Bhidayasiri Roongroj, Sringean Jirada, Phumphid Saisamorn, Anan Chanawat, Thanawattano Chusak, Deoisres Suwijak, Panyakaew Pattamon, Phokaewvarangkul Onanong, Maytharakcheep Suppata, Buranasrikul Vijittra, Prasertpan Tittaya, Khontong Rotjana, Jagota Priya, Chaisongkram Araya, Jankate Worawit, Meesri Jeeranun, Chantadunga Araya, Rattanajun Piyaporn, Sutaphan Phantakarn, Jitpugdee Weerachai, Chokpatcharavate Marisa, Avihingsanon Yingyos, Sittipunt Chanchai, Sittitrai Werasit, Boonrach Grisada, Phonsrithong Aekamorn, Suvanprakorn Pichit, Vichitcholchai Janprapa, Bunnag Tej
Chulalongkorn Centre of Excellence for Parkinson's Disease and Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand.
The Academy of Science, The Royal Society of Thailand, Bangkok, Thailand.
Front Neurol. 2024 May 13;15:1386608. doi: 10.3389/fneur.2024.1386608. eCollection 2024.
The rising prevalence of Parkinson's disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional "passive" approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson's disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a "pinch-to-size" test, gait and balance, with performance recorded using a mobile application and smartphone's sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.
帕金森病(PD)在全球范围内患病率不断上升,这给各国医疗保健系统带来了重大的公共卫生挑战,尤其是在泰国等中低收入国家,这些国家可能缺乏足够资源来满足不断增长的医疗保健需求。此外,还有许多早期帕金森病未被诊断出来,而在这个阶段进行治疗干预价值最大且成本最低。传统的“被动”方法,即临床医生等待有症状的帕金森病患者前来寻求治疗,是不够的。积极主动地早期识别帕金森病将有助于及时进行治疗干预,并且数字健康技术可以在病例识别和早期诊断中得到推广应用。帕金森病风险调查(TCTR20231025005)旨在评估一个数字人群筛查平台,以识别泰国人群中未被诊断出的帕金森病病例。鉴于帕金森病有较长的前驱期,筛查的目标人群是年龄≥40岁的人,这比运动症状通常出现的时间提前约20年。泰国拥有评价很高的医疗保健系统,并且为公民建立了全民医疗保健计划,这使其成为利用数字技术开展全国性筛查计划的理想之地。该数字平台由一组帕金森病多学科专家设计,包括一份关于帕金森病症状的20项问卷以及八项数字标志物的客观测试:语音元音、语音句子、静息和姿势性震颤、交替手指敲击、“捏合到合适大小”测试、步态和平衡,使用移动应用程序和智能手机传感器记录测试表现。机器学习工具利用收集到的数据来识别有患帕金森病风险或有早期症状的受试者。本文描述了问卷项目和数字标志物的选择与验证,结果表明所选参数和数据分析方法是稳健、可靠且可重复的。这个数字平台可以作为泰国其他非传染性疾病类似筛查策略的一个模型。