Characterizing and Predicting Post-Acute Sequelae of SARS CoV-2 infection (PASC) in a Large Academic Medical Center in the US (preprint)

ABSTRACT

Objective: The growing number of Coronavirus Disease-2019 (COVID-19) survivors who are affected by Post-Acute Sequelae of SARS CoV-2 infection (PACS) represent a worldwide public health challenge. Yet, the novelty of this condition and the resulting limited data on underlying pathomechanisms so far hampered the advancement of effective therapies. Using electronic health records (EHR) data, we aimed to characterize PASC-associated diagnoses and to develop risk prediction models. Methods: In our cohort of 63,675 COVID-19 positive patients seen at Michigan Medicine, 1,724 (2.7 %) had a recorded PASC diagnosis. We used a case control study design comparing PASC cases with 17,205 matched controls and performed phenome-wide association studies (PheWASs) to characterize enriched phenotypes of the post-COVID-19 period and potential PASC pre-disposing phenotypes of the pre-, and acute-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, cases were significantly enriched for known PASC symptoms (e.g., shortness of breath, malaise/fatigue, and cardiac dysrhythmias) but also many musculoskeletal, infectious, and digestive disorders. We found seven phenotypes in the pre-COVID-19 period (irritable bowel syndrome, concussion, nausea/vomiting, shortness of breath, respiratory abnormalities, allergic reaction to food, and circulatory disease) and 69 phenotypes in the acute-COVID-19 period (predominantly respiratory, circulatory, neurological, digestive, and mental health phenotypes) that were significantly associated with PASC. The derived pre-COVID-19 PheRS and acute-COVID-19 PheRS had low accuracy to differentiate cases from controls; however, they stratified risk well, e.g., a combination of the two PheRSs identified a quarter of the COVID-19 positive cohort at a 3.5-fold increased risk for PASC compared to the bottom 50% of their distributions. Conclusions: Our agnostic screen of time stamped EHR data uncovered a plethora of PASC-associated diagnoses across many categories and highlighted a complex arrangement of presenting and likely pre-disposing features -- the latter with a potential for risk stratification approaches. Yet, considerably more work will need to be done to better characterize PASC and its subtypes, especially long-term consequences, and to consider more comprehensive risk models.