Inflammation-Nutrition Biomarker Model for Survival Prediction in Lung Cancer Patients with Concurrent Tuberculosis

Hongqi Zhou¹Zihao Zhao²Jinhai Wang³Weiyun Jin^4*Bensong Xian^5*Lindi Li¹XiangWen Nie¹Weiwei Wu¹Ran Chen¹Qizhen Xie¹Haixia Wu¹Weiwei Jiang¹Min Tang¹Yuxin Li⁶

• ¹Oncology Department, Guiyang Public Health Treatment Center, Guiyang, China
• ²Orthopedics Department, Guiyang Public Health Treatment Center, Guiyang, China
• ³Medical Records Office, Guiyang Public Health Treatment Center, Guiyang, China
• ⁴College of Humanities Education, Inner Mongolia Medical University, Hohhot, China
• ⁵School of Health Management, Inner Mongolia Medical University, Hohhot, China
• ⁶Neurology Department, Guiyang Public Health Treatment Center, Guiyang, China

Objectives:To explore the prognostic value of eight inflammation-nutrition biomarkers in patients with lung cancer and tuberculosis as no multidimensional prognostic models for this comorbid population are available currently.Methodology:A retrospective study included 100 patients with lung cancer and tuberculosis admitted to a tertiary hospital from October 2019 to October 2024.Eight inflammation-nutrition markers (NLR, PLR, SII, LMR, PNI, HALP, HRR, ALB/GLB) were chosen as predictors while overall survival (OS) was the major event. Feature selection was implemented by LASSO regression; a Cox proportional hazards model was established afterwards. The nomogram's performance was assessed by ROC curve and C-index as well as the calibration using bootstrap resampling. The statistical power was calculated by PowerSurvEpi and sensitivity analyses were implemented to test the robustness of the model. Results:There were six predictors remaining in the final model including diabetes, ECOG PS, NLR, PNI, HRR and RDW. Among them, ECOG PS was an independent prognostic factor (HR=1.76, p=0.04). The nomogram achieved a good performance (C-index=0.71), an AUC of 0.693 for 3-year OS as well as an excellent calibration (Bootstrap P>0.05).In the high-risk subgroup with ECOG PS≥2 and NLR>8, the 5-year survival rate was close to zero. The model achieved an adequate statistical power (83%, α=0.05). Sensitivity analysis revealed an significant interaction between ECOG PS and NLR (p=0.032) and NLR>8 was the most robust threshold for this interaction. Conclusion: This is the first study to establish and validate a combined inflammation-nutrition prognostic model for patients with lung cancer and tuberculosis. Our model provides a quantitative tool to stratify individual risk and offers evidence for the usage of nutritional interventions in high-risk patients.