Hepatitis B screening to reduce the risk of viral reactivation in gynecologic oncology patients receiving chemotherapy at a regional tertiary cancer center: A quality improvement initiative.
Notice bibliographique
Résumé
332 Background: In 2020, ASCO released a Provisional Clinical Opinion recommending universal hepatitis B virus (HBV) screening prior to systemic chemotherapy to reduce the risk of reactivation and associated morbidities. There is limited data for HBV prevalence and risk factors in gynecologic oncology. In gynecologic oncology patients at the Juravinski Cancer Centre, median baseline screening rate over 6 months was 0%. Our aim was to increase the rate of HBV screening to 70% in gynecologic oncology patients initiating chemotherapy over 6 months and compare real-world efficacy of risk factor-based vs. universal screening. Methods: We performed an interrupted time series study using the Model for Improvement methodology. Four interventions were introduced to address identified screening barriers: provider education, standardization of a testing protocol, integration with existing clinical workflow, and biweekly feedback reports. These were modified in response to outcomes and stakeholder feedback in Plan-Do-Study-Act cycles. Process and outcome measures data were collected by chart review and analyzed on statistical process control and run charts. Retrospective chart review collected demographic and disease data including Centers for Disease Control (CDC) hepatitis risk factors. Results: From Dec 1/20 to Nov 30/21, there were 381 new chemotherapy initiations in gynecology patients. The proportion of physicians screening increased significantly from 0% to 85%, and HBV monthly screening rates increased significantly from 0% to 72.2% by month 8 and were sustained for 4 months at last analysis. The integrated clinic screening protocol and feedback report interventions were each associated with increased screening rates. Of 330 unique patients initiating chemotherapy, 175 were screened (53%). Although ≥95% lacked data for 4 CDC hepatitis risk factors, 60.9% had ≥1 risk factor, and 11.2% had ≥2. HBV surface antigen (HBSAg) was non-reactive in all screened patients, but anti-HBV core (HBc) antibody was reactive in 5 (2.9%), indicative of prior infection. Real world risk factor-based screening in those with ≥1 CDC risk factor would have only identified 3/5 seropositive patients. In the screened population, risk-factor based screening had sensitivity 60%, specificity 38.8%, PPV 2.8%, NPV 97.1%. There were no HBV reactivations. Conclusions: Implementation of 4 interventions to increase HBV screening in gynecologic oncology patients receiving chemotherapy significantly improved screening rates, achieving our target at 8 months with sustained improvement. Risk-factor based screening lacks sensitivity compared to universal screening which may impact management. Lessons learned from this initiative may be applicable to other interventions to reduce infectious morbidity in oncologic populations.
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 | 0,013 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 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 ».