Trial reportJACC. Heart failure2016
Do Non-Clinical Factors Improve Prediction of Readmission Risk?: Results From the Tele-HF Study.
Trial report in JACC. Heart failure, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00303212 (A Study of Telemonitoring to Improve Heart Failure Outcomes), which is not on this map. Cited by 26 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Study of Telemonitoring to Improve Heart Failure Outcomes
Who cites it
26 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Can prediction models for hospital readmission be improved by incorporating patient-reported outcome measures? A systematic review and narrative synthesis.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024Pooled it
- Evaluating risk prediction models for adults with heart failure: A systematic literature review.PloS one · 2020Pooled it
- Analysis of Machine Learning Techniques for Heart Failure Readmissions.Circulation. Cardiovascular quality and outcomes · 2016Pooled it
- Predictive Performance of Machine Learning Models for Heart Failure Readmission: A Systematic Review.Biomedicines · 2025Review
- Social Determinants of Health and 30-Day Readmission for Heart Failure Patients in U.S. Hospitals: Evidence from ICD-10 Z-Code Data.Healthcare (Basel, Switzerland) · 2025Article
- Leveraging deep survival models to predict quality of care risk in diverse hospital readmissions.Scientific reports · 2023Article
- Article
- Prognostic value of patient-reported outcomes in predicting 30 day all-cause readmission among older patients with heart failure.ESC heart failure · 2022Article
- Do patient-reported outcome measures measure up? A qualitative study to examine perceptions and experiences with heart failure proms among diverse, low-income patients.Journal of patient-reported outcomes · 2022Article
- Machine learning for predicting readmission risk among the frail: Explainable AI for healthcare.Patterns (New York, N.Y.) · 2022Article
- PRADOC: a trial on the efficiency of a transition care management plan for hospitalized patients with heart failure in France.ESC heart failure · 2021Article
- Predicting Cardiovascular Risk Using Social Media Data: Performance Evaluation of Machine-Learning Models.JMIR cardio · 2021Article
- A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders.ACM transactions on computing for healthcare · 2021Article
- The association of hospital teaching intensity with 30-day postdischarge heart failure readmission and mortality rates.Health services research · 2020Article
- Variability in Language used on Social Media prior to Hospital Visits.Scientific reports · 2020Article
- Patient-Associated Predictors of 15- and 30-Day Readmission After Hospitalization for Acute Heart Failure.Current heart failure reports · 2019Observational
- Development and Prospective Validation of a Machine Learning-Based Risk of Readmission Model in a Large Military Hospital.Applied clinical informatics · 2019Article
- Relation of Living in a "Food Desert" to Recurrent Hospitalizations in Patients With Heart Failure.The American journal of cardiology · 2019Article
- Predicting 30-day mortality and 30-day re-hospitalization risks in Medicare patients with heart failure discharged to skilled nursing facilities: development and validation of models using administrative data.The journal of nursing home research sciences · 2019Article
- Incorporating patient-centered factors into heart failure readmission risk prediction: A mixed-methods study.American heart journal · 2018Article
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Authors and funding
6 authors.
Funding
Abstract
objectivesThis 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.
backgroundExisting readmission risk models have poor discrimination and it is unknown whether they would be markedly improved by the inclusion of patient-reported information.
methodsAs 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.
resultsThe 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.
conclusionsSelf-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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.