TY -非盟的木头,迈克尔D AU -西方,尼古拉斯·C AU - Sreepada罗摩年代AU - Loftsgard,肯特C AU -彼得森,班图语州,非盟-罗毕拉德朱莉M盟页,帕特丽夏盟,Ridgway Randa查达盟——尼尔·K AU - Portales-Casamar Elodie盟——峡谷,马提亚PY - 2022 DA - 2022/11/15 TI -识别风险因素,Patient-Reported经验和措施,结果和数据采集工具,做一个个性化的儿科疼痛预测工具:焦点小组研究乔——JMIR Perioper地中海SP - e42341六世- 5 - 1千瓦patient-oriented研究KW - patient-reported结果措施千瓦patient-reported经验措施KW -风险预测KW -疼痛KW -个性化的风险KW -手术KW -麻醉KW -焦点小组KW -专题分析KW -围手术期KW -参与医学KW -数字卫生工具KW -手术后的疼痛KW -儿童KW -阿片类药物使用千瓦-虚拟焦点小组KW -术后KW -儿科千瓦背景:围手术期是一个数据丰富的环境，有潜力通过数字健康工具和预测分析来优化患者的健康，有针对性的预适应。虽然儿童手术术后疼痛的一些危险因素已经为人所知，但系统地使用术前信息来指导个性化干预在临床实践中还不广泛。目的:我们的长期目标是通过开发个性化的疼痛风险预测模型来降低儿童术后持续疼痛(PPSP)和长期阿片类药物使用的发生率，该模型可以指导临床医生和家庭确定有针对性的康复策略。为了开发这样一个系统，我们的第一个目标是确定风险因素、结果、相关的经验测量，以及数据收集工具，以用于未来的数据收集和风险建模研究。方法:本研究采用以患者为导向的研究方法，利用父母/护理人员和临床医生的专业知识。我们对在一家三级儿科医院招募的参与者进行了虚拟焦点小组;每次会议持续约1小时，由临床医生或家庭成员(有手术经验的人以及最近接受过全身麻醉手术的孩子的父母)或两者共同组成。对数据进行主题分析，以确定疼痛的潜在危险因素，以及相关患者报告的经验和结果测量(分别为PREMs和PROMs)，可用于评估术后在家恢复的进展。 This guidance was combined with a targeted literature review to select tools to collect risk factor and outcome information for implementation in a future study. Results: In total, 22 participants (n=12, 55%, clinicians and n=10, 45%, family members) attended 10 focus group sessions; participants included 12 (55%) of 22 persons identifying as female, and 12 (55%) were under 50 years of age. Thematic analysis identified 5 key domains: (1) demographic risk factors, including both child and family characteristics; (2) psychosocial risk factors, including anxiety, depression, and medical phobias; (3) clinical risk factors, including length of hospital stay, procedure type, medications, and pre-existing conditions; (4) PREMs, including patient and family satisfaction with care; and (5) PROMs, including nausea and vomiting, functional recovery, and return to normal activities of daily living. Participants further suggested desirable functional requirements, including use of standardized and validated tools, and longitudinal data collection, as well as delivery modes, including electronic, parent proxy, and self-reporting, that can be used to capture these metrics, both in the hospital and following discharge. Established PREM/PROM questionnaires, pain-catastrophizing scales (PCSs), and substance use questionnaires for adolescents were subsequently selected for our proposed data collection platform. Conclusions: This study established 5 key data domains for identifying pain risk factors and evaluating postoperative recovery at home, as well as the functional requirements and delivery modes of selected tools with which to capture these metrics both in the hospital and after discharge. These tools have been implemented to generate data for the development of personalized pain risk prediction models. SN - 2561-9128 UR - https://periop.www.mybigtv.com/2022/1/e42341 UR - https://doi.org/10.2196/42341 UR - http://www.ncbi.nlm.nih.gov/pubmed/36378509 DO - 10.2196/42341 ID - info:doi/10.2196/42341 ER -