Chronic Pain
Conditions
Brief summary
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.
Interventions
DIAGNOSTIC_TESTALGOS System
A Quantitative Electroencephalography (QEEG) based pain biomarker assessment that scales with patient reported Numeric Rating Scale (NRS)
Sponsors
National Institute on Drug Abuse (NIDA)
PainQx, Inc
Study design
Observational model
CASE_CONTROL
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to 85 Years
Healthy volunteers
Yes
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Area Under the Curve of Classification Versus Patient Self Report of Pain vs no Pain State | 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. | 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) |
| Sensitivity of Classification Versus Patient Self Report of Pain vs no Pain State | 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, 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) |
| Specificity of Classification Versus Patient Self Report of Pain vs no Pain State | 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, 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) |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Area Under the Curve of Classification Versus Patient Self Report of no/Mild Pain vs Moderate/Severe Pain State | 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. | 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) |
| Area Under the Curve of Classification Versus Patient Self Report of no, Mild, or Moderate Pain vs Severe Pain State | 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. | 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) |
Countries
United States
Participant flow
Participants by arm
| Arm | Count |
|---|---|
| Chronic Pain Patients Chronic pain patients who meet the IASP definition of chronic pain for a musculoskeletal pain disorder. | 280 |
| Healthy Controls Healthy control participants with no diagnosis of chronic pain nor other various neurological conditions | 54 |
| Total | 334 |
Withdrawals & dropouts
| Period | Reason | FG000 | FG001 |
|---|---|---|---|
| Overall Study | Withdrawal by Subject | 26 | 0 |
Baseline characteristics
| Characteristic | Chronic Pain Patients | Healthy Controls | Total |
|---|---|---|---|
| Age, Categorical <=18 years | 0 Participants | 0 Participants | 0 Participants |
| Age, Categorical >=65 years | 101 Participants | 3 Participants | 104 Participants |
| Age, Categorical Between 18 and 65 years | 179 Participants | 51 Participants | 230 Participants |
| Race (NIH/OMB) American Indian or Alaska Native | 2 Participants | 0 Participants | 2 Participants |
| Race (NIH/OMB) Asian | 10 Participants | 15 Participants | 25 Participants |
| Race (NIH/OMB) Black or African American | 80 Participants | 11 Participants | 91 Participants |
| Race (NIH/OMB) More than one race | 0 Participants | 0 Participants | 0 Participants |
| Race (NIH/OMB) Native Hawaiian or Other Pacific Islander | 0 Participants | 0 Participants | 0 Participants |
| Race (NIH/OMB) Unknown or Not Reported | 65 Participants | 2 Participants | 67 Participants |
| Race (NIH/OMB) White | 123 Participants | 26 Participants | 149 Participants |
| Region of Enrollment United States | 280 participants | 54 participants | 334 participants |
| Sex: Female, Male Female | 145 Participants | 34 Participants | 179 Participants |
| Sex: Female, Male Male | 135 Participants | 20 Participants | 155 Participants |
Adverse events
| Event type | EG000 affected / at risk | EG001 affected / at risk |
|---|---|---|
| deaths Total, all-cause mortality | 0 / 280 | 0 / 54 |
| other Total, other adverse events | 0 / 280 | 0 / 54 |
| serious Total, serious adverse events | 0 / 280 | 0 / 54 |
Outcome results
Primary
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)
Time frame: 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.
Population: Analysis was carried out using both arms combined: a control arm (negative class), and a pain arm (positive class). The reason for combining both arms is to assess the classifier's ability to differentiate the two classes and all outcome measures represent the performance of classification.
| Arm | Measure | Value (NUMBER) |
|---|---|---|
| Study Participants | Area Under the Curve of Classification Versus Patient Self Report of Pain vs no Pain State | .70 probability |
Primary
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)
Time frame: 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.
Population: Analysis was carried out using both arms combined: a control arm (negative class), and a pain arm (positive class). The reason for combining both arms is to assess the classifier's ability to differentiate the two classes and all outcome measures represent the performance of classification.
| Arm | Measure | Value (NUMBER) |
|---|---|---|
| Study Participants | Sensitivity of Classification Versus Patient Self Report of Pain vs no Pain State | .783 probability |
Primary
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)
Time frame: 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.
Population: Analysis was carried out using both arms combined: a control arm (negative class), and a pain arm (positive class). The reason for combining both arms is to assess the classifier's ability to differentiate the two classes and all outcome measures represent the performance of classification.
| Arm | Measure | Value (NUMBER) |
|---|---|---|
| Study Participants | Specificity of Classification Versus Patient Self Report of Pain vs no Pain State | .607 probability |
Secondary
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)
Time frame: 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.
Population: Analysis was carried out using both arms combined: No/Mild/Moderate vs Severe pain. The reason for combining both arms is to assess the classifier's ability to differentiate the two classes and all outcome measures represent the performance of classification.
| Arm | Measure | Value (NUMBER) |
|---|---|---|
| Study Participants | Area Under the Curve of Classification Versus Patient Self Report of no, Mild, or Moderate Pain vs Severe Pain State | .669 probability |
Secondary
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)
Time frame: 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.
Population: Analysis was carried out using both arms combined: No/Mild vs Moderate/Severe pain. The reason for combining both arms is to assess the classifier's ability to differentiate the two classes and all outcome measures represent the performance of classification.
| Arm | Measure | Value (NUMBER) |
|---|---|---|
| Study Participants | Area Under the Curve of Classification Versus Patient Self Report of no/Mild Pain vs Moderate/Severe Pain State | .694 probability |