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Factors inducing cutaneous adverse reactions in cancer patients treated with PD-1 and PD-L1 inhibitors: a
Young-Ah Cho1, Youngyun Moon2, Wooyoung Park2
1The Prime Hospital, Jinju, Gyeongsangnam-do, Republic of Korea.
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.
Insights
This study found that antihistamine use and cancer metastasis are linked to increased skin reactions from immune checkpoint inhibitors (ICIs). Diabetes and opioid use were associated with fewer skin issues, aiding in personalized ICI treatment strategies.
Area of Science:
- Oncology
- Dermatology
- Immunology
Background:
- Immune checkpoint inhibitors (ICIs) are vital cancer therapies but can cause immune-related adverse events (irAEs), particularly skin reactions.
- Understanding patient-specific factors influencing these cutaneous reactions is critical for managing ICI treatment.
- This research investigates associations between patient characteristics and skin adverse events in cancer patients receiving ICIs.
Purpose of the Study:
- To identify patient characteristics associated with cutaneous adverse reactions in cancer patients undergoing ICI therapy.
- To explore the utility of machine learning models for predicting ICI-induced skin toxicity.
Main Methods:
- A cohort of 209 cancer patients receiving ICIs was analyzed.
- Statistical analyses included chi-square tests, Fisher's exact tests, and multivariable logistic regression.
- Machine learning models (logistic regression, elastic net, random forest, SVM) were developed to predict skin adverse events.
Main Results:
- Antihistamine use and cancer metastasis were significantly associated with a higher incidence of skin reactions.
- Diabetes and opioid usage showed a correlation with lower rates of skin adverse events.
- Predictive models, especially logistic regression and elastic net, demonstrated robust performance in forecasting these events.
Conclusions:
- Patient characteristics significantly influence the risk of developing skin reactions to ICIs.
- Predictive models can aid in proactive management of ICI-induced dermatologic toxicity.
- Personalized treatment strategies informed by risk factors and predictive analytics can optimize ICI therapy outcomes.
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