Symptom Clusters Seen in Adult COVID-19 Recovery Clinic Care Seekers

COVID-19 symptom reports describe varying levels of disease severity with differing periods of recovery and symptom trajectories. Thus, there are a multitude of disease and symptom characteristics clinicians must navigate and interpret to guide care.

To find natural groups of patients with similar constellations of post-acute sequelae of COVID-19 (PASC) symptoms.

Cohort SETTING: Outpatient COVID-19 recovery clinic with patient referrals from 160 primary care clinics serving 36 counties in Texas.

Adult patients seeking COVID-19 recovery clinic care between November 15, 2020, and July 31, 2021, with laboratory-confirmed mild (not hospitalized), moderate (hospitalized), or severe (hospitalized with critical care) COVID-19.

Demographics, COVID illness onset, and duration of persistent PASC symptoms via semi-structured medical assessments.

Four hundred forty-one patients (mean age 51.5 years; 295 [66.9%] women; 99 [22%] Hispanic, and 170 [38.5%] non-White, racial minority) met inclusion criteria. Using a k-medoids algorithm, we found that PASC symptoms cluster into two distinct groups: neuropsychiatric (N = 186) (e.g., subjective cognitive dysfunction) and pulmonary (N = 255) (e.g., dyspnea, cough). The neuropsychiatric cluster had significantly higher incidences of otolaryngologic (X² = 14.3, p < 0.001), gastrointestinal (X² = 6.90, p = 0.009), neurologic (X² = 441, p < 0.001), and psychiatric sequelae (X² = 40.6, p < 0.001) with more female (X² = 5.44, p = 0.020) and younger age (t = 2.39, p = 0.017) patients experiencing longer durations of PASC symptoms before seeking care (t = 2.44, p = 0.015). Patients in the pulmonary cluster were more often hospitalized for COVID-19 (X² = 3.98, p = 0.046) and had significantly higher comorbidity burden (U = 20800, p = 0.019) and pulmonary sequelae (X² = 13.2, p < 0.001).

Health services clinic data from a large integrated health system offers insights into the post-COVID symptoms associated with care seeking for sequelae that are not adequately managed by usual care pathways (self-management and primary care clinic visits). These findings can inform machine learning algorithms, primary care management, and selection of patients for earlier COVID-19 recovery referral.

N/A.