Time-variant and tissue-level collaterals predict postoperative neurological recovery and clinical outcomes of patients with endovascular thrombectomy
Notice bibliographique
Résumé
Background: A comprehensive assessment of collateral status can yield profound insights into the ischemic mechanism in patients experiencing acute ischemic stroke. This study aims to investigate whether time-variant and tissue-level collateral characteristics may serve as predictors for functional outcomes in patients undergoing endovascular thrombectomy (EVT) through the application of machine learning (ML) algorithms, and to stratify postoperative neurological recovery of these patients. Methods: In this retrospective study, 128 acute ischemic stroke patients characterized by anterior large-vessel occlusion and received EVT between May 2020 and December 2022 were enrolled. These patients underwent multiphase computed tomography (CT) angiography (mCTA) and CT perfusion (CTP). The time-variant collateral score was defined as the Collateral Score on Color-Coded summation maps (CSCC) of mCTA. The hypoperfusion intensity ratio (HIR) was calculated from CTP data. The data were split into training and test sets in a ratio of 7:3, and univariable and multivariable regression analyses were employed for feature selection. For ML analyses, logistic regression (LR), support vector machine (SVM), random forest (RF), decision tree (DT), and eXtreme gradient boosting (XGBoost) algorithms were utilized. The receiver operating characteristic (ROC) curve and decision curve were employed for performance evaluation. The mixed effect model was established to estimate the impact of collateral stratification on the postoperative National Institutes of Health Stroke Scale (NIHSS). Results: Age [odds ratio (OR) =1.073; 95% confidence interval (CI): 1.008, 1.154; P=0.040], Alberta Stroke Program Early CT Score (ASPECTS) (OR =0.742; 95% CI: 0.546, 0.975; P=0.040), CSCC (OR =0.468; 95% CI: 0.213, 0.953; P=0.044), and HIR (OR =56.666; 95% CI: 3.843, 1,156.959; P=0.005) were significantly associated with good outcome in training set. By utilizing these four selected features, the RF algorithm achieved the best performance and the highest clinical suitability in predicting good clinical outcomes, with an area under the ROC curve (AUC) of 0.964 (95% CI: 0.902, 0.992) and 0.837 (95% CI: 0.684, 0.935) in training set and testing set, respectively. The Shapley Additive exPlanations (SHAP) analysis revealed that HIR was the most significant variable in predicting clinical outcomes. Fixed effects and group × time interaction effects [all P<0.01 at all time points (TPs)] were acquired in HIR stratification. HIR enabled better stratification and prediction of patients' postoperative NIHSS [Akaike information criterion (AIC): HIR =4,599.577 and CSCC =4,648.707]. Conclusions: RF model, which has been trained on time-variant and tissue-level collaterals, is capable of accurately predicting the clinical outcomes of patients undergoing EVT. Stratifying patients based on HIR may yield valuable insights into predicting trends in the potential postoperative neurological recovery.
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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 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| 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) | 0,000 | 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 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 ».