Risk of rash with nilotinib: A systematic review of the literature and meta-analysis.
Bibliographic record
Abstract
9088 Background: Nilotinib is indicated for the treatment of chronic myelogenous leukemia (CML). The reported incidence and risk of rash from this medication vary widely and have been inconsistently reported in published trials. Therefore we conducted a systematic review and meta-analysis of the literature to determine the incidence and risk of developing rash. Methods: Relevant studies were identified from the PubMed database (1998-2012), abstracts presented at ASCO and ASH Conferences (2004-2011) and Web of Science database (1998-2012). Eligible studies were limited to prospective Phase II-III clinical trials in which patients received nilotinib at doses of either 300 mg or 400 mg twice daily. Incidence, relative risk (RR), and 95% confidence intervals (CI) were calculated using random-effects or fixed-effects models based on heterogeneity of included studies. Results: Data from a total of 3,186 patients receiving nilotinib in 16 clinical trials were available for analysis. The overall incidence of all-grade and high-grade (grade ≥3) rash were 33.1% (95% CI: 27.7-39.1) and 2.6% (95% CI: 2.1-3.4), respectively. Incidence of all-grade rash for patients with CML, gastrointestinal stromal tumor (GIST) and systemic mastocytosis were 33.2% (95% CI: 27.2-39.9), 25.7% (95% CI: 14.0-42.5) and 25.0% (95% CI: 15.7-37.4), respectively. Nilotinib was associated with increased risk of all-grade rash (RR=2.891, 95% CI: 2.079-4.020; P<0.001) when compared to patients treated with imatinib. Risk of high-grade rash was increased compared to imatinib (RR=1.823, 95% CI: 0.670-4.957), but this was not statistically significant (P=0.24). Conclusions: Patients with hematologic malignancies and GIST who are treated with nilotinib are at significant risk for developing a rash. Further studies for characterization, prevention and treatment of this untoward toxicity are needed in order to maintain patients’ quality of life and minimize the need for dose modification, which may impact clinical outcome.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.043 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".