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Machine learning-based prediction of non-ionic iodinated contrast media-induced acute adverse reactions following contrast-enhanced CT.
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Li Xi, Wang Ke, Liu Huawei, Liu Yu, Qiu Haiyan, Huang Jing, Liu Heng, Li Xue
Аннотация
BACKGROUND: Iodinated contrast media (ICM) used in contrast-enhanced CT (CECT) examinations may induce acute adverse reactions (AAR) with different severity. To improve upon traditional linear risk assessments, this study aims to develop and validate a machine learning (ML)-based predictive model for ICM-AAR using routinely available clinical factors from a large-scale real-world cohort.
METHODS: Five ML models including Logistic Regression, Random Forest, XGBoost, CatBoost and LightGBM were trained and validated on a retrospective cohort of 332,090 patients who underwent CECT scans between 2014 and 2020. The final cohort was split into train, test and external validation datasets. Synthetic minority over-sampling technique (SMOTE) and under-sampling strategies were used to balance the data. Model training was performed using the GridsearchCV algorithm with 5-fold cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). The SHapley Additive exPlanations (SHAP) algorithm was performed to identify the most significant features.
RESULTS: The train set included 132,102 patients (median age, 60 years [IQR, 49-69 years]; 72,666 males). The CatBoost model exhibited the best performance, with an AUROC of 0.6916 (95%CI = [0.6555, 0.7272]) in test set (n = 56,616; median age, 60 years [IQR, 49-69 years]; 31,400 males) and 0.6565 (95%CI = [0.6206, 0.6913]) in external validation set (n = 111,334; median age, 58 years [IQR, 49-68 years]; 61,447 males). Age, Injection rate, Type of contrast media, Injection dose, and Examination site were identified as the five most significant features by SHAP analysis.
CONCLUSION: A ML model based on clinical factors was capable of predicting the occurrence of ICM-AAR, demonstrating improved ability in tackling extreme class imbalance. The model could be used to help clinical decision-making.
METHODS: Five ML models including Logistic Regression, Random Forest, XGBoost, CatBoost and LightGBM were trained and validated on a retrospective cohort of 332,090 patients who underwent CECT scans between 2014 and 2020. The final cohort was split into train, test and external validation datasets. Synthetic minority over-sampling technique (SMOTE) and under-sampling strategies were used to balance the data. Model training was performed using the GridsearchCV algorithm with 5-fold cross-validation. Model performance was evaluated using the area under the receiver operating characteristic curve (AUROC). The SHapley Additive exPlanations (SHAP) algorithm was performed to identify the most significant features.
RESULTS: The train set included 132,102 patients (median age, 60 years [IQR, 49-69 years]; 72,666 males). The CatBoost model exhibited the best performance, with an AUROC of 0.6916 (95%CI = [0.6555, 0.7272]) in test set (n = 56,616; median age, 60 years [IQR, 49-69 years]; 31,400 males) and 0.6565 (95%CI = [0.6206, 0.6913]) in external validation set (n = 111,334; median age, 58 years [IQR, 49-68 years]; 61,447 males). Age, Injection rate, Type of contrast media, Injection dose, and Examination site were identified as the five most significant features by SHAP analysis.
CONCLUSION: A ML model based on clinical factors was capable of predicting the occurrence of ICM-AAR, demonstrating improved ability in tackling extreme class imbalance. The model could be used to help clinical decision-making.