European journal of medical research · 2025
Interpretable prognostic modeling for long-term survival of Type A aortic dissection patients using support vector machine algorithm.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Cai Hao, Shao Yue, Liu Xuan-Yu, Li Chang-Ying, Ran Hao-Yu, Shi Hao-Ming, Zhang Cheng, Wu Qing-Chen
Аннотация
OBJECTIVES: This study aims to develop a reliable and interpretable predictive model for long-term survival in Type A aortic dissection (TAAD) patients, utilizing machine learning (ML) algorithms.
METHODS: We retrospectively reviewed the clinical data of patients diagnosed with TAAD who underwent open surgical repair at the First Affiliated Hospital of Chongqing Medical University, from September 2017 to December 2020, and at the Chongqing University Central Hospital between October 2019 and April 2020. Cases with less than 20% missing data were imputed using random forest algorithms. To identify significant prognostic factors, we performed LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression analysis, including preoperative blood markers, previous medical history and intraoperative condition. Based on the advantages of the model and the characteristics of the data set, we subsequently developed a machine learning-based prognostic model using Support Vector Machine (SVM) and evaluated its performance across key metrics. To further explain the decision-making process of the SVM model, we employed SHapley Additive exPlanation (SHAP) values for model interpretation.
RESULTS: A total of 171 patients with TAAD were included in model training and internal test groups; 73 patients with TAAD were included in external test group. Through LASSO Cox regression, univariate analysis, and clinical relevance assessment, seven feature variables were selected for modeling. Performance evaluation revealed that the SVM model showed excellent performance in both the training and test sets, with no significant overfitting, indicating strong clinical applicability. In the training set, the model achieved an AUC of 0.9137 (95% CI 0.9081-0.9203) and in the internal and external testing set, 0.8533 (95% CI 0.8503-0.8624) and 0.8770 (95% CI 0.8698-0.8982), respectively. The accuracy values were 0.8366, 0.8481 and 0.8030; precision values were 0.8696, 0.8374 and 0.8235; recall values were 0.8421, 0.7933 and 0.7651; F1 scores were 0.8290, 0.8148 and 0.7928; Brier scores were 0.1213, 0.1417 and 0.1323; average precision (AP) values were 0.9019, 0.8789 and 0.8548, respectively. SHAP analysis revealed that longer operation time, extended cardiopulmonary bypass (CPB) duration, prolonged aortic cross-clamp (ACC) time, advanced age, higher plasma transfusion volume, elevated serum creatinine and increased white blood cell (WBC) count significantly contributed to higher model predictions.
CONCLUSIONS: This study developed an interpretable predictive model based on the SVM algorithm to assess long-term survival in TAAD patients. The model demonstrated accuracy, precision, and robustness in identifying high-risk patients, providing reliable evidence for clinicians.
METHODS: We retrospectively reviewed the clinical data of patients diagnosed with TAAD who underwent open surgical repair at the First Affiliated Hospital of Chongqing Medical University, from September 2017 to December 2020, and at the Chongqing University Central Hospital between October 2019 and April 2020. Cases with less than 20% missing data were imputed using random forest algorithms. To identify significant prognostic factors, we performed LASSO (Least Absolute Shrinkage and Selection Operator) Cox regression analysis, including preoperative blood markers, previous medical history and intraoperative condition. Based on the advantages of the model and the characteristics of the data set, we subsequently developed a machine learning-based prognostic model using Support Vector Machine (SVM) and evaluated its performance across key metrics. To further explain the decision-making process of the SVM model, we employed SHapley Additive exPlanation (SHAP) values for model interpretation.
RESULTS: A total of 171 patients with TAAD were included in model training and internal test groups; 73 patients with TAAD were included in external test group. Through LASSO Cox regression, univariate analysis, and clinical relevance assessment, seven feature variables were selected for modeling. Performance evaluation revealed that the SVM model showed excellent performance in both the training and test sets, with no significant overfitting, indicating strong clinical applicability. In the training set, the model achieved an AUC of 0.9137 (95% CI 0.9081-0.9203) and in the internal and external testing set, 0.8533 (95% CI 0.8503-0.8624) and 0.8770 (95% CI 0.8698-0.8982), respectively. The accuracy values were 0.8366, 0.8481 and 0.8030; precision values were 0.8696, 0.8374 and 0.8235; recall values were 0.8421, 0.7933 and 0.7651; F1 scores were 0.8290, 0.8148 and 0.7928; Brier scores were 0.1213, 0.1417 and 0.1323; average precision (AP) values were 0.9019, 0.8789 and 0.8548, respectively. SHAP analysis revealed that longer operation time, extended cardiopulmonary bypass (CPB) duration, prolonged aortic cross-clamp (ACC) time, advanced age, higher plasma transfusion volume, elevated serum creatinine and increased white blood cell (WBC) count significantly contributed to higher model predictions.
CONCLUSIONS: This study developed an interpretable predictive model based on the SVM algorithm to assess long-term survival in TAAD patients. The model demonstrated accuracy, precision, and robustness in identifying high-risk patients, providing reliable evidence for clinicians.