@Article{info:doi/10.2196/42341，作者=“Wood, Michael D and West, Nicholas C and Sreepada, Rama S and Loftsgard, Kent C and Petersen, Luba and Robillard, Julie M and Page, Patricia and Ridgway, Randa and Chadha, Neil K and Portales-Casamar, Elodie and G{\ \ o}rges, Matthias”，标题=“儿科个性化疼痛预测工具的风险因素识别、患者报告经验和结果测量以及数据获取工具”;“焦点小组研究”，期刊=“JMIR Perioper Med”，年=“2022”，月=“11”，日=“15”，卷=“5”，号=“1”，页=“e42341”，关键词=“以患者为导向的研究;患者报告的结果测量;患者报告的体验测量;风险预测;疼痛;个性化的风险;手术;麻醉;焦点小组; thematic analysis; perioperative; participatory medicine; digital health tool; postsurgical pain; children; opioid use; virtual focus group; postoperative; pediatrics; health outcome", abstract="Background: The perioperative period is a data-rich environment with potential for innovation through digital health tools and predictive analytics to optimize patients' health with targeted prehabilitation. Although some risk factors for postoperative pain following pediatric surgery are already known, the systematic use of preoperative information to guide personalized interventions is not yet widespread in clinical practice. Objective: Our long-term goal is to reduce the incidence of persistent postsurgical pain (PPSP) and long-term opioid use in children by developing personalized pain risk prediction models that can guide clinicians and families to identify targeted prehabilitation strategies. To develop such a system, our first objective was to identify risk factors, outcomes, and relevant experience measures, as well as data collection tools, for a future data collection and risk modeling study. Methods: This study used a patient-oriented research methodology, leveraging parental/caregiver and clinician expertise. We conducted virtual focus groups with participants recruited at a tertiary pediatric hospital; each session lasted approximately 1 hour and was composed of clinicians or family members (people with lived surgical experience and parents of children who had recently undergone a procedure requiring general anesthesia) or both. Data were analyzed thematically to identify potential risk factors for pain, as well as relevant patient-reported experience and outcome measures (PREMs and PROMs, respectively) that can be used to evaluate the progress of postoperative recovery at home. 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. ", issn="2561-9128", doi="10.2196/42341", url="https://periop.www.mybigtv.com/2022/1/e42341", url="https://doi.org/10.2196/42341", url="http://www.ncbi.nlm.nih.gov/pubmed/36378509" }