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Risk Factors Analysis of Cutaneous Adverse Drug Reactions Caused by Targeted Therapy and Immunotherapy Drugs for
Zimin Zhang1,2, Mingyang Zhu1, Weiwei Jiang1
1Department of Pharmacy, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Abstract:
Targeted therapy and immunotherapy drugs for oncology have greater efficacy and tolerability than cytotoxic chemotherapeutic drugs. However, the cutaneous adverse drug reactions associated with these newer therapies are more common and remain poorly predicted. An effective prediction model is urgently needed and essential. This retrospective study included 1052 patients, divided into train set, test set, and external validation set. As a data-driven study, a total of 76 variables were collected. Univariate logistic analysis, least absolute shrinkage and selection operator regression, and stepwise logistic regression were utilized for feature screening. Finally, nine machine-learning models were constructed and compared, and grid search was performed to adjust the parameters. Model performance was evaluated using calibration curve and the area under the receiver operating characteristic curve (AUROC). Nine risk factors were eventually identified: age, treatment modality, cancer types, history of allergies, age-corrected Charlson comorbidity index, percentage of eosinophils, absolute number of monocytes, Eastern Cooperative Oncology Group Performance Status, and C-reactive protein. Among the models, the logistic model performed best, demonstrating strong performance in test set (AUROC = 0.734) and external validation set (AUROC = 0.817). This study identified nine significant risk factors and developed a nomogram prediction model. These findings have important implications for optimizing therapeutic efficacy and maintaining the quality of life of patients from the perspective of managing cutaneous adverse drug reactions. Trial Registration: ChiCTR2400088422.
Insights
New oncology drugs offer better outcomes but cause more skin reactions. This study identified nine key risk factors and developed a predictive model to manage these adverse drug reactions, improving patient quality of life.
Area of Science:
- Oncology
- Dermatology
- Biostatistics
Background:
- Targeted therapy and immunotherapy offer improved efficacy and tolerability over traditional chemotherapy for cancer treatment.
- However, these advanced oncology treatments are associated with a higher incidence of cutaneous adverse drug reactions (cADRs), which are difficult to predict.
- An effective predictive model for cADRs is crucial for optimizing patient care and quality of life.
Purpose of the Study:
- To identify significant risk factors for cutaneous adverse drug reactions in patients receiving targeted therapy or immunotherapy.
- To develop and validate a predictive model for cADRs.
Main Methods:
- A retrospective study of 1052 patients, with data split into training, testing, and external validation sets.
- Feature selection using univariate logistic analysis, least absolute shrinkage and selection operator (LASSO) regression, and stepwise logistic regression.
- Construction and comparison of nine machine-learning models, with parameter tuning via grid search. Model performance assessed using calibration curves and AUROC.
Main Results:
- Nine significant risk factors for cADRs were identified: age, treatment modality, cancer types, allergy history, age-corrected Charlson comorbidity index, eosinophil percentage, absolute monocyte count, ECOG Performance Status, and C-reactive protein.
- The logistic regression model demonstrated the best performance, achieving an AUROC of 0.734 in the test set and 0.817 in the external validation set.
- A nomogram prediction model was successfully developed based on these nine factors.
Conclusions:
- The study successfully identified key predictors of cADRs associated with modern cancer therapies.
- The developed logistic model and nomogram provide a valuable tool for predicting and managing cADRs.
- These findings can aid in optimizing treatment strategies and improving the quality of life for cancer patients experiencing skin-related side effects.
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