Objectives Complications after percutaneous coronary intervention (PCI) are common and costly. Risk models for predicting the likelihood of acute kidney injury (AKI), bleeding, stroke and death are limited by accuracy and inability to use non-linear relationships among predictors. Our objective was to develop and validate a set of artificial neural networks (ANN) models to predict five adverse outcomes after PCI—AKI, bleeding, stroke, death and any adverse outcome. Methods We conducted a study of 28 005 patients (training and test cohorts of 21 004 and 7001 patients, respectively) undergoing PCI at five hospitals in the Barnes-Jewish Hospital system. We used an ANN multi-layer perceptron (MLP) architecture based on a set of 278 preprocessed variables. Model accuracy was tested using area under the receiver operating-characteristic curve (AUC). Improved prediction by the MLP model was assessed using integrated discrimination improvement (IDI) and Brier score. Results The fully trained MLP model achieved convergence quickly (<10 epochs) and could accurately predict AKI (77.9%), bleeding (86.5%), death (90.3%) and any adverse outcome (80.6%) in the independent test set. Prediction of stroke was not satisfactory (69.9%). Compared with the currently used models for AKI, bleeding and death prediction, our models showed a significantly higher AUC, IDI and Brier score. Conclusions Using neural network-based models, we accurately predict major adverse events after PCI. Larger studies for replicability and longitudinal studies for evidence of impact are needed to establish these artificial intelligence methods in current PCI practice. No data are available. The patient-level data used in this study is confidential and cannot be shared.

中文翻译：

---

经皮冠状动脉介入治疗中的人工智能：使用人工神经网络改进 PCI 相关并发症的风险预测

目的 经皮冠状动脉介入治疗 (PCI) 后的并发症是常见且昂贵的。用于预测急性肾损伤 (AKI)、出血、中风和死亡可能性的风险模型受到准确性的限制，并且无法使用预测因子之间的非线性关系。我们的目标是开发和验证一组人工神经网络 (ANN) 模型，以预测 PCI 后的五种不良后果——AKI、出血、中风、死亡和任何不良后果。方法 我们对在 Barnes-Jewish 医院系统的 5 家医院接受 PCI 的 28 005 名患者（分别为 21 004 和 7001 名患者的训练和测试队列）进行了研究。我们使用了基于一组 278 个预处理变量的 ANN 多层感知器 (MLP) 架构。使用接收者操作特征曲线 (AUC) 下的面积测试模型准确性。使用综合辨别改进 (IDI) 和 Brier 分数评估 MLP 模型改进的预测。结果经过充分训练的 MLP 模型快速收敛（<10 个 epochs），并且可以准确预测独立测试集中的 AKI（77.9%）、出血（86.5%）、死亡（90.3%）和任何不良结果（80.6%）。中风的预测不令人满意（69.9%）。与目前用于 AKI、出血和死亡预测的模型相比，我们的模型显示出显着更高的 AUC、IDI 和 Brier 评分。结论使用基于神经网络的模型，我们可以准确预测 PCI 后的主要不良事件。需要更大规模的可重复性研究和影响证据的纵向研究，以在当前的 PCI 实践中建立这些人工智能方法。没有可用数据。