Validity of Administrative Data for the Diagnosis of Primary Sclerosing Cholangitis: A Population-Based Study
Notice bibliographique
Résumé
Purpose: Few studies have investigated the epidemiology of Primary Sclerosing Cholangitis (PSC) using administrative databases because information regarding the validity of the diagnostic codes is not available. Additionally, the codes for PSC (576.1 in ICD-9-CM and K83.0 in ICD-10) are not distinct, including common conditions such as ascending cholangitis. The objectives of this study were to assess the validity of the diagnostic codes for PSC in administrative data and generate a coding algorithm to identify PSC cases. Methods: Two study populations were identified: (1) from administrative databases; and (2) through review of medical records. The administrative data study population consisted of PSC cases residing in the Calgary Health Region (CHR; population ˜1.2 million) from 2000-2003. All patient contacts/claims with a diagnostic field coded 576.1 or K83.0 were extracted from three health service databases: (1) physician claims; (2) hospital discharge; and (3) emergency visits and ambulatory procedures (e.g., ERCP). Because residents of CHR have universal health care we were able to link these databases through unique personal health numbers. The chart review study population was defined as all residents of the CHR with a diagnosis of PSC between 2000 and 2003. Using the chart review as the reference standard, the sensitivity (Se) and positive predictive value (PPV) and their 95% CIs of a PSC diagnosis based on administrative data was calculated. Coding algorithms were developed by considering variables associated with PSC (e.g., coexistent IBD, procedures [e.g., ERCP], etc.), and PSC coding details (i.e., frequency of a PSC code) in order to maximize the PPV while maintaining a high Se. Results: A total of 86 confirmed PSC cases were identified from chart review and 998 potential PSC cases were identified from the three health service databases. In the administrative databases, 14 true cases were not captured resulting in Se and PPV estimates of 84% (95% CI 74%, 91%) and 7% (6%, 9%), respectively. When considering only inpatient data, 49 true cases were not captured resulting in Se and PPV estimates of 43% (32%, 54%) and 9% (6%, 12%), respectively. The optimal coding algorithm included one PSC code and one IBD code when all three databases were combined with corresponding Se and PPV estimates of 56% (45%, 66%) and 59% (48%, 70%), respectively. The Se and PPV estimates obtained when applying this algorithm to only inpatient data were 28% (19%, 39%) and 71% (53%, 85%), respectively. Conclusion: An algorithm for the accurate identification of true PSC cases from administrative data could not be derived. Thus, a distinct diagnostic code for PSC is required to facilitate investigation of the epidemiology of PSC using administrative data.
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 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 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 ».