#5644 SELECTED CARDIOVASCULAR AND NON-CARDIOVASCULAR EVENTS IN INCIDENT AND PREVALENT DIALYSIS PATIENTS IN THE US MEDICARE POPULATION
Bibliographic record
Abstract
Abstract Background and Aims The ASCEND-D/ID trial reported safety and efficacy of daprodustat in patients receiving dialysis [1,2]. Rates and types of clinical outcomes vary between incident and prevalent dialysis patients. This study assessed events, of which most were described as safety events in ASCEND-D/ID trial, among incident dialysis patients on erythropoiesis-stimulating agents (ESAs) and prevalent dialysis patients in the US Medicare population. Method This is a retrospective, observational cohort study using United States Renal Data System data. The study included incident dialysis-dependent (IDD) patients from 2017–2019 and, separately, prevalent dialysis-dependent (PDD) patients as of January 1, 2018. For IDD patients, the index date was the dialysis initiation date; for PDD patients, the index date was January 1, 2018. To establish the cohorts, we further required patients to have at least 6 continuous months of Medicare Parts A and B coverage prior to, and be aged ≥18 years on, the index date. For IDD patients, we also required ESA use on the index date to better support comparability to the PDD patients. Patients with a history of kidney transplant or who had cancer during the year prior to, or who had a hospitalization for heart failure (HF), myocardial infarction (MI), or a stroke in the 4 weeks before, the index date were excluded. Patients were followed from index date (for IDD patients, follow-up started from index date plus 60 days to ascertain dialysis modality stability) to the earliest date of death, loss of Medicare coverage, kidney transplantation, or December 31, 2019. Events assessed were cardiovascular (CV: all-cause death, HF, MI, stroke, thromboembolic events [TEE], major adverse CV events [MACE: composite of death, MI, stroke], and MACE+HF) and non-CV (cancer, gastric erosions, ocular events, seizures, serious infection) events, derived from medical claims in the follow-up period using ICD-10 diagnosis, Healthcare Common Procedure Coding System, and Current Procedural Terminology codes in the follow-up period. HF, MI, stroke, and serious infection events were defined as inpatient events. TEEs included vascular access thrombosis (VAT), deep venous thrombosis (DVT), and pulmonary embolism (PE). First event rates were calculated separately for IDD and PDD cohorts and expressed as number of events per 100 patient-years. Results The IDD cohort contained 23,909 patients; the PDD cohort, 230,055. Median follow-up time was 12.6 months for the IDD cohort and 24.0 months for PDD cohort. Mean ages of the IDD and PDD cohorts were 70.3 and 63.3 years, respectively. Compared with the PDD cohort, patients in the IDD cohort were more likely to be female (48.2% vs 43.7%), of non-Hispanic White race (61.6% vs 40.4%), less likely be of lower-income status (33.0% vs 47.4% for Medicare/Medicaid dual enrollment and 38.6% vs 57.0% for Part D low-income subsidy) and with higher proportion of comorbidities. First event rates are shown in the Table for the IDD and PDD cohorts. Compared with the PDD cohort, patients in the IDD cohort had higher rates of all-cause mortality (17.3 vs 16.8 per 100 patient-years), stroke (1.8 vs 1.4), VAT (109.4. vs 67.2, hemodialysis patients only), and MACE (20.9 vs 20.3); however, they had lower rates of MI (3.1 vs 3.5), HF (7.1 vs 8.9), DVT (3.2 vs 4.0), and MACE+HF (25.6 vs 26.9). For non-CV events, patients in the IDD cohort had lower event rates for nearly all events except malignancies. Conclusion ESA-treated IDD patients had higher event rates for some CV events but lower rates for most non-CV events compared with PDD patients. As expected, however, rates of VAT were higher in IDD patients than PDD patients. These background event rates in real-world populations with wide ESA use provide context when considering potential alternative treatments for anemia. Limitation of this descriptive study includes the reporting of crude (unadjusted) rates, and therefore should be interpreted with caution. Funding: GSK (study 217316).
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".