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DiSCERN: Advanced PD Therapy Candidacy and Evaluation System

DiSCERN：先进 PD 治疗候选资格和评估系统

基本信息

批准号：
10207343
负责人：
Aaron John Hadley
金额：
$ 80.27万
依托单位：
GREAT LAKES NEUROTECHNOLOGIES
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-20 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10207343
关键词：
Academic Medical Centers Activities of Daily Living Algorithms Awareness Caring Cellular Phone Cessation of life Chronic Clinic Clinical Clinical assessments Collection Communities Computer software Data Data Collection Decision Making Deep Brain Stimulation Detection Development Diagnosis Disadvantaged Dose Dyskinetic syndrome Emotional Evaluation Expert Systems Fatigue Feedback Health Health Care Costs Health Technology Healthcare Home Impaired cognition Implant Incidence Institution Lead Letters Levodopa Measures Medical Methods Modeling Monitor Motor Movement Disorders Neurology Operative Surgical Procedures Outcome Parkinson Disease Patient Selection Patients Perception Pharmaceutical Preparations Phase Pilot Projects Population Process Provider Pump Quality of life Questionnaires Resources Risk Rural Rural Population Screening procedure Selection Bias Self Assessment Sensitivity and Specificity Site Specialist Standardization Symptoms System Technology Telemedicine Therapeutic Unnecessary Surgery Validation Wireless Technology base compliance behavior cost data exchange design diagnostic platform diaries disparity reduction exhaust experience health care delivery health disparity improved innovation mHealth medical specialties mobile application motion sensor motor disorder motor symptom new technology novel patient screening predictive modeling prevent product development racial minority remote monitoring remote screening response screening side effect smartphone Application socioeconomics success therapy outcome tool wearable device wearable sensor technology

项目摘要

Summary The objective is to design, develop, and clinically assess DiSCERN, a standardized telemedicine tool for identifying patients with Parkinson’s disease (PD) who would benefit from advanced therapies (AT) and determining when AT recipients need therapy adjustments. Once chronic PD medication usage results in motor fluctuations and dyskinesias and all non-invasive therapies have been exhausted, AT (e.g., deep brain stimulation, drug pumps) is often recommended. While experts at academic medical centers may appropriately identify AT candidates, AT is underutilized due to limited access and inequitable utilization of limited evaluative resources for a sizable subset of the PD population. Remote screening and monitoring with DiSCERN will improve patient selection, reduce disparities, and expand access for rural populations and disadvantaged communities. The system will engage and empower patients, providers, and healthcare institutions and lead to improved health, healthcare delivery, and the reduction of health disparities. This mobile health technology will include a patient friendly smartphone app, non-motor assessments, and wireless wearable sensors for continuously monitoring PD motor symptoms, complications, and quality of life (QoL). We have previously commercialized wearables and mobile apps for remote monitoring of PD motor symptoms and side effects, which will significantly de-risk the project. Still, novel development and validation efforts are required to commercialize this new technology. Innovations include: 1) integration of PD monitoring algorithms with context aware activity detection for improved PD motor assessment and QoL quantification; 2) implementation of the algorithms on a smartphone and wearable device; 3) development of a predictive model that uses motor and non-motor features to accurately identify PD patients who would be good candidates for AT; and 4) implementation of a model that alerts clinicians when an AT recipient needs a therapy adjustment. Through integration with AT systems, DiSCERN will improve the clinician experience and allow the limited availability of specialists to scale care to a diverse and growing PD population, who may not otherwise have access to AT. Phase I includes: 1) validation of context aware activity detection algorithms on PD patient data; 2) determining the extent specific activities or activity levels correlate with PD QoL; 3) using clinician feedback to identify collected data features that are useful in informing AT clinical decisions; and 4) identification of wearables to be used in the final system. Phase II includes: 1) transition of context aware activity detection and PD symptom quantification algorithms onto a smartphone and wearable chips; 2) development of a smartphone app that integrates data collection, non-motor assessment, and data-transfer to the cloud; and 3) collecting data from AT candidates in the months before and after AT is initiated to develop models that accurately identify AT candidates and when AT adjustments are needed. DiSCERN will improve therapy efficiency, expand access, and result in more patients opting for AT.

总结