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Objectives <p>Intravenous contrast media extravasation (ICME) is a frequent and potentially severe complication during contrast-enhanced computed tomography (CECT). Although prevention of ICME is critical in radiology practice, valid and clinically practical prediction models remain limited. This study aimed to develop and validate an interpretable machine learning (ML) model for predicting the risk of ICME in patients undergoing CECT.</p> Patients and methods <p>This multicenter, retrospective case–control study analyzed 500 patients (252 ICME cases, 248 controls) from three institutions. Predictors encompassed patient demographics, clinical history, procedural details, venous access characteristics, and the diurnal temperature difference. From 22 initial candidates, 14 features were selected using least absolute shrinkage and selection operator (LASSO) regression. Five machine learning models—logistic regression, support vector machine (SVM), random forest, XGBoost, and K-nearest neighbors (KNN)—were trained and evaluated on an independent test set. The optimal model was assessed based on discrimination, calibration, and clinical utility, with its predictions explained using SHapley Additive exPlanations (SHAP).</p> Results <p>Among the five models, Logistic Regression demonstrated superior and the most generalizable performance on the test set, achieving an area under the receiver operating characteristic curve (AUC) of 0.82 (95% CI 0.72, 0.89), with a sensitivity of 78%, a precision of 75%, and an F1-score of 0.76. It also showed excellent calibration (Hosmer–Lemeshow <i>p</i> = 0.067). The other models' test AUCs were 0.81 (95% CI 0.71, 0.89) for SVM, 0.80 (95% CI 0.71, 0.88) for Random Forest, 0.79 (95% CI 0.68, 0.87) for XGBoost, and 0.74 (95% CI 0.64, 0.83) for KNN. Decision curve analysis confirmed the clinical utility of the Logistic Regression model across a broad range of threshold probabilities, with a clinically optimal threshold of 0.25. SHAP analysis identified needle gauge (18G, protective), poor vein quality (risk factor), and needle type (Y-type, risk factor) as the most influential predictors. The diurnal temperature difference was established as a novel, independent environmental risk factor.</p> Conclusions <p>The developed Logistic Regression model can effectively predict the occurrence of ICME, with a validated AUC of 0.82, indicating good discriminatory ability. It provides a practical and interpretable tool for risk stratification, highlighting key modifiable factors (e.g., needle choice and vein assessment) and a novel environmental cue (diurnal temperature difference), which can assist in guiding targeted preventive strategies to enhance patient safety during CECT.</p> <p>Clinical trial number.</p> <p>Not applicable.</p>

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Development and validation of an interpretable machine learning model for predicting intravenous contrast media extravasation risk in CT imaging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Wenyan Zhang,
  • Qin Li,
  • Xianrong Kong,
  • Basen Li,
  • Hanxiong Guan,
  • Beibei Shao

摘要

Objectives

Intravenous contrast media extravasation (ICME) is a frequent and potentially severe complication during contrast-enhanced computed tomography (CECT). Although prevention of ICME is critical in radiology practice, valid and clinically practical prediction models remain limited. This study aimed to develop and validate an interpretable machine learning (ML) model for predicting the risk of ICME in patients undergoing CECT.

Patients and methods

This multicenter, retrospective case–control study analyzed 500 patients (252 ICME cases, 248 controls) from three institutions. Predictors encompassed patient demographics, clinical history, procedural details, venous access characteristics, and the diurnal temperature difference. From 22 initial candidates, 14 features were selected using least absolute shrinkage and selection operator (LASSO) regression. Five machine learning models—logistic regression, support vector machine (SVM), random forest, XGBoost, and K-nearest neighbors (KNN)—were trained and evaluated on an independent test set. The optimal model was assessed based on discrimination, calibration, and clinical utility, with its predictions explained using SHapley Additive exPlanations (SHAP).

Results

Among the five models, Logistic Regression demonstrated superior and the most generalizable performance on the test set, achieving an area under the receiver operating characteristic curve (AUC) of 0.82 (95% CI 0.72, 0.89), with a sensitivity of 78%, a precision of 75%, and an F1-score of 0.76. It also showed excellent calibration (Hosmer–Lemeshow p = 0.067). The other models' test AUCs were 0.81 (95% CI 0.71, 0.89) for SVM, 0.80 (95% CI 0.71, 0.88) for Random Forest, 0.79 (95% CI 0.68, 0.87) for XGBoost, and 0.74 (95% CI 0.64, 0.83) for KNN. Decision curve analysis confirmed the clinical utility of the Logistic Regression model across a broad range of threshold probabilities, with a clinically optimal threshold of 0.25. SHAP analysis identified needle gauge (18G, protective), poor vein quality (risk factor), and needle type (Y-type, risk factor) as the most influential predictors. The diurnal temperature difference was established as a novel, independent environmental risk factor.

Conclusions

The developed Logistic Regression model can effectively predict the occurrence of ICME, with a validated AUC of 0.82, indicating good discriminatory ability. It provides a practical and interpretable tool for risk stratification, highlighting key modifiable factors (e.g., needle choice and vein assessment) and a novel environmental cue (diurnal temperature difference), which can assist in guiding targeted preventive strategies to enhance patient safety during CECT.

Clinical trial number.

Not applicable.