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RESEARCH PRODUCT
Common cardiovascular risk factors and in-hospital mortality in 3,894 patients with COVID-19: survival analysis and machine learning-based findings from the multicentre Italian CORIST Study.
Augusto Di CastelnuovoMarialaura BonaccioSimona CostanzoAlessandro GialluisiAndrea AntinoriNausicaa BerselliLorenzo BlandiRaffaele BrunoRoberto CaudaGiovanni GuaraldiIlaria MyLorenzo MenicantiGiustino ParrutiGiuseppe PattiStefano PerliniFrancesca SantilliCarlo SignorelliGiulio G. StefaniniAlessandra VergoriAmina AbdeddaimWalter AgenoAntonella AgodiPiergiuseppe AgostoniLuca AielloSamir Al MoghaziFilippo AucellaGreta BarbieriAlessandro BartoloniCarolina BolognaPaolo BonfantiSerena BrancatiFrancesco CacciatoreLucia CaianoFrancesco CannataLaura CarrozziAntonio CascioAntonella CingolaniFrancesco CipolloneClaudia ColombaAnnalisa CrisettiFrancesca CrostaGian B. DanziDamiano D'ardesKatleen De Gaetano DonatiFrancesco Di GennaroGisella Di PalmaGiuseppe Di TanoMassimo FantoniTommaso FilippiniPaola FiorettoFrancesco M. FuscoIvan GentileLeonardo GrisafiGabriella GuarnieriFrancesco LandiGiovanni LarizzaArmando LeoneGloria MaccagniSandro MaccarellaMassimo MapelliRiccardo MaragnaRossella MarcucciGiulio MarescaClaudia MarottaLorenzo MarraFranco MastroianniAlessandro MengozziFrancesco MenichettiJovana MilicRita MurriArturo MontineriRoberta MussinelliCristina MussiniMaria MussoAnna OdoneMarco OlivieriEmanuela PasiFrancesco PetriBiagio PincheraCarlo A. PivatoRoberto PizziVenerino PolettiFrancesca RaffaelliClaudia RavagliaGiulia RighettiAndrea RognoniMarco RossatoMarianna RossiAnna SabenaSalinaro FrancescoVincenzo SangiovanniCarlo SanroccoAntonio ScarafinoLaura ScorzoliniRaffaella SgarigliaPaola G. SimeoneEnrico SpinoniCarlo TortiEnrico M. TrecarichiFrancesca VezzaniGiovanni VeronesiRoberto VettorAndrea VianelloMarco VincetiRaffaele De CaterinaLicia Iacoviellosubject
MaleEpidemiologyEndocrinology Diabetes and MetabolismMedicine (miscellaneous)030204 cardiovascular system & hematologycomputer.software_genreMachine Learning0302 clinical medicineRetrospective StudieRisk FactorsCardiovascular DiseaseEpidemiology80 and overMedicineAge FactorViralHospital MortalityBetacoronavirus Hospital MortalityYoung adultAged 80 and overNutrition and DieteticsCOVID-19; Epidemiology; In-hospital mortality; Risk factorsMortality rateHazard ratioAge FactorsMiddle AgedIn-hospital mortalityC-Reactive ProteinCardiovascular DiseasesFemaleSurvival AnalysiCardiology and Cardiovascular MedicineCoronavirus InfectionsHumanGlomerular Filtration RateAdultmedicine.medical_specialtyAdolescentPneumonia Viral030209 endocrinology & metabolismSettore MED/17 - MALATTIE INFETTIVEMachine learningCOVID-19; Epidemiology; In-hospital mortality; Risk factors; Adolescent; Adult; Age Factors; Aged; Aged 80 and over; C-Reactive Protein; COVID-19; Cardiovascular Diseases; Coronavirus Infections; Female; Glomerular Filtration Rate; Humans; Male; Middle Aged; Pandemics; Pneumonia Viral; Retrospective Studies; Risk Factors; SARS-CoV-2; Survival Analysis; Young Adult; Betacoronavirus; Hospital Mortality; Machine LearningArticle03 medical and health sciencesBetacoronavirusYoung AdultHumansRisk factorPandemicsSurvival analysisAgedRetrospective StudiesPandemicBetacoronavirubusiness.industryCoronavirus InfectionSARS-CoV-2Risk FactorCOVID-19Retrospective cohort studyPneumoniaSurvival AnalysisConfidence intervalRisk factorsArtificial intelligencebusinesscomputerdescription
Background and aims There is poor knowledge on characteristics, comorbidities and laboratory measures associated with risk for adverse outcomes and in-hospital mortality in European Countries. We aimed at identifying baseline characteristics predisposing COVID-19 patients to in-hospital death. Methods and results Retrospective observational study on 3894 patients with SARS-CoV-2 infection hospitalized from February 19th to May 23rd, 2020 and recruited in 30 clinical centres distributed throughout Italy. Machine learning (random forest)-based and Cox survival analysis. 61.7% of participants were men (median age 67 years), followed up for a median of 13 days. In-hospital mortality exhibited a geographical gradient, Northern Italian regions featuring more than twofold higher death rates as compared to Central/Southern areas (15.6% vs 6.4%, respectively). Machine learning analysis revealed that the most important features in death classification were impaired renal function, elevated C reactive protein and advanced age. These findings were confirmed by multivariable Cox survival analysis (hazard ratio (HR): 8.2; 95% confidence interval (CI) 4.6–14.7 for age ≥85 vs 18–44 y); HR = 4.7; 2.9–7.7 for estimated glomerular filtration rate levels <15 vs ≥ 90 mL/min/1.73 m2; HR = 2.3; 1.5–3.6 for C-reactive protein levels ≥10 vs ≤ 3 mg/L). No relation was found with obesity, tobacco use, cardiovascular disease and related-comorbidities. The associations between these variables and mortality were substantially homogenous across all sub-groups analyses. Conclusions Impaired renal function, elevated C-reactive protein and advanced age were major predictors of in-hospital death in a large cohort of unselected patients with COVID-19, admitted to 30 different clinical centres all over Italy.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2020-10-01 | Nutrition, metabolism, and cardiovascular diseases : NMCD |