PROMIS Scales for Assessment of Persistent Post-COVID Symptoms: A Cross Sectional Study.

Objective: Persistent post-COVID symptoms are estimated to occur in up to 10% of patients who have had COVID-19. These lingering symptoms may persist for weeks to months after resolution of the acute illness. This study aimed to add insight into our understanding of certain post-acute conditions and clinical findings. The primary purpose was to determine the persistent post COVID impairments prevalence and characteristics by collecting post COVID illness data utilizing Patient-Reported Outcomes Measurement Information System (PROMIS). The resulting measures were used to assess surveyed patients physical, mental, and social health status.

Methods: A cross-sectional study and 6-months Mayo Clinic COVID recovered registry data were used to evaluate continuing symptoms severity among the 817 positive tested patients surveyed between March and September 2020. The resulting PROMIS data set was used to analyze patients post 30 days health status. The e-mailed questionnaires focused on fatigue, sleep, ability to participate in social roles, physical function, and pain.

Results: The large sample size (n = 817) represented post hospitalized and other managed outpatients. Persistent post COVID impairments prevalence and characteristics were determined to be demographically young (44 years), white (87%), and female (61%). Dysfunction as measured by the PROMIS scales in patients recovered from acute COVID-19 was reported as significant in the following domains: ability to participate in social roles (43.2%), pain (17.8%), and fatigue (16.2%).

Conclusion: Patient response on the PROMIS scales was similar to that seen in multiple other studies which used patient reported symptoms. As a result of this experience, we recommend utilizing standardized scales such as the PROMIS to obtain comparable data across the patients' clinical course and define the disease trajectory. This would further allow for effective comparison of data across studies to better define the disease process, risk factors, and assess the impact of future treatments.