2021
180-day readmission risk model for older adults with acute myocardial infarction: the SILVER-AMI study
Dodson JA, Hajduk AM, Murphy TE, Geda M, Krumholz HM, Tsang S, Nanna MG, Tinetti ME, Ouellet G, Sybrant D, Gill TM, Chaudhry SI. 180-day readmission risk model for older adults with acute myocardial infarction: the SILVER-AMI study. Open Heart 2021, 8: e001442. PMID: 33452007, PMCID: PMC7813425, DOI: 10.1136/openhrt-2020-001442.Peer-Reviewed Original ResearchConceptsAcute myocardial infarctionReadmission risk modelSelf-reported health statusMyocardial infarctionFunctional mobilityOlder adultsHealth statusDays of AMIFirst diastolic blood pressureChronic obstructive pulmonary diseaseIschemic ECG changesProspective cohort studyDiastolic blood pressureObstructive pulmonary diseaseLength of stayInitial heart rateFinal risk modelSILVER-AMI StudyRisk modelInitial hemoglobinCohort studyReadmission ratesBlood pressureEjection fractionHeart failure
2019
Thirty-Day Readmission Risk Model for Older Adults Hospitalized With Acute Myocardial Infarction
Dodson JA, Hajduk AM, Murphy TE, Geda M, Krumholz HM, Tsang S, Nanna MG, Tinetti ME, Goldstein D, Forman DE, Alexander KP, Gill TM, Chaudhry SI. Thirty-Day Readmission Risk Model for Older Adults Hospitalized With Acute Myocardial Infarction. Circulation Cardiovascular Quality And Outcomes 2019, 12: e005320. PMID: 31010300, PMCID: PMC6481309, DOI: 10.1161/circoutcomes.118.005320.Peer-Reviewed Original ResearchMeSH KeywordsAge FactorsAgedAged, 80 and overFemaleGeriatric AssessmentHealth Status IndicatorsHumansMaleMyocardial InfarctionPatient AdmissionPatient ReadmissionPredictive Value of TestsProspective StudiesReproducibility of ResultsRisk AssessmentRisk FactorsTime FactorsTreatment OutcomeUnited StatesConceptsAcute myocardial infarctionReadmission risk modelFinal risk modelFunctional mobilityFunctional impairmentMyocardial infarctionOlder adultsFirst diastolic blood pressureChronic obstructive pulmonary diseaseAge-related functional impairmentsP2Y12 inhibitor useAcute kidney injuryDaily living (ADL) disabilityPatient-level factorsProspective cohort studyDiastolic blood pressureObstructive pulmonary diseasePatients of ageGeneral health statusStrongest predictorRisk modelMore comorbiditiesCause readmissionKidney injuryCohort study
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