Ignite Creation Date:
2025-12-24 @ 11:40 PM
Ignite Modification Date:
2025-12-25 @ 9:31 PM
Study NCT ID:
NCT04585451
Status:
COMPLETED
Last Update Posted:
2023-05-06
First Post:
2020-10-06
Is NOT Gene Therapy:
False
Has Adverse Events:
True
Brief Title:
Expanded Development of a Medical Device Utilizing an EEG-Based Algorithm for the Objective Quantification of Pain
Sponsor:
Organization:
Raw JSON
{'hasResults': True, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D059350', 'term': 'Chronic Pain'}], 'ancestors': [{'id': 'D010146', 'term': 'Pain'}, {'id': 'D009461', 'term': 'Neurologic Manifestations'}, {'id': 'D012816', 'term': 'Signs and Symptoms'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'resultsSection': {'moreInfoModule': {'pointOfContact': {'email': 'bd@painqx.com', 'phone': '6179817753', 'title': 'VP R&D', 'organization': 'PainQx'}, 'certainAgreement': {'piSponsorEmployee': False, 'restrictiveAgreement': False}}, 'adverseEventsModule': {'timeFrame': '1 year post recruitment', 'description': 'Observational study without risk of mortality as result of study', 'eventGroups': [{'id': 'EG000', 'title': 'Chronic Pain Patients', 'description': 'ALGOS System: A Quantitative Electroencephalography (QEEG) based pain biomarker assessment that scales with patient reported Numeric Rating Scale (NRS)', 'otherNumAtRisk': 280, 'deathsNumAtRisk': 280, 'otherNumAffected': 0, 'seriousNumAtRisk': 280, 'deathsNumAffected': 0, 'seriousNumAffected': 0}, {'id': 'EG001', 'title': 'Healthy Controls', 'description': 'ALGOS System: A Quantitative Electroencephalography (QEEG) based pain biomarker assessment that scales with patient reported Numeric Rating Scale (NRS)', 'otherNumAtRisk': 54, 'deathsNumAtRisk': 54, 'otherNumAffected': 0, 'seriousNumAtRisk': 54, 'deathsNumAffected': 0, 'seriousNumAffected': 0}], 'frequencyThreshold': '0'}, 'outcomeMeasuresModule': {'outcomeMeasures': [{'type': 'PRIMARY', 'title': 'Area Under the Curve of Classification Versus Patient Self Report of Pain vs no Pain State', 'denoms': [{'units': 'Participants', 'counts': [{'value': '308', 'groupId': 'OG000'}]}], 'groups': [{'id': 'OG000', 'title': 'Study Participants', 'description': 'All participants in study, chronic pain patients and controls'}], 'classes': [{'categories': [{'measurements': [{'value': '.70', 'groupId': 'OG000'}]}]}], 'paramType': 'NUMBER', 'timeFrame': '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.', 'description': '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)', 'unitOfMeasure': 'probability', 'reportingStatus': 'POSTED', 'populationDescription': "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."}, {'type': 'PRIMARY', 'title': 'Sensitivity of Classification Versus Patient Self Report of Pain vs no Pain State', 'denoms': [{'units': 'Participants', 'counts': [{'value': '308', 'groupId': 'OG000'}]}], 'groups': [{'id': 'OG000', 'title': 'Study Participants', 'description': 'All participants in study, chronic pain patients and controls'}], 'classes': [{'categories': [{'measurements': [{'value': '.783', 'groupId': 'OG000'}]}]}], 'paramType': 'NUMBER', 'timeFrame': '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.', 'description': '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)', 'unitOfMeasure': 'probability', 'reportingStatus': 'POSTED', 'populationDescription': "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."}, {'type': 'PRIMARY', 'title': 'Specificity of Classification Versus Patient Self Report of Pain vs no Pain State', 'denoms': [{'units': 'Participants', 'counts': [{'value': '308', 'groupId': 'OG000'}]}], 'groups': [{'id': 'OG000', 'title': 'Study Participants', 'description': 'All participants in study, chronic pain patients and controls'}], 'classes': [{'categories': [{'measurements': [{'value': '.607', 'groupId': 'OG000'}]}]}], 'paramType': 'NUMBER', 'timeFrame': '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.', 'description': '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)', 'unitOfMeasure': 'probability', 'reportingStatus': 'POSTED', 'populationDescription': "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."