2022
Associations between hospitalist physician workload, length of stay, and return to the hospital
Djulbegovic M, Chen K, Cohen AB, Heacock D, Canavan M, Cushing W, Agarwal R, Simonov M, Chaudhry SI. Associations between hospitalist physician workload, length of stay, and return to the hospital. Journal Of Hospital Medicine 2022, 17: 445-455. PMID: 35662410, PMCID: PMC9248905, DOI: 10.1002/jhm.12847.Peer-Reviewed Original ResearchConceptsLength of stayEmergency departmentPhysician workloadYale-New Haven HospitalMedian patient ageSeverity of illnessElectronic health record dataNumber of patientsHealth record dataWeekend admissionHospital daysPatient ageClinical outcomesObservational studyHospitalist serviceMAIN OUTCOMEPatient complexityHospitalist workloadSociodemographic factorsPatient encountersPatientsLogistic regressionMultilevel PoissonRecord dataOutcomesAssociation of Financial Strain With Mortality Among Older US Adults Recovering From an Acute Myocardial Infarction
Falvey JR, Hajduk AM, Keys CR, Chaudhry SI. Association of Financial Strain With Mortality Among Older US Adults Recovering From an Acute Myocardial Infarction. JAMA Internal Medicine 2022, 182: 445-448. PMID: 35188537, PMCID: PMC8861896, DOI: 10.1001/jamainternmed.2021.8569.Peer-Reviewed Original Research
2018
Long-Term Cognitive Decline After Newly Diagnosed Heart Failure
Hammond CA, Blades NJ, Chaudhry SI, Dodson JA, Longstreth WT, Heckbert SR, Psaty BM, Arnold AM, Dublin S, Sitlani CM, Gardin JM, Thielke SM, Nanna MG, Gottesman RF, Newman AB, Thacker EL. Long-Term Cognitive Decline After Newly Diagnosed Heart Failure. Circulation Heart Failure 2018, 11: e004476. PMID: 29523517, PMCID: PMC6072263, DOI: 10.1161/circheartfailure.117.004476.Peer-Reviewed Original ResearchConceptsIncident heart failureHeart failureYears of ageEjection fractionHF diagnosisAtrial fibrillationModified Mini-Mental State Examination scoresCommunity-based prospective cohort studyCognitive declineHistory of HFMini-Mental State Examination scoreLong-term cognitive declineModified Mini-Mental State ExaminationAtrial fibrillation statusComorbid atrial fibrillationMini-Mental State ExaminationProspective cohort studyGlobal cognitive abilityState Examination scoreHospital discharge summariesPublic health implicationsCohort studyClinical strokeCardiovascular healthAdjusted model
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 scoresCareHospitalizationScoresAssessing the reliability of self-reported weight for the management of heart failure: application of fraud detection methods to a randomised trial of telemonitoring
Steventon A, Chaudhry SI, Lin Z, Mattera JA, Krumholz HM. Assessing the reliability of self-reported weight for the management of heart failure: application of fraud detection methods to a randomised trial of telemonitoring. BMC Medical Informatics And Decision Making 2017, 17: 43. PMID: 28420352, PMCID: PMC5395848, DOI: 10.1186/s12911-017-0426-4.Peer-Reviewed Original ResearchConceptsEnd-digit preferenceHeart failureHeart Failure Outcomes trialEffective preventive careCharacteristics of patientsSelf-reported weightHealth care professionalsSix-month trial periodIntervention patientsMore medicationsAccuracy of reportingOutcome trialsTrial enrollmentPreventive careClinical managementUnnecessary treatmentDesign of initiativesCare professionalsPatientsRegistration numberAlert fatigueElectronic medical dataTrial periodTrialsNumber of days
2016
“Deterioration to Door Time”: An Exploratory Analysis of Delays in Escalation of Care for Hospitalized Patients
Sankey CB, McAvay G, Siner JM, Barsky CL, Chaudhry SI. “Deterioration to Door Time”: An Exploratory Analysis of Delays in Escalation of Care for Hospitalized Patients. Journal Of General Internal Medicine 2016, 31: 895-900. PMID: 26969311, PMCID: PMC4945556, DOI: 10.1007/s11606-016-3654-x.Peer-Reviewed Original ResearchConceptsMedical intensive care unitEscalation of careIntensive care unitClinical deteriorationCare escalationHospitalized patientsCare unitDoor timeInpatient medicineRetrospective cohort studySeverity of illnessRisk of deathAcademic medical centerHospital mortalityICU transferCohort studyInpatient floorInpatient settingMedical CenterGeneral floorPatientsCareMortalityCurrent eraPrevious studies
