BACKGROUND Hyperglycemia or high blood glucose during surgery is associated with poor postoperative outcome. Knowing in advance which patients may develop hyperglycemia allows optimal assignment of resources and earlier initiation of glucose management plan. OBJECTIVE To develop predictive models to estimate peak glucose levels in surgical patients and to implement the best performing model as a point-of-care clinical tool to assist the surgical team to optimally manage glucose levels. METHODS Using a large perioperative dataset (6,579 patients) of patient- and surgery-specific parameters, we developed and validated linear regression and machine learning models (random forest, extreme gradient boosting [Xg Boost], classification and regression trees [CART], and neural network) to predict the peak glucose levels during surgery. The model performances were compared in terms of mean absolute percentage error (MAPE), logarithm of the ratio of the predicted to actual value (log ratio), median prediction error, and interquartile error range. The best performing model was implemented as part of a web-based application for optimal decision-making toward glucose management during surgery. RESULTS Accuracy of the machine learning models were higher (MAPE = 17%, log ratio = 0.029 for Xg Boost) when compared with that of the linear regression model (MAPE = 22%, log ratio = 0.041). The Xg Boost model had the smallest median prediction error (5.4 mg/dL) and the narrowest interquartile error range (-17 to 24 mg/dL) as compared with the other models. The best performing model, Xg Boost, was implemented as a web application, Hyper-G, which the perioperative providers can use at the point of care to estimate peak glucose levels during surgery. CONCLUSIONS Machine learning models are able to accurately predict peak glucose levels during surgery. Implementation of such a model as a web-based application can facilitate optimal decision-making and advance planning of glucose management strategies.

中文翻译：

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Hyper-G：一种用于在手术患者中进行最佳决策和管理血糖水平的人工智能工具。

背景技术手术期间的高血糖或高血糖与差的术后结果有关。提前知道哪些患者可能发生高血糖症，可以优化资源分配并尽早启动葡萄糖管理计划。目的开发预测模型以估计手术患者的峰值血糖水平，并将性能最佳的模型用作即时医疗工具，以协助手术团队最佳地管理血糖水平。方法使用围手术期患者和手术特定参数的大型数据集（6,579名患者），我们开发并验证了线性回归和机器学习模型（随机森林，极限梯度提升[Xg Boost]，分类和回归树[CART]，以及神经网络）以预测手术过程中的峰值葡萄糖水平。根据平均绝对百分比误差（MAPE），预测值与实际值之比的对数（对数比），中值预测误差和四分位数误差范围对模型性能进行比较。最佳性能模型是基于Web的应用程序的一部分，可在手术过程中对血糖管理进行最佳决策。结果与线性回归模型（MAPE = 22％，对数比= 0.041）相比，机器学习模型的准确性更高（MAPE = 17％，Xg Boost的对数比= 0.029）。与其他模型相比，Xg Boost模型的中值预测误差最小（5.4 mg / dL），四分位误差范围最窄（-17至24 mg / dL）。效果最好的模型Xg Boost被实现为Web应用程序Hyper-G，围手术期提供者可以在护理时使用它来估计手术期间的峰值葡萄糖水平。结论机器学习模型能够准确预测手术过程中的峰值葡萄糖水平。这种模型作为基于Web的应用程序的实现可以促进最佳决策和葡萄糖计划管理策略的提前规划。