Zhejiang Da Xue Xue Bao Yi Xue Ban. 2022 Dec 25;51(6):716-723. doi: 10.3724/zdxbyxb-2022-0303.
To construct and validate a nomogram for predicting the risk of secondary peripheral neuropathy in patients with advanced lung cancer.
The sociodemographic and clinical data of 335 patients with advanced lung cancer admitted to Department of Respiratory, the First Affiliated Hospital of Zhejiang University School of Medicine from May 2020 to May 2021 were retrospectively collected. Pearson correlation analysis, univariate and multivariate logistic regression analyses were used to identify the risk factors of secondary peripheral neuropathy in patients with advanced lung cancer. A nomogram was constructed according to the contribution of each risk factor to secondary peripheral neuropathy, and the receiver operating characteristic (ROC) curve, Calibration curve and clinical decision curve were used to evaluate differentiation, calibration, and the clinical utility of the model. The nomogram was further validated with data from 64 patients with advanced lung cancer admitted between June 2021 and August 2021.
The incidences of secondary peripheral neuropathy in two series of patients were 34.93% (117/335) and 40.63% (26/64), respectively. The results showed that drinking history ( =3.650, 95% : 1.523-8.746), comorbid diabetes ( =3.753, 95% : 1.396-10.086), chemotherapy ( =2.887, 95% : 1.046-7.970), targeted therapy ( =8.671, 95% : 4.107-18.306), immunotherapy ( =2.603, 95% : 1.337-5.065) and abnormal liver and kidney function ( =12.409, 95% : 4.739-32.489) were independent risk factors for secondary peripheral neuropathy (all <0.05). A nomogram was constructed based on the above risk factors. The area under the ROC curve (AUC) of the nomogram for predicting the secondary peripheral neuropathy was 0.913 (95% : 0.882-0.944); and sensitivity, specificity, positive and negative predictive values were 85.47%, 81.65%, 71.43% and 91.28%, respectively. The Calibration curve and clinical decision curve showed good calibration and clinical utility. External validation results showed that the AUC was 0.764 (95% : 0.638-0.869); and sensitivity, specificity, positive and negative predictive values were 79.28%, 85.79%, 73.25% and 85.82%, respectively.
Advanced lung cancer patients have a high risk of secondary peripheral neuropathy after anticancer therapy. Drinking history, comorbid diabetes, chemotherapy, targeted therapy, immunotherapy, abnormal liver and kidney function are independent risk factors. The nomogram prediction model constructed in the study is effective and may be used for the risk assessment of secondary peripheral neuropathy in patients with advanced lung cancer.
构建并验证预测晚期肺癌患者继发周围神经病变风险的列线图。
回顾性收集 2020 年 5 月至 2021 年 5 月浙江大学医学院附属第一医院呼吸科收治的 335 例晚期肺癌患者的人口统计学和临床资料。采用 Pearson 相关性分析、单因素和多因素 logistic 回归分析确定晚期肺癌患者继发周围神经病变的风险因素。根据每个风险因素对继发周围神经病变的贡献构建列线图，并使用受试者工作特征（ROC）曲线、校准曲线和临床决策曲线评估模型的区分度、校准度和临床实用性。使用 2021 年 6 月至 2021 年 8 月收治的 64 例晚期肺癌患者的数据对该列线图进行进一步验证。
两批患者继发周围神经病变的发生率分别为 34.93%（117/335）和 40.63%（26/64）。结果表明，饮酒史（ =3.650，95%：1.523-8.746）、合并糖尿病（ =3.753，95%：1.396-10.086）、化疗（ =2.887，95%：1.046-7.970）、靶向治疗（ =8.671，95%：4.107-18.306）、免疫治疗（ =2.603，95%：1.337-5.065）和肝肾功能异常（ =12.409，95%：4.739-32.489）是继发周围神经病变的独立风险因素（均<0.05）。根据上述风险因素构建了列线图。该列线图预测继发周围神经病变的 ROC 曲线下面积（AUC）为 0.913（95%：0.882-0.944）；灵敏度、特异度、阳性预测值和阴性预测值分别为 85.47%、81.65%、71.43%和 91.28%。校准曲线和临床决策曲线显示出良好的校准度和临床实用性。外部验证结果显示，AUC 为 0.764（95%：0.638-0.869）；灵敏度、特异度、阳性预测值和阴性预测值分别为 79.28%、85.79%、73.25%和 85.82%。
晚期肺癌患者在接受抗癌治疗后发生继发周围神经病变的风险较高。饮酒史、合并糖尿病、化疗、靶向治疗、免疫治疗、肝肾功能异常是独立的风险因素。本研究构建的列线图预测模型有效，可用于评估晚期肺癌患者继发周围神经病变的风险。
