The development of a prediciton index for patients at high risk of severe chemotherapy induced nausea and vomiting
Bibliographic record
Abstract
8156 Background: Despite modern antiemetic therapy, 20% to 40% of cancer patients receiving chemotherapy fail to achieve complete control of emesis. Several risk factors for acute and delayed nausea/vomiting (n/w) have been identified; these include female gender, daily alcohol intake, chemotherapy emetogenicity and tumour type. Given the many risk factors, it is difficult to subjectively combine them for an overall risk assessment. To address this need we conducted a prospective cohort study to identify risk factors associated with the development of acute and delayed n/w in patients receiving chemotherapy. Methods: Two hundred patients receiving outpatient chemotherapy were asked to complete a questionnaire assessing presence of risk factors prior to their first cycle of chemotherapy. Outcomes were collected using diaries following each cycle of chemotherapy up to 6 cycles. To determine which factors were associated with severe acute and delayed n/w, multivariable logistic regression analysis adjusted for clustering was used. The likelihood ratio test in a backward elimination process was then used to select the final covariates into the model. Risk score categories were calculated along with the area under the receiver operator curve (AUROC). Results: The 200 cancer patients enrolled completed 864 cycles of chemotherapy. Mean age was 58 and 62% were female. The median cycles of chemotherapy completed was 3. Incidence of severe acute n/w was 7.2% (62 of 864 chemo cycles). Incidence of severe delayed n/w was 9.3% (80 of 864 chemo cycles). On multivariate analysis for acute and delayed n/w, 6 factors were identified for acute and 8 for delayed. Based on these regression models, two prediction indices were developed, one for severe acute nausea/vomiting and one for severe delayed nausea/vomiting. AUROC was 0.85 (95%CI: 0.78–0.89) and 0.79(95%CI: 0.74–0.88), respectively. Conclusion: To our knowledge, such indices for prediction of high risk nausea/vomiting are the first in oncology. These tools can be used to identify a priori patients at high risk for nausea/vomiting and allow for optimization of their antiemetic therapy. External validation of the indices is required before wide-spread application. Author Disclosure Employment or Leadership Consultant or Advisory Role Stock Ownership Honoraria Research Funding Expert Testimony Other Remuneration GlaxoSmithKline GlaxoSmithKline, Merck
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".