2021
The Association of COVID-19 With Acute Kidney Injury Independent of Severity of Illness: A Multicenter Cohort Study
Moledina DG, Simonov M, Yamamoto Y, Alausa J, Arora T, Biswas A, Cantley LG, Ghazi L, Greenberg JH, Hinchcliff M, Huang C, Mansour SG, Martin M, Peixoto A, Schulz W, Subair L, Testani JM, Ugwuowo U, Young P, Wilson FP. The Association of COVID-19 With Acute Kidney Injury Independent of Severity of Illness: A Multicenter Cohort Study. American Journal Of Kidney Diseases 2021, 77: 490-499.e1. PMID: 33422598, PMCID: PMC7791318, DOI: 10.1053/j.ajkd.2020.12.007.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAgedCohort StudiesCOVID-19C-Reactive ProteinCreatinineDiureticsFemaleHospital MortalityHumansIntensive Care UnitsLength of StayMaleMiddle AgedProportional Hazards ModelsRenal DialysisRenal Insufficiency, ChronicRespiration, ArtificialRisk FactorsSARS-CoV-2Severity of Illness IndexUnited StatesVasoconstrictor AgentsConceptsAcute kidney injurySARS-CoV-2Cohort studyRisk factorsCOVID-19Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testingTime-updated Cox proportional hazards modelsDialysis-requiring acute kidney injuryYale New Haven Health SystemHigher inflammatory marker levelsMore acute kidney injuryCox proportional hazards modelMulticenter cohort studyHigh rateInflammatory marker levelsTraditional risk factorsProportional hazards modelCoronavirus disease 2019KDIGO criteriaNephrotoxin exposureKidney injuryInjury independentUnivariable analysisNasopharyngeal samplesMarker levels
2018
Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study
Huang C, Murugiah K, Mahajan S, Li SX, Dhruva SS, Haimovich JS, Wang Y, Schulz WL, Testani JM, Wilson FP, Mena CI, Masoudi FA, Rumsfeld JS, Spertus JA, Mortazavi BJ, Krumholz HM. Enhancing the prediction of acute kidney injury risk after percutaneous coronary intervention using machine learning techniques: A retrospective cohort study. PLOS Medicine 2018, 15: e1002703. PMID: 30481186, PMCID: PMC6258473, DOI: 10.1371/journal.pmed.1002703.Peer-Reviewed Original ResearchMeSH KeywordsAcute Kidney InjuryAgedClinical Decision-MakingData MiningDecision Support TechniquesFemaleHumansMachine LearningMaleMiddle AgedPercutaneous Coronary InterventionProtective FactorsRegistriesReproducibility of ResultsRetrospective StudiesRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeConceptsPercutaneous coronary interventionNational Cardiovascular Data RegistryRisk prediction modelAKI eventsAKI riskCoronary interventionAKI modelMean ageCardiology-National Cardiovascular Data RegistryAcute kidney injury riskAKI risk predictionRetrospective cohort studyIdentification of patientsCandidate variablesAvailable candidate variablesCohort studyPCI proceduresPoint of careBrier scoreAmerican CollegeData registryPatientsCalibration slopeInjury riskSame cohort