The systemic inflammation response index (SIRI) predicts survival in advanced non-small cell lung cancer patients undergoing immunotherapy and the construction of a nomogram model

Abstract

Background: Inflammation and immune evasion are associated with tumorigenesis and progression. The Systemic Inflammation Response Index (SIRI) has been proposed as a pre-treatment peripheral blood biomarker. This study aims to compare the relationship between SIRI, various serum biomarkers, and the prognosis of NSCLC patients before and after treatment.

Methods: A retrospective study was conducted on advanced NSCLC patients treated with anti-PD-1 drugs from December 2018 to September 2021. Peripheral blood markers were measured pre- and post-treatment, and hazard ratios were calculated to assess the association between serum biomarkers and progression-free survival (PFS) and overall survival (OS). Kaplan-Meier curves and Cox proportional hazards models were employed for survival analysis. A nomogram model was built based on multivariate Cox proportional hazards regression analysis using the R survival package, with internal validation via the bootstrap method (1,000 resamples). Predictive performance was expressed using the concordance index (C-index), and calibration plots illustrated predictive accuracy.The application value of the model was evaluated by decision curve analysis (DCA).

Results: Among 148 advanced NSCLC patients treated with PD-1 inhibitors, the median PFS was 12.9 months (range: 5.4-29.2 months), and the median OS was 19.9 months (range: 9.6-35.2 months). Univariate analysis identified pre- and post-treatment SIRI, mGRIm-Score, and PNI as independent prognostic factors for both PFS and OS (p < 0.05). Multivariate analysis demonstrated that high post-SIRI and post-mGRIm-Score independently predicted poor PFS (P < 0.001, P = 0.004) and OS (P = 0.048, P = 0.001). The C-index of the nomogram model for OS was 0.720 (95% CI: 0.693-0.747) and for PFS was 0.715 (95% CI: 0.690-0.740). Internal validation via bootstrap resampling (B = 1,000) showed good agreement between predicted and observed OS and PFS at 1, 2, and 3 years, as depicted by calibration plots.

Conclusion: SIRI is an important independent predictor of early progression in advanced NSCLC patients treated with PD-1 inhibitors and may assist in identifying patients who will benefit from PD-1 inhibitors therapy in routine clinical practice.

Keywords: immunotherapy; nomogram model; non-small cell lung cancer; prognostic; systemic inflammation response index.

Figure 1

Display the baseline characteristics of patients using a pie chart.

Figure 2

Kaplan-Meier estimates of PFS according to (A) pre-PNI, (B) post-PNI, (C) pre-SIRI, (D) post-SIRI, (E) pre-mGRIm-Score, (F) post-mGRIm-Score.

Figure 3

Kaplan-Meier estimates of OS according to (A) pre-PNI, (B) post-PNI, (C) pre-SIRI, (D) post-SIRI, (E) pre-mGRIm-Score, (F) post-mGRIm-Score.

Figure 4

Nomogram and calibration curve for predicting PFS and OS of NSCLC patients and DCA curve of prediction model. (A) Nomogram model predicting 1-year, 2-year, and3-yearPFS; (B) calibration curves for 1-year, 2-year and 3-year disease Response rates; (C) 1-year PFS, 2-year PFS, and 3-year PFS clinical value DCA curves; (D) Nomogram model for OS; (E) calibration curves for 1-year, 2-year and 3-year survival rates; (F) 1-year, 2-year, and 3-year clinical value DCA curves.

Figure 5

Kaplan-Meier estimates of PFS and OS according to (A) PFS of ΔPNI, (B) PFS of ΔPNI, (C) OS of ΔSIRI, (D) OS of ΔSIRI.