34 Executive Function as a Protective Factor for Post-Surgical Quality of Life in Unilateral Epilepsy Surgery
Notice bibliographique
Résumé
Objective: Many epilepsy syndromes are medically refractory, leading patients to be referred for surgical work-up to control their seizures and improve their quality of life (QOL). Although surgical treatments may reduce or stop seizures, many patients continue to present with declines in mood and/or cognition post-operatively. In addition, pre-operative QOL of patients with medically refractory epilepsy is impacted by executive function (EF). The present study aims to investigate the relationship between post-operative mood/QOL and pre-operative EF in adults with epilepsy. It was hypothesized that mood would remain stable or decline post-operatively; pre-operative EF would be a protective factor for mood decline and QOL. Participants and Methods: The sample consisted of 47 adult patients (57.4% female; Age, M= 34.02(11.59)) with medically refractory epilepsy at the UCSF Epilepsy Center. Participants were included if they received surgical treatment for their epilepsy (42.6% right anterior temporal lobectomy [ATL], 46.8% left ATL, 2.1% laser ablation, 6.4% responsive neurostimulation, 2.1% multiple surgical interventions) and received both a pre- and post-surgical neuropsychological evaluation. Most patients were right-handed (95.7% right). Mood and QOL were assessed from pre- and post-operative evaluations using the Beck Depression Inventory- Second Edition (BDI-II), Beck Anxiety Inventory (BAI), and Quality of Life in Epilepsy- 31 (QOLIE-31). Executive function was assessed using the Trail Making Test, and the Delis-Kaplan Executive Function Scale (D-KEFS) subtests Color-Word Interference (CW-I) and Verbal Fluency. Descriptive statistics were obtained for each of the measures listed. A paired sample t-test was conducted between time A and B to determine whether mood and QOL were significantly different. Two multiple regressions were conducted. One analysis for post-operative depression and QOL respectively with pre-operative EF. Results: At time A, both anxiety and depression were minimal (BDI M= 17.8, SD= 10.34; BAI M= 13; SD= 8.94). QOL was borderline clinically significant (QOLIE M= 37.46, SD= 9.74). Depression at time B was positively correlated with depression at time A (r[45]= 0.316, p=0.035). A paired sample t-test indicated that depression and QOL were significantly different at time A and time B (t[44]= 2.04, p= 0.047; t[31]= -3.34, p= 0.002), with improved scores post-operatively. Anxiety was not significantly different across time points (t[39]= 1.20, p=0.238). Multiple regression analyses indicated that pre-operative depression and EF did not predict post-operative depression (F(5,27)= 1.62, p= 0.189). Pre-operative EF (CW-I Inhibition-Switching), but not pre-operative depression, predicted post-operative QOL (F(4(24)= 3.13, p=.03, R2= .343). Conclusions: Results were somewhat discrepant from prior research in that depression and QOL improved post-surgically. Notably, while the observed change in depression was statistically significant it was not clinically significant according to literature (Doherty et al., 2021). Pre-surgical inhibitory control predicted QOL, illustrating that EF may serve as a protective factor post-surgically. The present study did not include a measure of seizure freedom classification post-operatively, therefore, future studies should investigate how seizure freedom classification impacts the relationship between mood, QOL, and cognitive outcomes.
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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 | 0,000 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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 ».