Retrospective clinical study analysis of skin adverse reactions related to epidermal growth factor receptor...

Epidermal growth factor receptor inhibitors (EGFRIs) represent a cornerstone in the targeted therapy of malignant tumors. While effective, dermatological adverse events (dAEs) associated with EGFRIs pose a significant challenge, often necessitating treatment discontinuation due to their severity and potential to impede the continuity of cancer therapy. Despite extensive research, the specific mechanisms and predictors of these adverse events remain poorly understood, particularly in diverse populations. This gap in knowledge underscores the need for targeted studies to better predict and manage these events, enhancing patient outcomes and adherence to life-saving therapies.

This observational study was conducted at The First Affiliated Hospital of Guangxi Medical University, covering cancer patients treated with EGFRIs from 2020 to 2022. We analyzed clinical data including patient demographics, treatment specifics, and the development and timing of dAEs. The study employed SPSS 26.0 software for data analysis, focusing on the incidence of dAEs and factors influencing their occurrence. We used Kaplan-Meier and Cox regression methods to establish a predictive model for dAEs, tracking their onset and impact on treatment continuity.

In our study of 120 patients treated with EGFR inhibitors at The First Affiliated Hospital of Guangxi Medical University, we found a high prevalence of dAEs, with 84.2% of patients experiencing such effects. The most common manifestations were papulopustular rashes, observed as pustules in 52.5% and papules in 57.4% of cases, followed by nail lesions in 62.4% of patients, oral or other mucosal ulcers in 34.7%, and hair changes in 26.7%. The median incubation time (MIT) for dAEs was 5 weeks. We identified drug type, ethnicity, and occupation as statistically significant risk factors (P<0.05 for all) that influenced the MIT, which the Cox regression model further identified as protective factors. Nomograms were developed to assess the risk of dAEs, although it is important to note that these models have only been internally validated, lacking external validation data at this stage.

The study highlights the high incidence of EGFRIs-associated dAEs, with specific dermatological manifestations posing significant challenges in cancer therapy. The identification of drug type, ethnicity, and occupation as influential factors on the MIT for dAEs informs clinical decisions. Our prediction model serves as a practical tool for evaluating the risk of developing dAEs over time, aiming to optimize patient management and mitigate treatment interruptions.