Association between elevated multiple circulating biomarkers and short-term mortality in critically ill lung cancer patients: a meta-analysis

Abstract

Background and aims While traditional circulating biomarkers have demonstrated prognostic value in general lung cancer patients, their short-term predictive value in critical care settings remains unclear. This study aims to systematically evaluate the association between elevated multiple circulating biomarkers and short-term mortality in critically ill lung cancer patients. Methods This study searched databases including PubMed, Embase, Web of Science, Cochrane Library, and CNKI from inception to February 2026. Literatures that enrolled patients with severe lung cancer, reported data on the association between biomarkers and short-term mortality, and provided relative risks (RR) or convertible data were included. Meta-analysis was conducted using R software with the Hartung-Knapp-Sidik-Jonkman (HKSJ) method for random effects models and robust variance estimation (RVE) to handle correlated effect sizes from the same study. Heterogeneity was assessed, and subgroup analyses, meta-regression, and sensitivity analyses were performed to evaluate pooled effect sizes and publication bias. Results This study included 9 literatures, extracting 19 studies covering 4,436 critically ill lung cancer patients. The primary meta-analysis using RVE showed that elevated biomarkers were significantly associated with short-term mortality (pooled RR = 1.62, 95% CI: 1.09-2.41, P = 0.022). Among these, 17 studies showed positive associations, with pooled RR = 1.93 (95% CI: 1.50-2.48, P < 0.001). Subgroup analysis revealed significant differences in effect sizes across different outcomes, biomarker types, and geographical regions (all P < 0.01 between subgroups), with the inflammatory biomarker subgroup showing the highest effect size (RR = 2.44). Subgroup analysis by critical illness definition showed that the effect was significant in both ICU-admitted patients (RR = 1.38, 95% CI: 1.07-1.78) and patients with advanced/terminal disease (RR = 2.45, 95% CI: 1.66-3.62). Meta-regression analysis indicated that outcome measures (90-day mortality) and patient region (North America) were significant factors influencing heterogeneity. Sensitivity analysis confirmed the robustness of results. Conclusion Multi-biomarker detection provides important reference for risk stratification and treatment decision-making in critically ill lung cancer patients. Future clinical practice could combine traditional critical care scoring systems to establish individualized prognostic prediction models based on multiple biomarkers.