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

使用 Fitbit 早期检测儿科手术患者的感染并减少出院后的医疗保健利用率

• 批准号: 10729412
• 负责人: FIZAN ABDULLAH
• 金额: $ 84.99万
• 依托单位: LURIE CHILDREN'S HOSPITAL OF CHICAGO
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-22 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10729412
• 关键词: 18 year old; Abdominal Pain; Abscess; Academic Medical Centers; Algorithms; Appendectomy; Appendicitis; Caregivers; Caring; Chicago; Child; Childhood; Clinical; Community Hospitals; Critical Incident Technic; Data; Decision Making; Desire for food; Development; Diagnosis; Disparity; Early Diagnosis; Early identification; Electronic Health Record; Emergency Care; Emergency Situation; Emergency department visit; Feeds; Fever; Health system; Healthcare; Healthcare Systems; Heart Rate; Hospitalization; Hospitals; Infection; Inpatients; Intervention; Intra-abdominal; Machine Learning; Measures; Methods; Monitor; Operative Surgical Procedures; Organ; Pain; Parents; Patient Monitoring; Patients; Pattern; Pediatric Hospitals; Pediatric Surgical Procedures; Perforation; Phlegmon; Physical activity; Physiological; Postoperative Period; Procedures; Proxy; Recommendation; Recovery; Reporting; Research Priority; Resources; Signal Transduction; Sleep; Sleep disturbances; Surgeon; Surgical Wound Infection; System; Technology; Thermometers; Time; University Hospitals; Validation; Visit; Work; adverse outcome; care seeking; clinical care; clinical decision-making; cloud based; cost; dashboard; data exchange; fitbit; health care service utilization; heart rate monitor; hospital readmission; improved; machine learning algorithm; machine learning method; non-invasive monitor; operation; parental monitoring; patient population; postoperative recovery; prediction algorithm; prospective; recruit; remote patient monitoring; sensor; symptom science; tertiary care; tool; transmission process; uptake; wasting; wearable device

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.

项目摘要 小儿阑尾切除术是儿童中最常见的住院手术， 患者、其父母、医疗保健系统和第三方支付者的负担。出院后，监测 父母只包括这种“代理”主观评估，据报道，这是不准确的， 导致并发症增加（例如，再入院），以及浪费的医疗资源（例如， 手术后可能避免的急诊科（艾德）就诊）。消费者可穿戴设备的进展 被动地和非侵入性地监测身体活动（PA）、心率（HR）和睡眠的设备（“CWD”） 开创了症状科学的新纪元，尤其是在手术后。他们的扩张能力， 在儿童中近乎实时地产生连续、有效、客观和可操作的措施， 有机会及早发现术后恢复模式的改变，从而提高精确度， 及时采取必要的临床干预措施。拟议的研究将使用CWD，Fitbit智能手表2， 将机器学习方法应用于Fitbit数据（身体活动，HR和睡眠），以在临床上创建 早期发现术后感染的有意义的警报。住院期间和之后继续 discharge，Fitbit智能手表2，一种广泛使用的，商业上可穿戴的设备，幼儿（3- 18岁）将用于测量步数，睡眠和HR。该提案有2个目标。目标1开发 并使用Fitbit验证感染的机器学习算法。Aim 2前瞻性近实时馈送 Fitbit将阑尾切除术后患者的数据提供给临床医生，并检查其对临床决策的影响 第一次接触医疗保健系统的时间，以及整体医疗保健使用模式。该提案 与NINR的研究重点一致。从这项工作中开发的方法将为开发 类似的算法用于需要代理的其他患者群体，以及表征其他手术， 应改善所有手术患者的整体术后管理。