Journal of the American Heart Association · 2026
Machine-Learning Prediction of Tumor Comorbidity and Cancer-Associated Mortality in Patients With Coronary Heart Disease: A 2-Stage Modeling Study.
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Zheng Wei, Cai Hongsen, Li Yahui, Huang Man, Fang Qin, Wang Luyun, Wang Daowen, Jiang Jie, Zhang Yan, Ding Hu, Hou Wenguang, Zhu Hongling, Wang Yan
Аннотация
BACKGROUND: Coronary heart disease (CHD) with concomitant tumors is increasingly prevalent and is associated with a higher mortality. We aimed to develop 2 predictive models to enable early identification of tumor risk in patients with CHD and mortality risk in those with CHD and cancer.
METHODS: We analyzed 134 141 patients with CHD treated at Tongji Hospital (Huazhong University of Science and Technology, Wuhan, Hubei, China) during 2001 to 2023. Data collected from the First Hospital of Peking University (Beijing, China) were used for external validation. We developed and evaluated 6 machine learning models (extreme gradient boosting, light gradient boosting machine, random forest, neural network, decision tree, and logistic regression). Model explainability was improved using Shapley additive explanation, and an online prediction platform was subsequently implemented.
RESULTS: For the model of predicting tumor risk in patients with CHD, extreme gradient boosting showed the best performance in the test set (area under the receiver operating characteristic curve =0.935), achieving the lowest Brier score (0.080). Its sensitivity, accuracy, and F1 score were 0.740, 0.882, and 0.751, respectively. Meanwhile, light gradient boosting machine performed well for in-hospital mortality prediction in the test set (area under the receiver operating characteristic curve =0.965), achieving the lowest Brier score (0.019), and the highest sensitivity (0.677), and F1 score (0.599). The 2 models also showed good performance in the external validation, suggesting a degree of generalizability.
CONCLUSIONS: We constructed 2 predictive models to assess the risk of tumor and cancer-associated mortality in patients with CHD. The web platform assists clinicians in decision-making and patient management.
METHODS: We analyzed 134 141 patients with CHD treated at Tongji Hospital (Huazhong University of Science and Technology, Wuhan, Hubei, China) during 2001 to 2023. Data collected from the First Hospital of Peking University (Beijing, China) were used for external validation. We developed and evaluated 6 machine learning models (extreme gradient boosting, light gradient boosting machine, random forest, neural network, decision tree, and logistic regression). Model explainability was improved using Shapley additive explanation, and an online prediction platform was subsequently implemented.
RESULTS: For the model of predicting tumor risk in patients with CHD, extreme gradient boosting showed the best performance in the test set (area under the receiver operating characteristic curve =0.935), achieving the lowest Brier score (0.080). Its sensitivity, accuracy, and F1 score were 0.740, 0.882, and 0.751, respectively. Meanwhile, light gradient boosting machine performed well for in-hospital mortality prediction in the test set (area under the receiver operating characteristic curve =0.965), achieving the lowest Brier score (0.019), and the highest sensitivity (0.677), and F1 score (0.599). The 2 models also showed good performance in the external validation, suggesting a degree of generalizability.
CONCLUSIONS: We constructed 2 predictive models to assess the risk of tumor and cancer-associated mortality in patients with CHD. The web platform assists clinicians in decision-making and patient management.