Comparison of Risk Scores for Lower Gastrointestinal Bleeding
Notice bibliographique
Résumé
Importance: Clinical prediction models, or risk scores, can be used to risk stratify patients with lower gastrointestinal bleeding (LGIB), although the most discriminative score is unknown. Objective: To identify all LGIB risk scores available and compare their prognostic performance. Data Sources: A systematic search of Ovid MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials from January 1, 1990, through August 31, 2021, was conducted. Non-English-language articles were excluded. Study Selection: Observational and interventional studies deriving or validating an LGIB risk score for the prediction of a clinical outcome were included. Studies including patients younger than 16 years or limited to a specific patient population or a specific cause of bleeding were excluded. Two investigators independently screened the studies, and disagreements were resolved by consensus. Data Extraction and Synthesis: Data were abstracted according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guideline independently by 2 investigators and pooled using random-effects models. Main Outcomes and Measures: Summary diagnostic performance measures (sensitivity, specificity, and area under the receiver operating characteristic curve [AUROC]) determined a priori were calculated for each risk score and outcome combination. Results: A total of 3268 citations were identified, of which 9 studies encompassing 12 independent cohorts and 4 risk scores (Oakland, Strate, NOBLADS [nonsteroidal anti-inflammatory drug use, no diarrhea, no abdominal tenderness, blood pressure ≤100 mm Hg, antiplatelet drug use (nonaspirin), albumin <3.0 g/dL, disease score ≥2 (according to the Charlson Comorbidity Index), and syncope], and BLEED [ongoing bleeding, low systolic blood pressure, elevated prothrombin time, erratic mental status, and unstable comorbid disease]) were included in the meta-analysis. For the prediction of safe discharge, the AUROC for the Oakland score was 0.86 (95% CI, 0.82-0.88). For major bleeding, the AUROC was 0.93 (95% CI, 0.90-0.95) for the Oakland score, 0.73 (95% CI, 0.69-0.77) for the Strate score, 0.58 (95% CI, 0.53-0.62) for the NOBLADS score, and 0.65 (95% CI, 0.61-0.69) for the BLEED score. For transfusion, the AUROC was 0.99 (95% CI, 0.98-1.00) for the Oakland score and 0.88 (95% CI, 0.85-0.90) for the NOBLADS score. For hemostasis, the AUROC was 0.36 (95% CI, 0.32-0.40) for the Oakland score, 0.82 (95% CI, 0.79-0.85) for the Strate score, and 0.24 (95% CI, 0.20-0.28) for the NOBLADS score. Conclusions and Relevance: The Oakland score was the most discriminative LGIB risk score for predicting safe discharge, major bleeding, and need for transfusion, whereas the Strate score was best for predicting need for hemostasis. This study suggests that these scores can be used to predict outcomes from LGIB and guide clinical care accordingly.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».