan, Italy.
Int J Med Inform ; 164: 104807, 2022 08.
Article in English | MEDLINE | ID: covidwho-2076190
ABSTRACT

PURPOSE:

COVID-19 disease frequently affects the lungs leading to bilateral viral pneumonia, progressing in some cases to severe respiratory failure requiring ICU admission and mechanical ventilation. Risk stratification at ICU admission is fundamental for resource allocation and decision making. We assessed performances of three machine learning approaches to predict mortality in COVID-19 patients admitted to ICU using early operative data from the Lombardy ICU Network.

METHODS:

This is a secondary analysis of prospectively collected data from Lombardy ICU network. A logistic regression, balanced logistic regression and random forest were built to predict survival on two datasets dataset A included patient demographics, medications before admission and comorbidities, and dataset B included respiratory data the first day in ICU.

RESULTS:

Models were trained on 1484 patients on four outcomes (7/14/21/28 days) and reached the greatest predictive performance at 28 days (F1-score 0.75 and AUC 0.80). Age, number of comorbidities and male gender were strongly associated with mortality. On dataset B, mode of ventilatory assistance at ICU admission and fraction of inspired oxygen were associated with an increase in prediction performances.

CONCLUSIONS:

Machine learning techniques might be useful in emergency phases to reach good predictive performances maintaining interpretability to gain knowledge on complex situations and enhance patient management and resources.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Case report / Observational study / Prognostic study / Randomized controlled trials Limits: Humans / Male Language: English Journal: Int J Med Inform Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: J.ijmedinf.2022.104807

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Case report / Observational study / Prognostic study / Randomized controlled trials Limits: Humans / Male Language: English Journal: Int J Med Inform Journal subject: Medical Informatics Year: 2022 Document Type: Article Affiliation country: J.ijmedinf.2022.104807