Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the effectiveness of predicting postoperative complications using a novel surgical Variational Autoencoder (surgVAE) that uncovers intrinsic patterns via cross-task and cross-cohort presentation learning. This retrospective cohort study used data from the electronic health records of adult surgical patients over four years (2018 - 2021). Six key postoperative complications for cardiac surgery were assessed: acute kidney injury, atrial fibrillation, cardiac arrest, deep vein thrombosis or pulmonary embolism, blood transfusion, and other intraoperative cardiac events. We compared prediction performances of surgVAE against widely-used ML models and advanced representation learning and generative models under 5-fold cross-validation. 89,246 surgeries (49% male, median (IQR) age: 57 (45-69)) were included, with 6,502 in the targeted cardiac surgery cohort (61% male, median (IQR) age: 60 (53-70)). surgVAE demonstrated superior performance over existing ML solutions across all postoperative complications of cardiac surgery patients, achieving macro-averaged AUPRC of 0.409 and macro-averaged AUROC of 0.831, which were 3.4% and 3.7% higher, respectively, than the best alternative method (by AUPRC scores). Model interpretation using Integrated Gradients highlighted key risk factors based on preoperative variable importance. surgVAE showed excellent discriminatory performance for predicting postoperative complications and addressing the challenges of data complexity, small cohort sizes, and low-frequency positive events. surgVAE enables data-driven predictions of patient risks and prognosis while enhancing the interpretability of patient risk profiles.

翻译：早期发现手术并发症有助于及时治疗和主动风险防控。机器学习可用于识别和预测患者术后并发症风险。本研究开发并验证了一种新型手术变分自编码器（surgVAE），该模型通过跨任务与跨队列表征学习揭示内在规律，用于预测术后并发症的有效性。这项回顾性队列研究使用了四年间（2018-2021年）成年手术患者的电子健康记录数据。评估了心脏手术的六项关键术后并发症：急性肾损伤、心房颤动、心脏骤停、深静脉血栓或肺栓塞、输血以及其他术中心脏事件。通过五折交叉验证，我们将surgVAE的预测性能与广泛使用的机器学习模型、先进表征学习及生成模型进行了比较。研究共纳入89,246例手术（男性占49%，中位年龄57岁[四分位距45-69]），其中目标心脏手术队列6,502例（男性占61%，中位年龄60岁[四分位距53-70]）。surgVAE在所有心脏手术患者术后并发症的预测中均表现出优于现有机器学习解决方案的性能，其宏观平均AUPRC达0.409，宏观平均AUROC达0.831，分别比最佳替代方法（按AUPRC评分）高出3.4%和3.7%。通过积分梯度法进行模型可解释性分析，揭示了基于术前变量重要性的关键风险因素。surgVAE在预测术后并发症方面展现出优异的判别性能，并能有效应对数据复杂性、小队列规模和低频阳性事件等挑战。该模型不仅支持基于数据的患者风险与预后预测，同时增强了患者风险特征的可解释性。