Accuracy of Administrative Data in Identifying Ulcerative Colitis Patients Presenting with Acute Flare and Undergoing Colectomy: 2010 Presidential Poster
Notice bibliographique
Résumé
Purpose: Administrative databases have been widely used to evaluate in-hospital outcomes among ulcerative colitis (UC) patients admitted for a flare. However, the validity of administrative data defining admission diagnoses in UC has not been adequately validated. This study evaluates the accuracy of International Classification of Diseases coding in identifying patients who are admitted for UC flare or undergo colectomy. Methods: Population-based surveillance was conducted in the Calgary Health Region between January 1, 1996 and December 31, 2007 using the Discharge Abstract Database to identify adults (≥18 years) admitted for UC. Two cohorts were identified: patients admitted for UC (ICD-9:556.X, ICD-10:K51.X) and patients admitted with UC who underwent colectomy (ICD-9:45.7, 45.8; ICD-10/CCI:1.NM.87, 1.NM.89, 1.NM.91, 1.NQ.89, 1.NQ.90). UC patients who did not undergo colectomy were stratified by diagnostic position (i.e. UC coded as the primary diagnosis vs. UC in diagnostic position 2 or 3). All medical charts were comprehensively reviewed and 100 charts were randomly audited to confirm agreement. The accuracy of the administrative data in correctly identifying patients presenting with UC flare and patients admitted for colectomy was assessed. Results: The administrative database identified 697 admissions of UC that underwent colectomy; 665 charts were available for review. The administrative data correctly identified both UC and colectomy in 85.9% [95% CI: 83.2-88.5%] of cases. Reasons for misclassification included: repeat admissions (3.8%); patients did not have UC (5.4%); and patients did not undergo colectomy (5.0%). Chart review was performed for 569 patients admitted for UC flare but without colectomy. UC was the primary diagnosis in 66.1%. Overall, the administrative data was 57.1% [53.1-61.2%] accurate in identifying patients presenting with a flare. 10 patients (1.8%) underwent colectomy, which was not recorded in the administrative data. Other reasons for misclassification included: prior colectomy (12.3%); patients without UC (10.4%), or UC was a comorbidity for an unrelated admission (18.5%). The accuracy of administrative data identifying a UC flare varied by diagnostic position: 79.3% [75.2-83.4%] in the primary diagnostic position; 18.7% [11.3-26.1%] in the second diagnostic position; and 8.1% [2.4-13.9%] in the third diagnostic position. Conclusion: Administrative data accurately identifies UC patients who underwent a colectomy, but inappropriately included a subset of patients without UC or colectomy and missed a small proportion of colectomy patients. Administrative data is less reliable in identifying UC patients presenting to hospital with a flare, particularly if UC is not coded in the primary diagnostic position.
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 | 0,008 | 0,037 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 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 ».