2023
Developing Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study
Bikdeli B, Lo Y, Khairani C, Bejjani A, Jimenez D, Barco S, Mahajan S, Caraballo C, Secemsky E, Klok F, Hunsaker A, Aghayev A, Muriel A, Wang Y, Hussain M, Appah-Sampong A, Lu Y, Lin Z, Aneja S, Khera R, Goldhaber S, Zhou L, Monreal M, Krumholz H, Piazza G. Developing Validated Tools to Identify Pulmonary Embolism in Electronic Databases: Rationale and Design of the PE-EHR+ Study. Thrombosis And Haemostasis 2023, 123: 649-662. PMID: 36809777, PMCID: PMC11200175, DOI: 10.1055/a-2039-3222.Peer-Reviewed Original ResearchConceptsElectronic health recordsNLP algorithmNatural language processing toolsLanguage processing toolsPrincipal discharge diagnosisICD-10 codesDischarge diagnosisNLP toolsChart reviewHealth systemProcessing toolsYale New Haven Health SystemPatient identificationElectronic databasesHealth recordsData validationHigh-risk PEPulmonary Embolism ResearchSecondary discharge diagnosisIdentification of patientsManual chart reviewNegative predictive valueCodeRadiology reportsAlgorithm
2021
Assessing Performance of Machine Learning—Reply
Khera R, Mortazavi BJ, Krumholz HM. Assessing Performance of Machine Learning—Reply. JAMA Cardiology 2021, 6: 1466-1466. PMID: 34586344, DOI: 10.1001/jamacardio.2021.3715.Commentaries, Editorials and Letters
2020
Association of Ticagrelor vs Clopidogrel With Net Adverse Clinical Events in Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention
You SC, Rho Y, Bikdeli B, Kim J, Siapos A, Weaver J, Londhe A, Cho J, Park J, Schuemie M, Suchard MA, Madigan D, Hripcsak G, Gupta A, Reich CG, Ryan PB, Park RW, Krumholz HM. Association of Ticagrelor vs Clopidogrel With Net Adverse Clinical Events in Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention. JAMA 2020, 324: 1640-1650. PMID: 33107944, PMCID: PMC7592033, DOI: 10.1001/jama.2020.16167.Peer-Reviewed Original ResearchMeSH KeywordsAcute Coronary SyndromeAdultAgedAged, 80 and overAlgorithmsAspirinCase-Control StudiesCause of DeathClopidogrelDatabases, FactualDyspneaFemaleHemorrhageHumansIschemiaMaleMiddle AgedMyocardial InfarctionNetwork Meta-AnalysisPercutaneous Coronary InterventionPropensity ScorePurinergic P2Y Receptor AntagonistsRecurrenceRepublic of KoreaRetrospective StudiesStrokeTicagrelorUnited StatesConceptsNet adverse clinical eventsAcute coronary syndromePercutaneous coronary interventionAdverse clinical eventsHemorrhagic eventsIschemic eventsHazard ratioCause mortalityCoronary syndromeCoronary interventionClinical eventsRisk of NACEClinical practiceLarge randomized clinical trialsPrimary end pointRetrospective cohort studyPropensity-matched pairsSummary hazard ratioRandomized clinical trialsRoutine clinical practiceSignificant differencesP2Y12 platelet inhibitorsTicagrelor groupCohort studySecondary outcomes
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 comparisonAdmission diagnoses among patients with heart failure: Variation by ACO performance on a measure of risk-standardized acute admission rates
Benchetrit L, Zimmerman C, Bao H, Dharmarajan K, Altaf F, Herrin J, Lin Z, Krumholz HM, Drye EE, Lipska KJ, Spatz ES. Admission diagnoses among patients with heart failure: Variation by ACO performance on a measure of risk-standardized acute admission rates. American Heart Journal 2018, 207: 19-26. PMID: 30404047, DOI: 10.1016/j.ahj.2018.09.006.Peer-Reviewed Original ResearchMeSH KeywordsAccountable Care OrganizationsAgedAlgorithmsAnalysis of VarianceCardiovascular DiseasesComorbidityFemaleHeart FailureHospitalizationHumansInternational Classification of DiseasesMaleMedicare Part AMedicare Part BPatient AdmissionPatient DischargePatient-Centered CareSex DistributionTime FactorsUnited StatesConceptsHeart failureAccountable care organizationsMean admission rateAdmission ratesAdmission typeAcute admission ratesNoncardiovascular conditionsAdmission diagnosisCause admission ratesMedicare Shared Savings Program Accountable Care OrganizationsRate of hospitalizationPrincipal discharge diagnosisProportion of admissionsType of admissionNoncardiovascular causesHF admissionsHF patientsPerson yearsDischarge diagnosisPatient populationPatientsAdmissionKey quality metricDiagnosisSubstantial proportion
