Abstract
Introduction: in patients with advanced non-small cell lung cancer (ANSCLC) undergoing treatment with programmed cell death protein 1 (PD-1) inhibitors (PD-1Is), adverse reactions (ARs) may occur. Establishing a risk assessment model can facilitate personalized treatment. Materials and Methods: clinical data were collected from 215 ANSCLC patients treated with PD-1Is. Patients who experienced ARs were classified as the observation group (OG, 92 cases), while those who did not experience ARs were classified as the control group (CG, 123 cases). A multivariable logistic regression (LR) model was employed to analyze independent risk factors (RFs) associated with ARs, and R Studio software was utilized to create a nomogram predictive model. Results:the concordance index for the nomogram predictive model for ARs in ANSCLC patients treated with PD-1Is was 0.911. The threshold for predicting ARs using the nomogram was greater than 0.25, providing a clinical net benefit superior to individual indicators such as smoking, tumor-node-metastasis (TNM) staging, neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), and prognostic nutritional index (PNI). The proportion of smokers in the OG was markedly superior to that in the CG (P<0.05). Conclusion: smoking, TNM staging, and peripheral blood indicators such as NLR, SII, and PNI are independent RFs for the occurrence of ARs. The constructed nomogram predictive model demonstrates greater clinical utility than individual indicators, enhancing the accuracy of AR predictions.
Keywords
References
The published articles will be distributed under the Creative Commons Attribution 4.0 International License (CC BY). It is allowed to copy and redistribute the material in any medium or format, and remix, transform, and build upon it for any purpose, even commercially, as long as appropriate credit is given to the original author(s), a link to the license is provided and it is indicated if changes were made. Users are required to provide full bibliographic description of the original publication (authors, article title, journal title, volume, issue, pages), as well as its DOI code. In electronic publishing, users are also required to link the content with both the original article published in Journal of Medical Biochemistry and the licence used.
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.