Automated CT Perfusion Imaging to Aid in the Selection of Patients With Acute Ischemic Stroke for Mechanical Thrombectomy: A Health Technology Assessment.
Notice bibliographique
Résumé
BACKGROUND: Stroke is a sudden interruption in the blood supply to a part of the brain, causing loss of neurological function. It is the third leading cause of death in Canada and affects mainly older people. In the acute setting, neuroimaging is integral to stroke evaluation and decision-making. The neuroimaging results guide patient selection for mechanical thrombectomy. Using automated image processing techniques facilitates efficient review of this information and communication between centres. We conducted a health technology assessment of automated CT perfusion imaging as a tool for selecting stroke patients with anterior circulation occlusion for mechanical thrombectomy. This assessment included an evaluation of clinical effectiveness, cost-effectiveness, and the budget impact of publicly funding automated CT perfusion imaging. METHODS: We performed a systematic literature search of the clinical evidence. We assessed the risk of bias of each study using QUADAS-2 or the Cochrane risk-of-bias tool, and the quality of the body of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) Working Group criteria. We performed a systematic economic literature search and approximated cost-effectiveness based on previous analyses. We also analyzed the budget impact of publicly funding automated CT perfusion imaging to evaluate people with acute ischemic stroke in Ontario. RESULTS: Automated CT perfusion imaging had a sensitivity of 84% for identifying the infarct core (dead tissue that does not recover despite restoring blood flow with mechanical thrombectomy), compared with diffusion-weighted MRI imaging at 24 hours. One study reported that 7% of patients were misclassified with respect to eligibility for mechanical thrombectomy (either erroneously classified as eligible or erroneously classified non-eligible). Two randomized controlled trials (DEFUSE 3 and DAWN) demonstrated the efficacy of mechanical thrombectomy up to 24 hours after stroke onset, with patient selection guided by automated CT perfusion imaging. These data showed that a significantly higher proportion of patients in the mechanical thrombectomy group achieved functional independence compared with the standard care group (DEFUSE 3: risk ratio: 2.67 [95% confidence interval 1.60-4.48]; DAWN: adjusted rate difference: 33% [95% credible interval 21%-44%]; GRADE: Moderate).A previous health technology assessment in stroke patients presenting at 0 to 6 hours after stroke symptom onset and the results from recent randomized controlled trials for patients presenting at 6 to 24 hours informed the evaluation of cost-effectiveness. Mechanical thrombectomy informed by automated CT perfusion imaging to assess eligibility is likely to be cost-effective for patients presenting at 6 to 24 hours after stroke symptom onset. The annual budget impact of publicly funding automated CT perfusion imaging in Ontario over the next 5 years would be $1.3 million in year 1 and $0.9 million each year thereafter. Some of the costs of automated CT perfusion imaging could be offset by avoiding unnecessary patient transfers between hospitals. CONCLUSIONS: Automated CT perfusion imaging has an acceptable sensitivity and specificity for detecting brain areas that have been affected by stroke. In patients selected for mechanical thrombectomy using automated CT perfusion imaging, there was significant improvement in functional independence. Mechanical thrombectomy informed by automated CT perfusion imaging is likely to be cost-effective. We estimate that publicly funding automated CT perfusion imaging in Ontario would result in additional costs of $1.3 million in year 1 and $0.9 million per year thereafter.
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 | 0,052 | 0,197 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,009 |
| Bibliométrie | 0,014 | 0,010 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| 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 ».