A novel nomogram based on the patient’s clinical data and CT signs to predict poor outcomes in AIS patients
Notice bibliographique
Résumé
Background The 2019 American Heart Association/American Stroke Association (AHA/ASA) guidelines strongly advise using non-contrast CT (NCCT) of the head as a mandatory test for all patients with suspected acute ischemic stroke (AIS) due to CT’s advantages of affordability and speed of imaging. Therefore, our objective was to combine patient clinical data with head CT signs to create a nomogram to predict poor outcomes in AIS patients. Methods A retrospective analysis was conducted on 161 patients with acute ischemic stroke who underwent mechanical thrombectomy at the Guangzhou Hospital of Integrated Traditional and Western Medicine from January 2019 to June 2023. All patients were randomly assigned to either the training cohort ( n = 113) or the validation cohort ( n = 48) at a 7:3 ratio. According to the National Institute of Health Stroke Scale (NIHSS) score 7 days after mechanical thrombectomy, the patients were divided into the good outcome group (<15) and the poor outcome group (≥15). Predictive factors were selected through univariate analyses, LASSO regression analysis, and multivariate logistic regression analysis, followed by the construction of a nomogram predictive model. The receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of the model, and bootstrapped ROC area under the curve (AUC) estimates were calculated to provide a more stable evaluation of the model’s accuracy. The model’s calibration performance was evaluated through the Hosmer-Lemeshow goodness-of-fit test and calibration plot, and the clinical effectiveness of the model was analyzed through decision curve analysis (DCA). Results Multivariate logistic regression analysis showed that hyperdense middle cerebral artery sign (HMCAS) (OR 9.113; 95% CI [1.945–42.708]; P = 0.005), the Alberta Stroke Program Early Computed Tomography Score (ASPECTS) > 6 (OR 7.707; 95% CI [2.201–26.991]; P = 0.001), NIHSS score (OR 1.085; 95% CI [1.009–1.166]; P = 0.027), age (OR 1.077; 95% CI [1.020–1.138]; P = 0.008) and white blood cell count (WBC) (OR 1.200; 95% CI [1.008–1.428]; P = 0.040) were independent risk factors for early poor outcomes after mechanical thrombectomy. The nomogram model was constructed based on the above factors. The training set achieved an AUC of 0.894, while the validation set had an AUC of 0.848. The bootstrapped ROC AUC estimates were 0.905 (95% CI [0.842–0.960]) for the training set and 0.848 (95% CI [0.689–0.972]) for the validation set. Results from the Hosmer-Lemeshow goodness-of-fit test and calibration plot indicated consistent performance of the prediction model across both training and validation cohorts. Furthermore, the DCA curve demonstrated the model’s favorable clinical practicality. Conclusion This study introduces a novel practical nomogram based on HMCAS, ASPECTS > 6, NIHSS score, age, and WBC that can well predict the probability of poor outcomes after MT in patients with AIS.
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 | 0,000 | 0,002 |
| 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 | 0,001 |
| 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 ».