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
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