British journal of hospital medicine (London, England : 2005) · 2026
Development of a LASSO Regression-Based Nomogram to Predict Post-Neoadjuvant Chemotherapy Infection Risk in Lung Cancer Patients.
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Zhou Lijie, Chen Xuejia, Zeng Juanli, Cao Jun, Chen Jiangchuan, Li Jianmin, Yang Huan
Аннотация
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.
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.