The Impact of Outcome and Hepatic Toxicity in HCV-Infected Patients with Diffuse Large B-Cell Lymphoma Treated with Rituximab Plus CHOP Therapy; A Retrospective Multicenter Japanese Analysis.
Bibliographic record
Abstract
Abstract Abstract 2700 Poster Board II-676 Background: Hepatitis B virus reactivation after systemic chemotherapy including rituximab is a well-documented complication. However, no studies have investigated the influence of hepatitis C virus (HCV) infection for hepatic toxicity of diffuse large B-cell lymphoma (DLBCL) patients treated by rituximab containing chemotherapy. The prognostic value of HCV infection in DLBCL in the era of rituximab was also unclear. Herein we conducted a multicenter retrospective analysis to compare the outcome and hepatic toxicity of DLBCL patients with and without HCV infection treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (RCHOP) as an initial therapy. Methods: We analyzed 548 patients: HCV-positive (n=126) or -negative (n=422) patients with CD20-positive DLBCL receiving RCHOP between January 2004 and March 2008. HCV-negative patients treated during same period with HCV-positive patients in each institute were enrolled. Hepatitis B surface antigen positive patients were excluded in this study. For survival analysis, event-free survival (EFS) and overall survival (OS) were compared according to HCV infection. The definition of severe hepatic toxicity was more than Grade 3 transaminases increase according to National Cancer Institute of Canada criteria. The change of serum HCV-RNA levels was examined in 33 HCV-positive patients. Results: Before the treatment, HCV-positive patients had higher age (P<0.001), more frequently elevated lactate dehydrogenase levels (P=0.004), spleen involvement (P=0.001) and higher international prognostic index (P=0.01) than HCV-negative patients. HCV infection was not a significant risk factor for EFS and a borderline risk factor for OS (3-year EFS, 66% vs. 74%, P=0.20; OS, 77% vs. 84%, P=0.07). Cox multivariate analysis showed that HCV was not a significant poor risk factor. Thirty-three out of 126 (26%) HCV-positive patients had severe hepatic toxicity, compared to 3% HCV-negative patients, and multivariate analysis revealed that HCV infection was significantly strong risk factor for hepatic toxicity (HR:14.72 (95%CI: 6.34–34.02)) (Table 1). The severe hepatic toxicity was not significantly associated with poor prognosis of HCV-positive patients, but chemotherapy was stopped in four of 33 patients due to severe hepatic toxicity and died of disease progression. The monitoring of HCV viral load demonstrated that HCV-RNA significantly increased during rituximab treatment (P=0.006) (median: 320 to 2000KIU/ml) and then decreased after treatment (1200KIU/ml) (P=0.003). The results of HCV viral load showed that liver dysfunction might be due to HCV activation. Conclusion: Our data show that HCV infection is not prognostic factor, but was the significantly influential for hepatic toxicity in the patients with DLBCL treated by RCHOP therapy. Further prospective studies are warranted to clear whether antiviral therapy should be added to rituximab and chemotherapy. Disclosures: Kinoshita: Chugai Pharmaceutical Co Ltd, Zenyaku Kogyo Co. Ltd: Honoraria; Zenyaku Kogyo Co. Ltd: Research Funding.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".