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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

查看全文 作　　者：Jonathan [1]Montomoli;Luca [2]Romeo;Sara [2,3]Moccia;Michele [2]Bernardini;Lucia [2]Migliorelli;Daniele [2]Berardini;Abele [4,5]Donati;Andrea [4,5]Carsetti;Maria Grazia [6]Bocci;Pedro David Wendel [7]Garcia;Thierry [8]Fumeaux;Philippe [9]Guerci;Reto Andreas [7]Schüpbach;Can [10]Ince;Emanuele [2]Frontoni;Matthias Peter [7]Hilty;RISC-19-ICU [11]Investigators 高影响力作者 机构地区：[1]Department of Anaesthesia and Intensive Care,Infermi Hospital,AUSL della Romagna,Rimini 47923,Italy;[2]Department of Information Engineering,UniversitàPolitecnica delle Marche,Ancona 60131,Italy;[3]The BioRobotics Institute and Department of Excellence in Robotics and AI,Scuola Superiore Sant’Anna,Pisa 56127,Italy;[4]Anesthesia and Intensive Care Unit,Azienda Ospedaliero Universitaria Ospedali Riuniti,Ancona 60126,Italy;[5]Department of Biomedical Sciences and Public Health,UniversitàPolitecnica delle Marche,Ancona 60126,Italy;[6]Department of Anaesthesia and Intensive Care,Fondazione Policlinico Universitario A.Gemelli IRCCS,UniversitàCattolica del Sacro Cuore,Rome 00168,Italy;[7]Institute of Intensive Care Medicine,University Hospital of Zurich,Zurich 8091,Switzerland;[8]Swiss Society of Intensive Care Medicine,Basel 4001,Switzerland;[9]Department of Anesthesiology and Critical Care Medicine,University Hospital of Nancy,Nancy 54511,France;[10]Department of Intensive Care Erasmus MC,University Medical Center Rotterdam,Rotterdam,3015 GD,Netherlands;[11]不详高影响力机构 出　　处：《Journal of Intensive Medicine》索引2021年第1卷第2期,共7页高影响力期刊 基　　金：supported by the“Microsoft Grant Award:AI for Health COVID-19″;The RISC-19-ICU reg-istry is supported by the Swiss Society of Intensive Care Medicine and funded by internal resources of the Institute of Intensive Care Medicine,of the University Hospital Zurich and by unrestricted grants from CytoSorbents Europe GmbH(Berlin,Germany);Union Bancaire Privée(Zurich,Switzerland);The sponsors had no role in the design of the study,the collection and analysis of the data,or the preparation of the manuscript. 摘　　要：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 probability.Machine 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 care.Methods: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)registry.We 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 admission.The model was iteratively cross-validated in different subsets of the study cohort.Results:The final study population consisted of 675 patients.The 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%)patients.The 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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