Weiyun Jin

This study aims to develop a multidimensional risk prediction model, identify characteristic inflammation-nutrition biomarkers, and optimize clinical decision-making. The study included 500 lung cancer patients diagnosed between October 2019 and October 2024 at a tertiary medical institution in Guiyang, China. The exposure variables included eight inflammation-nutrition biomarkers: neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), hemoglobin-albumin-lymphocyte-platelet score (HALP), prognostic nutritional index (PNI), hemoglobin-to-red cell distribution width ratio (HRR), and albumin-to-globulin ratio (ALB/GLB). The outcome variable was overall survival (OS). This study aimed to predict 1-year mortality rather than conduct traditional time-to-event survival analysis. All patients were followed until death or a uniform administrative censoring point.LASSO logistic regression was employed to model the outcome as a binary classification (death within 1 year: yes/no).This study employed a small-sample modeling approach, initially using LASSO regression for feature selection and dimensionality reduction, followed by variance inflation factor and collinearity screening for secondary feature selection. Finally, the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm was used to optimize feature variables. The results showed that age, clinical stage, poor differentiation, ECOG PS 0–1, serum albumin level, LMR, HRR, and ALB/GLB were independent prognostic factors. Based on these factors, a lung cancer mortality risk prediction model was developed, and a corresponding web-based calculator was created, providing a practical tool to support clinical decision-making and personalized treatment strategies.

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.