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

使用可穿戴技术评估心胸外科手术的恢复情况并检测术后并发症

• 批准号: 10522199
• 负责人: Xiao Li
• 金额: $ 74.89万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10522199
• 关键词: Algorithms; Biometry; COVID-19; COVID-19 detection; COVID-19 early detection; Cardiac; Cardiovascular system; Caring; Chest; Complication; Data; Detection; Development; Early Diagnosis; Family member; Fatigue; Frequencies; Future; Goals; Health; Health Care Costs; Health Personnel; Health Status; Heart Diseases; Home; Hospitals; Intervention; Lead; Learning; Link; Lung; Lung diseases; Lyme Disease; Machine Learning; Methods; Monitor; Operative Surgical Procedures; Outcome; Outcome Measure; Outpatients; Pain; Patient Discharge; Patient Monitoring; Patient Outcomes Assessments; Patients; Pattern; Physiological; Pilot Projects; Postoperative Care; Postoperative Complications; Postoperative Period; Public Health; Quality of life; Recovery; Research; Resolution; Severities; Signal Transduction; Sleep disturbances; Symptoms; System; Telemedicine; Testing; Thoracic Surgical Procedures; Time; Validation; Viral; Visit; algorithm development; cost; critical period; experience; high risk; hospital readmission; improved; improved outcome; innovation; machine learning algorithm; mortality; mortality risk; novel; operation; personalized care; personalized predictions; postoperative recovery; preemptive intervention; randomized trial; real time monitoring; wearable device

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.

项目摘要。每年，超过500,000名患者接受心脏和肺部病的手术。 手术后，患者经常会遭受疼痛，疲劳和睡眠干扰，可能会持续数周到几个月。 此外，多达32％的患者发生术后并发症，通常发生在出院后 医院，可能会导致再入院。并发症是昂贵的，可能是致命的；它们是相关的 医疗保健成本增加了200-300％，术后90天增加了6倍。 目前，手术后，患者从医院出院时，患者及其家人 负责监视患者的健康状况。医生通常看不到患者2-4 出院后几周。尝试改善术后监测的尝试包括家庭健康访问和 远程医疗方法。但是，这些方法已被证明是无效，昂贵的，并且允许 仅投票和间歇性评估。他们直到在 更严重的阶段。因此，准确，易于实现和廉价的方法来评估术后 在符号发作之前，恢复并检测并发症。 我们先前表明，可穿戴物收集的生物识别技术的机器学习分析可以检测到莱姆 疾病和共同-19。然后，在一项试点研究中，我们应用了我们的算法，以前开发出来识别共证 19，对接受胸腔手术的患者，表明该算法可以检测到89％的并发症A 症状发作前3天的中位数。当我们评估心胸术的术后恢复时 患者，我们表明生物识别技术的机器学习分析可以将患者分类为明显的康复 组。这是可穿戴设备和机器学习算法可能导致高度准确且易于访问的 早期预测并发症并改善恢复评估的方法。 我们的总体目标是优化和验证我们的机器学习算法，并为此开发了 早期发现Covid-19-用于在症状发作之前检测术后并发症 使用机器学习分析使用术前和术中数据来预测患者恢复的质量。 我们的项目旨在首先使用可穿戴设备来收集心胸外科手术的高分辨率生理数据 患者。然后，我们将扩展我们先前开发的算法，以供术后早期检测 并发症并开发出一种算法，以预测患者术后康复的质量。 拟议的项目将开发一种创新的方法，以检测后部并发症 症状发作并使用术前和术中数据预测患者术后恢复的质量。 重要的是，我们提出的方法可以缩放以改善心胸外科手术的预后 患者，但适用于接受其他类型手术的患者。这项研究的结果将使未来 随机试验评估是否使用机器学习算法进行实时术后监视 可穿戴设备可能导致1）早期发现并发症，2）早期的门诊干预措施会改善 恢复和/或减少并发症的严重程度，以及3）计划外医院再入院的下降。