Characterization of Chronic Pain and its Biopsychosocial Mechanisms in Lupus using Electronic Health Records

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Up to 100 million Americans live with ongoing pain, costing $635 billion annually. In the United States, there are more than 200,000 people living with SLE, a chronic inflammatory rheumatic disease with multi-organ involvement that disproportionately affects females and racial minorities. Living with a chronic disease such as SLE confers multiple challenges. Pain is a frequent self-reported symptom in SLE and is often one of the first symptoms of the disease. Despite treatment advances, pain remains the most prominent, unaddressed patient complaint. The management of pain in SLE has recently become more challenging because of the alarming epidemic of addiction and mortality attributed to opioid misuse. An estimated 31-46% of patients with SLE use prescription opioids. In one study, 70% of individuals using opioids used them for ≥1 year, and 22% were taking ≥2 opioid medications at the same time. Patients with SLE are nearly twice as likely as the general population to have opioid-related overdose hospitalizations. However, efforts to mitigate opioid misuse cannot be achieved without a detailed understanding and sustained investment in clinical research on the underlying mechanisms that produce and maintain chronic pain. Characterizing the burden of chronic pain in SLE is challenging on at least two counts. First, we lack data on the prevalence and burden of chronic pain in SLE, partly due to the absence of reliable approaches to identify patients with clinically significant pain in electronic health records (EHR). Second, there is a critical need to understand the biopsychosocial mechanisms and correlates that drive the pain experience in SLE. In this mentored career development award (K01), Dr. Titilola Falasinnu will use computational methods to increase the understanding of the clinical management chronic pain in SLE using EHR. In Aim 1, Dr. Falasinnu will develop a computational chronic pain phenotyping algorithm using diagnostic codes, pain scores, narrative clinic notes and medications extracted from the EHRs of two large healthcare systems (n~2,400). She will then use the algorithm to estimate chronic pain prevalence in a population-based registry (n~76,000). In Aim 2, Dr. Falasinnu will comprehensively phenotype biopsychosocial correlates of chronic pain using an existing registry of ~500 patients with SLE attending a multi-disciplinary pain center. Throughout the award, Dr. Falasinnu will build on her doctoral training as an epidemiologist and biostatistician to develop new skills in biomedical informatics to conduct impactful pain medicine research. These skills will include working with EHR and registry data, machine learning and natural language processing, pain science, grant-writing, and scientific communication. Through coursework, clinical observation in pain medicine clinics, mentorship, and external conferences and workshops, Dr. Falasinnu will gain the skills needed to apply for her first R01 and pursue a career as a tenure-track principal investigator.