Using Wearable Technology to Assess Recovery and Detect Post-Operative Complications Following Cardiothoracic Surgery

项目摘要。每年有超过50万名患者接受心脏和肺部疾病的手术。 手术后，患者经常经历疼痛、疲劳和睡眠障碍，可持续数周至数月。 此外，多达32%的患者会出现术后并发症，这些并发症通常发生在出院后。 并可能导致再次入院。并发症代价高昂，可能是致命的；它们与 医疗费用增加了200-300%，术后90天的死亡率增加了6倍。 目前，手术后，当患者出院时，患者及其家人 负责监测病人的健康状况。病人通常在2-4个月内不去看医生 出院后几周。改善术后监测的努力包括家庭健康访问和 远程医疗即将到来。然而，这些方法已经被证明是无效的，成本很高，并且允许 只有对复苏的模糊和断断续续的评估。他们不会发现并发症，直到他们在 更严重的阶段。因此，准确、易于实施和廉价的术后评估方法 迫切需要恢复并在症状出现之前的最早阶段发现并发症。 我们之前展示了对可穿戴设备收集的生物特征进行机器学习分析可以检测到莱姆 疾病和新冠肺炎。然后，在一项初步研究中，我们应用了我们之前开发的算法来识别Covid- 19，并表明该算法可以检测89%的并发症 症状出现前的中位数为3天。当我们评估心胸手术后的恢复情况时 患者，我们表明，机器学习分析的生物识别可以将患者分为不同的恢复期 组。因此，可穿戴设备和机器学习算法可以导致高度准确和可访问的 方法早期预测并发症，提高康复评估水平。 我们的总体目标是优化和验证我们的机器学习算法--以前是为 早期检测新冠肺炎--用于在症状出现之前检测术后并发症 使用机器学习分析来使用术前和术中数据来预测患者恢复的质量。 我们的项目旨在首次使用可穿戴设备收集心胸外科手术的高分辨率生理数据 病人。然后，我们将扩展我们之前开发的用于术后早期检测的算法 并开发一种算法来预测患者术后恢复的质量。 拟议的项目将开发一种创新的方法，在手术前检测术后并发症 使用术前和术中的数据，对患者术后症状的出现和术后恢复的质量进行预测。 重要的是，我们建议的方法可以扩展，不仅可以改善心胸外科手术的结果 患者，但接受其他类型的手术的患者。这项研究的结果将使未来 一项随机试验，评估是否使用机器学习算法进行实时术后监测 可穿戴设备可以导致1)更早地发现并发症，2)更早地进行门诊干预，从而改善 恢复和/或减少并发症的严重程度，以及3)计划外再次住院的减少。

Project Summary. Every year, more than 500,000 patients undergo operations for heart and lung disease. After surgery, patients often experience pain, fatigue, and disturbed sleep that can persist for weeks to months. In addition, up to 32% of patients develop postoperative complications, which often occur after discharge from the hospital and may lead to readmission. Complications are costly and can be deadly; they are associated with a 200-300% increase in healthcare costs and a 6-fold increase in 90-day postoperative mortality. Currently, after surgery, when a patient is discharged from the hospital, the patient and their family members are responsible for monitoring the patient’s health status. Patients are usually not seen by a doctor for 2-4 weeks after discharge. Attempts to improve postoperative monitoring include home health visits and telemedicine approaches. However, these methods have been shown to be ineffective, costly, and allow for only vague and intermittent assessments of recovery. They do not detect complications until they are at a more severe stage. As such, accurate, easy-to-implement and inexpensive methods to assess postoperative recovery and to detect complications at their earliest stage—before symptom onset—are urgently needed. We previously showed that machine learning analysis of biometrics collected by wearables could detect Lyme Disease and Covid-19. We then, in a pilot study, applied our algorithm, previously developed to identify Covid- 19, to patients undergoing thoracic surgery and showed that this algorithm could detect 89% of complications a median of 3 days before symptom onset. When we evaluated the postoperative recovery of cardiothoracic patients, we showed that machine learning analysis of biometrics could classify patients into distinct recovery groups. Thus, wearables and machine learning algorithms could lead to a highly accurate and accessible method to predict complications early and improve assessments of recovery. Our overall objective is to optimize and validate our machine learning algorithm—previously developed for the early detection of Covid-19—for the detection of postoperative complications prior to symptom onset and to use machine learning analysis to predict the quality of a patient’s recovery using pre- and intraoperative data. Our project aims to first use wearables to collect high-resolution physiologic data of cardiothoracic surgical patients. We will then extend our previously developed algorithm for early detection of postoperative complications and develop an algorithm to predict the quality of a patient’s postoperative recovery. The proposed project will develop an innovative method to detect postoperative complications prior to symptom onset and predict the quality of a patient’s postoperative recovery using pre- and intraoperative data. Importantly, our proposed method could be scaled to not only improve outcomes for cardiothoracic surgical patients, but for patients undergoing other types of surgery. The results of this study will enable a future randomized trial that evaluates whether real-time postoperative monitoring with machine learning algorithms and wearables can lead to 1) earlier detection of complications, 2) earlier outpatient interventions that improve recovery and/or reduce severity of complications, and 3) decreases in unplanned hospital readmissions.