Ding Sirui, Liang Yafen, Chang Chia-Yuan, Brown Cheryl, Jiang Xiaoqian, Hu Xia, Zou Na
Department of Computer Science and Engineering, Texas A&M University, College Station, TX 77840, United States.
Department of Anesthesiology, University of Texas Health Center at Houston, Houston, TX 77030, United States.
J Am Med Inform Assoc. 2025 Jul 1;32(7):1101-1109. doi: 10.1093/jamia/ocaf066.
Primary graft dysfunction (PGD) is an essential outcome after the heart transplant, which causes severe complications and symptoms for recipients. The in advance prediction of PGD can help the transplant physician better manage the risks of PGD occurrence for patients. Domain experts have identified some important risk factors leading to PGD. However, a widely accepted PGD prediction method is lacking from a computational perspective. In this work, we focus on the prediction of PGD after heart transplant with machine learning (ML).
With the strong power of artificial intelligence, we propose to design a ML algorithm to precisely predict the PGD with the donor and recipient features. Moreover, we apply the computational method to automatically identify important features and interactions between them.
To evaluate the effectiveness of the ML algorithm in PGD prediction, we curated a PGD patients' cohort from the United Network for Organ Sharing database, which contains 8008 recipients. 5 commonly used ML models are used for performance comparison. The multi-layer perceptron model achieves superior performance, as measured by area under the receiver operating characteristic curve (AUROC), at 0.868. We identify the top 20 important features and interactions between donors and recipients. Clinical analyses are conducted on the identified features and interactions.
We summarize the contributions of this work from three aspects including methodology, clinical analysis, and insights. We discuss the limitations of this work on data, model, and real-world implementation perspectives. Additionally, we further discuss the future directions to extend this work to more organ types and diseases.
In summary, ML has promising applications in PGD prediction as a computational tool for clinical study. We can also use the ML model to help us identify and discover new risk factors and interactions between donor and recipient.
原发性移植肝无功能（PGD）是心脏移植后的一项关键结果，会给受者带来严重并发症和症状。对PGD进行提前预测有助于移植医生更好地管理患者发生PGD的风险。领域专家已经确定了一些导致PGD的重要风险因素。然而，从计算角度来看，缺乏一种被广泛接受的PGD预测方法。在这项工作中，我们专注于使用机器学习（ML）预测心脏移植后的PGD。
借助人工智能的强大力量，我们提议设计一种ML算法，利用供体和受体特征精确预测PGD。此外，我们应用计算方法自动识别重要特征及其之间的相互作用。
为了评估ML算法在PGD预测中的有效性，我们从器官共享联合网络数据库中精心挑选了一个PGD患者队列，其中包含8008名受者。使用5种常用的ML模型进行性能比较。多层感知器模型表现出色，通过受试者操作特征曲线下面积（AUROC）衡量，达到了0.868。我们确定了前20个重要特征以及供体和受体之间的相互作用。对识别出的特征和相互作用进行了临床分析。
我们从方法学、临床分析和见解三个方面总结了这项工作的贡献。我们从数据、模型和实际应用角度讨论了这项工作的局限性。此外，我们进一步讨论了将这项工作扩展到更多器官类型和疾病的未来方向。
总之，作为临床研究的计算工具，ML在PGD预测中具有广阔的应用前景。我们还可以使用ML模型帮助我们识别和发现新的风险因素以及供体和受体之间的相互作用。
