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.
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".