School of Nursing, Midwifery & Social Work, University of Manchester, Manchester, UK,
Support Care Cancer. 2013 Oct;21(10):2759-67. doi: 10.1007/s00520-013-1843-2. Epub 2013 May 30.
A number of risk factors have been implicated in the development of chemotherapy-induced nausea/vomiting (CINV). Our aim was to develop a risk prediction model and identify patients at high risk for developing CINV before their chemotherapy treatment.
A multisite, observational, prospective longitudinal design was used. Participants were 336 chemotherapy-naïve cancer patients providing 791 assessments. They completed measures to assess potential risk factors for CINV, including socio-demographic and clinical/treatment-related characteristics, symptom distress, expectations for CINV and state-trait anxiety. CINV was measured with the MASCC Antiemesis Tool. Participants were divided randomly to a training set (=286) and a test set (=50). Random-effects models were run to ascertain the contribution of risk factors in the development of CINV using the training sample. Specificity and sensitivity of the model were assessed in both sets of samples.
Younger age, history of nausea/vomiting, trait anxiety and fatigue were linked with higher levels of CINV, and use of moderately and low emetogenic chemotherapy were linked with lower CINV. The model's specificity were 55.4 and 50.0 % and sensitivity were 80.3 and 79.0 % in the training and test sample, respectively. A dynamic web-based tool is freely available for use by clinicians.
This model of risk prediction for CINV can be an aid to clinical decision-making and assist clinicians to rationalise antiemetic use with their patients.
许多风险因素与化疗引起的恶心/呕吐(CINV)的发展有关。我们的目的是在化疗治疗前为患者建立一种风险预测模型,以识别出发生 CINV 的高风险患者。
采用多中心、观察性、前瞻性纵向设计。共有 336 名首次接受化疗的癌症患者参与研究,共进行了 791 次评估。他们完成了评估 CINV 潜在风险因素的措施,包括社会人口统计学和临床/治疗相关特征、症状困扰、对 CINV 的预期以及状态-特质焦虑。使用 MASCC 止吐工具来测量 CINV。患者被随机分为训练集(=286)和测试集(=50)。使用训练样本运行随机效应模型,以确定风险因素在 CINV 发展中的作用。在两个样本集中评估了模型的特异性和敏感性。
年龄较小、恶心/呕吐史、特质焦虑和疲劳与 CINV 水平较高有关,而使用中度和低度致吐性化疗与 CINV 水平较低有关。该模型在训练和测试样本中的特异性分别为 55.4%和 50.0%,敏感性分别为 80.3%和 79.0%。一个动态的网络工具可免费供临床医生使用。
该 CINV 风险预测模型可以辅助临床决策,帮助临床医生与患者一起合理使用止吐药物。