#5644 SELECTED CARDIOVASCULAR AND NON-CARDIOVASCULAR EVENTS IN INCIDENT AND PREVALENT DIALYSIS PATIENTS IN THE US MEDICARE POPULATION
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».