Artificial Intelligence-based Parkinson's Disease Risk Assessment (AI-PRA) Study

Обзор

The study aims to provide initial proof-of-concept validation data of an artificial intelligence-based model to estimate individual Parkinson's disease risk using demographic, clinical, genetic information and digital biomarker data collected via a smartwatch and a mobile application.

Подробное описание

Background: Everyday electronic devices may detect subtle motor and non-motor abnormalities years before the clinical diagnosis of Parkinson's disease (PD) providing opportunities for early detection.

Study aim and impact: This study aims to validate an artificial intelligence based model that provides an individualised risk of PD based on demographic, clinical, genetic and digital biomarker data (smartwatch and a phone app). An early diagnosis will allow timely interventions to manage symptoms and risk stratification of participants for early clinical trials.

Methods: 60 people at risk of PD (either with polysomnography confirmed REM sleep behaviour disorder; OR neurogenic orthostatic hypotension; OR objective hyposmia on smell test) will be recruited.

Participants will complete study assessments to provide PD risk estimation using current research clinical criteria and the artificial intelligence model. Study assessments will include:

* In-person visits (baseline and 6 months) to complete validated questionnaires and a neurological examination (including cognitive and motor assessments). * Brain dopamine (DAT) scan (baseline only). * blood tests for PD polygenic risk score (baseline only) and plasma urate (in males only at baseline and 6 months). * Smartwatch and phone app: a smartwatch linked to the participants' smartphone will provide digital biomarker and additional clinical information through questionnaires via study phone app.

An artificial intelligence based model (AI-PROGNOSIS model) will use these digital data in combination with demographics, clinical and genetic information to provide an individualised PD risk estimation.

Accuracy measures of the risk estimates from the current research diagnostic criteria and artificial intelligence model using the presence of abnormal dopamine DAT scan as the ground truth for PD diagnosis will be provided.

Вмешательства

• Устройство Smartwatch and phone app
  Wearing a smartwatch and using a mobile phone application for 6 months in order to provide digital biomarker data and additional self reported clinical information.

Первичные конечные точки

• Classification performance of the PD risk artificial intelligence-based model [Срок оценки: From enrolment to 6 months]

Вторичные конечные точки (1)

• Usability of study digital environment (mAI-Health phone app) [Срок оценки: At 6 month visit]

Критерии участия

Критерии включения

• Age ≥ 50 years.
• At least one of the following clinical markers for PD risk:

• REM sleep behaviour disorder (RBD) confirmed with polysomnography.
• Neurogenic orthostatic hypotension (nOH) defined as a drop in systolic / diastolic blood pressure ≥ 20/10mmHg within 3 minutes of active standing or tilt-table test, and with a blunted heart rate response (ΔHeart rate/ΔSBP ratio < 0.5 bpm/mmHg).
• Objective hyposmia defined as University of Pennsylvania Smell Identification Test (UPSIT) score ≤ 15th percentile for age and sex.
• Able and willing to give informed written consent.
• Use of compatible smartphone (mobile operating system Android version 11 or newer). A smartwatch will be provided to each participant for the duration of the study.

Критерии исключения

• Clinical diagnosis of Parkinson's disease (PD) according to MDS clinical diagnostic criteria.
• Currently taking levodopa, dopamine agonists, MAO-B inhibitors, amantadine or another PD medication, except for low-dose treatment of restless leg syndrome (with permission of investigator).
• Dementia defined as deterioration of cognitive function severe enough to impair functioning on daily activities.
• Active treatment with neuroleptics, reserpine or metoclopramide (these drugs should be discontinued for at least 6 months before screening visit) due to their interference with dopamine transporter SPECT imaging acquisition and interpretation.
• Pregnant women.
• Concomitant participation in interventional studies.
• Unwilling or unable to give informed written consent.
• Vulnerable individuals as defined by the HRA.
• Inability to use the smartwatch and/or the mAI-Health app for the purpose of the study as judged by the investigator.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Модель наблюдения

Когортное

Центры проведения

Франция · 1 центр

• Centre Hospitalier Universitaire de Toulouse — Toulouse

Испания · 1 центр

• Fundación Iniciativa para las Neurociencias. Hospital Ruber Internacional. — Madrid

Великобритания · 1 центр

• Queen Mary University of London — London

Публикации

• Hastings A, Cullinane P, Wrigley S, Revesz T, Morris HR, Dickson JC, Jaunmuktane Z, Warner TT, De Pablo-Fernandez E. Neuropathologic Validation and Diagnostic Accuracy of Presynaptic Dopaminergic Imaging in the Diagnosis of Parkinsonism. Neurology. 2024 Jun 11;102(11):e209453. doi: 10.1212/WNL.0000000000209453. Epub 2024 May 17. PMID 38759132
• Heinzel S, Berg D, Gasser T, Chen H, Yao C, Postuma RB; MDS Task Force on the Definition of Parkinson's Disease. Update of the MDS research criteria for prodromal Parkinson's disease. Mov Disord. 2019 Oct;34(10):1464-1470. doi: 10.1002/mds.27802. Epub 2019 Aug 14. PMID 31412427
• Berg D, Postuma RB, Adler CH, Bloem BR, Chan P, Dubois B, Gasser T, Goetz CG, Halliday G, Joseph L, Lang AE, Liepelt-Scarfone I, Litvan I, Marek K, Obeso J, Oertel W, Olanow CW, Poewe W, Stern M, Deuschl G. MDS research criteria for prodromal Parkinson's disease. Mov Disord. 2015 Oct;30(12):1600-11. doi: 10.1002/mds.26431. PMID 26474317

Идентификаторы

NCT: NCT07706829 · 370634

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

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