Outcomes after transvenous defibrillator implantation in cardiac sarcoidosis: a systematic review
Bibliographic record
Abstract
Introduction Sarcoidosis is a systemic inflammatory disorder associated with ventricular arrhythmias (VAs) and sudden death in the context of cardiac involvement. Established guidelines advocate implantable cardioverter-defibrillator (ICD) implantation in specific subcohorts, but there is a paucity of data on outcomes. Methods and Results We conducted a systematic review of published literature to assess outcomes in patients with cardiac sarcoidosis (CS) treated with ICD. Observational studies of patients with definite or probable CS and ICD implantation were identified from multiple databases from inception to 21 st May 2021. Outcomes of interest included appropriate and inappropriate ICD therapies in addition to all-cause mortality. Study quality was assessed individually using the Newcastle Ottawa Scale (NOS). Eight studies were identified comprising 530 patients, with follow-up period of 24 to 66 months (weighted average 40 months). Mean age was 53.9 years with ejection fraction of 41.3%. Overall incidence of appropriate therapy, reported in all studies, was 39% during follow-up. Left ventricular systolic dysfunction (LVSD) with ejection fraction < 40% was a predictor of appropriate therapy in the majority of studies, as were sustained VAs during electrophysiological testing (EP) in one study. Inappropriate therapy was documented in six studies and primarily driven by atrial arrhythmias. All-cause mortality was reported in six studies, with incidence of 6.0% over a median follow-up period of 42 months. Only three studies achieved good quality in the comparability domain of NOS. Conclusions Appropriate ICD therapy in patients with CS is commonly associated with LVSD, which may act as a surrogate for scar burden. The utility of EP testing in this setting remains unclear.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".