Phase II year
2019
(last award dollars: 2020)
Phase II Amount
$1,381,707
Chronic pain affects over 100 million Americans representing a major public health imperative. Objective biomarkers of pathology exist for several diseases, and their development is one of the great advances of modern allopathic medicine; however, objective assessment of pain has lagged far behind. Currently, there are no objectively verifiable and clinically useful means to identify or quantify the presence or severity of pain. The current standard of care relies on patient self-report, such as the visual analog scale (VAS), which presents a serious barrier to the effective assessment and treatment of pain. Self-reported pain is influenced by nociceptive, affective, and cognitive processes, and though many treatments effect reported pain, they likely do so through a varied set of neurophysiological mechanisms, with different consequences for health and long-term well-being. Some patients have difficulty assigning themselves a pain rating, especially those with pain that falls towards the middle of the rating scale. In addition, communications issues, drug-seeking behavior, the desire of some patients to appear stoic, and other issues can create problems with establishing an accurate pain rating. As a result, despite a long history of research, current assessment and treatment of pain is not optimal, with enormous costs to patients and society. PainQx is currently developing the PQX-MED system, a system that will objectively evaluate an individuals pain level using quantitative EEG (QEEG). Advanced signal processing, machine learning, classification methodologies and a large reference database will be used to develop algorithms that quantify features of an individuals EEG that are associated with the perception of pain. Before the PainQx platform is ready for its FDA Validation Study, PainQx needs to demonstrate the ability to assess pain in a representative set of patients with chronic pain. To ensure commercial viability, PainQx also needs to be able to generate its pain biomarker using a limited montage of EEG electrodes which can be rapidly applied prior to data acquisition and processing. PainQx proposes to achieve these objectives through the proposed SBIR project. In Phase I, PainQx will conduct a clinical study of 50 chronic pain patients utilizing 19 lead EEG acquisition, add those cases to an existing database of 19 lead pain cases, and demonstrate that 19 Lead EEG data can be used to assess the intensity of pain a patient is experiencing. In Phase II, PainQx will demonstrate that the relationship between the VAS and a QEEG based biomarker demonstrated using 19 leads can be demonstrated using a subset of EEG recording locations to significantly improve clinical utility. Further, predictive accuracy using the reduced montage will meet targets for performance established using 19 lead data.
Public Health Relevance Statement: Project Narrative The nature of self-reported pain rating scales leads to difficulty in accurately identifying, evaluating and therefore, optimally treating pain due to issues such as patient communication difficulties, drug-seeking behavior, differences in pain tolerance, and other challenges. As a result, patients can be either over-treated, leading to (or perpetuating) addiction as manifested by the opioid epidemic, or under-treated, leading to readmissions, lost productivity, unnecessary pain and suffering, and significant costs to the healthcare system. By providing physicians an objective pain measurement tool, PainQx believes it will allow physicians to increase certainty in dosing and treatment selection, thereby addressing the over and under treatment paradigm, and consequently reducing opioid abuse and overall healthcare costs.
NIH Spending Category: Bioengineering; Brain Disorders; Chronic Pain; Clinical Research; Clinical Trials and Supportive Activities; Drug Abuse (NIDA only); Machine Learning and Artificial Intelligence; Networking and Information Technology R&D (NITRD); Neurosciences; Pain Research; Substance Abuse
Project Terms: Achievement; addiction; Address; Affect; Affective; Algorithms; American; analog; base; Biological Markers; Brain; brain electrical activity; Chronic; chronic pain; chronic pain patient; Classification; classification algorithm; Clinical; Clinical Research; cognitive process; Communication; comorbidity; computerized data processing; cost; Data; data acquisition; Databases; Development; Disease; Dose; drug seeking behavior; Electrodes; Electroencephalography; Ensure; Evaluation; expectation; experience; falls; Future; Health; Health Care Costs; Healthcare Systems; hospital readmission; improved; Individual; Lead; Literature; Location; Logistic Regressions; Logistics; Machine Learning; machine learning algorithm; Mathematics; Measures; Medical Device; Medicine; meetings; Methodology; Modernization; Nature; neurophysiology; Nociception; opioid abuse; opioid epidemic; Output; Pain; Pain intensity; Pain management; Pain Measurement; pain perception; pain score; Pain Threshold; Pathology; Patient Self-Report; Patients; Performance; Personal Satisfaction; Phase; Physicians; Population; product development; Productivity; Public Health; Recording of previous events; Regulatory Pathway; Reporting; Research; Selection for Treatments; Severities; signal processing; Small Business Innovation Research Grant; Societies; standard of care; System; Testing; Time; tool; Training; treatment effect; Validation; validation studies; Visual; Visual Analogue Pain Scale
