Systematic review and meta-analysis of cardiovascular event incidence and risk factors in pediatric dialysis patients
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) and left ventricular hypertrophy (LVH) are prevalent complications in pediatric and adolescent dialysis patients, elevating morbidity and mortality risks. Despite existing studies on cardiovascular risks, a systematic synthesis of their prevalence and contributing factors is lacking. This study aims to establish an evidence-based foundation to guide clinical interventions. METHODS: We systematically searched PubMed, Embase, Cochrane Library, and Web of Science until March 2025. Cardiovascular events and determinants were descriptively analyzed, while LVH prevalence underwent meta-analysis using a random-effects model. Heterogeneity sources were explored via sensitivity and subgroup analyses (stratified by dialysis modality and study quality), with intergroup differences assessed by mixed-effects meta-regression. Study quality was evaluated using the Newcastle-Ottawa scale (NOS) for observational studies and the Agency for Healthcare Research and Quality checklist for cross-sectional studies. Heterogeneity was quantified with Cochran Q and I2 statistics. RESULTS: Ten observational studies (5 cohorts, 5 cross-sectional) enrolling 6012 pediatric and adolescent dialysis patients (publication years 1996-2023) were included in the final analysis. The random-effects meta-analysis revealed a pooled LVH prevalence of 56% (95% confidence intervals (CI): 44-69%; I2 = 81.9%, P < .001), indicating substantial heterogeneity. Subgroup analyses demonstrated a numerically higher LVH prevalence in hemodialysis (HD) (66.0%, 95% CI: 52-78%) versus peritoneal dialysis (PD) patients (51.5%, 95% CI: 38-65%), though this difference lacked statistical significance (P = .204). Age, gender, and HD modality were independent risk factors. The incidence of cardiovascular-related events was significantly higher in female patients than in males. CONCLUSION: Pediatric dialysis patients show significantly higher risks of cardiovascular events and LVH versus controls. This necessitates regular primary echocardiographic monitoring, blood pressure optimization, and risk stratification. Future multicenter studies should: provide optimal dialysis modalities; conduct high-quality research to inform clinical interventions.
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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 | 0.017 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".