Validation of the Enhanced Recovery After Surgery (ERAS) database in Alberta, Canada and a comparative analysis with Swedish and Swiss data
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Notice bibliographique
Résumé
The Enhanced Recovery After Surgery (ERAS) Interactive Audit System (EIAS) is a retrospective database containing information about the pre-, intra-, and post-operative components of surgical patient care. EIAS was created to allow centers that have adopted ERAS protocols to assess their performance. To have confidence in the data collected by EIAS, its completeness, accuracy and validity must be assessed. This study aims to assess the validity of the Alberta EIAS when compared to the gold standard measurement for patient data, the patient electronic medical record (EMR). Four sites that implemented ERAS across Alberta were included, with 20 to 60 patient EMRs pulled from each site. Data on 12 pre-specified ERAS elements and three outcome variables was abstracted from patient EMRs and compared to the corresponding variables from EIAS. Validation criteria included (I) accuracy (agreement between EMR and EIAS) and (II) missingness (percent of data that was missing in patients EMR and EIAS). The estimates of accuracy were compared to estimates of accuracy from two other EIAS validation studies using meta-analysis. A total of 113 patient charts were reviewed across four sites. The mean agreement between chart review and EIAS was 73.6% (standard deviation, SD = 14.5) with a mean sensitivity of 70.3 (SD = 32.8) and mean specificity of 50.1 (SD = 42.5). Agreement between chart review and EIAS was better among outcomes (agreement for re-operation was 93.7%) than it was for accuracy of documentation of the ERAS elements (mean agreement = 73.6%). Agreement varied by site (68.5% to 94.4%) and reviewer (68.0% to 96.6%). Across all 12 ERAS elements and three outcome variables, a mean of 11.4% of data were missing, with re-operation having the greatest proportion of missing data (15.9%) and termination of drains and early mobilization with the lowest proportion of missing data (9.7%). Estimates of accuracy were not different between studies (I2 = 56.4%, p = 0.101). In Alberta, the accuracy and completeness of EIAS data is similar to that of Sweden and Switzerland, but is varied. This study found that data abstractors that are medically trained, and trained in standardized data abstraction are important determinants of generating high quality data, highlighting the need for adequate resources for data collection.
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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) | 0,005 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| 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