Cardiovascular Morbidity Patterns in Patients on Dialysis Globally in Apollo Dial DB
Bibliographic record
Abstract
Background: Cardiovascular diseases (CVDs) affect most people with kidney failure but are undefined globally. We aimed to analyze CVD prevalence among dialysis patients treated in 40 countries across six continents, as represented in the first version of a global dialysis database (Apollo Dial DB). Methods: Apollo Dial DB includes adult dialysis patient data from a global kidney network during Jan 2018-Mar 2021 (Fresenius Medical Care, Bad Homburg, DE). Data anonymization was performed in alignment with recommendations from a re-identification risk determination (Privacy Analytics, Ontario, CA). This analysis assessed CVD comorbidities based on ICD-10 codes. Results: Among 543,169 patients included, 79% reported ≥1 CVD condition. The prevalence of CVD conditions showed some differences by age and sex (Figure 1). Hypertension was the most common, affecting 73.6% of patients. Atherosclerotic heart disease affected 19.0%, increasing with age (9.9% in 18-44 years to 24.1% in ≥75) and more common in males (20.3%) than females (17.2%). Congestive heart failure affected 17.5%, also increasing with age. Other conditions included peripheral vascular disease (11.5%), cardiomyopathy (7.3%), and cardiac dysrhythmias (7.1%), all more prevalent in older age groups and slightly higher in males. Conclusion: Hypertension is the most common CVD comorbidity among dialysis patients globally, followed by atherosclerotic heart disease and congestive heart failure. The prevalence of these conditions increases with age and is slightly higher in males. Future analyses are needed to explore differences by world region, which could inform region-specific management strategies. Funding: Commercial Support - Fresenius Medical CareFigure 1: Distribution of cardiovascular diseases by age group and gender
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".