Using the Fitbit for early detection of Infection and reduction of healthcare utilization after Discharge in Pediatric Surgical Patients

项目总结 儿科阑尾切除术是儿童中最常见的住院手术，与显著的 给患者、他们的父母、医疗保健系统和第三方付款人带来负担。出院后，由 父母只包括这种被报道为不准确的“代理”主观评估，以及 导致了增加的并发症(例如，重新入院)和浪费医疗资源(例如， 手术后可能避免的急诊科(ED)就诊)。消费者可穿戴设备的研究进展 被动和非侵入性地监测体力活动(PA)、心率(HR)和睡眠的设备(CWD) 正在开创症状科学的新时代，特别是在手术后。他们的扩展能力可以 在儿童中近乎实时地生成持续、有效、客观和可操作的措施，提供 及早发现术后恢复模式改变的机会，从而提高精确度和 任何必要的临床干预的及时性。拟议的研究将使用CWD、Fitbit Inspire 2和 将机器学习方法应用于Fitbit数据(体力活动、HR和睡眠)，以创建临床 早期发现术后感染的有意义的警报。在住院期间和之后继续 放电，Fitbit Inspire 2，这是一款广泛使用的商业可穿戴设备，儿童很容易接受(3- 18岁)将被用来测量步数、睡眠和心率。该提案有两个目标。目标1开发 并使用Fitbit验证了机器学习算法对感染的检测。AIM 2预期提供接近实时的数据 阑尾切除术后病人的临床资料匹配及对临床决策的影响 与医疗保健系统进行第一次接触的时间，以及总体医疗保健使用模式。这项建议 与NINR的研究优先事项保持一致。通过这项工作开发的方法将为开发 需要代理的其他患者群体的类似算法，以及描述其他手术和， 应改善所有手术患者的整体术后管理。

PROJECT SUMMARY Pediatric appendectomy, the most prevalent inpatient procedure in children, is associated with significant burden to the patient, their parents, healthcare systems and third party payors. After discharge, monitoring by parents consists only of such “proxy” subjective assessments, which have been reported as inaccurate, and resulted in both increased complications (e.g., readmissions), and wasted healthcare resources (e.g., potentially avoidable emergency department (ED) visits after surgery). Advances in consumer wearable devices (“CWDs”) that passively and non-invasively monitor physical activity (PA), heart rate (HR), and sleep are ushering in a new era of symptoms science, particularly after surgery. Their expanding capability to generate continuous, valid, objective, and actionable measures in near-real time in children, provide opportunities to detect altered post-operative recovery patterns early, and therefore improve the precision and timeliness of any necessary clinical interventions. The proposed study will use a CWD, the Fitbit Inspire 2, and will apply machine learning methods to the Fitbit data (physical activity, HR, and sleep) to create clinically meaningful alerts for early detection of postoperative infection. During hospitalization and continuing after discharge, a Fitbit Inspire 2, a widely-used, commercially wearable device well-tolerated by young children (3- 18 years old) will be used to measure step counts, sleep, and HR. The proposal has 2 aims. Aim 1 develops and validates machine learning algorithm for infection using the Fitbit. Aim 2 prospectively feeds near-real time Fitbit data on postoperative appendectomy patients to clinicians, and examines their effect on clinical decision making, time to first contact with the healthcare system, and on overall healthcare use patterns. The proposal is aligned with NINR’s research priorities. Methods developed from this work will pave the way to develop similar algorithms for other patient populations needing a proxy, as well as to characterize other surgeries and, should improve overall postoperative management for all surgical patients.