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Characterizing and Predicting Post-Acute Sequelae of SARS CoV-2 infection (PASC) in a Large Academic Medical Center in the US
Preprint
in English
| medRxiv
| ID: ppmedrxiv-22281356
ABSTRACT
ObjectiveA growing number of Coronavirus Disease-2019 (COVID-19) survivors are affected by Post-Acute Sequelae of SARS CoV-2 infection (PACS). Using electronic health records data, we aimed to characterize PASC-associated diagnoses and to develop risk prediction models. MethodsIn our cohort of 63,675 COVID-19 positive patients, 1,724 (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. ResultsIn 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 69 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 COVID-19 positive cohort with an at least 2.9-fold increased risk for PASC. ConclusionsThe uncovered PASC-associated diagnoses across categories highlighted a complex arrangement of presenting and likely predisposing features, some with a potential for risk stratification approaches. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/22281356v3_ufig1.gif" ALT="Figure 1"> View larger version (35K) org.highwire.dtl.DTLVardef@2ba229org.highwire.dtl.DTLVardef@a3651forg.highwire.dtl.DTLVardef@1440df1org.highwire.dtl.DTLVardef@ef7806_HPS_FORMAT_FIGEXP M_FIG C_FIG
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Full text:
Available
Collection:
Preprints
Database:
medRxiv
Type of study:
Cohort_studies
/
Experimental_studies
/
Observational study
/
Prognostic study
Language:
English
Year:
2022
Document type:
Preprint