Profiles of Home Medication Use in Patients on Dialysis Globally
Bibliographic record
Abstract
Background: Global patterns of medication use in dialysis are undefined. This project aims to provide a real-world view of home medication use in dialysis using a global dialysis dataset from 40 countries called Apollo Dial DB. The most common prescribed medications administered at home by patients were compared by modality. 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). Home medications were analyzed based on top (most common) five pharmacological classes defined by first 4-digits of ATC code; classes were stratified by modality. Results: In Apollo Dial DB 109,888 in-center hemodialysis (HD) patients had 1,459,954 ATC code medication entries (13.3 drugs/patient), 6,466 peritoneal dialysis (PD) patients had 35,873 ATC codes (5.5 drugs/patient), and 1,899 home HD patients 10,228 ATC codes (5.4 drugs/patient). Top pharmacological classes included phosphate binders and antihypertensives for all modalities, yet differences existed by modality (Figure 1). Conclusion: Home medication use appears 2-fold higher for in-center HD versus PD and home HD. Most common medications included calcium phosphate binders, 50% of in-center, 57% of home HD, and 33% of PD patients. Beta-blockers were commonly used for hypertension management in HD (48% in-center, 37% home patients), while calcium channel blockers were most used in PD (33%). Antithrombotics were more prevalent in HD (48% in-center, 54% home patients) and gastroesophageal medications in home HD patients (33%). Antiemetics and diuretics were more prevalent in PD patients (42% and 33%). Insights provide benchmarks for the community. Further research is needed accounting for duration of use and considering real-world application of various drugs (e.g., GLP1 drugs, HIF-PH inhibitors).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".