Prediction of 1-year mortality after heart transplantation using machine learning approaches: A single-center study from China
Prediction of 1-year mortality after heart transplantation using machine learning approaches: A single-center study from China
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DOI：
10.1016/j.ijcard.2021.07.024
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发表时间：
2021-08-30
影响因子：
3.5
通讯作者：
Li, Fei
中科院分区：
文献类型：
--
作者：
Zhou, Ying;Chen, Si;Li, Fei
背景：心脏移植（HTx）仍然是终末期心力衰竭的金标准治疗。本研究的目的是使用机器学习方法建立一个评估HTx预后的风险预测模型。方法：本研究纳入了2015年1月1日至2018年12月31日在我院接受原位HTx的连续受者。主要结局为1年死亡率。最小绝对收缩和选择算子方法被用来选择变量，并采用七种不同的机器学习方法来开发风险预测模型。Bootstrap方法用于模型验证。模型解释采用SHAP方法。结果：共纳入受者381例，平均年龄43.783岁。白蛋白、受体年龄和左心房直径是影响HTx 1年死亡率的前3个最重要的变量。其他重要变量包括红细胞、血红蛋白、淋巴细胞%、吸烟史、使用冻干rhBNP、使用左西孟旦、高血压、心脏手术史、恶性肿瘤和气管插管史。随机森林（RF）模型实现了0.801的最佳曲线下面积（AUC），梯度增强机（GBM）显示了0.271的最佳灵敏度。引入SHAP方法，在个体水平上显示RF模型对“生存”或“死亡”的预测过程。结论：我们使用机器学习方法建立了HTx患者术后预后的风险预测模型，并证明RF模型在验证时具有最大AUC的最高区分度。该预测模型有助于识别高危HTx受体，提供个性化治疗方案，减少器官浪费。
Background: Heart transplantation (HTx) remains the gold-standard treatment for end-stage heart failure. The aim of this study was to establish a risk-prediction model for assessing prognosis of HTx using machine-learning approach. Methods: Consecutive recipients of orthotopic HTx at our institute between January 1st, 2015 and December 31st, 2018 were included in this study. The primary outcome was 1-year mortality. Least absolute shrinkage and selection operator method was used to select variables and seven different machine-learning approaches were employed to develop the risk-prediction model. Bootstrap method was used for model validation. Shapley Additive exPlanations (SHAP) method was used for model interpretation. Results: 381 recipients were included with average age of 43.783 years old. Albumin, recipient age and left atrium diameter ranked top three most important variables that affected the 1-year mortality of HTx. Other important variables included red blood cell, hemoglobin, lymphocyte%, smoking history, use of lyophilized rhBNP, use of Levosimendan, hypertension, cardiac surgery history, malignancy and endotracheal intubation history. Random Forest (RF) model achieved the best area under curves (AUC) of 0.801 and gradient boosting machine (GBM) showed the best sensitivity of 0.271. SHAP method was introduced to display the RF model's predicting processes of "survival" or "death" in individual level. Conclusions: We established the risk-prediction model for postoperative prognosis of HTx patients by using machine learning method and demonstrated that the RF model performed the highest discrimination with the largest AUC when validated. This prediction model could help to recognize high-risk HTx recipients, provide personalized therapy plan and reduce organ wastage.