[...] An Examination of the Effects of [LC] in an Underserved Population, Utilizing Manual Extraction of Electronic Health Records, 2025, Musachia+

Post Acute Sequelae from SARS-CoV-2 (PASC) at University of Illinois Hospital and Clinics – An Examination of the Effects of Long COVID in an Underserved Population, Utilizing Manual Extraction of Electronic Health Records

John Musachia, Jon Radosta, Dirin Ukwode, Shahrukh Rizvi, Romani Wahba

Background
Although there has been a steady decrease in morbidity and mortality from the SARS-CoV-2 virus since the 2020-2021 period, thousands of Americans are still infected with the SARS-CoV-2 virus daily. Some proportion of these infected individuals will go on to develop Post-Acute Sequelae from SARS-CoV-19 (PASC, or Long COVID), manifesting symptoms four weeks or more after recovery from COVID-19. PASC and its underlying pathophysiology are still poorly described and understood. Although hundreds of peer-reviewed, published investigations on Long COVID exist, few have focused on underserved urban patient populations. Most of the published research has involved reviews of diagnostic codes from electronic health records, or responses to questionnaires. We sought to review Long COVID in an underserved population in Chicago, and to go beyond electronic health record reviews of diagnostic codes, utilizing in-depth chart reviews, gleaned via manual extraction, focusing on notations of care providers. We investigated which specific pre-existing conditions, if any, might be associated with specific Long COVID symptomatology's, and if any pre-existing conditions predicted Long COVID. There have been inadequate investigations looking at Long COVID's effects in underserved populations (Appendix A). To our knowledge, there exists only one investigation examining Long COVID via manual extraction of physician notes from electronic health records (Appendix B).

Methods
204 Long COVID patients, 98 COVID-19-postive patients, and 104 healthy (no history of COVID-19 infection) patients from an inner-city health system caring for underserved communities were reviewed via manual data extraction from electronic health records, focusing on provider notes in patient charts.

Results and Conclusions
Our Long COVID symptom frequencies were distinct compared to frequencies from other reviews that did not focus on underserved populations, and done with medical records when only diagnostic codes are utilized. Pre-existing medical conditions did not predict similar Long COVID symptomologies, save for the significant association between pre-existing cough/dyspnea/pulmonary conditions and pre-existing migraine/headache, and their analogous Long COVID symptoms. The odds of having Long COVID increased comparatively in subjects hospitalized with COVID-19, subjects with BMI>30, and female subjects.

Link | PDF (American Journal of Medicine Open) [Open Access]