肺癌患者化疗相关恶心呕吐的危险因素分析和列线图预测模型构建

Analysis of risk factors for chemotherapy-induced nausea and vomiting in lung cancer patients and construction of a nomogram prediction model

目的:通过单因素和多因素Logistics回归分析肺癌患者CINV的危险因素,构建和验证列线图预测模型。方法:收集2021年10月至2022年6月郑州大学第一附属医院肿瘤科肺癌患者临床数据。选取符合纳入标准的950例患者,根据随机数表,患者按7∶3的比例分为训练队列(665例)和验证队列(285例)。在对训练队列进行单因素和多因素Logistics回归分析后,筛选CINV的独立危险因素,建立列线图模型。使用受试者工作特征曲线下面积(AUC)、Hosmer-Lemeshow检验对列线图预测模型的准确性和一致性进行评估。结果:通过单因素和多因素Logistics回归筛选出肺癌患者CINV 6个独立危险因素,并且构建CINV风险预测列线图,该模型训练集和内部验证集的ROC曲线下面积分别为0.887[95%可信区间(CI):0.861~0.891],0.823[95%CI:0.806~0.831];Hosmer-Lemeshow拟合优度检验显示训练集和内部验证集预测观测值与实际观测值之间均具有良好一致性(训练集χ^(2)=7.89,P>0.05;内部验证集χ^(2)=5.55,P>0.05)。结论:非吸烟、晕动史、使用铂类化疗药、无化疗史、使用阿片类镇痛药、睡眠时间<7 h是肺癌患者CINV的危险因素,以此构建肺癌患者CINV列线图预测模型预测准确性和一致性较好。

Objective To analyze the risk factors of chemotherapy-induced nausea and vomiting(CINV)in lung cancer patients using univariate and multivariate logistic regression,and to construct and validate a nomogram prediction model.Methods Clinical data of lung cancer patients from the Department of Oncology,the First Affiliated Hospital of Zhengzhou University,from October 2021 to June 2022 were collected.A total of 950 patients meeting the inclusion criteria were selected.According to a random number table,patients were divided into a training cohort(n=665)and a validation cohort(n=285)at a ratio of 7∶3.After conducting univariate and multivariate logistic regression analyses on the training cohort,independent risk factors for CINV were screened,and a nomogram model was established.The accuracy and consistency of the nomogram prediction model were evaluated using the area under the receiver operating characteristic curve(AUC)and the Hosmer-Lemeshow(HL)test.Results A total of 6 independent risk factors for CINV in non-smoking lung cancer patients were selected through univariate and multivariate logistic regression.A nomogram predicting CINV risk was constructed,with AUCs of 0.887[95%confidence interval(CI):0.861-0.891]and 0.823(95%CI:0.806-0.831)for the training and internal validation cohorts,respectively.The HL goodness-of-fit test showed good consistency between predicted and actual observations in both the training and internal validation cohorts(χ^(2)=7.89,P>0.05 for the training cohort;χ^(2)=5.55,P>0.05 for the internal validation cohort).Conclusion Non-smoking status,history of motion sickness,use of platinum-based chemotherapy drugs,absence of chemotherapy history,use of opioid analgesics,and sleep duration less than 7 h are risk factors for CINV in lung cancer patients.A nomogram prediction model for CINV in lung cancer patients based on these factors demonstrates good accuracy and consistency.