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.

项目总结。每年有超过50万名患者接受心肺疾病手术。