Dialysis Outcomes Across Countries and Regions: A Global Perspective From the International Society of Nephrology Global Kidney Health Atlas Study
Bibliographic record
Abstract
Introduction: Kidney failure treated with hemodialysis (HD), or peritoneal dialysis (PD) is a major global health problem that is associated with increased risks of death and hospitalization. This study aimed to compare the incidence and causes of death and hospitalization during the first year of HD or PD among countries. Methods: The third iteration of the International Society of Nephrology Global Kidney Health Atlas (ISN-GKHA) was conducted between June and September 2022. For this analysis, data were obtained from the cross-sectional survey of key stakeholders from ISN-affiliated countries. Results: A total of 167 countries participated in the survey (response rate 87.4%). In 48% and 58% of countries, 1% to 10% of people treated with HD and PD died within the first year, respectively, with cardiovascular disease being the main cause. Access-related infections or treatment withdrawal owing to cost were important causes of death in low-income countries (LICs). In most countries, <30% and <20% of patients with HD and PD, respectively, required hospitalization during the first year. A greater proportion of patients with HD and PD in LICs were hospitalized in the first year than those in high-income countries (HICs). Access-related infection and cardiovascular disease were the commonest causes of hospitalization among patients with HD, whereas PD-related infection was the commonest cause in patients with PD. Conclusion: There is significant heterogeneity in the incidence and causes of death and hospitalization in patients with kidney failure treated with dialysis. These findings highlight opportunities to improve care, especially in LICs where infectious and social factors are strong contributors to adverse outcomes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".