Personalised antiemetic prophylaxis with NEPA for patients at high risk of chemotherapy-induced nausea and vomiting receiving moderately emetogenic chemotherapy: results from the randomised, multinational MyRisk trial
Bibliographic record
Abstract
Background Patients receiving moderately emetogenic chemotherapy (MEC) are commonly prescribed a 5-HT 3 receptor antagonist (RA) and dexamethasone (DEX) as standard of care (SOC) antiemetic prophylaxis. However, in patients with an elevated risk of chemotherapy-induced nausea and vomiting (CINV) due to individual risk factors, prophylaxis with an NK 1 RA-containing regimen may optimise their antiemetic prevention. To address this unmet need for a more personalised antiemetic strategy, the MyRisk trial incorporated a predictive risk factor algorithm to select patients at increased risk of CINV who may benefit from enhanced antiemetic prophylaxis. Patients and Methods MyRisk was a phase IV, randomised, open-label, multicentre, multinational trial. Adult patients scheduled to receive 3 cycles of MEC with a high-risk CINV score were randomised to NEPA (a fixed combination of an NK 1 RA, netupitant, and 5-HT 3 RA, palonosetron) + DEX or SOC. The CINV risk score was calculated based on an algorithm that considered 7 risk factors. The primary endpoint was complete response (CR: no emesis/no rescue medication) during the overall phase (0-120h) across 3 consecutive cycles. Results Of 401 randomised patients, 388 were included in the efficacy analysis. The most common cancers were colorectal and lung; oxaliplatin and carboplatin were the most common MEC. Patients randomised to NEPA were significantly more likely to experience a CR compared to SOC (OR=1.67, 95%CI: 1.12 to 2.49; p=0.012). The NEPA group had a significantly higher probability of CR, no nausea, no emesis, and complete protection (81.0%, 63.7%, 95.4%, 71.8%, respectively) compared to the SOC arm (71.8%, 54.9%, 86.7%, 62.4, respectively) across 3 cycles of chemotherapy. Conclusions When individual risk factors are considered prior to MEC, a 3-drug regimen including NEPA provides superior CINV prevention across multiple cycles compared to the standard 2-drug approach. These findings underscore the value of personalised risk-adapted antiemetic strategies and have practice-changing potential for optimizing antiemetic control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".