Construction and validation of a nomogram for predicting the risk of secondary peripheral neuropathy in patients with advanced lung cancer.

OBJECTIVE

To construct and validate a nomogram for predicting the risk of secondary peripheral neuropathy in patients with advanced lung cancer.

METHODS

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.

RESULTS

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 ( OR=3.650, 95% CI: 1.523-8.746), comorbid diabetes ( OR=3.753, 95% CI: 1.396-10.086), chemotherapy ( OR=2.887, 95% CI: 1.046-7.970), targeted therapy ( OR=8.671, 95% CI: 4.107-18.306), immunotherapy ( OR=2.603, 95% CI: 1.337-5.065) and abnormal liver and kidney function ( OR=12.409, 95% CI: 4.739-32.489) were independent risk factors for secondary peripheral neuropathy (all P<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% CI: 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% CI: 0.638-0.869); and sensitivity, specificity, positive and negative predictive values were 79.28%, 85.79%, 73.25% and 85.82%, respectively.

CONCLUSIONS

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.