Abstract P6-09-07: The development of a prediction tool for moderate to severe diarrhea in HER-2/hormone positive metastatic breast cancer (MBC) patients receiving lapatinib in combination with letrozole (L-L)
Bibliographic record
Abstract
Abstract Background: For patients with HER-2/hormone positive MBC, the addition of lapatinib to letrozole is associated improvements in tumour response rates and a prolongation of progression free survival. However, moderate to severe diarrhea (≥ grade 2) is a potentially serious toxicity which can lead to dose reductions, delays, hospitalizations and even the premature discontinuation of treatment. Patient care could substantially be improved if these diarrhea events could be accurately predicted through the use of validated and easy-to-use mathematical models. In this study, the development of a repeated measures model to predict the risk of ≥ grade 2 diarrhea prior to each month of L-L therapy is described. Methods Data from 111 patients who received the L-L combination as part of a clinical trial were reviewed [Johnston, 2009]. Generalized estimating equations (GEE) were used to develop the final risk model using a backwards elimination process. Internal validation of the final regression coefficients was done using nonparametric bootstrapping. A risk scoring algorithm (range 0-250) was then derived from the final model coefficients. A receiver operating characteristic curve (ROC) analysis was then undertaken to measure the predictive accuracy of the final scoring algorithm. Results: Presence of skin and lung metastases at baseline, cumulative lapatinib dose and Hg level (nadir) were identified as being important predictors for ≥ grade 2 diarrhea. There was also a negative association between time on therapy and risk of diarrhea where a higher frequency was observed in the first few months. The ROC analysis indicated good predictive accuracy with an area under the curve of 0.80 (95%CI: 0.72 – 0.88). Prior to each new month of therapy, patients with risk scores > 125 units would be considered at high risk for developing ≥ grade 2 diarrhea. Conclusions: Risk of ≥ grade 2 diarrhea is associated with cumulative lapatinib exposure, disease related factors as well as Hg level. The planned external validation and eventual clinical application of this prediction tool will be an important source of risk information for the practicing oncologist and can enhance patient care by optimizing preventative therapies earlier in a proactive manner. Citation Format: George Dranitsaris, Mario E Locouture. The development of a prediction tool for moderate to severe diarrhea in HER-2/hormone positive metastatic breast cancer (MBC) patients receiving lapatinib in combination with letrozole (L-L) [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P6-09-07.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".