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 predictionScoresThe 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 schemesPhysicians' perceptions of the Thrombolysis in Myocardial Infarction (TIMI) risk score in older adults with acute myocardial infarction
Feder SL, Schulman-Green D, Geda M, Williams K, Dodson JA, Nanna MG, Allore HG, Murphy TE, Tinetti ME, Gill TM, Chaudhry SI. Physicians' perceptions of the Thrombolysis in Myocardial Infarction (TIMI) risk score in older adults with acute myocardial infarction. Heart & Lung 2015, 44: 376-381. PMID: 26164651, PMCID: PMC4567390, DOI: 10.1016/j.hrtlng.2015.05.005.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionMyocardial Infarction (TIMI) risk scoreRisk scoreOlder adultsMyocardial infarctionMedian sample ageTIMI risk scoreRisk stratification modelSemi-structured telephone interviewsRisk factorsNew risk modelAMI treatmentPhysicians' perceptionsMortality riskClinical experienceClinical practiceStratification modelTelephone interviewsAdultsThrombolysisInfarctionConstant comparative methodPhysiciansScoresQualitative studyRacial 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 modelsBaselineTrialsStatusRisk Stratification in Older Patients With Acute Myocardial Infarction
Feder SL, Schulman-Green D, Dodson JA, Geda M, Williams K, Nanna MG, Allore HG, Murphy TE, Tinetti ME, Gill TM, Chaudhry SI. Risk Stratification in Older Patients With Acute Myocardial Infarction. Journal Of Aging And Health 2015, 28: 387-402. PMID: 26100619, PMCID: PMC4886275, DOI: 10.1177/0898264315591005.Peer-Reviewed Original Research
2014
Procedure timing as a predictor of inhospital adverse outcomes from implantable cardioverter-defibrillator implantation: Insights from the National Cardiovascular Data Registry
Hsu JC, Varosy PD, Parzynski CS, Chaudhry SI, Dewland TA, Curtis JP, Marcus GM. Procedure timing as a predictor of inhospital adverse outcomes from implantable cardioverter-defibrillator implantation: Insights from the National Cardiovascular Data Registry. American Heart Journal 2014, 169: 45-52.e3. PMID: 25497247, DOI: 10.1016/j.ahj.2014.10.006.Peer-Reviewed Original ResearchConceptsHospital stayAdverse eventsICD recipientsWeekends/holidaysInhospital deathGreater oddsNational Cardiovascular Data Registry ICD RegistryFirst-time ICD recipientsImplantable cardioverter defibrillator implantationNational Cardiovascular Data RegistryImplantable cardioverter-defibrillator (ICD) proceduresImplantable cardioverter defibrillator recipientsHierarchical multivariable logistic regressionInhospital adverse outcomesCardioverter-defibrillator implantationProlonged hospital stayMultivariable logistic regressionReal-world populationAfternoon/eveningTotal complicationsMultivariable adjustmentAdverse outcomesHospital characteristicsICD implantsData registryPlace of Residence and Outcomes of Patients With Heart Failure
Bikdeli B, Wayda B, Bao H, Ross JS, Xu X, Chaudhry SI, Spertus JA, Bernheim SM, Lindenauer PK, Krumholz HM. Place of Residence and Outcomes of Patients With Heart Failure. Circulation Cardiovascular Quality And Outcomes 2014, 7: 749-756. PMID: 25074375, PMCID: PMC5323058, DOI: 10.1161/circoutcomes.113.000911.Peer-Reviewed Original ResearchConceptsNeighborhood socioeconomic statusIndividual socioeconomic statusOutcomes of patientsHeart failureHigher SES neighborhoodsSocioeconomic statusClinical factorsHeart Failure Outcomes trialPrimary end pointPatient-level factorsUS internal medicineCause mortalityCause readmissionMultivariable adjustmentOutcome trialsMedical chartsPatient interviewsLow-SES neighborhoodsCardiology practiceMortality ratePatientsPlace of residenceInternal medicineReadmissionEnd point
2013
Use of Remote Monitoring of Newly Implanted Cardioverter-Defibrillators
