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Enregistrement W3040552320 · doi:10.1001/jamanetworkopen.2020.9630

External Validation of the Oakland Score to Assess Safe Hospital Discharge Among Adult Patients With Acute Lower Gastrointestinal Bleeding in the US

2020· article· en· W3040552320 sur OpenAlexaff
Kathryn Oakland, Sandeepkumar Kothiwale, Tyler Forehand, Edmund S. Jackson, C. Bucknall, Michael Sey, Siddharth Singh, Vipul Jairath, Jonathan B. Perlin

Notice bibliographique

RevueJAMA Network Open · 2020
Typearticle
Langueen
DomaineMedicine
ThématiqueGastrointestinal Bleeding Diagnosis and Treatment
Établissements canadiensWestern University
Organismes subventionnairesJanssen PharmaceuticalsNational Institute of Diabetes and Digestive and Kidney DiseasesHCA HealthcarePfizer
Mots-clésMedicineTriageLower gastrointestinal bleedingGastrointestinal bleedingReceiver operating characteristicPopulationInternal medicineColonoscopyEmergency medicineColorectal cancer

Résumé

récupéré en direct d'OpenAlex

Importance: Lower gastrointestinal bleeding (LGIB), which manifests as blood in the colon or anorectum, is a common reason for hospitalization. In most patients, LGIB stops spontaneously with no in-hospital intervention. A risk score that could identify patients at low risk of experiencing adverse outcomes could help improve the triage process and allow greater numbers of patients to receive outpatient management of LGIB. Objective: To externally validate the Oakland Score, which was previously developed using a score threshold of 8 points to identify patients with LGIB who are at low risk of adverse outcomes. Design, Setting, and Participants: This multicenter prognostic study was conducted in 140 US hospitals in the Hospital Corporation of America network. A total of 46 179 adult patients (aged ≥16 years) admitted to the hospital with a primary diagnosis of LGIB between June 1, 2016, and October 15, 2018, were initially identified using diagnostic codes. Of those, 51 patients were excluded because they were more likely to have upper gastrointestinal bleeding, leaving a study population of 46 128 patients with LGIB. For the statistical analysis of the Oakland Score, an additional 8061 patients were excluded because they were missing data on Oakland Score components or clinical outcomes, resulting in 38 067 patients included in the analysis. The study used area under the receiver operating characteristic curves with 95% CIs for external validation of the model. Sensitivity and specificity were calculated for each score threshold (≤8 points, ≤9 points, and ≤10 points). Data were analyzed from October 16, 2018, to September 4, 2019. Main Outcomes and Measures: Identification of patients who met the criteria for safe discharge from the hospital and comparison of the performance of 2 score thresholds (≤8 points vs ≤10 points). Safe discharge was defined as the absence of blood transfusion, rebleeding, hemostatic intervention, hospital readmission, and death. Results: Among 46 128 adult patients with LGIB, the mean (SD) age was 70.1 (16.5) years; 23 091 patients (50.1%) were female. Of those, 22 074 patients (47.9%) met the criteria for safe discharge from the hospital. In this group, the mean (SD) age was 67.9 (18.1) years, and 11 056 patients (50.1%) were female. In the statistical analysis of the Oakland Score, which included only the 38 067 patients with complete data, the area under the receiver operating characteristic curve for safe discharge was 0.87 (95% CI, 0.87-0.87). An Oakland Score threshold of 8 points or lower identified 3305 patients (8.7%), with a sensitivity and specificity for safe discharge of 98.4% and 16.0%, respectively. Extension of the Oakland Score threshold to 10 points or lower identified 6770 patients (17.8%), with a sensitivity and specificity for safe discharge of 96.0% and 31.9%, respectively. Conclusions and Relevance: In this study, the Oakland Score consistently identified patients with acute LGIB who were at low risk of experiencing adverse outcomes and whose conditions could safely be managed without hospitalization. The score threshold to identify low-risk patients could be extended from 8 points or lower to 10 points or lower to allow identification of a greater proportion of low-risk patients.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,005
Score d'incertitude au seuil0,408

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,021
Tête enseignante GPT0,258
Écart entre enseignants0,237 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations69
Publié2020
Routes d'admission1
Résumé présentoui

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