Characterizing and Predicting Post-Acute Sequelae of SARS CoV-2 Infection (PASC) in a Large Academic Medical Center in the US
BACKGROUND: A growing number of Coronavirus Disease-2019 (COVID-19) survivors are affected by post-acute sequelae of SARS CoV-2 infection (PACS). Using electronic health record data, we aimed to characterize PASC-associated diagnoses and develop risk prediction models.
METHODS: In our cohort of 63,675 patients with a history of COVID-19, 1724 (2.7%) had a recorded PASC diagnosis. We used a case-control study design and phenome-wide scans to characterize PASC-associated phenotypes of the pre-, acute-, and post-COVID-19 periods. We also integrated PASC-associated phenotypes into phenotype risk scores (PheRSs) and evaluated their predictive performance.
RESULTS: In the post-COVID-19 period, known PASC symptoms (e.g., shortness of breath, malaise/fatigue) and musculoskeletal, infectious, and digestive disorders were enriched among PASC cases. We found seven phenotypes in the pre-COVID-19 period (e.g., irritable bowel syndrome, concussion, nausea/vomiting) and sixty-nine phenotypes in the acute-COVID-19 period (predominantly respiratory, circulatory, neurological) associated with PASC. The derived pre- and acute-COVID-19 PheRSs stratified risk well, e.g., the combined PheRSs identified a quarter of the cohort with a history of COVID-19 with a 3.5-fold increased risk (95% CI: 2.19, 5.55) for PASC compared to the bottom 50%.
CONCLUSIONS: The uncovered PASC-associated diagnoses across categories highlighted a complex arrangement of presenting and likely predisposing features, some with potential for risk stratification approaches.
Errataetall: | |
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Medienart: |
E-Artikel |
Erscheinungsjahr: |
2023 |
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Erschienen: |
2023 |
Enthalten in: |
Zur Gesamtaufnahme - volume:12 |
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Enthalten in: |
Journal of clinical medicine - 12(2023), 4 vom: 07. Feb. |
Sprache: |
Englisch |
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Beteiligte Personen: |
Fritsche, Lars G [VerfasserIn] |
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Links: |
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Anmerkungen: |
Date Revised 16.02.2024 published: Electronic UpdateOf: medRxiv. 2022 Nov 23;:. - PMID 36415469 Citation Status PubMed-not-MEDLINE |
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doi: |
10.3390/jcm12041328 |
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funding: |
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Förderinstitution / Projekttitel: |
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PPN (Katalog-ID): |
NLM353419982 |
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520 | |a BACKGROUND: A growing number of Coronavirus Disease-2019 (COVID-19) survivors are affected by post-acute sequelae of SARS CoV-2 infection (PACS). Using electronic health record data, we aimed to characterize PASC-associated diagnoses and develop risk prediction models | ||
520 | |a METHODS: In our cohort of 63,675 patients with a history of COVID-19, 1724 (2.7%) had a recorded PASC diagnosis. We used a case-control study design and phenome-wide scans to characterize PASC-associated phenotypes of the pre-, acute-, and post-COVID-19 periods. We also integrated PASC-associated phenotypes into phenotype risk scores (PheRSs) and evaluated their predictive performance | ||
520 | |a RESULTS: In the post-COVID-19 period, known PASC symptoms (e.g., shortness of breath, malaise/fatigue) and musculoskeletal, infectious, and digestive disorders were enriched among PASC cases. We found seven phenotypes in the pre-COVID-19 period (e.g., irritable bowel syndrome, concussion, nausea/vomiting) and sixty-nine phenotypes in the acute-COVID-19 period (predominantly respiratory, circulatory, neurological) associated with PASC. The derived pre- and acute-COVID-19 PheRSs stratified risk well, e.g., the combined PheRSs identified a quarter of the cohort with a history of COVID-19 with a 3.5-fold increased risk (95% CI: 2.19, 5.55) for PASC compared to the bottom 50% | ||
520 | |a CONCLUSIONS: The uncovered PASC-associated diagnoses across categories highlighted a complex arrangement of presenting and likely predisposing features, some with potential for risk stratification approaches | ||
650 | 4 | |a Journal Article | |
650 | 4 | |a Coronavirus Disease-2019 (COVID-19) | |
650 | 4 | |a electronic health records | |
650 | 4 | |a phenome-wide association study | |
650 | 4 | |a phenotype risk score | |
650 | 4 | |a post-acute sequelae of SARS CoV-2 (PASC, long COVID, post-COVID conditions) | |
700 | 1 | |a Jin, Weijia |e verfasserin |4 aut | |
700 | 1 | |a Admon, Andrew J |e verfasserin |4 aut | |
700 | 1 | |a Mukherjee, Bhramar |e verfasserin |4 aut | |
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