Development and validation of a novel nomogram for predicting overall survival of lung cancer patients treated with immune checkpoint inhibitors: A retrospective cohort study in China

Abstract

Abstract Background The purpose of this study was to establish a simple-to-use nomogram for predicting overall survival (OS) among lung cancer patients treated with immune checkpoint inhibitors (ICIs) based on robust clinicopathological prognostic factors. Materials and Methods A total of 1314 patients with lung cancer who had received ICIs therapy were followed up, and R statistical software was used for statistical analysis. The independent prognostic factors of OS were obtained by Cox regression, The consistency index (C-index) value, calibration curve and decision curve analysis (DCA) are used to evaluate the performance and identification ability of nomogram. Results Nine prognostic factors, including age, tumor node metastasis classification stage (TNM stage), surgery, radiation, Karnofsky performance status (KPS), histology, multidrug Therapy, D-dimer, albumin (ALB) were obtained by variable screening and combining with clinical practice. On this basis, the nomogram was developed to predict lung cancer patients' prognoses with ICIs treatment. Nomogram's C-index was calculated to predict 1-, 2-, and 3-year OS response 0.720(95% CI, 0.667–0.723), 0.742(95% CI, 0.686–0.797), 0.683(95% CI, 0.604–0.763), respectively, in the training cohort (P < 0.001). In the validation cohort, the C-indexes were 0.727 (95% CI, 0.649–0.806), 0.659 (95% CI, 0.562–0.755), and 0.637(95% CI, 0.482–0.792), respectively. DCA demonstrated that the nomogram was beneficial to clinical practice, Using the nomogram, lung cancer patients were categorized into two groups based on their mortality risk. In addition, a dynamic nomogram of the network services calculator was built. Conclusion A predictive nomogram based on commonly available factors could help clinicians screen lung cancer patients who would benefit from ICIs treatment and provide robust personalized prognostication.