dc.contributor.advisor | Wei, Wei-Qi | |
dc.creator | Kerchberger, Vern Eric | |
dc.date.accessioned | 2023-01-06T21:25:55Z | |
dc.date.available | 2023-01-06T21:25:55Z | |
dc.date.created | 2022-12 | |
dc.date.issued | 2022-10-10 | |
dc.date.submitted | December 2022 | |
dc.identifier.uri | http://hdl.handle.net/1803/17879 | |
dc.description.abstract | COVID-19 survivors are at risk for long-term health effects, but assessing the sequelae of COVID-19 at large scales is challenging. High-throughput methods to efficiently identify new medical problems arising after acute medical events using the electronic health record (EHR) could improve surveillance for long-term consequences of acute medical problems like COVID-19. We augmented an existing high-throughput phenotyping method (PheWAS) to identify new diagnoses occurring after an acute temporal event in the EHR. We then used the temporal-informed phenotypes to assess development of new medical problems among COVID-19 survivors enrolled in an EHR cohort of adults tested for COVID-19 at Vanderbilt University Medical Center. The study cohort included 186,105 adults tested for COVID-19 from March 5, 2020 to November 1, 2021; of which 30,088 (16.2%) tested positive. Median follow-up after testing was 412 days (IQR 274–528). Our temporal-informed phenotyping was able to distinguish phenotype chapters based on chronicity of their constituent diagnoses. PheWAS with temporal-informed phenotypes identified increased risk for 43 diagnoses among COVID-19 survivors during outpatient follow-up, including multiple new respiratory, cardiovascular, neurological, and pregnancy-related conditions. Findings were robust to sensitivity analyses, and several phenotypic associations were supported by changes in outpatient vital signs or laboratory tests from the pre-testing to post-recovery period. Temporal-informed PheWAS identified new diagnoses affecting multiple organ systems among COVID-19 survivors. These findings can inform future efforts to enable longitudinal health surveillance for survivors of COVID-19 and other acute medical conditions using the EHR. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dc.subject | COVID-19 | |
dc.subject | COVID-19/complications | |
dc.subject | electronic health records | |
dc.subject | cohort study | |
dc.subject | phenome-wide association study | |
dc.title | Temporal-informed phenotyping scans the medical phenome to identify new diagnoses after recovery from COVID-19 | |
dc.type | Thesis | |
dc.date.updated | 2023-01-06T21:25:55Z | |
dc.contributor.committeeMember | Ware, Lorraine B | |
dc.contributor.committeeMember | Feng, QiPing | |
dc.contributor.committeeMember | Walsh, Colin G | |
dc.type.material | text | |
thesis.degree.name | MS | |
thesis.degree.level | Masters | |
thesis.degree.discipline | Biomedical Informatics | |
thesis.degree.grantor | Vanderbilt University Graduate School | |
dc.creator.orcid | 0000-0002-0342-1965 | |