Incidence of sudden cardiac death in adults with end-stage renal disease: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Although sudden cardiac death (SCD) is recognized as a distinct cause of death in patients with end stage renal disease (ESRD), its incidence has not been well summarized. METHODS: We performed a systematic review and meta-analysis of the literature based on a protocol developed a priori. We searched MEDLINE and EMBASE (inception to March 2015) for randomized controlled trials and cohort studies reporting the incidence of SCD in adult patients with ESRD on hemodialysis or peritoneal dialysis. We collected data on number of SCD as well as the definition of SCD for each individual study. A random-effects model was used to summarize the incidence of SCD. We conducted subgroup analyses to explore sources of heterogeneity. RESULTS: Forty two studies (n = 80,382 patients) were included in the meta-analysis. The incidence of SCD among adults with ESRD ranged from 0.4 to 10.04 deaths per 100 person-years. The definitions and assessment of SCD varied across the included studies. There was evidence of significant heterogeneity (I(2) = 98; p < 0.001), which was not explained by subgroup analyses stratified by mean age, proportion of hypertensive or diabetic patients, follow-up time, study size, or type of cohort studied. CONCLUSION: Current estimates of the incidence of SCD among adults with ESRD vary widely. There is a need for further studies to more accurately estimate the incidence of SCD in patients with ESRD.
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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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.036 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".