Improving HFpEF diagnosis in pacemaker patients: a call for structured screening
Bibliographic record
Abstract
Abstract Background Pacemaker (PM) patients may be at an increased risk of developing heart failure with preserved ejection fraction (HFpEF) due to pacing-induced cardiac remodeling and age-related cardiovascular changes. However, HFpEF remains underdiagnosed and poorly understood in this population. Early detection is crucial for timely intervention, improving outcomes and reducing complications. Purpose Evaluate the impact of a systematic screening program of HFpEF in a large cohort of PM patients. Methods This was a cross-sectional, single-center study of patients with right ventricular apical pacing PMs implanted between January 2018 and December 2022. Each participant underwent a comprehensive assessment, including echocardiography, NT-proBNP measurement, clinical evaluation, PM interrogation and frailty assessment using the Edmonton Frail Scale. HF was classified per the 2023 ESC guidelines, with detailed systolic and diastolic function evaluation, including strain analysis and diastolic stress testing when needed. Results A total of 510 PM patients were screened, revealing a high prevalence of previously unrecognized HF. Of these, 194(38.0%) were diagnosed wth HFpEF, 31(6.1%) HFmrEF and 15(2.9%) previously undiagnosed HFrEF. Notably, only 47(9.2%) of HFpEF patients had a prior diagnosis. HFpEF patients were older (median age 83 years; IQR 78–86; p=0.003), and had a higher proportion of females (50.5% vs 19.4% in HFmrEF and 33.3% in HFrEF; p=0.003). They also had a high-risk CV profile, namely hypertension (84.5%), dyslipidemia (72.2%) and diabetes (43.3%). HFpEF patients had similar PM type distribution and ventricular pacing burden (75.6%±29.4, p =0.135) compared to other HF groups, with significantly higher ventricular pacing compared to non-HF patients (75.6%±29.4 vs 60.7%±43.4, p<0.001). Echocardiography characteristics of HFpEF patients revealed higher left ventricular global longitudinal strain (GLS) (-13.5±4.32%) and left atrial reservoir strain (17.56±9.43%) compared to HFmrEF and HFrEF, but still significantly lower than non-HF patients (GLS:13.5 vs 15.1, p<0.001). Systematic screening led to key interventions: 28% of patients were referred to cardiology (17% for LVEF<40%, 11% for severe valvular disease), 18% were referred to other specialties for additional management and 28.8% of newly diagnosed HFpEF patients for therapeutic reassessment to improve guideline-directed treatment. Conclusion Systematic screening in PM patients revealed a substantial burden of unrecognized HFpEF, highlighting the need for proactive diagnostics in this high-risk group. HFpEF patients had significant cardiovascular risk factors, high ventricular pacing burden, and suboptimal guideline-directed therapy. Structured screening enabled earlier detection, specialist referrals, and therapeutic reassessment. These findings support integrating routine HF screening into PM management to enhance early diagnosis, treatment optimization, and patient outcomes.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".