Development of a LASSO Regression-Based Nomogram to Predict Post-Neoadjuvant Chemotherapy Infection Risk in Lung
Lijie Zhou1, Xuejia Chen1, Juanli Zeng1
1Department of Pulmonary and Critical Care Medicine, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, 410005 Changsha, Hunan, China.
Aims/Background:
Post-chemotherapy infection is a major cause of treatment failure in lung cancer (LC) patients undergoing neoadjuvant chemotherapy (NAC). This study aimed to identify risk factors for post-NAC infection using the Least Absolute Shrinkage and Selection Operator (LASSO) regression and to develop and validate a corresponding risk prediction model.
Methods:
Clinical data from 144 LC patients who underwent NAC at Hunan Provincial People's Hospital between January 2021 and December 2024 were retrospectively analysed. Patients were stratified into a non-infection group (n = 81) and an infection group (n = 63) based on the occurrence of infection. LASSO-logistic regression was used to identify risk factors for post-chemotherapy infection. A risk prediction nomogram was subsequently constructed and validated based on these factors.
Results:
The optimal LASSO model was selected at lambda.1se (λ = 0.074), which retained 9 of the 15 candidate predictors. Eastern Cooperative Oncology Group Performance Status (ECOG-PS) ≥2, recent invasive procedures/catheterization, and Nutritional Risk Screening 2002 (NRS-2002) score ≥3 were identified as significant risk factors for post-chemotherapy infection, while a high cluster of differentiation 4-positive (CD4+) count served as a protective factor (p < 0.05). A nomogram incorporating these variables was subsequently developed. Internal validation using the bootstrap method (1000 iterations) demonstrated a good predictive performance with an area under the curve (AUC) of 0.831 (95% confidence interval [CI]: 0.761-0.901, p < 0.001), sensitivity of 76.2%, and specificity of 79.0% at the optimal cutoff. The calibration curve demonstrated good agreement between predicted and observed outcomes. Decision curve analysis (DCA) confirmed the clinical utility of the nomogram by showing a positive net benefit across a wide range of threshold probabilities.
Conclusion:
The LASSO-derived nomogram integrates key variables (ECOG-PS ≥2, invasive procedures/catheterization, NRS-2002 score ≥3, and CD4+ level) to enable individualised prediction of post-NAC infection risk in LC patients. Targeted interventions addressing these risk factors, together with maintenance of protective factors, may help reduce infection rates and improve treatment outcomes, providing a scientific basis for infection prevention and management.