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Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients

引用

Journal of Intensive Medicine 2021年第2期1卷 110-116页

作者： Jonathan Montomoli Luca Romeo Sara Moccia Michele Bernardini Lucia Migliorelli Daniele Berardini Abele Donati Andrea Carsetti Maria Grazia Bocci Pedro David Wendel Garcia Thierry Fumeaux philippe guerci Reto Andreas Schüpbach Can Ince Emanuele Frontoni Matthias Peter Hilty RISC-19-ICU Investigators Department of Anaesthesia and Intensive Care Infermi HospitalAUSL della RomagnaRimini 47923Italy Department of Information Engineering UniversitàPolitecnica delle MarcheAncona 60131Italy The BioRobotics Institute and Department of Excellence in Robotics and AI Scuola Superiore Sant’AnnaPisa 56127Italy Anesthesia and Intensive Care Unit Azienda Ospedaliero Universitaria Ospedali RiunitiAncona 60126Italy Department of Biomedical Sciences and Public Health UniversitàPolitecnica delle MarcheAncona 60126Italy Department of Anaesthesia and Intensive Care Fondazione Policlinico Universitario A.Gemelli IRCCSUniversitàCattolica del Sacro CuoreRome 00168Italy Institute of Intensive Care Medicine University Hospital of ZurichZurich 8091Switzerland Swiss Society of Intensive Care Medicine Basel 4001Switzerland Department of Anesthesiology and Critical Care Medicine University Hospital of NancyNancy 54511France Department of Intensive Care Erasmus MC University Medical Center RotterdamRotterdam3015 GDNetherlands 不详

Background:Accurate risk stratification of critically ill patients with coronavirus disease 2019(COVID-19)is essential for optimizing resource allocation,delivering targeted interventions,and maximizing patient survival *** learning(ML)techniques are attracting increased interest for the development of prediction models as they excel in the analysis of complex signals in data-rich environments such as critical ***:We retrieved data on patients with COVID-19 admitted to an intensive care unit(ICU)between March and October 2020 from the RIsk Stratification in COVID-19 patients in the Intensive Care Unit(RISC-19-ICU)*** applied the Extreme Gradient Boosting(XGBoost)algorithm to the data to predict as a binary out-come the increase or decrease in patients’Sequential Organ Failure Assessment(SOFA)score on day 5 after ICU *** model was iteratively cross-validated in different subsets of the study ***:The final study population consisted of 675 *** XGBoost model correctly predicted a decrease in SOFA score in 320/385(83%)critically ill COVID-19 patients,and an increase in the score in 210/290(72%)*** area under the mean receiver operating characteristic curve for XGBoost was significantly higher than that for the logistic regression model(0.86 vs.0.69,P<0.01[paired t-test with 95%confidence interval]).Conclusions:The XGBoost model predicted the change in SOFA score in critically ill COVID-19 patients admitted to the ICU and can guide clinical decision support systems(CDSSs)aimed at optimizing available resources.

关键词： Machine learning Extreme gradient boosting(XGBoost) COVID-19 Multiple organ failure Clinical decision support system(CDSS) Organ dysfunction score

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