Long-term clinical course and prognostic factors of heart failure with reduced ejection fraction (HFrEF) patients underwent primary prophylactic implantable cardioverter defibrillator (ICD)
Bibliographic record
Abstract
Abstract Background For decades, ICD is a well-established therapy for improving prognosis of structural heart disease with severe cardiac dysfunction, and ICD for primary prophylaxis against sudden cardiac death were routinely provided. However, long-term prognosis and clinical course are different in each individual patient with an ICD, and it is moreover unclear what kind of factors might have influences on their clinical outcomes. Purpose The aim of this study is to clarify long-term prognosis and predictors of future major adverse cardiac events (MACEs) in HFrEF patients with an ICD as primary prophylaxis in Japanese population. Methods We retrospectively analyzed our ICD database. Patients underwent primary prophylactic ICD implantation from 2006 to 2020 at our institute and met the criteria of ICD recommendation of the latest Japanese guideline. Its requirements are receiving optimal medication therapy, symptomatic heart failure (New York Heart Association classification II or greater), and severe cardiac dysfunction (left ventricular ejection fraction (LVEF) is 35% or less). Additionally, prior NSVT is considered Class I ICD recommendation. In the case of ischemic cardiomyopathy (ICM), ICD implantation was done at least 40 days after myocardial infarction and at least 90 days after revascularization. MACEs were defined as composite outcome of cardiovascular death, heart failure hospitalization, and appropriate ICD therapies. Results A total of 148 consecutive patients were enrolled (male, 120 (81%); age, 62.1±11.8 years; LVEF, 23.0±5.86%; left ventricular end-diastolic diameter (LVDd), 67.6±9.26mm; paroxysmal or persistent atrial fibrillation (AF), 38 (26%); NSVT, 113 (76%); use of class III antiarrhythmic drugs, 48 (32%); ICM, 49 (33%); cardiac resynchronization therapy (CRT), 63 (43%)). One hundred twenty patients (81%) were programmed with a shock-only zone over 200 beats per minute. The median follow-up duration was 58.5 months. Among those 148 patients, MACEs were occurred to 60 patients (41%). As a result of dividing all patients into two groups by the occurrence of MACE, LVEF and LVDd were worse in MACE(+) group, whereas, MACE(−) had greater number of co morbidities. The results of cox-regression analysis showed LVDd (HR: 1.07, 95% CI: 1.03–1.12, p<0.001), AF (HR: 2.88, 95% CI: 1.56–5.31, p<0.001) and ICM (HR: 1.78, 95% CI: 1.00–3.16, p=0.049) were the independent predictors of MACEs (Table). However, initial ICD programming was not related to the occurrence of MACE. Conclusions The incidence of MACEs in patients with an ICD and severe HFrEF was substantially high in this Japanese population. Etiology of ICM, left ventricle size, and AF were the potential risk factors for future MACEs. Funding Acknowledgement Type of funding sources: None.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".