Healthcare Resource Utilization (HCRU) in Prevalent Dialysis Patients Undergoing Peritoneal Dialysis in the United States
Bibliographic record
Abstract
Background: CKD is associated with increased HCRU. The US prevalence of patients (pts) undergoing peritoneal dialysis (PD) has more than doubled since 2000. We characterized the HCRU rate in prevalent PD pts in the US. Methods: This observational cohort study used 2017-2019 United States Renal Data System data. Adults (≥18 years [yrs]) undergoing PD on Jan, 1 2018 (index) and with 6 months of Medicare fee-for-service coverage prior to index (incl.) were included. Follow-up was from index until death, loss of Medicare coverage, kidney transplantation, or Dec 31, 2019. All-cause HCRU was measured as the total number of services provided/total follow-up time (rate per patient-year [PY]). Results: 20,647 PD pts were included (median age, 62.2 yrs; median dialysis duration, 2.7 yrs; Table 1). Diabetes was the most prevalent comorbidity (55.4%) and cause of ESKD (39.9%). Median follow-up was 2 yrs. During follow-up, rate of hospitalization was 1.52 per PY; of intensive care unit use 0.37; of skilled nursing facility (SNF) use 0.29; and of emergency department (ED) encounters 1.09 (Table 2). Consultation rates were highest for nephrologists (15.72). Pts treated in units with the largest numbers of pts (>100) had generally lower HCRU; pts outside of large metro areas had a lower hospitalization, but higher outpatient and ED encounters rates. Table 1. - Baseline characteristics Baseline characteristics Total, N (%) 20,647 (100) Median age, years (25th, 75th percentile) 62.2 (51.0, 71.0) Male sex, n (%) 11,322 (54.8) Median dialysis duration, years (25th, 75th percentile) 2.7 (1.3, 4.4) Race, n (%) Non-Hispanic white 11,037 (53.5) Non-Hispanic black 5,094 (24.7) Hispanic 2,810 (13.6) Other 1,706 (8.3) Medicare/Medicare dual enrollment, n (%) 7,020 (34.0) Part D drug coverage low-income subsidy No part D coverage 4,969 (24.1) Without low-income subsidy 6,894 (33.4) With low-income subsidy 8,784 (42.5) Comorbidities in baseline period, n (%) Diabetes 11,439 (55.4) Atherosclerotic heart disease 5,382 (26.1) Heart failure 5,093 (24.7) Other cardiac disease 4,192 (20.3) Peripheral vascular disease 3,792 (18.4) Dysrhythmia 3,017 (14.6) Chronic obstructive pulmonary disease 2,439 (11.8) Stoke or transient ischemic attack 1,910 (9.3) Acute myocardial infarction 1,142 (5.5) Liver disease 878 (4.3) Gastrointestinal disease 593 (2.9) Cause of end-stage kidney disease, n (%) Diabetes 8,242 (39.9) Hypertension 6,346 (30.7) Glomerulonephritis 3,141 (15.2) Other/unknown/missing 2,918 (14.1) Conclusions: In the US, PD pts had 1.52 hospitalizations and 1.09 ED encounters per PY. Pts treated in units with >100 pts had generally lower HCRU rates. Funding: Commercial Support - Funded by GSK (Study 213716)
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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 | 0.003 |
| 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".