title: Personalized Estimate of Chemotherapy-Induced Nausea and Vomiting: Development and External Validation of a Nomogram in Cancer Patients Receiving Highly/Moderately Emetogenic Chemotherapy.
abstract: Chemotherapy-induced nausea and vomiting (CINV) is presented in over 30% of cancer patients receiving highly/moderately emetogenic chemotherapy (HEC/MEC). The currently recommended antiemetic therapy is merely based on the emetogenic level of chemotherapy, regardless of patient's individual risk factors. It is, therefore, critical to develop an approach for personalized management of CINV in the era of precision medicine.A number of variables were involved in the development of CINV. In the present study, we pooled the data from 2 multi-institutional investigations of CINV due to HEC/MEC treatment in Asian countries. Demographic and clinical variables of 881 patients were prospectively collected as defined previously, and 862 of them had full documentation of variables of interest. The data of 548 patients from Chinese institutions were used to identify variables associated with CINV using multivariate logistic regression model, and then construct a personalized prediction model of nomogram; while the remaining 314 patients out of China (Singapore, South Korea, and Taiwan) entered the external validation set. C-index was used to measure the discrimination ability of the model.The predictors in the final model included sex, age, alcohol consumption, history of vomiting pregnancy, history of motion sickness, body surface area, emetogenicity of chemotherapy, and antiemetic regimens. The C-index was 0.67 (95% CI, 0.62-0.72) for the training set and 0.65 (95% CI, 0.58-0.72) for the validation set. The C-index was higher than that of any single predictor, including the emetogenic level of chemotherapy according to current antiemetic guidelines. Calibration curves showed good agreement between prediction and actual occurrence of CINV.This easy-to-use prediction model was based on chemotherapeutic regimens as well as patient's individual risk factors. The prediction accuracy of CINV occurrence in this nomogram was well validated by an independent data set. It could facilitate the assessment of individual risk, and thus improve the personalized management of CINV.
authors.affiliation: From the Collaborative Innovation Center for Cancer Medicine (ZH, YY, YM, YZ, CX, YH, HZ, LC, LZ); State Key Laboratory of Oncology in South China (ZH, YY, YM, HZ, LZ); Department of Medical Oncology, Sun Yat-sen University Cancer Center (ZH, WL, YY, YZ, CX, YH, LC, LZ); Department of Thoracic Surgery and Oncology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China (WL); Faculty of Health Sciences, University of Adelaide, Adelaide, Australia (DK); and Department of Pharmacy, National University of Singapore, National Cancer Center Singapore, Singapore, Republic of Singapore (AC).
authors.forename: Zhihuang
authors.initials: Z
authors.lastname: Hu
authors.forename: Wenhua
authors.initials: W
authors.lastname: Liang
authors.forename: Yunpeng
authors.initials: Y
authors.lastname: Yang
authors.forename: Dorothy
authors.initials: D
authors.lastname: Keefe
authors.forename: Yuxiang
authors.initials: Y
authors.lastname: Ma
authors.forename: Yuanyuan
authors.initials: Y
authors.lastname: Zhao
authors.forename: Cong
authors.initials: C
authors.lastname: Xue
authors.forename: Yan
authors.initials: Y
authors.lastname: Huang
authors.forename: Hongyun
authors.initials: H
authors.lastname: Zhao
authors.forename: Likun
authors.initials: L
authors.lastname: Chen
authors.forename: Alexandre
authors.initials: A
authors.lastname: Chan
authors.forename: Li
authors.initials: L
authors.lastname: Zhang
mesh_terms: D000328:Adult; D017677:Age Distribution; D000368:Aged; D000932:Antiemetics / Q000627:therapeutic use; D000971:Antineoplastic Combined Chemotherapy Protocols / Q000009:adverse effects* / Q000627:therapeutic use; D002681:China; D015331:Cohort Studies; D005260:Female; D006801:Humans; D015994:Incidence; D038622:Internationality; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009325:Nausea / Q000139:chemically induced* / Q000453:epidemiology / Q000517:prevention & control; D009369:Neoplasms / Q000188:drug therapy*; D049451:Nomograms; D011237:Predictive Value of Tests; D011379:Prognosis; D056910:Republic of Korea; D012720:Severity of Illness Index; D017678:Sex Distribution; D012846:Singapore; D013624:Taiwan; D014839:Vomiting / Q000139:chemically induced* / Q000453:epidemiology / Q000517:prevention & control; D055815:Young Adult
chemical_list: D000932:Antiemetics