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| ID | Type | Description | Link |
|---|---|---|---|
| 5R44DA046964-03 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
|---|---|
| National Institute on Drug Abuse (NIDA) | NIH |
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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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Chronic Pain Patients |
| ||
| Healthy Controls |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| ALGOS System | Diagnostic Test | A Quantitative Electroencephalography (QEEG) based pain biomarker assessment that scales with patient reported Numeric Rating Scale (NRS) |
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| 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. |
| 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) |
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Inclusion Criteria:
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:
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.
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Two hundred fifty (250) male and female pain patients with symptoms in excess of 3 months duration (per the IASP definition of Chronic Pain) between the ages of 18-85 years will be enrolled in this phase of the study. Fifty (50) healthy normal subjects between the ages of 18-85 years will also be enrolled. The normal subjects are added to assure that the study spans the entire pain scale including those with an NRS of 0.
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| Name | Affiliation | Role |
|---|---|---|
| William Koppes | PainQx, Inc | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Panorama Orthopedics & Spine Center | Golden | Colorado | 80401 | United States | ||
| Comprehensive Spine and Pain Center of New York |
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| ID | Title | Description |
|---|---|---|
| FG000 | Chronic Pain Patients | ALGOS System: A Quantitative Electroencephalography (QEEG) based pain biomarker assessment that scales with patient reported Numeric Rating Scale (NRS) |
| FG001 | Healthy Controls | ALGOS System: A Quantitative Electroencephalography (QEEG) based pain biomarker assessment that scales with patient reported Numeric Rating Scale (NRS) |
| Title | Milestones | Reasons Not Completed | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Overall Study |
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| ID | Title | Description |
|---|---|---|
| BG000 | Chronic Pain Patients | Chronic pain patients who meet the IASP definition of chronic pain for a musculoskeletal pain disorder. |
| BG001 | Healthy Controls | Healthy control participants with no diagnosis of chronic pain nor other various neurological conditions |
| Units | Counts |
|---|---|
| Participants |
|
| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Categorical | Count of Participants |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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) | 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. | Posted | Number | probability | 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. |
1 year post recruitment
Observational study without risk of mortality as result of study
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | Chronic Pain Patients | ALGOS System: A Quantitative Electroencephalography (QEEG) based pain biomarker assessment that scales with patient reported Numeric Rating Scale (NRS) |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| VP R&D | PainQx | 6179817753 | bd@painqx.com |
| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot | Yes | No | No | Study Protocol | Sep 28, 2020 | Feb 27, 2023 | Prot_000.pdf |
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| ID | Term |
|---|---|
| D059350 | Chronic Pain |
| ID | Term |
|---|---|
| D010146 | Pain |
| D009461 | Neurologic Manifestations |
| D012816 | Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
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| 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. |
| New Hyde Park |
| New York |
| 11042 |
| United States |
| Pain Management at Comprehensive Pain and Wellness Center | New York | New York | 10016 | United States |
| Comprehensive Spine and Pain Center of New York | New York | New York | 10017 | United States |
| Comprehensive Spine & Pain Center of New York | Valley Stream | New York | 11580 | United States |
| BG002 | Total | Total of all reporting groups |
| Participants |
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| Sex: Female, Male | Count of Participants | Participants |
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| Race (NIH/OMB) | Count of Participants | Participants |
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| Region of Enrollment | Number | participants |
|
| ID |
|---|
| Title |
|---|
| Description |
|---|
| OG000 | Study Participants | All participants in study, chronic pain patients and controls |
|
|
| 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) | 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. | Posted | Number | probability | 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. |
|
|
|
| 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) | 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. | Posted | Number | probability | 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. |
|
|
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| 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) | 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. | Posted | Number | probability | 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 | 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) | 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. | Posted | Number | probability | 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. |
|
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| 0 |
| 280 |
| 0 |
| 280 |
| 0 |
| 280 |
| EG001 | Healthy Controls | ALGOS System: A Quantitative Electroencephalography (QEEG) based pain biomarker assessment that scales with patient reported Numeric Rating Scale (NRS) | 0 | 54 | 0 | 54 | 0 | 54 |
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