Abstract 4364669: Systematic Review and Meta-Analysis on CardioMEMS for Enhanced Heart Failure Management: Examining Randomized Controlled Trials and Observational Studies
Bibliographic record
Abstract
Introduction: Patients with severe or advanced heart failure symptoms are often hospitalized for acute decompensated heart failure despite being on guideline-directed medical therapies (GDMT). Some randomized control trials have shown that implanting CardioMEMS, a pressure sensor device placed inside the pulmonary artery to measure systolic and diastolic pulmonary pressure, can improve rates of heart failure hospitalization without affecting mortality. However, many non-randomized observational studies have not been thoroughly investigated and compared with the findings from RCTs. Objectives: Compare the one-year heart failure hospitalization rates, cardiovascular mortality, and all-cause mortality between patients with and without CardioMEMS. Methods: A systematic search of PubMed, Scopus, Embase, Cochrane Library, and Web of Science was conducted through April 30, 2025. A total of 420 studies, including randomized control trials, prospective single-arm studies, retrospective cohorts, and case-control studies, were screened. Studies that reported a Hazard ratio (HR) or relative risk (RR) were included. Random effects models were utilized to derive pooled HR and RR. Study quality and bias were evaluated using the Newcastle-Ottawa scale, funnel plots, and Egger’s test. Results: A total of 19 studies, three RCTs and 16 observational studies, with 11,343 participants, were included. The data showed a mean age ranging from 60.9 to 75.5 years, 34.5% women, 76.1-83.7% Whites, and 12.6-18.4% Blacks. 65.3% had HFrEF, mostly NYHA class III. Pooled RCTs showed a 31% relative risk reduction (HR: 0.69; 95% CI: 0.55–0.86; I2 = 61.3%). Observational studies also showed benefit: those reporting hazard ratios had a pooled HR of 0.48 (95% CI: 0.36–0.62; I2 = 90.5%), while those reporting risk ratios yielded a pooled RR of 0.38 (95% CI: 0.30–0.47; I2 = 68.7%). All-cause mortality from RCTs showed no difference (HR: 0.97, CI: 0.74-1.28). However, the observational studies lacked sufficient data on cardiovascular and all-cause mortality. Conclusion: CardioMEMS implantation was associated with a significant reduction in one-year heart failure hospitalizations across study designs. Both randomized and observational studies reported comparable outcomes in controlled and real-world settings.
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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 | 0.037 | 0.087 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.034 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".