Akar JG, Bao H, Jones P, Wang Y, Chaudhry SI, Varosy P, Masoudi FA, Stein K, Saxon LA, Curtis JP. Use of Remote Monitoring of Newly Implanted Cardioverter-Defibrillators. Circulation 2013, 128: 2372-2383. PMID: 24043302, DOI: 10.1161/circulationaha.113.002481.Peer-Reviewed Original ResearchConceptsMedian odds ratioOdds ratioNational Cardiovascular Data Registry ICD RegistryRemote patient monitoringMultivariable logistic regressionHealth-related factorsImplanted cardioverter defibrillatorEligible patientsICD RegistryLack of enrollmentCurrent guidelinesCardioverter defibrillatorPatientsLogistic regressionLocal practice environmentMajor causeHealth insuranceSubsequent activationEnrollmentPractice environmentPatient monitoringPhysiciansActivationRPM systemSuccessful use
2011
Trends in Comorbidity, Disability, and Polypharmacy in Heart Failure
Wong CY, Chaudhry SI, Desai MM, Krumholz HM. Trends in Comorbidity, Disability, and Polypharmacy in Heart Failure. The American Journal Of Medicine 2011, 124: 136-143. PMID: 21295193, PMCID: PMC3237399, DOI: 10.1016/j.amjmed.2010.08.017.Peer-Reviewed Original ResearchConceptsProportion of patientsHeart failureSelf-reported heart failureHeart failure populationComorbid chronic conditionsNumber of comorbiditiesNutrition Examination SurveyCare of patientsPrevalence of disabilityPhenotype of patientsComplexity of patientsMedication useRecent patientsFunctional disabilityExamination SurveyFailure populationPhysical functionPrescription medicationsPatient preferencesChronic conditionsNational HealthPatient's abilityComorbiditiesPatientsOlder individuals
2010
Racial Disparities in Health Literacy and Access to Care Among Patients With Heart Failure
Chaudhry SI, Herrin J, Phillips C, Butler J, Mukerjhee S, Murillo J, Onwuanyi A, Seto TB, Spertus J, Krumholz HM. Racial Disparities in Health Literacy and Access to Care Among Patients With Heart Failure. Journal Of Cardiac Failure 2010, 17: 122-127. PMID: 21300301, PMCID: PMC3053061, DOI: 10.1016/j.cardfail.2010.09.016.Peer-Reviewed Original ResearchConceptsHeart failure patientsHealth literacyFailure patientsHeart failureInsurance statusWorse health literacyOutpatient medical careRacial differencesImportant racial differencesNoncardiac comorbiditiesUnadjusted analysesBlack raceCare existMedical homePatientsMedical careRacial disparitiesSocioeconomic statusStrong associationPotential mediatorsHealth carePoor accessCareSocial supportStatusTelemonitoring 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 monitoringTelemonitoring for Patients With Chronic Heart Failure: A Systematic Review
Chaudhry SI, Phillips CO, Stewart SS, Riegel B, Mattera JA, Jerant AF, Krumholz HM. Telemonitoring for Patients With Chronic Heart Failure: A Systematic Review. Journal Of Cardiac Failure 2007, 13: 56-62. PMID: 17339004, PMCID: PMC1910700, DOI: 10.1016/j.cardfail.2006.09.001.BooksConceptsHeart failure patientsFailure patientsHeart failureHigh-risk heart failure patientsHigh-risk Hispanic populationMonitoring of signsChronic heart failureHeart failure hospitalizationLow-risk patientsDisease managementHigh-quality careFailure hospitalizationNegative studiesSymptom monitoringPatientsHealth statusMEDLINE databaseSystematic reviewQuality carePhysiologic monitoringIntervention typeEvidence baseSimilar effectivenessIntervention costsHispanic population
2005
Do Age and Comorbidity Affect Intensity of Pharmacological Therapy for Poorly Controlled Diabetes Mellitus?
Chaudhry SI, Berlowitz DR, Concato J. Do Age and Comorbidity Affect Intensity of Pharmacological Therapy for Poorly Controlled Diabetes Mellitus? Journal Of The American Geriatrics Society 2005, 53: 1214-1216. PMID: 16108941, DOI: 10.1111/j.1532-5415.2005.53370.x.Peer-Reviewed Original ResearchConceptsDiabetes mellitusHypoglycemic medicationsOlder patientsVeterans Affairs national databasesIntensification of therapyRegular medical careObservational cohort studySingle clinic visitVA Medical CenterInfluence of ageClinic visitsCohort studyYounger patientsMedical therapyPharmacological therapyMedical CenterComorbiditiesMedical carePatientsTherapyMedicationsNational databaseMellitusOverall rateVisits