}, {'type': 'SECONDARY', 'title': 'Area Under the Curve of Classification Versus Patient Self Report of no/Mild Pain vs Moderate/Severe Pain State', 'denoms': [{'units': 'Participants', 'counts': [{'value': '308', 'groupId': 'OG000'}]}], 'groups': [{'id': 'OG000', 'title': 'Study Participants', 'description': 'All participants in study, chronic pain patients and controls'}], 'classes': [{'categories': [{'measurements': [{'value': '.694', 'groupId': 'OG000'}]}]}], 'paramType': 'NUMBER', 'timeFrame': '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.', 'description': '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)', 'unitOfMeasure': 'probability', 'reportingStatus': 'POSTED', 'populationDescription': "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."}, {'type': 'SECONDARY', 'title': 'Area Under the Curve of Classification Versus Patient Self Report of no, Mild, or Moderate Pain vs Severe Pain State', 'denoms': [{'units': 'Participants', 'counts': [{'value': '308', 'groupId': 'OG000'}]}], 'groups': [{'id': 'OG000', 'title': 'Study Participants', 'description': 'All participants in study, chronic pain patients and controls'}], 'classes': [{'categories': [{'measurements': [{'value': '.669', 'groupId': 'OG000'}]}]}], 'paramType': 'NUMBER', 'timeFrame': '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.', 'description': '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)', 'unitOfMeasure': 'probability', 'reportingStatus': 'POSTED', 'populationDescription': "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."}]}, 'participantFlowModule': {'groups': [{'id': 'FG000', 'title': 'Chronic Pain Patients', 'description': 'ALGOS System: A Quantitative Electroencephalography (QEEG) based pain biomarker assessment that scales with patient reported Numeric Rating Scale (NRS)'}, {'id': 'FG001', 'title': 'Healthy Controls', 'description': 'ALGOS System: A Quantitative Electroencephalography (QEEG) based pain biomarker assessment that scales with patient reported Numeric Rating Scale (NRS)'}], 'periods': [{'title': 'Overall Study', 'milestones': [{'type': 'STARTED', 'achievements': [{'groupId': 'FG000', 'numSubjects': '280'}, {'groupId': 'FG001', 'numSubjects': '54'}]}, {'type': 'COMPLETED', 'achievements': [{'groupId': 'FG000', 'numSubjects': '254'}, {'groupId': 'FG001', 'numSubjects': '54'}]}, {'type': 'NOT COMPLETED', 'achievements': [{'groupId': 'FG000', 'numSubjects': '26'}, {'groupId': 'FG001', 'numSubjects': '0'}]}], 'dropWithdraws': [{'type': 'Withdrawal by Subject', 'reasons': [{'groupId': 'FG000', 'numSubjects': '26'}, {'groupId': 'FG001', 'numSubjects': '0'}]}]}]}, 'baselineCharacteristicsModule': {'denoms': [{'units': 'Participants', 'counts': [{'value': '280', 'groupId': 'BG000'}, {'value': '54', 'groupId': 'BG001'}, {'value': '334', 'groupId': 'BG002'}]}], 'groups': [{'id': 'BG000', 'title': 'Chronic Pain Patients', 'description': 'Chronic pain patients who meet the IASP definition of chronic pain for a musculoskeletal pain disorder.'}, {'id': 'BG001', 'title': 'Healthy Controls', 'description': 'Healthy control participants with no diagnosis of chronic pain nor other various neurological conditions'}, {'id': 'BG002', 'title': 'Total', 'description': 'Total of all reporting groups'}], 'measures': [{'title': 'Age, Categorical', 'classes': [{'categories': [{'title': '<=18 years', 'measurements': [{'value': '0', 'groupId': 'BG000'}, {'value': '0', 'groupId': 'BG001'}, {'value': '0', 'groupId': 'BG002'}]}, {'title': 'Between 18 and 65 years', 'measurements': [{'value': '179', 'groupId': 'BG000'}, {'value': '51', 'groupId': 'BG001'}, {'value': '230', 'groupId': 'BG002'}]}, {'title': '>=65 years', 'measurements': [{'value': '101', 'groupId': 'BG000'}, {'value': '3', 'groupId': 'BG001'}, {'value': '104', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Sex: Female, Male', 'classes': [{'categories': [{'title': 'Female', 'measurements': [{'value': '145', 'groupId': 'BG000'}, {'value': '34', 'groupId': 'BG001'}, {'value': '179', 'groupId': 'BG002'}]}, {'title': 'Male', 'measurements': [{'value': '135', 'groupId': 'BG000'}, {'value': '20', 