Age-Related Emergency Department Reliance and Healthcare Resource Utilization in Patients with Sickle Cell Disease
Notice bibliographique
Résumé
Abstract Abstract 475 Introduction: For sickle cell disease (SCD) patients (pts), inadequate care during pediatric to adult transition may result in increased emergency department (ED) utilization. Emergency department reliance (EDR: total ED visits/total ambulatory [outpatient + ED] visits) identifies the proportion of ED visits in relation to all ambulatory visits and differentiates between acute episodic ED users from those who may not have adequate access to outpatient care. The aim of this study is to investigate age-related patterns of EDR and associated healthcare costs in pediatric SCD pts and those transitioning from pediatric to adult care. Methods: State Medicaid data from FL (1998–2009), NJ (1996–2009), MO (1997–2010), IA (1998–2010), and KS (2001–2009) were used for this study. Pts with ≥2 SCD diagnoses (ICD-9 282.6x) and ≥1 blood transfusion were included in the analysis. Pts were followed for as long as they were enrolled in Medicaid. Quarterly rates of outpatient visits, ED visits, EDR, SCD complications associated with ED visits, and ED visits resulting in hospitalization were evaluated. Total healthcare costs were calculated and stratified by outpatient (OP), inpatient (IP), ED, and prescription drug (Rx). SCD complications included pain, stroke, leg ulcers, avascular necrosis, infections, as well as pulmonary, renal, and cardiovascular events. Based on published thresholds, high EDR was defined as >0.33. A logistic regression model was used to assess associations between high EDR and transition age (<18 vs. ≥18 years [yrs]), transfusions, hydroxyurea use, and SCD complications. Other covariates included transfusions during the previous quarter, other relevant medications (e.g.: pain medication, diuretics, anticoagulants), comorbidities (e.g.: hypertension, myocardial infarction, liver disease), and, serving as proxies for overall health status, the frequency of OP, IP, and ED visits during the previous quarter. Regressions analyses were also used to calculate adjusted costs differences between pts with high vs. low EDR. Findings: A total of 3,208 pts were included (FL: 1,550, NJ: 992, MO: 489, KS: 121, IA: 56) in the study. Each pt was observed for an average (SD) of 6.0 (3.1) yrs. Average ED visits/quarter increased from 0.76 to 2.29 between age 15 and 24, reaching a peak of 2.9 at age 36 (Figure 1). Regardless of age, the most common SCD complications associated with ED visits were pain, infection, and pneumonia. Beginning at age 15, EDR rose from 0.17 to reach 0.29 at age 22, and remained high throughout adulthood. The quarterly rate of ED visits resulting in hospitalizations followed a similar pattern. Regression analysis indicated that pts were more likely to have high EDR during the post-transition period (≥18 yrs old, odds ratio [OR]: 2.38, p<0.001) and when experiencing an SCD complication (OR: 4.18, p<0.001). Pts with high EDR incurred statistically significantly higher inpatient and ED costs, resulting in significantly higher total costs (high vs. low EDR, unadjusted costs difference, OP: -$441, p<.001; IP: $7,427, p<.001; ED: $442, p<.001; Rx: -$447, p=0.182; total: $7,376, p<.001 [Table 1]; adjusted costs difference, OP: -$285; IP: $3,485; ED: $120; Rx: -$91; total: $3,086, p<.001 for all). Conclusion: Compared to children, pts transitioning to adulthood relied more on ED for their care. Moreover, pts with high EDR incurred more frequent hospitalizations and significantly higher healthcare costs, highlighting the need to improve transition related support including better access to primary care and increased engagement with SCD patients. Disclosures: Blinder: Novartis Pharmaceuticals: Consultancy, Research Funding. Vekeman:Novartis Pharmaceuticals: Research Funding. Sasane:Novartis Pharmaceuticals: Employment. Trahey:Novartis Pharmaceuticals: Research Funding. Paley:Novartis Pharmaceuticals: Employment. Magestro:Novartis Pharmaceuticals: Employment. Duh:Novartis Pharmaceuticals: Research Funding.
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 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 ».