Diagnosing Parkinson's disease (PD) depends on the clinical history of the patient and the patient's response to specific treatments such as levodopa. Unfortunately, a definitive diagnosis of PD is still limited to post-mortem evaluation of brain tissues. Furthermore, diagnosis of idiopathic PD is even more challenging because symptoms of PD overlap with symptoms of other conditions such as essential tremor (ET) or Parkinsonian syndromes (PSs) such as progressive supranuclear palsy (PSP), multiple system atrophy (MSA), corticobasal degeneration (CBD), or vascular Parkinsonism (VaP). Based on the principle that PD and PSs affect brain areas involved in eye movement control, this trial will utilize a platform that records complex eye movements and use a proprietary algorithm to characterize PSs. Preliminary data demonstrate that by monitoring oculomotor alterations, the process can assign PD-specific oculomotor patterns, which have the potential to serve as a diagnostic tool for PD. This study will evaluate capabilities of the process and its ability to differentiate PD from other PSs with statistical significance. The specific aims of this proposal are: To optimize the detection and analysis algorithms, and then to evaluate the process against neurological diagnoses of PD patients in a clinical study.
In the Phase I, complex eye movements, including Fixation, Optokinetic Nystagmus (OKN), Guided Saccades, Microsaccades, Smooth Pursuit, and Pupillometry will be measured in 90 subjects (30 PD, 30 non-PD with other movement disorders (PSP, ET, CBD, etc.), and 30 normal defined as not having any symptoms of any neurological condition.) The patients will be classified according to clinical evaluations and clinical follow ups performed by Dr. Holly Shill, Director of the Lonnie and Muhammad Ali Parkinson Center at the Barrow Neurological Institute (Phoenix, AZ). A 3-way analysis will be performed to troubleshoot and optimize the detection and classification algorithms. At this stage, these results will only be used for the evaluation of the diagnostic capability of the tool and not to treat or diagnose the patient. The product is portable with the potential to be an accurate tool to diagnose PD. This tool will provide substantial support to neurologists by validating or complementing the clinical tests currently used to diagnose PD. Successful diagnosis of PD can open new avenues for diagnosing other neurological conditions.
Study Type
OBSERVATIONAL
Enrollment
90
Subjects will undergo a complex eye exam which will capture fixation, optokinetic nystagmus, guided saccades, microsaccades, smooth pursuit, and pupillometry. Eye tracking equipment will be set and calibrated to the participant who will then perform the full 10 minute testing protocol with instructions from the investigator. After this, they will take a five minute break. The oculomotor testing protocol will be repeated twice.
Dignity Health / St. Joseph's Hospital and Medical Center
Phoenix, Arizona, United States
Percent of patients accurately diagnosed with SaccadeDX
The primary outcome measure will be accurate diagnosis in over 75% of patients; accuracy will be determined with statistical significance. Each eye movement test will produce a unique signature. Using a historical database and machine learning, the SaccadeDX algorithm will match the signature to a specific patient group (PD, non-PD movement disorder, control) resulting in a "SaccadeDX diagnosis". Once the patients are fully enrolled, the SaccadeDX diagnosis will be compared to the diagnosis previously made by the sub-investigator. Accurate diagnosis will be defined as a SaccadeDX diagnosis that matches the initial diagnosis provided by the sub-investigator.
Time frame: One test (approx. 1 hour)
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