PDF(Pubmed)
Abstract：
Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool.
摘要：
手术是许多类型疾病的常见一线治疗方法，包括癌症.一般选择性手术后的死亡率显着下降，而术后并发症仍然经常发生。术前评估工具用于支持患者风险分层，但并不总是提供精确和可访问的评估。可穿戴传感器（WS）提供了一种可访问的替代方案，可在非临床环境中进行连续监测。他们在围手术期显示出一致的摄取，但尚未将WS作为术前评估工具进行审查。本文回顾了WS研究在术前阶段的应用进展。加速度计在研究中一直被用作传感器，并且经常与光电体积描记术或心电图传感器结合使用。对预处理方法进行了讨论，数据缺失是一个共同的主题；这在几个方面进行了处理，通常通过采用提取阈值或使用插补技术。研究很少处理原始数据;采用内部专有算法和预先计算的心率和步数的商业设备最常用，限制了进一步的特征提取。一系列机器学习模型被用来预测结果，包括支持向量机，随机森林和回归模型。没有哪个模型明显优于其他模型。深度学习在预测运动测试结果方面被证明是成功的，但仅在大样本量研究中。这篇综述概述了WS的挑战，并为未来研究提供了建议，以开发WS作为可行的术前评估工具。