2016
Analysis of Machine Learning Techniques for Heart Failure Readmissions
Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, Negahban SN, Krumholz HM. Analysis of Machine Learning Techniques for Heart Failure Readmissions. Circulation Cardiovascular Quality And Outcomes 2016, 9: 629-640. PMID: 28263938, PMCID: PMC5459389, DOI: 10.1161/circoutcomes.116.003039.Peer-Reviewed Original Research
2015
The Variation in Recovery
Spatz ES, Curry LA, Masoudi FA, Zhou S, Strait KM, Gross CP, Curtis JP, Lansky AJ, Soares Barreto-Filho JA, Lampropulos JF, Bueno H, Chaudhry SI, D'Onofrio G, Safdar B, Dreyer RP, Murugiah K, Spertus JA, Krumholz HM. The Variation in Recovery. Circulation 2015, 132: 1710-1718. PMID: 26350057, PMCID: PMC4858327, DOI: 10.1161/circulationaha.115.016502.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAge of OnsetAlgorithmsAortic DissectionClassificationCoronary DiseaseDiagnostic Techniques, CardiovascularFemaleHumansMaleMedical RecordsMiddle AgedMyocardial InfarctionMyocardiumOxygen ConsumptionPhenotypePlaque, AtheroscleroticProspective StudiesReproducibility of ResultsRisk FactorsSex FactorsTreatment OutcomeYoung AdultConceptsAcute myocardial infarctionCoronary artery diseaseArtery diseaseClinical phenotypeNonobstructive coronary artery diseaseYoung AMI Patients (VIRGO) studyObstructive coronary artery diseaseYoung womenType 2 acute myocardial infarctionBiological disease mechanismsSubset of patientsThird universal definitionUnique clinical phenotypeCulprit lesionClinical characteristicsMyocardial infarctionTherapeutic efficacyUniversal definitionStudy participantsPatientsSupply-demand mismatchYoung adultsDisease mechanismsPatient studiesCurrent classification schemesDevelopment and Validation of an Algorithm to Identify Planned Readmissions From Claims Data
Horwitz LI, Grady JN, Cohen DB, Lin Z, Volpe M, Ngo CK, Masica AL, Long T, Wang J, Keenan M, Montague J, Suter LG, Ross JS, Drye EE, Krumholz HM, Bernheim SM. Development and Validation of an Algorithm to Identify Planned Readmissions From Claims Data. Journal Of Hospital Medicine 2015, 10: 670-677. PMID: 26149225, PMCID: PMC5459369, DOI: 10.1002/jhm.2416.Peer-Reviewed Original ResearchConceptsSame-hospital readmissionsNegative predictive valuePositive predictive valuePredictive valueReadmission measuresHospital-wide readmission measureGold standard chart reviewAdministrative claims-based algorithmDiagnostic cardiac catheterizationClaims-based algorithmLarge teaching centersAcute care hospitalsSmall community hospitalUnplanned readmissionChart reviewCardiac catheterizationScheduled careSpecificity 96.5Community hospitalReadmissionClaims dataCardiac devicesHealth systemTeaching centerPublic reporting
2014
National Trends in Patient Safety for Four Common Conditions, 2005–2011
Wang Y, Eldridge N, Metersky ML, Verzier NR, Meehan TP, Pandolfi MM, Foody JM, Ho SY, Galusha D, Kliman RE, Sonnenfeld N, Krumholz HM, Battles J. National Trends in Patient Safety for Four Common Conditions, 2005–2011. New England Journal Of Medicine 2014, 370: 341-351. PMID: 24450892, PMCID: PMC4042316, DOI: 10.1056/nejmsa1300991.Peer-Reviewed Original ResearchConceptsCongestive heart failureAcute myocardial infarctionMore adverse eventsProportion of patientsAdverse event ratesAdverse eventsHeart failureMyocardial infarctionRate of occurrenceCommon medical conditionsMedical recordsMedicare patientsMedical conditionsPatientsSurgeryInfarctionPneumoniaHospitalizationPatient safetyNational trendsMonitoring System dataSignificant declineFailureProportionRate
2010
