Machine learning for identification of short-term all-cause and cardiovascular deaths among patients undergoing peritoneal dialysis.
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Xu Xiao, Xu Zhiyuan, Ma Tiantian, Li Shaomei, Pei Huayi, Zhao Jinghong, Zhang Ying, Xiong Zibo, Liao Yumei, Li Ying, Lin Qiongzhen, Hu Wenbo, Li Yulin, Zheng Zhaoxia, Duan Liping, Fu Gang, Guo Shanshan, Zhang Beiru, Yu Rui, Sun Fuyun, Ma Xiaoying, Hao Li, Liu Guiling, Zhao Zhanzheng, Xiao Jing, Shen Yulan, Zhang Yong, Du Xuanyi, Ji Tianrong, Wang Caili, Deng Lirong, Yue Yingli, Chen Shanshan, Ma Zhigang, Li Yingping, Zuo Li, Zhao Huiping, Zhang Xianchao, Wang Xuejian, Liu Yirong, Gao Xinying, Chen Xiaoli, Li Hongyi, Du Shutong, Zhao Cui, Xu Zhonggao, Zhang Li, Chen Hongyu, Li Li, Wang Lihua, Yan Yan, Ma Yingchun, Wei Yuanyuan, Zhou Jingwei, Li Yan, Dong Jie, Niu Kai, He Zhiqiang, PDTAP Working Group
Аннотация
Although more and more cardiovascular risk factors have been verified in peritoneal dialysis (PD) populations in different countries and regions, it is still difficult for clinicians to accurately and individually predict death in the near future. We aimed to develop and validate machine learning-based models to predict near-term all-cause and cardiovascular death. Machine learning models were developed among 7539 PD patients, which were randomly divided into a training set and an internal test set by five random shuffles of 5-fold cross-validation, to predict the cardiovascular death and all-cause death in 3 months. We chose objectively collected markers such as patient demographics, clinical characteristics, laboratory data, and dialysis-related variables to inform the models and assessed the predictive performance using a range of common performance metrics, such as sensitivity, positive predictive values, the area under the receiver operating curve (AUROC), and the area under the precision recall curve. In the test set, the CVDformer models had a AUROC of 0.8767 (0.8129, 0.9045) and 0.9026 (0.8404, 0.9352) and area under the precision recall curve of 0.9338 (0.8134,0.9453) and 0.9073 (0.8412, 0.9164) in predicting near-term all-cause death and cardiovascular death, respectively. The CVDformer models had high sensitivity and positive predictive values for predicting all-cause and cardiovascular deaths in 3 months in our PD population. Further calibration is warranted in the future.