'groupId': 'BG001'}, {'value': '155', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Race (NIH/OMB)', 'classes': [{'categories': [{'title': 'American Indian or Alaska Native', 'measurements': [{'value': '2', 'groupId': 'BG000'}, {'value': '0', 'groupId': 'BG001'}, {'value': '2', 'groupId': 'BG002'}]}, {'title': 'Asian', 'measurements': [{'value': '10', 'groupId': 'BG000'}, {'value': '15', 'groupId': 'BG001'}, {'value': '25', 'groupId': 'BG002'}]}, {'title': 'Native Hawaiian or Other Pacific Islander', 'measurements': [{'value': '0', 'groupId': 'BG000'}, {'value': '0', 'groupId': 'BG001'}, {'value': '0', 'groupId': 'BG002'}]}, {'title': 'Black or African American', 'measurements': [{'value': '80', 'groupId': 'BG000'}, {'value': '11', 'groupId': 'BG001'}, {'value': '91', 'groupId': 'BG002'}]}, {'title': 'White', 'measurements': [{'value': '123', 'groupId': 'BG000'}, {'value': '26', 'groupId': 'BG001'}, {'value': '149', 'groupId': 'BG002'}]}, {'title': 'More than one race', 'measurements': [{'value': '0', 'groupId': 'BG000'}, {'value': '0', 'groupId': 'BG001'}, {'value': '0', 'groupId': 'BG002'}]}, {'title': 'Unknown or Not Reported', 'measurements': [{'value': '65', 'groupId': 'BG000'}, {'value': '2', 'groupId': 'BG001'}, {'value': '67', 'groupId': 'BG002'}]}]}], 'paramType': 'COUNT_OF_PARTICIPANTS', 'unitOfMeasure': 'Participants'}, {'title': 'Region of Enrollment', 'classes': [{'title': 'United States', 'categories': [{'measurements': [{'value': '280', 'groupId': 'BG000'}, {'value': '54', 'groupId': 'BG001'}, {'value': '334', 'groupId': 'BG002'}]}]}], 'paramType': 'NUMBER', 'unitOfMeasure': 'participants'}]}}, 'documentSection': {'largeDocumentModule': {'noSap': True, 'largeDocs': [{'date': '2020-09-28', 'size': 170512, 'label': 'Study Protocol', 'hasIcf': False, 'hasSap': False, 'filename': 'Prot_000.pdf', 'typeAbbrev': 'Prot', 'uploadDate': '2023-02-27T15:42', 'hasProtocol': True}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 334}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2020-07-23', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2023-04', 'completionDateStruct': {'date': '2022-01-05', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2023-04-11', 'studyFirstSubmitDate': '2020-10-06', 'resultsFirstSubmitDate': '2023-02-27', 'studyFirstSubmitQcDate': '2020-10-06', 'lastUpdatePostDateStruct': {'date': '2023-05-06', 'type': 'ACTUAL'}, 'resultsFirstSubmitQcDate': '2023-04-11', 'studyFirstPostDateStruct': {'date': '2020-10-14', 'type': 'ACTUAL'}, 'resultsFirstPostDateStruct': {'date': '2023-05-06', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2022-01-05', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Area Under the Curve of Classification Versus Patient Self Report of Pain vs no Pain State', 'timeFrame': '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.', 'description': '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)'}, {'measure': 'Sensitivity of Classification Versus Patient Self Report of Pain vs no Pain State', 'timeFrame': '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.', 'description': '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)'}, {'measure': 'Specificity of Classification Versus Patient Self Report of Pain vs no Pain State', 'timeFrame': '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.', 'description': '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)'}], 'secondaryOutcomes': [{'measure': 'Area Under the Curve of Classification Versus Patient Self Report of no/Mild Pain vs Moderate/Severe Pain State', 'timeFrame': '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.', 'description': '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)'}, {'measure': 'Area Under the Curve of Classification Versus Patient Self Report of no, Mild, or Moderate Pain vs Severe Pain State', 'timeFrame': '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.', 'description': '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)'}]}, 'oversightModule': {'isUsExport': True, 'oversightHasDmc': True, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': True}, 'conditionsModule': {'conditions': ['Chronic Pain']}, 'descriptionModule': {'briefSummary': '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.