2022
A risk model for decline in health status after acute myocardial infarction among older adults
Hajduk A, Dodson J, Murphy T, Chaudhry S. A risk model for decline in health status after acute myocardial infarction among older adults. Journal Of The American Geriatrics Society 2022, 71: 1228-1235. PMID: 36519774, PMCID: PMC10089939, DOI: 10.1111/jgs.18162.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionHealth status declineImportant patient-centered outcomesPatient-centered outcomesMyocardial infarctionStatus declineHealth statusOlder adultsShort Form-12 Physical Component ScorePhysical component scoreProspective cohort studyRisk prediction modelLogistic regression modelsOlder adult survivorsPredictive risk modelRisk modelCohort studyPatient ageValidation cohortC-statisticRisk factorsHigh riskLower riskAdult survivorsHospitalizationRacial disparities among older adults with acute myocardial infarction: The SILVER‐AMI study
Demkowicz P, Hajduk A, Dodson J, Oladele C, Chaudhry S. Racial disparities among older adults with acute myocardial infarction: The SILVER‐AMI study. Journal Of The American Geriatrics Society 2022, 71: 474-483. PMID: 36415964, PMCID: PMC9957871, DOI: 10.1111/jgs.18084.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionFunctional impairmentGeriatric conditionsClinical characteristicsMyocardial infarctionBlack participantsOlder adultsRacial disparitiesRisk of mortalityMore functional impairmentSILVER-AMI StudyBlack older adultsYounger average ageUnadjusted oddsDaily livingAMI hospitalizationAverage ageGeriatric phenotypesUS hospitalsHospitalizationLogistic regressionMortalityImpairmentInfarctionUnintentional loss
2017
Impact of Telemonitoring on Health Status
Jayaram NM, Khariton Y, Krumholz HM, Chaudhry SI, Mattera J, Tang F, Herrin J, Hodshon B, Spertus JA. Impact of Telemonitoring on Health Status. Circulation Cardiovascular Quality And Outcomes 2017, 10: e004148. PMID: 29237746, PMCID: PMC5776725, DOI: 10.1161/circoutcomes.117.004148.Peer-Reviewed Original ResearchConceptsKansas City Cardiomyopathy QuestionnaireUsual careHealth statusHeart failureKCCQ overall summary scoreRecent heart failure hospitalizationDisease-specific health statusKCCQ overall summaryHeart failure hospitalizationWeeks of dischargeRandomized clinical trialsOverall summary scoreKCCQ scoresNoninvasive TelemonitoringFailure hospitalizationBaseline characteristicsSecondary outcomesTreatment armsClinical trialsSummary scoresPatientsSubscale scoresCareHospitalizationScores
2015
Racial Differences in Heart Failure Outcomes Evidence From the Tele-HF Trial (Telemonitoring to Improve Heart Failure Outcomes)
Qian F, Parzynski CS, Chaudhry SI, Hannan EL, Shaw BA, Spertus JA, Krumholz HM. Racial Differences in Heart Failure Outcomes Evidence From the Tele-HF Trial (Telemonitoring to Improve Heart Failure Outcomes). JACC Heart Failure 2015, 3: 531-538. PMID: 26160368, PMCID: PMC8635169, DOI: 10.1016/j.jchf.2015.03.005.Peer-Reviewed Original ResearchConceptsPatient-reported health statusKansas City Cardiomyopathy QuestionnaireHeart failureBlack patientsHealth statusHF hospitalizationRacial differencesBaseline KCCQ scoresClinical laboratory valuesPatient-reported outcomesSignificant racial differencesKCCQ scoresHF admissionsPatient demographicsWhite patientsLaboratory valuesPropensity score methodsPatientsHospitalizationOutcome evidenceMonthsLinear mixed modelsBaselineTrialsStatus
2010
Telemonitoring in Patients with Heart Failure
Chaudhry SI, Mattera JA, Curtis JP, Spertus JA, Herrin J, Lin Z, Phillips CO, Hodshon BV, Cooper LS, Krumholz HM. Telemonitoring in Patients with Heart Failure. New England Journal Of Medicine 2010, 363: 2301-2309. PMID: 21080835, PMCID: PMC3237394, DOI: 10.1056/nejmoa1010029.Peer-Reviewed Original ResearchConceptsPrimary end pointUsual care groupSecondary end pointsHeart failureEnd pointHeart failure outcomesNumber of hospitalizationsTelephone-based interactive voice response systemUsual careAdverse eventsPatient's clinicianMedian ageCare groupLarge trialsInteractive voice response systemPatientsSmall studyVoice response systemNumber of daysHospitalizationReadmissionTelemonitoringSignificant differencesCliniciansDeath
2007
Patterns of Weight Change Preceding Hospitalization for Heart Failure
Chaudhry SI, Wang Y, Concato J, Gill TM, Krumholz HM. Patterns of Weight Change Preceding Hospitalization for Heart Failure. Circulation 2007, 116: 1549-1554. PMID: 17846286, PMCID: PMC2892745, DOI: 10.1161/circulationaha.107.690768.Peer-Reviewed Original ResearchConceptsHeart failure hospitalizationHeart failureControl patientsFailure hospitalizationBody weightCase patientsWeight gainDecompensated heart failureHeart failure severityBaseline body weightHeart failure decompensationPatient's body weightAdjusted odds ratioCase-control studyGradual weight gainDisease management programsHigh-risk periodBasis of ageOdds ratioHospitalizationPatientsMean increaseDaily weightWeight changeHome monitoring
2004
Educational disadvantage impairs functional recovery after hospitalization in older persons
Chaudhry SI, Friedkin RJ, Horwitz RI, Inouye SK. Educational disadvantage impairs functional recovery after hospitalization in older persons. The American Journal Of Medicine 2004, 117: 650-656. PMID: 15501202, DOI: 10.1016/j.amjmed.2004.06.026.Peer-Reviewed Original ResearchConceptsPoor functional recoveryImpairs functional recoveryFunctional recoveryLower educationBaseline impairmentOlder personsPoor self-rated healthDaily living scoreImpairment of activitiesSelf-rated healthLow educational levelClinical factorsIndependent predictorsLiving scoreHigh comorbidityDaily livingHospitalizationCognitive impairmentOlder adultsImpairmentPoor recoveryMonthsEducational levelSocial supportSignificant relation