Table 1.

PASC Computable Phenotype Definition Across Three EHR Networks

Network	Approach	PASC computable phenotype
N3C	Machine-learning-based definition	Adult patients with all of the following within 100 to 190 days after the index date: • ≥18 years old • ≥2 days of data • ≥1 diagnosis and ≥1 medication in the pre-/post-index period Who are likely to have PASC based on a model trained to identify patients who have previously visited a Long COVID clinic
PCORnet	Rules-based definition	Adult patients with all of the following: • ≥1 diagnosis and ≥1 medication in the pre-/post-index period. • ≥1 incident diagnosis belonging to a list of 25 CCSR codes within 30 to 180 days after the index date (truncated at end of study period) with no prior diagnosis recorded before the index date. The 25 CCSR codes are established to have higher incidence rates in COVID positive patients, derived through prior literature and clinician review.
PEDSnet	Rules-based definition	Pediatric patients with any of the following within 28 to 179 days after the index date: • Clinician-assigned PASC or MIS-C diagnosis. • ≥2 occurrences of diagnosis codes for one of the following: ° Abnormal liver enzymes ° Myocarditis ° Anosmia ° Myositis ° Dysgeusia ° Pericarditis ° Chest pain ° Thrombophlebitis/thromboembolism ° COVID-19

Networks took different approaches to identify patients with probable PASC within the EHR. CCSR, Clinical Classifications Software Refined; MIS-C, Multisystem Inflammatory Syndrome in Children.