Harlan M Krumholz1, Sarwat I Chaudhry2, John A Spertus3, Jennifer A Mattera4, Beth Hodshon2, Jeph Herrin5. 1. Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut; Robert Wood Johnson Foundation Clinical Scholars Program, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut; Department of Health Policy and Management, Yale School of Public Health, New Haven, Connecticut. Electronic address: harlan.krumholz@yale.edu. 2. Section of General Internal Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut. 3. Mid America Heart Institute, University of Missouri-Kansas City School of Medicine, Kansas City, Missouri. 4. Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, Connecticut. 5. Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut; Health Research and Educational Trust, Chicago, Illinois.
Abstract
OBJECTIVES: This study sought to determine whether a model that included self-reported socioeconomic, health status, and psychosocial characteristics obtained from patients recently discharged from hospitalizations for heart failure substantially improved 30-day readmission risk prediction compared with a model that incorporated only clinical and demographic factors. BACKGROUND: Existing readmission risk models have poor discrimination and it is unknown whether they would be markedly improved by the inclusion of patient-reported information. METHODS: As part of the Tele-HF (Telemonitoring to Improve Heart Failure Outcomes) trial, we conducted medical record abstraction and telephone interviews in a sample of 1,004 patients recently hospitalized for heart failure to obtain clinical, functional, and psychosocial information within 2 weeks of discharge. Candidate risk factors included 110 variables divided into 2 groups: demographic and clinical variables generally available from the medical record; and socioeconomic, health status, adherence, and psychosocial variables from patient interview. RESULTS: The 30-day readmission rate was 17.1%. Using the 3-level risk score derived from the restricted medical record variables, patients with a score of 0 (no risk factors) had a readmission rate of 10.9% (95% confidence interval [CI]: 8.2% to 14.2%), and patients with a score of 2 (all risk factors) had a readmission rate of 32.1% (95% CI: 22.4% to 43.2%), a C-statistic of 0.62. Using the 5-level risk score derived from all variables, patients with a score of 0 (no risk factors) had a readmission rate of 9.6% (95% CI: 6.1% to 14.2%), and patients with a score of 4 (all risk factors) had a readmission rate of 55.0% (95% CI: 31.5% to 76.9%), a C-statistic of 0.65. CONCLUSIONS: Self-reported socioeconomic, health status, adherence, and psychosocial variables are not dominant factors in predicting readmission risk for patients with heart failure. Patient-reported information improved model discrimination and extended the predicted ranges of readmission rates, but the model performance remained poor. (Telemonitoring to Improve Heart Failure Outcomes [Tele-HF]; NCT00303212).
RCT Entities:
OBJECTIVES: This study sought to determine whether a model that included self-reported socioeconomic, health status, and psychosocial characteristics obtained from patients recently discharged from hospitalizations for heart failure substantially improved 30-day readmission risk prediction compared with a model that incorporated only clinical and demographic factors. BACKGROUND: Existing readmission risk models have poor discrimination and it is unknown whether they would be markedly improved by the inclusion of patient-reported information. METHODS: As part of the Tele-HF (Telemonitoring to Improve Heart Failure Outcomes) trial, we conducted medical record abstraction and telephone interviews in a sample of 1,004 patients recently hospitalized for heart failure to obtain clinical, functional, and psychosocial information within 2 weeks of discharge. Candidate risk factors included 110 variables divided into 2 groups: demographic and clinical variables generally available from the medical record; and socioeconomic, health status, adherence, and psychosocial variables from patient interview. RESULTS: The 30-day readmission rate was 17.1%. Using the 3-level risk score derived from the restricted medical record variables, patients with a score of 0 (no risk factors) had a readmission rate of 10.9% (95% confidence interval [CI]: 8.2% to 14.2%), and patients with a score of 2 (all risk factors) had a readmission rate of 32.1% (95% CI: 22.4% to 43.2%), a C-statistic of 0.62. Using the 5-level risk score derived from all variables, patients with a score of 0 (no risk factors) had a readmission rate of 9.6% (95% CI: 6.1% to 14.2%), and patients with a score of 4 (all risk factors) had a readmission rate of 55.0% (95% CI: 31.5% to 76.9%), a C-statistic of 0.65. CONCLUSIONS: Self-reported socioeconomic, health status, adherence, and psychosocial variables are not dominant factors in predicting readmission risk for patients with heart failure. Patient-reported information improved model discrimination and extended the predicted ranges of readmission rates, but the model performance remained poor. (Telemonitoring to Improve Heart Failure Outcomes [Tele-HF]; NCT00303212).
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