Expanded Development of a Medical Device Utilizing an EEG-Based Algorithm for the Objective Quantification of Pain

Study Overview
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Brief Summary
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PainQx is conducting a study to collect electroencephalography (EEG) data from 250 people with chronic pain and 50 healthy controls in order to develop algorithms that will objectively assess the level of pain a person is experiencing.

Official Title
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Expanded Development of a Medical Device Utilizing an EEG-Based Algorithm for the Objective Quantification of Pain

Conditions
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Chronic Pain

Intervention / Treatment
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* Diagnostic Test: ALGOS System


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Male and female chronic pain patients Patients between the ages of 18-85 years Patients exhibiting the presence of symptoms in excess of 3 months duration Patients suffering from neuropathic (e.g., lower back pain), osteoarthritis, or muscular skeletal pain Patients with evidence of pathology related to the painful condition on which diagnosis was made (e.g., results of imaging or diagnostic pain code) Patients with NRS pain scores across the full range (1-10) at the time of testing Inclusion Criteria, Normal (no-pain) Group o Subjects will be included with no history of pain with a duration of greater than 3 months, and no report of pain at the time of testing (or within 3 months of testing) Exclusion Criteria: Patients with medically diagnosed psychotic illness Patients with medically diagnosed drug or alcohol dependence in the past 12 months Patients with a medical history of head injury with loss of consciousness and amnesia (within the last 2 years) Patients with skull abnormalities that preclude the proper placement of the electrodes for the EEG data acquisition Patients who have a spinal cord stimulator, or other implantable devices Patients for whom the source of pain at the time of the evaluation is associated with: neurological disorders (multiple sclerosis, Parkinson, dementia), diabetes, migraines, or those with reflex / sympathetic dystrophy disorder/complex regional pain syndrome, fibromyalgia, or visceral pain Note: This does not exclude patients who suffer from these disorders if the current source of pain is not due to the disorder. For example, patients with diabetes are NOT excluded, but patients whose pain at the time of the evaluation is a result of diabetic neuropathy are excluded. Similarly, patients with a history of migraines but for whom a migraine is not the current source of pain at the time of the evaluation are NOT excluded. Patients with cancer Patients on workers compensation or disability Patient on anticonvulsant medication Patients who have a history of seizures

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 85 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Chronic Pain Patients<br> | Diagnostic Test: ALGOS System<br>* A Quantitative Electroencephalography (QEEG) based pain biomarker assessment that scales with patient reported Numeric Rating Scale (NRS)<br>|
| Healthy Controls<br> | Diagnostic Test: ALGOS System<br>* A Quantitative Electroencephalography (QEEG) based pain biomarker assessment that scales with patient reported Numeric Rating Scale (NRS)<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Area Under the Curve of Classification Versus Patient Self Report of Pain vs no Pain State | This measure is the performance of the classification of pain vs no pain compared to the patient self-report in the form of Numerical Rating Scale (NRS). The primary outcome measure is Area Under the Curve (AUC), derived from the Receiver Operating Characteristic (ROC) curve, a standard metric of performance for binary classifiers. AUC is a numeric quantity ranging from 0 to 1, where the value of 1 indicates perfect separation, while 0.5 represents zero separation. AUC represents a fundamental expression of classifier separation performance without the complexity of threshold selection. (NRS 0 vs 1-10) | Self-reported pain using average of NRS value at the start and end of EEG collection, and classification based on 15 minutes of EEG collection. | 
| Sensitivity of Classification Versus Patient Self Report of Pain vs no Pain State | Sensitivity, or true positive rate is the probability of a positive result in the true chronic pain patients. This measure is calculated by dividing true positives by the summation of true positives and false negatives. (NRS 0 vs 1-10) | Self-reported pain using average of NRS value at the start and end of EEG collection, and classification based on 15 minutes of EEG collection. | 
| Specificity of Classification Versus Patient Self Report of Pain vs no Pain State | Specificity, or true negative rate is the probability of a negative result in the true healthy control patients. This measure is calculated by dividing true negatives by the summation of true negatives and false positives. (NRS 0 vs 1-10) | Self-reported pain using average of NRS value at the start and end of EEG collection, and classification based on 15 minutes of EEG collection. | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Area Under the Curve of Classification Versus Patient Self Report of no/Mild Pain vs Moderate/Severe Pain State | This measure is the performance of the classification of No/Mild vs Moderate/Severe pain compared to the patient self-report in the form of Numerical Rating Scale (NRS). The outcome measure is Area Under the Curve (AUC), derived from the Receiver Operating Characteristic (ROC) curve, a standard metric of performance for binary classifiers. AUC is a numeric quantity ranging from 0 to 1, where the value of 1 indicates perfect separation (the classifier is correct on every subject), while 0.5 represents zero separation (no better than guessing). AUC represents a fundamental expression of classifier separation performance without the complexity of threshold selection. (NRS 0-3.5 vs 4-10) | Self-reported pain using average of NRS value at the start and end of EEG collection, and classification based on 15 minutes of EEG collection. | 
| Area Under the Curve of Classification Versus Patient Self Report of no, Mild, or Moderate Pain vs Severe Pain State | This measure is the performance of the classification of No/Mild/Moderate vs Severe pain compared to the patient self-report in the form of Numerical Rating Scale (NRS). The outcome measure is Area Under the Curve (AUC), derived from the Receiver Operating Characteristic (ROC) curve, a standard metric of performance for binary classifiers. AUC is a numeric quantity ranging from 0 to 1, where the value of 1 indicates perfect separation (the classifier is correct on every subject), while 0.5 represents zero separation (no better than guessing). AUC represents a fundamental expression of classifier separation performance without the complexity of threshold selection. (NRS 0-6.5 vs 7-10) | Self-reported pain using average of NRS value at the start and end of EEG collection, and classification based on 15 minutes of EEG collection. |