Prediction models for physical function in COVID-19 survivors

Abstract

BackgroundThe burden of caring for patients who have survived COVID-19 will be enormous in the coming years, especially with respect to physical function. Physical function has been routinely assessed using the Post-COVID-19 Functional Status (PCFS) scale. AimThis study built prediction models for the PCFS scale using sociodemographic data, clinical findings, lung function, and muscle strength. MethodTwo hundred and one patients with post-COVID-19 syndrome (PCS) completed the PCFS scale to assess physical function. Their levels of general fatigue were also assessed using the Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F) scale, handgrip strength (HGS), and spirometry. ResultsThe number of participants who scored 0 (none), 1 (negligible), 2 (slight), 3 (moderate), and 4 (severe) on the PCFS scale was 25 (12%), 40 (20%), 39 (19%), 49 (24%), and 48 (24%), respectively. The PCFS scale was significantly correlated with the following variables: FACIT-F score (r = −0.424, P < 0.001), HGS (r = −0.339, P < 0.001), previous hospitalization (r = 0.226, P = 0.001), body mass index (r = 0.163, P = 0.021), and sex (r = −0.153, P = 0.030). The regression model with the highest coefficient of regression (R = 0.559) included the following variables: age, sex, body mass index, FACIT-F, HGS, and previous hospitalization. ConclusionsWorse general fatigue and HGS are associated with more severe physical function impairments in PCS patients. Furthermore, a history of prior hospitalization results in worse physical function. Thus, prediction models for the PCFS scale that incorporate objective measures enable a better assessment of the physical function of these patients, thus helping in the selection of candidates for a program of physical reconditioning.