Evaluation of Glial and Neuronal Blood Biomarkers Compared With Clinical Decision Rules in Assessing the Need for Computed Tomography in Patients With Mild Traumatic Brain Injury
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
Importance: In 2018, the combination of glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase (UCH-L1) levels became the first US Food and Drug Administration-approved blood test to detect intracranial lesions after mild to moderate traumatic brain injury (MTBI). How this blood test compares with validated clinical decision rules remains unknown. Objectives: To compare the performance of GFAP and UCH-L1 levels vs 3 validated clinical decision rules for detecting traumatic intracranial lesions on computed tomography (CT) in patients with MTBI and to evaluate combining biomarkers with clinical decision rules. Design, Setting, and Participants: This prospective cohort study from a level I trauma center enrolled adults with suspected MTBI presenting within 4 hours of injury. The clinical decision rules included the Canadian CT Head Rule (CCHR), New Orleans Criteria (NOC), and National Emergency X-Radiography Utilization Study II (NEXUS II) criteria. Emergency physicians prospectively completed data forms for each clinical decision rule before the patients' CT scans. Blood samples for measuring GFAP and UCH-L1 levels were drawn, but laboratory personnel were blinded to clinical results. Of 2274 potential patients screened, 697 met eligibility criteria, 320 declined to participate, and 377 were enrolled. Data were collected from March 16, 2010, to March 5, 2014, and analyzed on August 11, 2021. Main Outcomes and Measures: The presence of acute traumatic intracranial lesions on head CT scan (positive CT finding). Results: Among enrolled patients, 349 (93%) had a CT scan performed and were included in the analysis. The mean (SD) age was 40 (16) years; 230 patients (66%) were men, 314 (90%) had a Glasgow Coma Scale score of 15, and 23 (7%) had positive CT findings. For the CCHR, sensitivity was 100% (95% CI, 82%-100%), specificity was 33% (95% CI, 28%-39%), and negative predictive value (NPV) was 100% (95% CI, 96%-100%). For the NOC, sensitivity was 100% (95% CI, 82%-100%), specificity was 16% (95% CI, 12%-20%), and NPV was 100% (95% CI, 91%-100%). For NEXUS II, sensitivity was 83% (95% CI, 60%-94%), specificity was 52% (95% CI, 47%-58%), and NPV was 98% (95% CI, 94%-99%). For GFAP and UCH-L1 levels combined with cutoffs at 67 and 189 pg/mL, respectively, sensitivity was 100% (95% CI, 82%-100%), specificity was 25% (95% CI, 20%-30%), and NPV was 100%; with cutoffs at 30 and 327 pg/mL, respectively, sensitivity was 91% (95% CI, 70%-98%), specificity was 20% (95% CI, 16%-24%), and NPV was 97%. The area under the receiver operating characteristic curve (AUROC) for GFAP alone was 0.83; for GFAP plus NEXUS II, 0.83; for GFAP plus NOC, 0.85; and for GFAP plus CCHR, 0.88. The AUROC for UCH-L1 alone was 0.72; for UCH-L1 plus NEXUS II, 0.77; for UCH-L1 plus NOC, 0.77; and for UCH-L1 plus CCHR, 0.79. The GFAP biomarker alone (without UCH-L1) contributed the most improvement to the clinical decision rules. Conclusions and Relevance: In this cohort study, the CCHR, the NOC, and GFAP plus UCH-L1 biomarkers had equally high sensitivities, and the CCHR had the highest specificity. However, using different cutoff values reduced both sensitivity and specificity of GFAP plus UCH-L1. Use of GFAP significantly improved the performance of the clinical decision rules, independently of UCH-L1. Together, the CCHR and GFAP had the highest diagnostic performance.
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.
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 | 0,003 | 0,000 |
| 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) | 0,000 | 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 découle