Missed Hemodialysis Treatments: International Variation, Predictors, and Outcomes in the Dialysis Outcomes and Practice Patterns Study (DOPPS)
Bibliographic record
Abstract
RATIONALE & OBJECTIVE: Missed hemodialysis (HD) treatments not due to hospitalization have been associated with poor clinical outcomes and related in part to treatment nonadherence. Using data from the Dialysis Outcomes and Practice Patterns Study (DOPPS) phase 5 (2012-2015), we report findings from an international investigation of missed treatments among patients prescribed thrice-weekly HD. STUDY DESIGN: Prospective observational study. SETTING & PARTICIPANTS: 8,501 patients participating in DOPPS, on HD therapy for more than 120 days, from 20 countries. Longitudinal and cross-sectional analyses were performed based on the 4,493 patients from countries in which 4-month missed treatment risk was > 5%. PREDICTORS: The main predictor of patient outcomes was 1 or more missed treatments in the 4 months before DOPPS phase 5 enrollment; predictors of missed treatments included country, patient characteristics, and clinical factors. OUTCOMES: Mortality, hospitalization, laboratory measures, patient-reported outcomes, and 4-month missed treatment risk. ANALYTICAL APPROACH: Outcomes were assessed using Cox proportional hazards, logistic, and linear regression, adjusting for case-mix and country. RESULTS: The 4-month missed treatment risk varied more than 50-fold across all 20 DOPPS countries, ranging from < 1% in Italy and Japan to 24% in the United States. Missed treatments were more likely with younger age, less time on dialysis therapy, shorter HD treatment time, lower Kt/V, longer travel time to HD centers, and more symptoms of depression. Missed treatments were positively associated with all-cause mortality (HR, 1.68; 95% CI, 1.37-2.05), cardiovascular mortality, sudden death/cardiac arrest, hospitalization, serum phosphorus level > 5.5mg/dL, parathyroid hormone level > 300pg/mL, hemoglobin level < 10g/dL, higher kidney disease burden, and worse general and mental health. LIMITATIONS: Possible residual confounding; temporal ambiguity in the cross-sectional analyses. CONCLUSIONS: In the countries with a 4-month missed treatment risk > 5%, HD patients were more likely to die, be hospitalized, and have poorer patient-reported outcomes and laboratory measures when 1 or more missed treatments occurred in a 4-month period. The large variation in missed treatments across 20 nations suggests that their occurrence is potentially modifiable, especially in the United States and other countries in which missed treatment risk is high.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".