ArticleBMC pulmonary medicine2026
Association between C-reactive protein-to-albumin ratio and the risk of overall survival in advanced non-small cell lung cancer patients with anlotinib treatment: a retrospective cohort study.
Article in BMC pulmonary medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe C-reactive protein-to-albumin ratio (CAR) reflects systemic inflammation and malnutrition, but its association with survival in anlotinib-treated advanced non-small cell lung cancer (NSCLC) remains unexplored.
methodsWe retrospectively analyzed 417 patients with stage IIIB-IV NSCLC treated with anlotinib at Zhongshan Hospital, Fudan University and Zhongshan Hospital (Xiamen), Fudan University. Baseline CAR was calculated as serum C-reactive protein (mg/L) divided by albumin (g/L). Overall survival (OS) was defined as the time from treatment initiation to death or last follow-up. Cox proportional hazards models, restricted cubic spline (RCS) analysis, and two-piecewise linear regression model were applied to evaluate the association between CAR and OS. Subgroup analyses were further performed for robustness.
resultsAmong 417 patients (mean age 62.2 ± 10.33 years), 216 deaths (51.8%) occurred during follow-up. The median CAR was 0.34 (0.08–1.09). In multivariable Cox models, elevated CAR was independently associated with worse OS (HR 1.10, 95% CI 1.01–1.21, p = 0.031). RCS analysis revealed a nonlinear association between CAR and OS (p for nonlinearity < 0.001). The inflection point was 2.12: below this threshold, higher CAR significantly increased mortality risk (HR 1.92, 95% CI 1.54–2.40, p < 0.0001), whereas above 2.12, CAR showed a non-significant trend toward lower risk (HR 0.82, 95% CI 0.68–1.00, p = 0.050). Subgroup analyses demonstrated consistent associations across most clinical strata.
conclusionBaseline CAR is a strong and independent prognostic marker for OS in advanced NSCLC patients treated with anlotinib. Its simplicity and availability make it a valuable tool for clinical risk stratification and treatment planning.
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