Global and Temporal Trends in Utilization and Outcomes of Implantable Cardioverter Defibrillators in Hypertrophic Cardiomyopathy
Bibliographic record
Abstract
BACKGROUND: Over the past decades, hypertrophic cardiomyopathy has become a contemporary treatable disease. However, limited data exist on the global trends of implantable cardioverter defibrillator (ICD) utilization and its impact on mortality/morbidity burden reduction. METHODS: Electronic databases were systematically searched up to March 2024 for studies reporting on ICD utilization rates in hypertrophic cardiomyopathy. A random effects model was used to pool study estimates across time-era, geographic region, and age group. Primary outcome was global trends in ICD utilization. Secondary outcomes included trends of sudden cardiac death, appropriate/inappropriate shocks, and ICD-related complications. RESULTS: In total, 234 studies (N=92 500, 514 748 patient-years) met inclusion criteria. Mean age was 46.2 (12.4) years and 37.49% were women. A total of 12 139 patients (16.43%) received an ICD over 429 766 person-years of follow-up, with an ICD implantation rate of 2.79%/y ([95% CI, 2.35%–3.32%] I²=97.80%). Rates of ICD implantation steadily increased over time from 1990 (1.09%) to 2021 (4.01%; P =0.002), with noticeable geographic variation ( P =0.008). The overall rate of appropriate ICD discharges and ICD-related complications was 3.44%/y ([95% CI, 3.08%–3.84%] I²=88.40%) and 1.98%/y ([95% CI, 1.52%–2.59%] I²=90.44%), respectively, with no significant trend over time. The overall rate of inappropriate discharges was 3.58%/y ([95% CI, 3.08%–4.16%] I 2 =88.03%), and declined significantly over time ( P =0.044). There was a significant decline in the rates of sudden cardiac death from 1990 (0.84%/y) to 2020 (0.31%/y). CONCLUSIONS: Dramatic increases in ICD utilization have occurred, representing a 3.7-fold increase, with appropriate therapies occurring in 3.44%/y. In parallel a significant reduction in sudden cardiac death was observed, but there are insufficient data to demonstrate that a causative relationship exists. Geographic disparities in ICD utilization were evident, highlighting the need to improve access to specialized care for patients with hypertrophic cardiomyopathy. Geographic disparities in ICD utilization were evident, highlighting the need to improve access to specialized care for patients with hypertrophic cardiomyopathy. REGISTRATION: URL: https://www.crd.york.ac.uk/PROSPERO/ ; Unique identifier: CRD42023407126
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".