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Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation.
Jung, Christian; Mamandipoor, Behrooz; Fjølner, Jesper; Bruno, Raphael Romano; Wernly, Bernhard; Artigas, Antonio; Bollen Pinto, Bernardo; Schefold, Joerg C; Wolff, Georg; Kelm, Malte; Beil, Michael; Sviri, Sigal; van Heerden, Peter V; Szczeklik, Wojciech; Czuczwar, Miroslaw; Elhadi, Muhammed; Joannidis, Michael; Oeyen, Sandra; Zafeiridis, Tilemachos; Marsh, Brian; Andersen, Finn H; Moreno, Rui; Cecconi, Maurizio; Leaver, Susannah; De Lange, Dylan W; Guidet, Bertrand; Flaatten, Hans; Osmani, Venet.
  • Jung C; Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Duesseldorf, University Hospital Duesseldorf, Duesseldorf, Germany.
  • Mamandipoor B; Fondazione Bruno Kessler Research Institute, Trento, Italy.
  • Fjølner J; Department of Intensive Care, Aarhus University Hospital, Aarhus, Denmark.
  • Bruno RR; Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Duesseldorf, University Hospital Duesseldorf, Duesseldorf, Germany.
  • Wernly B; Department of Anaesthesiology, Paracelsus Medical University, Salzburg, Austria.
  • Artigas A; Department of Intensive Care Medicine, CIBER Enfermedades Respiratorias, Corporacion Sanitaria Universitaria Parc Tauli, Autonomous University of Barcelona, Sabadell, Spain.
  • Bollen Pinto B; Department of Acute Medicine, Geneva University Hospitals, Geneva, Switzerland.
  • Schefold JC; Department of Intensive Care Medicine, Inselspital, Universitätsspital, University of Bern, Bern, Switzerland.
  • Wolff G; Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Duesseldorf, University Hospital Duesseldorf, Duesseldorf, Germany.
  • Kelm M; Division of Cardiology, Pulmonology and Vascular Medicine, Medical Faculty, Heinrich-Heine-University Duesseldorf, University Hospital Duesseldorf, Duesseldorf, Germany.
  • Beil M; Department of Medical Intensive Care, Hadassah University Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
  • Sviri S; Department of Medical Intensive Care, Hadassah University Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
  • van Heerden PV; Department of Anesthesia, Intensive Care and Pain Medicine, Hadassah Medical Center and Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.
  • Szczeklik W; Center for Intensive Care and Perioperative Medicine, Jagiellonian University Medical College, Krakow, Poland.
  • Czuczwar M; Second Department of Anesthesiology and Intensive Care, Medical University of Lublin, Lublin, Poland.
  • Elhadi M; Faculty of Medicine, University of Tripoli, Tripoli, Libyan Arab Jamahiriya.
  • Joannidis M; Division of Intensive Care and Emergency Medicine, Department of Internal Medicine, Medical University Innsbruck, Innsbruck, Austria.
  • Oeyen S; Department of Intensive Care 1K12IC, Ghent University Hospital, Ghent, Belgium.
  • Zafeiridis T; Intensive Care Unit, General Hospital of Larissa, Larissa, Greece.
  • Marsh B; Mater Misericordiae University Hospital, Dublin, Ireland.
  • Andersen FH; Department of Anaesthesia and Intensive Care, Ålesund Hospital, Alesund, Norway.
  • Moreno R; Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway.
  • Cecconi M; Hospital de São José, Centro Hospitalar Universitário de Lisboa Central, Lisbon, Portugal.
  • Leaver S; Faculdade de Ciências Médicas de Lisboa, Nova Medical School - Faculdade de Ciências Médicas, Universidade da Beira Interior, Lisbon, Portugal.
  • De Lange DW; Department of Anaesthesia, IRCCS Instituto Clínico Humanitas, Humanitas University, Milan, Italy.
  • Guidet B; General Intensive Care, St George's University Hospitals, NHS Foundation Trust, London, United Kingdom.
  • Flaatten H; Department of Intensive Care Medicine, University Medical Center, Utrecht University, Utrecht, Belgium.
  • Osmani V; Épidémiologie Hospitalière Qualité et Organisation des Soins, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Sorbonne Universités, UPMC Univ Paris 06, INSERM, UMR_S 1136, Paris, France.
JMIR Med Inform ; 10(3): e32949, 2022 Mar 31.
Article in English | MEDLINE | ID: covidwho-1770908
ABSTRACT

BACKGROUND:

The COVID-19 pandemic caused by SARS-CoV-2 is challenging health care systems globally. The disease disproportionately affects the elderly population, both in terms of disease severity and mortality risk.

OBJECTIVE:

The aim of this study was to evaluate machine learning-based prognostication models for critically ill elderly COVID-19 patients, which dynamically incorporated multifaceted clinical information on evolution of the disease.

METHODS:

This multicenter cohort study (COVIP study) obtained patient data from 151 intensive care units (ICUs) from 26 countries. Different models based on the Sequential Organ Failure Assessment (SOFA) score, logistic regression (LR), random forest (RF), and extreme gradient boosting (XGB) were derived as baseline models that included admission variables only. We subsequently included clinical events and time-to-event as additional variables to derive the final models using the same algorithms and compared their performance with that of the baseline group. Furthermore, we derived baseline and final models on a European patient cohort, which were externally validated on a non-European cohort that included Asian, African, and US patients.

RESULTS:

In total, 1432 elderly (≥70 years old) COVID-19-positive patients admitted to an ICU were included for analysis. Of these, 809 (56.49%) patients survived up to 30 days after admission. The average length of stay was 21.6 (SD 18.2) days. Final models that incorporated clinical events and time-to-event information provided superior performance (area under the receiver operating characteristic curve of 0.81; 95% CI 0.804-0.811), with respect to both the baseline models that used admission variables only and conventional ICU prediction models (SOFA score, P<.001). The average precision increased from 0.65 (95% CI 0.650-0.655) to 0.77 (95% CI 0.759-0.770).

CONCLUSIONS:

Integrating important clinical events and time-to-event information led to a superior accuracy of 30-day mortality prediction compared with models based on the admission information and conventional ICU prediction models. This study shows that machine-learning models provide additional information and may support complex decision-making in critically ill elderly COVID-19 patients. TRIAL REGISTRATION ClinicalTrials.gov NCT04321265; https//clinicaltrials.gov/ct2/show/NCT04321265.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Language: English Journal: JMIR Med Inform Year: 2022 Document Type: Article Affiliation country: 32949

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Experimental Studies / Observational study / Prognostic study / Randomized controlled trials Language: English Journal: JMIR Med Inform Year: 2022 Document Type: Article Affiliation country: 32949