Machine learning applied to electronic health records from the RECOVER initiative identified 4 long COVID19 subphenotypes which differ in the involvement of organ systems, previous SARSCoV2 infection severity and underlying conditions LongCOVID
Subphenotype 1 consisted of 7,047 patients. It was dominated by cardiac-related, renal-related and circulation-related topics , including cardiac and circulatory conditions, renal failure, anemia and fluid and electrolyte disorders. Compared to other subphenotypes, patients in this subphenotype were older ) and had the highest proportion of males . The patients also had a higher acute severity of COVID-19 , use of mechanical ventilation and critical care services ).
Subphenotype 3 consisted of 4,879 patients who mainly had musculoskeletal and nervous system sequelae , such as musculoskeletal pain, headaches and sleep-wake disorders. This subphenotype included patients with a median age of 57.0 years ), and 60.71% were female.
For each subphenotype, we also provide the sex-disaggregated analysis results with respect to patient demographics, baseline and acute infection phase medical utilizations, prior condition prevalence and newly PASC incidence rates in Supplementary TableComparison with patients without SARS-CoV-2 infection
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