The development of a prediciton index for patients at high risk of severe chemotherapy induced nausea and vomiting
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».