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RESEARCH PRODUCT
Predicting in-hospital mortality from Coronavirus Disease 2019: A simple validated app for clinical use
Francesco Antonio MancarellaMassimo AttanasioMauro GoriValentina ZuccaroAntonio GasbarriniMatteo TacelliLuca Ferdinando LoriniLaura ParisCalogero CammàFederico RaimondiBianca MagroRaffaele BrunoLorenzo ZileriSalvatore BattagliaVincenzo Giuseppe GenovaFabiano Di MarcoCiro CelsaGiulia CammàStefano FagiuoliMichele SenniLuca NovelliMarco Eneasubject
MaleViral DiseasesEpidemiologyClinical prediction ruleCardiovascular MedicineVascular MedicineSteroid TherapyChronic Liver DiseaseCohort StudiesMedical ConditionsEndocrinologyRetrospective StudieRisk FactorsMedicine and Health Sciences80 and overCoronary Heart DiseaseHospital MortalityAged 80 and overMultidisciplinaryPharmaceuticsLiver DiseasesQHazard ratioRMiddle AgedMobile ApplicationsHospitalsHospitalizationInfectious DiseasesBrier scoreItalyCardiovascular DiseasesMedicineFemaleHumanResearch ArticleCohort studyAdultmedicine.medical_specialtyEndocrine DisordersCorticosteroid TherapyScienceSettore MED/12 - GASTROENTEROLOGIAMobile ApplicationCardiologyGastroenterology and HepatologyRisk AssessmentDrug TherapyInternal medicineDiabetes MellitusmedicineHumansAgedRetrospective StudiesReceiver operating characteristicbusiness.industrySARS-CoV-2Risk FactorSettore MED/09 - MEDICINA INTERNACOVID-19Covid 19Retrospective cohort studyCardiovascular Disease RiskTriageConfidence intervalHealth CareROC CurveHealth Care FacilitiesMedical Risk FactorsMetabolic DisordersCohort Studiebusinessdescription
Backgrounds Validated tools for predicting individual in-hospital mortality of COVID-19 are lacking. We aimed to develop and to validate a simple clinical prediction rule for early identification of in-hospital mortality of patients with COVID-19. Methods and findings We enrolled 2191 consecutive hospitalized patients with COVID-19 from three Italian dedicated units (derivation cohort: 1810 consecutive patients from Bergamo and Pavia units; validation cohort: 381 consecutive patients from Rome unit). The outcome was in-hospital mortality. Fine and Gray competing risks multivariate model (with discharge as a competing event) was used to develop a prediction rule for in-hospital mortality. Discrimination and calibration were assessed by the area under the receiver operating characteristic curve (AUC) and by Brier score in both the derivation and validation cohorts. Seven variables were independent risk factors for in-hospital mortality: age (Hazard Ratio [HR] 1.08, 95% Confidence Interval [CI] 1.07–1.09), male sex (HR 1.62, 95%CI 1.30–2.00), duration of symptoms before hospital admission <10 days (HR 1.72, 95%CI 1.39–2.12), diabetes (HR 1.21, 95%CI 1.02–1.45), coronary heart disease (HR 1.40 95% CI 1.09–1.80), chronic liver disease (HR 1.78, 95%CI 1.16–2.72), and lactate dehydrogenase levels at admission (HR 1.0003, 95%CI 1.0002–1.0005). The AUC was 0.822 (95%CI 0.722–0.922) in the derivation cohort and 0.820 (95%CI 0.724–0.920) in the validation cohort with good calibration. The prediction rule is freely available as a web-app (COVID-CALC: https://sites.google.com/community.unipa.it/covid-19riskpredictions/c19-rp). Conclusions A validated simple clinical prediction rule can promptly and accurately assess the risk for in-hospital mortality, improving triage and the management of patients with COVID-19.
year | journal | country | edition | language |
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2021-01-14 |