ABSTRACT
Background: Autonomic dysfunction is a known complication of post-acute sequelae of SARS-CoV-2 (PASC)/long COVID, however prevalence and severity are unknown. Objective: To assess the frequency, severity, and risk factors of autonomic dysfunction in PASC, and to determine whether severity of acute SARS-CoV-2 infection is associated with severity of autonomic dysfunction. Design: Cross-sectional online survey of adults with PASC recruited through long COVID support groups between October 2020 and August 2021. Participants: 2,413 adults ages 18-64 years with PASC including patients who had a confirmed positive test for COVID-19 (test-confirmed) and participants who were diagnosed with COVID-19 based on clinical symptoms alone. Main measures: The main outcome measure was the Composite Autonomic Symptom 31 (COMPASS-31) total score, used to assess global autonomic dysfunction. Test-confirmed hospitalized vs. test-confirmed non-hospitalized participants were compared to determine if the severity of acute SARS-CoV-2 infection was associated with the severity autonomic dysfunction. Key results: Sixty-six percent of PASC patients had a COMPASS-31 score >20, suggestive of moderate to severe autonomic dysfunction. COMPASS-31 scores did not differ between test-confirmed hospitalized and test-confirmed non-hospitalized participants [28.95 (15.62, 46.60) vs. 26.4 (13.75, 42.10); p = 0.06]. Conclusions: Evidence of moderate to severe autonomic dysfunction was seen in 66% of PASC patients in our study, independent of hospitalization status, suggesting that autonomic dysfunction is highly prevalent in the PASC population and independent of the severity of acute COVID-19 illness.
ABSTRACT
Importance: Physicians are required to work with rapidly growing amounts of medical data. Approximately 62% of time per patient is devoted to reviewing electronic health records (EHRs), with clinical data review being the most time-consuming portion. Objective: To determine whether an artificial intelligence (AI) system developed to organize and display new patient referral records would improve a clinician's ability to extract patient information compared with the current standard of care. Design, Setting, and Participants: In this prognostic study, an AI system was created to organize patient records and improve data retrieval. To evaluate the system on time and accuracy, a nonblinded, prospective study was conducted at a single academic medical center. Recruitment emails were sent to all physicians in the gastroenterology division, and 12 clinicians agreed to participate. Each of the clinicians participating in the study received 2 referral records: 1 AI-optimized patient record and 1 standard (non-AI-optimized) patient record. For each record, clinicians were asked 22 questions requiring them to search the assigned record for clinically relevant information. Clinicians reviewed records from June 1 to August 30, 2020. Main Outcomes and Measures: The time required to answer each question, along with accuracy, was measured for both records, with and without AI optimization. Participants were asked to assess overall satisfaction with the AI system, their preferred review method (AI-optimized vs standard), and other topics to assess clinical utility. Results: Twelve gastroenterology physicians/fellows completed the study. Compared with standard (non-AI-optimized) patient record review, the AI system saved first-time physician users 18% of the time used to answer the clinical questions (10.5 [95% CI, 8.5-12.6] vs 12.8 [95% CI, 9.4-16.2] minutes; P = .02). There was no significant decrease in accuracy when physicians retrieved important patient information (83.7% [95% CI, 79.3%-88.2%] with the AI-optimized vs 86.0% [95% CI, 81.8%-90.2%] without the AI-optimized record; P = .81). Survey responses from physicians were generally positive across all questions. Eleven of 12 physicians (92%) preferred the AI-optimized record review to standard review. Despite a learning curve pointed out by respondents, 11 of 12 physicians believed that the technology would save them time to assess new patient records and were interested in using this technology in their clinic. Conclusions and Relevance: In this prognostic study, an AI system helped physicians extract relevant patient information in a shorter time while maintaining high accuracy. This finding is particularly germane to the ever-increasing amounts of medical data and increased stressors on clinicians. Increased user familiarity with the AI system, along with further enhancements in the system itself, hold promise to further improve physician data extraction from large quantities of patient health records.