Factors inducing cutaneous adverse reactions in cancer patients treated with PD-1 and PD-L1 inhibitors: a machine-learning algorithm approach.

Abstract

Background: Immune checkpoint inhibitors (ICIs) show promise in cancer treatment but can lead to immune-related adverse events (irAEs), notably affecting the skin. Understanding the factors behind these skin reactions is crucial for effective management during treatment. Hence, the aim of this study was to uncover associations between patient characteristics and cutaneous adverse reactions among cancer patients undergoing ICI treatment.

Methods: The study involved 209 cancer patients receiving ICIs. Statistical methods, including the chi-square test, Fisher's exact test, and multivariable logistic regression, were employed to analyze variables such as hypertension, antihistamine use, cancer metastasis, diabetes, and opioid usage. Additionally, machine learning techniques, including logistic regression, elastic net, random forest, and support vector machines (SVM), were utilized to develop predictive models anticipating skin-related adverse events.

Results: Results highlighted significant associations between specific patient attributes and the incidence of skin reactions post-ICI treatment. Notably, patients using antihistamines or with cancer metastasis exhibited higher rates of skin adverse reactions, while those with diabetes or using opioids displayed lower incidence rates. Robust performance in forecasting these adverse events was observed, particularly in the predictive models employing logistic regression and elastic net.

Conclusions: This pioneering study contributes crucial insights into predictive modeling for ICI-induced skin reactions, emphasizing the importance of personalized treatment strategies. By identifying risk factors and utilizing tailored predictive models, healthcare providers can proactively manage adverse events, optimizing the benefits of ICIs while mitigating potential side effects.