2019
Submissions from the SPRINT Data Analysis Challenge on clinical risk prediction: a cross-sectional evaluation
Jackevicius CA, An J, Ko DT, Ross JS, Angraal S, Wallach JD, Koh M, Song J, Krumholz HM. Submissions from the SPRINT Data Analysis Challenge on clinical risk prediction: a cross-sectional evaluation. BMJ Open 2019, 9: e025936. PMID: 30904868, PMCID: PMC6475140, DOI: 10.1136/bmjopen-2018-025936.Peer-Reviewed Original ResearchConceptsRisk prediction toolsCross-sectional evaluationClinical risk predictionClinical performanceCardiovascular disease historyClinical risk scoreHigh-risk patientsLow-risk patientsClinical prediction toolRisk predictionEfficacy outcomesC-statisticDisease historyInclusion criteriaIndependent reviewersRisk scoreExternal validationPatientsPrediction toolsEfficacyOutcomesSame outcome
2018
Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies
Pennells L, Kaptoge S, Wood A, Sweeting M, Zhao X, White I, Burgess S, Willeit P, Bolton T, Moons KGM, van der Schouw YT, Selmer R, Khaw KT, Gudnason V, Assmann G, Amouyel P, Salomaa V, Kivimaki M, Nordestgaard BG, Blaha MJ, Kuller LH, Brenner H, Gillum RF, Meisinger C, Ford I, Knuiman MW, Rosengren A, Lawlor DA, Völzke H, Cooper C, Ibañez A, Casiglia E, Kauhanen J, Cooper JA, Rodriguez B, Sundström J, Barrett-Connor E, Dankner R, Nietert PJ, Davidson KW, Wallace RB, Blazer DG, Björkelund C, Donfrancesco C, Krumholz HM, Nissinen A, Davis BR, Coady S, Whincup PH, Jørgensen T, Ducimetiere P, Trevisan M, Engström G, Crespo CJ, Meade TW, Visser M, Kromhout D, Kiechl S, Daimon M, Price JF, de la Cámara A, Jukema J, Lamarche B, Onat A, Simons LA, Kavousi M, Ben-Shlomo Y, Gallacher J, Dekker JM, Arima H, Shara N, Tipping RW, Roussel R, Brunner EJ, Koenig W, Sakurai M, Pavlovic J, Gansevoort RT, Nagel D, Goldbourt U, Barr ELM, Palmieri L, Njølstad I, Sato S, Verschuren W, Varghese CV, Graham I, Onuma O, Greenland P, Woodward M, Ezzati M, Psaty BM, Sattar N, Jackson R, Ridker PM, Cook NR, D'Agostino RB, Thompson SG, Danesh J, Di Angelantonio E, Tipping R, Simpson L, Pressel S, Couper D, Nambi V, Matsushita K, Folsom A, Shaw J, Magliano D, Zimmet P, Knuiman M, Whincup P, Wannamethee S, Willeit J, Santer P, Egger G, Casas J, Amuzu A, Ben-Shlomo Y, Gallacher J, Tikhonoff V, Casiglia E, Sutherland S, Nietert P, Cushman M, Psaty B, Søgaard A, Håheim L, Ariansen I, Tybjærg-Hansen A, Jensen G, Schnohr P, Giampaoli S, Vanuzzo D, Panico S, Palmieri L, Balkau B, Bonnet F, Marre M, de la Cámara A, Herrera M, Friedlander Y, McCallum J, McLachlan S, Guralnik J, Phillips C, Guralnik J, Guralnik J, Guralnik J, Khaw K, Wareham N, Schöttker B, Saum K, Holleczek B, Nissinen A, Tolonen H, Giampaoli S, Donfrancesco C, Vartiainen E, Jousilahti P, Harald K, D’Agostino R, Massaro J, Pencina M, Vasan R, D’Agostino R, Massaro J, Pencina M, Vasan R, Kayama T, Kato T, Oizumi T, Jespersen J, Møller L, Bladbjerg E, Chetrit A, Rosengren A, Wilhelmsen L, Björkelund C, Lissner L, Nagel D, Dennison E, Kiyohara Y, Ninomiya T, Doi Y, Rodriguez B, Nijpels G, Stehouwer C, Sato S, Kazumasa Y, Iso H, Goldbourt U, Salomaa V, Vartiainen E, Kurl S, Tuomainen T, Salonen J, Visser M, Deeg D, Meade T, Nilsson P, Hedblad B, Melander O, De Boer I, DeFilippis A, Verschuren W, Sattar N, Watt G, Meisinger C, Koenig W, Koenig W, Meisinger C, Verschuren W, Rosengren A, Kuller L, Tverdal A, Gillum R, Cooper J, Kirkland S, Shimbo D, Shaffer J, Sato S, Kazumasa Y, Iso H, Ducimetiere P, Bakker S, van der Harst P, Hillege H, Crespo C, Amouyel P, Dallongeville J, Assmann G, Schulte H, Trompet S, Smit R, Stott D, van der Schouw Y, Després J, Cantin B, Dagenais G, Laughlin G, Wingard D, Khaw K, Trevisan M, Aspelund T, Eiriksdottir G, Gudmundsson E, Ikram A, van Rooij F, Franco O, Rueda-Ochoa O, Muka T, Glisic M, Tunstall-Pedoe H, Völzke H, Howard B, Zhang Y, Jolly S, Gallacher J, Davey-Smith G, Can G, Yüksel H, Nakagawa H, Morikawa Y, Miura K, Njølstad I, Ingelsson M, Giedraitis V, Ridker P, Gaziano J, Kivimaki M, Shipley M, Brunner E, Shipley M, Arndt V, Brenner H, Cook N, Ridker P, Ford I, Sattar N, Ibañez A, Geleijnse J. Equalization of four cardiovascular risk algorithms after systematic recalibration: individual-participant meta-analysis of 86 prospective studies. European Heart Journal 2018, 40: 621-631. PMID: 30476079, PMCID: PMC6374687, DOI: 10.1093/eurheartj/ehy653.Peer-Reviewed Original ResearchConceptsSystematic Coronary Risk EvaluationReynolds Risk ScoreFramingham risk scoreRisk algorithmStatin therapyCVD riskProspective studyRisk discriminationRisk scoreCardiovascular disease risk estimationPrimary prevention guidelinesCoronary Risk EvaluationRisk factor profileYear CVD riskCardiovascular risk algorithmsCVD risk algorithmsIndividual participant dataDisease risk estimationCohort EquationsCVD eventsCVD incidencePrevention guidelinesHigh riskFactor profileHead comparisonAccurate estimation of cardiovascular risk in a non-diabetic adult: detecting and correcting the error in the reported Framingham Risk Score for the Systolic Blood Pressure Intervention Trial population
