Predicting Pain at the Bedside in the Context of Patients’ Living Environments

Tristan Andres Bekinschtein


We are planning to develop a cost-effective method to prognosticate the course of pain levels in chronic pain disorders using measures of brain activity combined with patient self-reports. Our goal is to enable treating clinicians and patients to obtain rich online feedback about the state of the condition to facilitate challenging decisions about treatment, e.g., the dosage of analgesic medication.

Considerable controversy surrounds the neural signature of chronic pain (Ploner et al., Trends Cogn Sci, 2016), making it difficult to develop a biomarker to diagnose the condition. As a consequence, diagnosis and prognosis of the conditions rest on clinical expert evaluation of the patient’s symptom presentation and their subjective reports. Instead of attempting to develop a unique diagnostic marker for CRPS, we capitalize on clinical and patient expertise to collapse first-person data with clinical observations and brain data to predict pain levels in CRPS. Here we pilot a big-data study powered to allow for the prediction of chronic pain levels per patient by integrating precision telemedicine with 129-channel high-density Electroencephalograph (EEG) data. Our project is enriched by the charity “Burning Night CRPS’s first-hand expertise of living with complex regional pain.

At multiple points throughout the course of several months, we are planning to sample EEG data from chronic pain patients to investigate effective connectivity using portable devices at the patients’ home. To complement the prognostic power of the model, we are planning to collect online information about patients’ pain levels, psychological and functional state. Data collection will take place via the bot while the patients pursue daily tasks. At multiple points throughout the course of several months, we are planning to sample EEG data from a tactile hierarchical oddball paradigm using portable devices at the patients’ home. The paradigm has been developed in our lab (Bekinschtein et al., Proc Natl Acad Sci, 2009) and has already been used to investigate effective connectivity in the cortical hierarchy (Chennu et al., J Neurosci, 2016). We envision that the project might contribute to enhancing patients’ functional treatment outcomes in the context of their life circumstances and goals to aid their rehabilitation in the community.

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Predicting Pain at the Bedside

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