Pleural effusions in patients treated with dasatinib: Results from two institutions, risk factors and management
Bibliographic record
Abstract
17503 Background: Dasatinib (SPRYCEL, formerly BMS354825) is a multi-targeted kinase inhibitor that has been shown to be very effective in the therapy of imatinib-resistant and -intolerant Ph-positive CML/ALL patients, frequently resulting in hematologic and cytogenetic remissions. Therapy with kinase inhibitors has been limited to some extent by fluid retention, the type of which is dependent on the individual drug. Pleural effusions have been relatively more common with dasatinib. We report on the experience at two large teaching hospitals - incidence, risk factors and management. Methods: 27 patients were treated on 5 BMS Phase 2 Studies (2CP, 1AP, 1BP-M, 1BP-L, 1ALL) - 17 CP, 5 AP, 3 BP-M, 2 BP-L/ALL. All patients started on a dose of 70mg BID of dasatinib. Results: In all 13 patients developed effusions. All but 1 were Grade 2; grades 2–4 are symptomatic and require intervention. Of the 14 who did not, 9 were either withdrawn from study because of CML progression (3) or dose-reduced because of hematological toxicity (6). The development of effusions was more common in patients with more advanced disease (BC>AP>CP), in those with previous lung problems (smoking, infections), and in those maintained on starting doses of dasatinib. Effusions could develop even 1–2 years (1>100d, 2>200, 2>300d, 1>500d) after starting therapy often triggered by a lung infection. Effective management included ongoing combinations of diuretics, temporary drug discontinuation, dose reduction, and more recently a once daily drug dosing schedule (Hochhaus et al , Blood 2006, 108: 166a). One patient required thoracentesis and chest tube. Conclusions: Dose monitoring and adjustments and management of the effusions have permitted continuation of the therapy in 10 of the 13 patients with good hematological outcomes. A change in dose schedule will be most effective. No significant financial relationships to disclose.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".