Underutilization of Neurodiagnostic Resources in Drug-Resistant Epilepsy
Notice bibliographique
Résumé
Use of Recommended Neurodiagnostic Evaluation Among Patients With Drug-Resistant Epilepsy Matthew Spotnitz, Cameron D Ekanayake, Anna Ostropolets, Guy M McKhann, Hyunmi Choi, Ruth Ottman, Alfred I Neugut, George Hripcsak, Karthik Natarajan, Brett E Youngerman. JAMA Neurol . 2024;81(5):499–506. DOI: 10.1001/jamaneurol.2024.0551 . PMID: 38557864 Importance: Interdisciplinary practice parameters recommend that patients with drug-resistant epilepsy (DRE) undergo comprehensive neurodiagnostic evaluation, including presurgical assessment. Reporting from specialized centers suggests long delays to referral and underuse of surgery; however, longitudinal data are limited to characterize neurodiagnostic evaluation among patients with DRE in more diverse US settings and populations. Objective: To examine the rate and factors associated with neurodiagnostic studies and comprehensive evaluation among patients with DRE within 3 US cohorts. Design, setting, and participants: A retrospective cross-sectional study was conducted using the Observational Medical Outcomes Partnership Common Data Model including US multistate Medicaid data, commercial claims data, and Columbia University Medical Center (CUMC) electronic health record data. Patients meeting a validated computable phenotype algorithm for DRE between January 1, 2015, and April 1, 2020, were included. No eligible participants were excluded. Exposure: Demographic and clinical variables were queried. Main outcomes and measures: The proportion of patients receiving a composite proxy for comprehensive neurodiagnostic evaluation, including (1) magnetic resonance or other advanced brain imaging, (2) video-electroencephalography, and (3) neuropsychological evaluation within 2 years of meeting the inclusion criteria. Results: A total of 33 542 patients with DRE were included in the Medicaid cohort, 22 496 in the commercial insurance cohort, and 2741 in the CUMC database. A total of 31 516 patients (53.6%) were women. The proportion of patients meeting the comprehensive evaluation main outcome in the Medicaid cohort was 4.5% ( n = 1520); in the commercial insurance cohort, 8.0% ( n = 1796); and in the CUMC cohort, 14.3% ( n = 393). Video-electroencephalography (24.9% Medicaid, 28.4% commercial, and 63.2% CUMC) and magnetic resonance imaging of the brain (35.6% Medicaid, 43.4% commercial, and 52.6% CUMC) were performed more regularly than neuropsychological evaluation (13.0% Medicaid, 16.6% commercial, and 19.2% CUMC) or advanced imaging (3.2% Medicaid, 5.4% commercial, and 13.1% CUMC). Factors independently associated with greater odds of evaluation across all 3 data sets included the number of inpatient and outpatient nonemergency epilepsy visits and focal rather than generalized epilepsy. Conclusions and relevance: The findings of this study suggest there is a gap in the use of diagnostic studies to evaluate patients with DRE. Care setting, insurance type, frequency of nonemergency visits, and epilepsy type are all associated with evaluation. A common data model can be used to measure adherence to best practices across a variety of observational data sources.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».