Abstract 4367189: Clinical Efficacy of Telemedicine vs Standard Outpatient Management for Heart Failure: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Telemedicine (TM) modalities—home tele-monitoring and structured telephone support—are proposed to improve outcomes in heart failure (HF), yet pooled effects remain inconsistent. Methods: PubMed, Embase, Cochrane, and Web of Science were searched to 1 June 2025 for randomised controlled trials (RCTs) or prospective cohorts comparing TM with usual outpatient HF care. Primary endpoints were all-cause mortality and HF-related readmission; quality of life (QoL) was secondary. Risk ratios (RRs) were pooled with a random-effects model (I2 for heterogeneity). Risk of bias was assessed with Cochrane RoB 2 and Newcastle–Ottawa tools. Protocol: PROSPERO. Results: Twenty-eight studies (24 RCTs, 4 cohorts) enrolling 11 334 patients (mean age 69 y; 37 % women) met criteria. TM reduced mortality (pooled RR 0.81, 95 % CI 0.72–0.91; I2 18 %). Benefit was greatest in tele-monitoring trials (RR 0.66, 0.54–0.81; 2 710 patients), whereas telephone support showed a non-significant trend (RR 0.88, 0.76–1.01).TM lowered HF readmissions by 22 % (RR 0.78, 0.69–0.88; I2 25 %) and had no effect on all-cause hospitalisations (RR 0.96, 0.88–1.04). QoL improved in six of eight studies reporting the Minnesota Living with HF Questionnaire (mean difference –3.4 points). Funnel-plot symmetry and Egger’s test (p = 0.28) suggested no publication bias; most trials were low-to-moderate risk. Conclusions: Across >11 000 HF patients, TM—especially home tele-monitoring—significantly reduces mortality and HF-specific readmissions and modestly enhances QoL compared with standard outpatient management. These contemporary data support routine integration of TM into HF care pathways and its inclusion in future guideline recommendations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.025 | 0.041 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".