Warner F, Dhruva SS, Ross JS, Dey P, Murugiah K, Krumholz HM. Accurate estimation of cardiovascular risk in a non-diabetic adult: detecting and correcting the error in the reported Framingham Risk Score for the Systolic Blood Pressure Intervention Trial population. BMJ Open 2018, 8: e021685. PMID: 30037874, PMCID: PMC6059296, DOI: 10.1136/bmjopen-2018-021685.Peer-Reviewed Original ResearchConceptsSystolic Blood Pressure Intervention TrialFramingham risk scoreCardiovascular riskRisk scoreStudy populationStudy participantsNon-diabetic adultsTotal study populationHigh-risk populationClinical trial dataClinical trial sitesTrial populationIntervention trialsRisk populationsNew England JournalIndependent investigatorsTrial dataSecondary analysisSPRINT trialSPRINT dataTrialsRiskScoresParticipantsPopulation
2015
Do Non-Clinical Factors Improve Prediction of Readmission Risk? Results From the Tele-HF Study
Krumholz HM, Chaudhry SI, Spertus JA, Mattera JA, Hodshon B, Herrin J. Do Non-Clinical Factors Improve Prediction of Readmission Risk? Results From the Tele-HF Study. JACC Heart Failure 2015, 4: 12-20. PMID: 26656140, PMCID: PMC5459404, DOI: 10.1016/j.jchf.2015.07.017.Peer-Reviewed Original ResearchConceptsReadmission ratesPatient-reported informationHeart failureHealth statusReadmission riskC-statisticRisk scorePsychosocial variablesMedical record abstractionWeeks of dischargeReadmission risk modelNon-clinical factorsCandidate risk factorsReadmission risk predictionRecord abstractionClinical variablesPatient interviewsMedical recordsRisk factorsPatientsPsychosocial informationPsychosocial characteristicsTelephone interviewsRisk predictionScores
2009
Determinants of Cardiac Catheterization Use in Older Medicare Patients With Acute Myocardial Infarction
Ko DT, Ross JS, Wang Y, Krumholz HM. Determinants of Cardiac Catheterization Use in Older Medicare Patients With Acute Myocardial Infarction. Circulation Cardiovascular Quality And Outcomes 2009, 3: 54-62. PMID: 20123672, PMCID: PMC3024143, DOI: 10.1161/circoutcomes.109.858456.Peer-Reviewed Original ResearchMeSH KeywordsAge FactorsAgedAged, 80 and overCardiac CatheterizationChi-Square DistributionComorbidityFemaleHemorrhageHospitalizationHumansLikelihood FunctionsLogistic ModelsMaleMedicareMyocardial InfarctionPatient SelectionPractice Patterns, Physicians'Risk AssessmentRisk FactorsSeverity of Illness IndexSex FactorsTime FactorsTreatment OutcomeUnited StatesConceptsAcute myocardial infarctionCardiac catheterization useCardiac catheterizationInappropriate indicationsAMI patientsAppropriate indicationsMyocardial infarctionRisk scoreCardiology/American Heart Association class IBaseline cardiovascular riskOlder Medicare patientsHigh-risk patientsDemographic factorsMore comorbiditiesCardiovascular riskOlder patientsMale sexProcedure indicationFemale sexMedicare patientsAmerican CollegeAMI admissionsMedicare feePatientsCatheterization
2005
Performance of the Thrombolysis in Myocardial Infarction (TIMI) ST-elevation myocardial infarction risk score in a national cohort of elderly patients
Rathore SS, Weinfurt KP, Foody JM, Krumholz HM. Performance of the Thrombolysis in Myocardial Infarction (TIMI) ST-elevation myocardial infarction risk score in a national cohort of elderly patients. American Heart Journal 2005, 150: 402-410. PMID: 16169316, PMCID: PMC2790534, DOI: 10.1016/j.ahj.2005.03.069.Peer-Reviewed Original ResearchConceptsThirty-day mortality rateTIMI scoreElderly patientsMortality rateRisk scoreMyocardial Infarction (TIMI) risk scoreHigh TIMI scoreMyocardial Infarction (TIMI) scoreCommunity-based cohortReperfusion therapyTrial cohortPrognostic discriminationTrial populationNational cohortModest discriminationPatientsUS hospitalsSTEMICohortScore discriminationScoresThrombolysis