2019
World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions
Group T, Kaptoge S, Pennells L, De Bacquer D, Cooney M, Kavousi M, Stevens G, Riley L, Savin S, Khan T, Altay S, Amouyel P, Assmann G, Bell S, Ben-Shlomo Y, Berkman L, Beulens J, Björkelund C, Blaha M, Blazer D, Bolton T, Beaglehole R, Brenner H, Brunner E, Casiglia E, Chamnan P, Choi Y, Chowdry R, Coady S, Crespo C, Cushman M, Dagenais G, D'Agostino R, Daimon M, Davidson K, Engström G, Ford I, Gallacher J, Gansevoort R, Gaziano T, Giampaoli S, Grandits G, Grimsgaard S, Grobbee D, Gudnason V, Guo Q, Tolonen H, Humphries S, Iso H, Jukema J, Kauhanen J, Kengne A, Khalili D, Koenig W, Kromhout D, Krumholz H, Lam T, Laughlin G, Ibañez A, Meade T, Moons K, Nietert P, Ninomiya T, Nordestgaard B, O'Donnell C, Palmieri L, Patel A, Perel P, Price J, Providencia R, Ridker P, Rodriguez B, Rosengren A, Roussel R, Sakurai M, Salomaa V, Sato S, Schöttker B, Shara N, Shaw J, Shin H, Simons L, Sofianopoulou E, Sundström J, Völzke H, Wallace R, Wareham N, Willeit P, Wood D, Wood A, Zhao D, Woodward M, Danaei G, Roth G, Mendis S, Onuma O, Varghese C, Ezzati M, Graham I, Jackson R, Danesh J, Di Angelantonio E. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. The Lancet Global Health 2019, 7: e1332-e1345. PMID: 31488387, PMCID: PMC7025029, DOI: 10.1016/s2214-109x(19)30318-3.Peer-Reviewed Original ResearchConceptsRisk prediction modelSystolic blood pressureCardiovascular disease riskBritish Heart FoundationIndividual participant dataCardiovascular diseaseBlood pressureTotal cholesterolC-indexFatal coronary heart diseaseNon-fatal cardiovascular diseaseDisease riskCardiovascular disease risk predictionParticipant dataIncident cardiovascular eventsHistory of diabetesRisk factor profileRisk prediction chartsWHO STEPwise approachSex-specific incidenceUK Medical Research CouncilCoronary heart diseaseFirst myocardial infarctionExternal validation cohortHarrell's C-index
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
2008
Investigation of 89 candidate gene variants for effects on all-cause mortality following acute coronary syndrome
Morgan TM, Xiao L, Lyons P, Kassebaum B, Krumholz HM, Spertus JA. Investigation of 89 candidate gene variants for effects on all-cause mortality following acute coronary syndrome. BMC Medical Genomics 2008, 9: 66. PMID: 18620593, PMCID: PMC2483267, DOI: 10.1186/1471-2350-9-66.Peer-Reviewed Original ResearchConceptsAcute coronary syndromeRisk factorsACS mortalityCoronary syndromeTraditional cardiac risk factorsKaplan-Meier survival analysisMultivariate risk prediction modelCardiac risk factorsPutative genetic risk factorsGene variantsUniversity-affiliated hospitalBorderline statistical significanceGenetic risk factorsSystematic literature searchRisk prediction modelGenetic variantsCandidate gene variantsACS survivorsCause mortalityACS casesCox regressionPatient cohortClinical prognosisPotential confoundingSurvival analysis