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '85 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '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.', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Male and female chronic pain patients\n* Patients between the ages of 18-85 years\n* Patients exhibiting the presence of symptoms in excess of 3 months duration\n* Patients suffering from neuropathic (e.g., lower back pain), osteoarthritis, or muscular skeletal pain\n* Patients with evidence of pathology related to the painful condition on which diagnosis was made (e.g., results of imaging or diagnostic pain code)\n\nPatients with NRS pain scores across the full range (1-10) at the time of testing Inclusion Criteria, Normal (no-pain) Group\n\no 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)\n\nExclusion Criteria:\n\n* Patients with medically diagnosed psychotic illness\n* Patients with medically diagnosed drug or alcohol dependence in the past 12 months\n* Patients with a medical history of head injury with loss of consciousness and amnesia (within the last 2 years)\n* Patients with skull abnormalities that preclude the proper placement of the electrodes for the EEG data acquisition\n* Patients who have a spinal cord stimulator, or other implantable devices\n* 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\n\nNote: 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.\n\n* Patients with cancer\n* Patients on workers compensation or disability\n* Patient on anticonvulsant medication\n* Patients who have a history of seizures'}, 'identificationModule': {'nctId': 'NCT04585451', 'briefTitle': 'Expanded Development of a Medical Device Utilizing an EEG-Based Algorithm for the Objective Quantification of Pain', 'organization': {'class': 'INDUSTRY', 'fullName': 'PainQx, Inc'}, 'officialTitle': 'Expanded Development of a Medical Device Utilizing an EEG-Based Algorithm for the Objective Quantification of Pain', 'orgStudyIdInfo': {'id': 'PQXNIH2'}, 'secondaryIdInfos': [{'id': '5R44DA046964-03', 'link': 'https://reporter.nih.gov/quickSearch/5R44DA046964-03', 'type': 'NIH'}]}, 'armsInterventionsModule': {'armGroups': [{'label': 'Chronic Pain Patients', 'interventionNames': ['Diagnostic Test: ALGOS System']}, {'label': 'Healthy Controls', 'interventionNames': ['Diagnostic Test: ALGOS System']}], 'interventions': [{'name': 'ALGOS System', 'type': 'DIAGNOSTIC_TEST', 'description': 'A Quantitative Electroencephalography (QEEG) based pain biomarker assessment that scales with patient reported Numeric Rating Scale (NRS)', 'armGroupLabels': ['Chronic Pain Patients', 'Healthy Controls']}]}, 'contactsLocationsModule': {'locations': [{'zip': '80401', 'city': 'Golden', 'state': 'Colorado', 'country': 'United States', 'facility': 'Panorama Orthopedics & Spine Center', 'geoPoint': {'lat': 39.75554, 'lon': -105.2211}}, {'zip': '11042', 'city': 'New Hyde Park', 'state': 'New York', 'country': 'United States', 'facility': 'Comprehensive Spine and Pain Center of New York', 'geoPoint': {'lat': 40.7351, 'lon': -73.68791}}, {'zip': '10016', 'city': 'New York', 'state': 'New York', 'country': 'United States', 'facility': 'Pain Management at Comprehensive Pain and Wellness Center', 'geoPoint': {'lat': 40.71427, 'lon': -74.00597}}, {'zip': '10017', 'city': 'New York', 'state': 'New York', 'country': 'United States', 'facility': 'Comprehensive Spine and Pain Center of New York', 'geoPoint': {'lat': 40.71427, 'lon': -74.00597}}, {'zip': '11580', 'city': 'Valley Stream', 'state': 'New York', 'country': 'United States', 'facility': 'Comprehensive Spine & Pain Center of New York', 'geoPoint': {'lat': 40.66427, 'lon': -73.70846}}], 'overallOfficials': [{'name': 'William Koppes', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'PainQx, Inc'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'PainQx, Inc', 'class': 'INDUSTRY'}, 'collaborators': [{'name': 'National Institute on Drug Abuse (NIDA)', 'class': 'NIH'}], 'responsibleParty': {'type': 'SPONSOR'}}}}