Background: Small cell lung cancer (SCLC) is an aggressive malignancy with poor prognosis despite available treatment options. Although immunotherapy now constitutes the standard of care for extensive-stage SCLC (ES-SCLC), reliable biomarkers for patient stratification remain scarce. Objectives: This study aimed to evaluate the prognostic value of baseline inflammatory, metabolic, and nutritional blood biomarkers and to construct an integrated dynamic prediction model for patient stratification. Design: This was a retrospective analysis conducted at a single center. Methods: We analyzed 191 SCLC patients treated between 2021 and 2024. Primary endpoints were overall survival (OS) and progression-free survival (PFS). The prognostic utility of inflammatory, metabolic, and nutritional blood biomarkers (neutrophil-to-lymphocyte ratio (NLR), lactate dehydrogenase (LDH), and prognostic nutritional index (PNI)) was systematically evaluated with quantitative comparisons across stage-treatment subgroups. We subsequently developed a novel composite metric, the LDH/Age (LA) ratio. Multivariable Cox regression was used to identify independent predictors, facilitating the construction and bootstrap validation of a nomogram and accompanying web-based calculator. Results: In the ES-SCLC cohort ( n = 73) receiving first-line chemoimmunotherapy (CIT), elevated baseline LDH (⩾240 U/L) and low PNI (<46.7) were independent risk factors for reduced OS and PFS. High NLR (⩾2.94) correlated significantly with diminished PFS and, among programmed death-ligand 1 inhibitor users, shorter OS. The novel LA ratio demonstrated superior predictive power for PFS compared to LDH alone (hazard ratio = 2.80, p < 0.001). Leveraging these factors, an integrated prognostic model combining Age, NLR, LDH, and PNI successfully stratified patients into high- and low-risk groups (log-rank p < 0.001). Conclusion: LDH, PNI, and NLR are established prognostic biomarkers for ES-SCLC patients undergoing CIT. Crucially, the novel LA ratio demonstrates enhanced predictive value for disease progression. The resulting web-based nomogram constitutes a practical, cost-effective mechanism for precise clinical risk stratification.
Wang et al. (2026) studied this question.