Can we accurately identify chemotherapy-related acute care visits in administrative data?
Notice bibliographique
Résumé
185 Background: Administrative data is increasingly being used to study treatment related complications that lead to acute care visits such as emergency department visits or hospitalizations (ED+H). We evaluated the accuracy of diagnosis codes for identifying chemotherapy related acute care visits (CRVs) among women with breast cancer. Methods: We prospectively developed algorithms to identify CRVs from administrative data in women receiving adjuvant chemotherapy for breast cancer in Ontario, Canada. Sensitivity (SN) and specificity (SP) were calculated for 3 scenarios: chemotherapy related ED visit, chemotherapy related H, and febrile neutropenia (FN) related visit using the chart as the gold standard. Since there is no specific diagnosis code for FN, three definitions of FN were considered: general (defined as fever or infection or neutropenia as main reason for visit), moderate (neutropenia as main reason for visit) or strict (fever or infection plus neutropenia). The population based cohort was generated by linking several health databases to identify women who had at least one ED+H during adjuvant chemotherapy for breast cancer between 2007-2009. The validation cohort consisted of 490 randomly selected cases from this cohort. Results: The population-based cohort consisted of 8,359 patients of whom 43.4% had at least one ED+H including 1,496 women who had multiple visits resulting in 6,293 unique ED+H. Of these, 73.1% were considered CRVs based on our algorithm. The algorithm performed well in identifying CRVs that included an H either from ED (SN 90%, SP 100%) or directly from home (SN 91%, SP 93%) but less well for ED visits that did not result in H (SN 65%, SP 80%). Depending on which FN algorithm was used, 1.4-24% of visits were considered FN related. The general FN algorithm had excellent SN regardless of whether the visit involved H (94-98%) but SP was moderate (66-80%). The strict FN algorithm had good SP (78-99%) but SN was highly variable (13-89%). The moderate FN algorithm provided the best tradeoff between SN (69-97%) and SP (76-98%). Conclusions: CRVs can be identified from administrative data with reasonable confidence, obviating the need for chart abstraction to evaluate chemotherapy related serious events.
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) | 0,000 | 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 | 0,001 |
| 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 ».