Relative Frequency of Treatment Modality, Mortality, and Hospitalization Causes in Patients on Dialysis Across 41 Countries and Five Global Regions: A MONDO Initiative Report
Bibliographic record
Abstract
Background: This study aims to describe treatment modality, mortality, and hospitalization causes using a large international database from nine providers Methods: We present descriptive data from the MONDO Initiative, an academic-industry partnership collected anonymized data from multiple providers between 2000 and 2019. Data anonymization was performed in alignment with recommendations from a re-identification risk determination (Privacy Analytics, Ontario, CA). MONDO has U.S.-based ethics approval hosted by the Renal Research Institute Results: We report data on 292,531 dialysis patients from 41 countries across five regions: North America (6.4%), Latin America (33.4%), Europe (45.6%), Asia-Pacific (10.4%), and Africa/Other (4.2%). Of these, 172,301 (58.9%) were male. Age distribution (in years) was as follows: 0–17 (0.13%), 18–44 (1.5%), 45–64 (17.3%), 65–74 (38.3%), and ≥75 (23.6%). Self-reported ethnicity was unavailable for 39.3%, when 42.5% identified as White, 13.4% as Other, 3.8% as Black, and 1.1% as Asian. Across the follow-up period, 103, 380, 638 treatments were performed, mostly hemodialysis (56%), followed by hemodiafiltration (35.3%), and peritoneal dialysis (8.3%). The five leading causes of death were1) cardiovascular disease (41.2%), 2) circulatory disease (38.9%), 3) infectious disease (12.1%), 4) respiratory disease [non-infectious] (5.8%), and 5) cerebrovascular disease (5.3%). While the five leading causes for hospitalization were 1) circulatory disease (9%), 2) genitourinary disease (5.7%), 3) infectious disease (5.3%), 4) respiratory disease [non-infectious] (3.9%), and digestive disease (3.4%). Other causes accounted for smaller portion of events, not detailed in this abstract Conclusion: Our study presents one of the largest international cohorts of dialysis-dependent patients, with broad regional representation, including countries lacking formal registries. It contrasts with existing data and underscores the value of global registries for comprehensive population insights
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".