Patterns of moderate and vigorous physical activity in obese and overweight compared with non‐overweight children
DORSEY KB, HERRIN J, KRUMHOLZ HM. Patterns of moderate and vigorous physical activity in obese and overweight compared with non‐overweight children. Pediatric Obesity 2010, 6: e547-e555. PMID: 20883127, PMCID: PMC3815589, DOI: 10.3109/17477166.2010.490586.Peer-Reviewed Original ResearchConceptsVigorous physical activityOW/OBNon-overweight childrenMVPA boutsPhysical activityGreater body mass index z-scoreVPA boutsOW/OB groupBody mass index z-scoreMean daily MVPANon-overweight groupLess physical activityIndex z-scoreMinutes of MVPANon-overweight peersObese childrenObese participantsOverweight childrenOB groupDaily MVPASustained MVPADistinct patternsOB participantsMVPAConsecutive boutsThirty-Day Outcomes in Medicare Patients With Heart Failure at Heart Transplant Centers
Hummel SL, Pauli NP, Krumholz HM, Wang Y, Chen J, Normand SL, Nallamothu BK. Thirty-Day Outcomes in Medicare Patients With Heart Failure at Heart Transplant Centers. Circulation Heart Failure 2010, 3: 244-252. PMID: 20061519, DOI: 10.1161/circheartfailure.109.884098.Peer-Reviewed Original ResearchConceptsHeart transplant centersRisk-standardized readmission ratesRisk-standardized mortality ratesTransplant centersStandardized mortality ratioHeart failureTransplant hospitalsReadmission ratesMortality rateStandardized readmission ratioMortality ratioMedicare patientsReadmission ratiosMean standardized mortality ratioThirty-day outcomesCoronary artery bypassHeart failure careElderly Medicare patientsElderly Medicare beneficiariesArtery bypassElderly patientsTransplant candidatesMedicare beneficiariesHospitalPatients
2008
An algorithm for identifying physical activity patterns from motion data.
Dorsey KB, Herrin J, Krumholz HM. An algorithm for identifying physical activity patterns from motion data. Pediatric Exercise Science 2008, 20: 305-18. PMID: 18714120, DOI: 10.1123/pes.20.3.305.Peer-Reviewed Original Research
2007
Failure to Rescue
Horwitz LI, Cuny JF, Cerese J, Krumholz HM. Failure to Rescue. Medical Care 2007, 45: 283-287. PMID: 17496710, DOI: 10.1097/01.mlr.0000250226.33094.d4.Peer-Reviewed Original ResearchConceptsChart reviewQuality Patient Safety IndicatorsRetrospective chart reviewPatient safety indicatorsRate of deathAdministrative dataHospital complicationsComplication typePatient agePatient characteristicsPrimary outcomeNonsurgical casesInsurance statusComplicationsHealthcare ResearchPatientsMortalityConsortium institutionsAgeSafety indicatorsFailureReviewHospitalizationAdmissionCases
2006
Recursive partitioning–based preoperative risk stratification for atrial fibrillation after coronary artery bypass surgery
Sedrakyan A, Zhang H, Treasure T, Krumholz HM. Recursive partitioning–based preoperative risk stratification for atrial fibrillation after coronary artery bypass surgery. American Heart Journal 2006, 151: 720-724. PMID: 16504639, DOI: 10.1016/j.ahj.2005.05.010.Peer-Reviewed Original ResearchConceptsCoronary artery bypass graft surgeryAtrial fibrillationLow-risk groupAggressive prophylaxisRelative riskPredictors of AFArtery bypass graft surgeryCoronary artery bypass surgeryRisk of AFYale-New Haven HospitalOnly ejection fractionPreoperative atrial fibrillationBypass graft surgeryArtery bypass surgeryCoronary artery diseasePreoperative risk stratificationHigh-risk groupHeart disease severityPossible adverse eventsOlder age subgroupsArrhythmia prophylaxisGraft surgeryProphylactic therapyAdverse eventsBypass surgery
2004
Reporting of model validation procedures in human studies of genetic interactions
Coffey CS, Hebert PR, Krumholz HM, Morgan TM, Williams SM, Moore JH. Reporting of model validation procedures in human studies of genetic interactions. Nutrition 2004, 20: 69-73. PMID: 14698017, DOI: 10.1016/j.nut.2003.09.012.Peer-Reviewed Original Research