Cucchiaro Giovanni, Ahumada Luis, Gray Geoffrey, Fierstein Jamie, Yates Hannah, Householder Kym, Frye William, Rehman Mohamed
Johns Hopkins All Children's Hospital, St. Petersburg, FL, United States.
JMIR Form Res. 2022 Aug 15;6(8):e37054. doi: 10.2196/37054.
Machine learning uses algorithms that improve automatically through experience. This statistical learning approach is a natural extension of traditional statistical methods and can offer potential advantages for certain problems. The feasibility of using machine learning techniques in health care is predicated on access to a sufficient volume of data in a problem space.
This study aimed to assess the feasibility of data collection from an adolescent population before and after a posterior spine fusion operation.
Both physical and psychosocial data were collected. Adolescents scheduled for a posterior spine fusion operation were approached when they were scheduled for the surgery. The study collected repeated measures of patient data, including at least 2 weeks prior to the operation and 6 months after the patients were discharged from the hospital. Patients were provided with a Fitbit Charge 4 (consumer-grade health tracker) and instructed to wear it as often as possible. A third-party web-based portal was used to collect and store the Fitbit data, and patients were trained on how to download and sync their personal device data on step counts, sleep time, and heart rate onto the web-based portal. Demographic and physiologic data recorded in the electronic medical record were retrieved from the hospital data warehouse. We evaluated changes in the patients' psychological profile over time using several validated questionnaires (ie, Pain Catastrophizing Scale, Patient Health Questionnaire, Generalized Anxiety Disorder Scale, and Pediatric Quality of Life Inventory). Questionnaires were administered to patients using Qualtrics software. Patients received the questionnaire prior to and during the hospitalization and again at 3 and 6 months postsurgery. We administered paper-based questionnaires for the self-report of daily pain scores and the use of analgesic medications.
There were several challenges to data collection from the study population. Only 38% (32/84) of the patients we approached met eligibility criteria, and 50% (16/32) of the enrolled patients dropped out during the follow-up period-on average 17.6 weeks into the study. Of those who completed the study, 69% (9/13) reliably wore the Fitbit and downloaded data into the web-based portal. These patients also had a high response rate to the psychosocial surveys. However, none of the patients who finished the study completed the paper-based pain diary. There were no difficulties accessing the demographic and clinical data stored in the hospital data warehouse.
This study identifies several challenges to long-term medical follow-up in adolescents, including willingness to participate in these types of studies and compliance with the various data collection approaches. Several of these challenges-insufficient incentives and personal contact between researchers and patients-should be addressed in future studies.
机器学习使用通过经验自动改进的算法。这种统计学习方法是传统统计方法的自然延伸，对于某些问题可能具有潜在优势。在医疗保健中使用机器学习技术的可行性取决于在问题空间中获取足够数量的数据。
本研究旨在评估在青少年进行后路脊柱融合手术后收集数据的可行性。
收集了身体和心理社会数据。在计划进行后路脊柱融合手术的青少年患者安排手术时与他们接触。该研究收集了患者数据的重复测量值，包括手术前至少2周以及患者出院后6个月的数据。为患者提供了Fitbit Charge 4（消费级健康追踪器），并指示他们尽可能经常佩戴。使用基于网络的第三方门户来收集和存储Fitbit数据，并对患者进行培训，使其了解如何将个人设备上关于步数、睡眠时间和心率的数据下载并同步到基于网络的门户上。从医院数据仓库中检索电子病历中记录的人口统计学和生理学数据。我们使用几份经过验证的问卷（即疼痛灾难化量表、患者健康问卷、广泛性焦虑症量表和儿童生活质量量表）评估患者心理状况随时间的变化。问卷通过Qualtrics软件发放给患者。患者在住院前、住院期间以及术后3个月和6个月时接受问卷。我们发放纸质问卷以进行每日疼痛评分的自我报告和镇痛药使用情况的调查。
从研究人群中收集数据存在若干挑战。我们接触的患者中只有38%（32/84）符合入选标准，在随访期间，登记患者中有50%（16/32）退出——平均在研究进行到17.6周时。在完成研究的患者中，69%（9/13）可靠地佩戴了Fitbit并将数据下载到基于网络的门户中。这些患者对心理社会调查的回复率也很高。然而，完成研究的患者中没有一人完成纸质疼痛日记。获取存储在医院数据仓库中的人口统计学和临床数据没有困难。
本研究确定了青少年长期医学随访中的若干挑战，包括参与这类研究的意愿以及对各种数据收集方法的依从性。未来的研究应解决其中一些挑战——激励不足以及研究人员与患者之间缺乏个人联系。
