Subphenotypes of Self-Reported Symptoms and Outcomes in Long COVID: a prospective cohort study with latent class analysis. (preprint)

Objective: To characterize subphenotypes of self-reported symptoms and outcomes(SRSOs) in Post-acute sequelae of COVID-19(PASC). Design: Prospective, observational cohort study of PASC subjects. Setting: Academic tertiary center from five clinical referral sources. Participants: Adults with COVID-19 [≥] 20 days before enrollment and presence of any new self-reported symptoms following COVID-19. Exposures: We collected data on clinical variables and SRSOs via structured telephone interviews and performed standardized assessments with validated clinical numerical scales to capture psychological symptoms, neurocognitive functioning, and cardiopulmonary function. We collected saliva and stool samples for quantification of SARS-CoV-2 RNA via qPCR. Primary and Secondary outcomes of measure: Description of PASC SRSOs burden and duration, derivation of distinct PASC subphenotypes via latent class analysis (LCA), and relationship between viral load with SRSOs and PASC subphenotypes. Results: Baseline data for 214 individuals were analyzed. The study visit took place at a median of 197.5 days after COVID-19 diagnosis, and participants reported ever having a median of 9/16 symptoms (interquartile range 6-11) after acute COVID, with muscle-aches, dyspnea, and headache being the most common. Fatigue, cognitive impairment, and dyspnea were experienced for a longer time. Participants had a lower burden of active symptoms (median 3, interquartile range 1-6) than those ever experienced (p<0.001). Unsupervised LCA of symptoms revealed three clinically-active PASC subphenotypes: a high burden constitutional symptoms (21.9%) , a persistent loss/change of smell and taste (20.6%) , and a minimal residual symptoms subphenotype (57.5%). Subphenotype assignments were strongly associated with self-assessments of global health, recovery and PASC impact on employment (p<0.001). Viral persistence (5.6% saliva and 1% stool samples positive) did not explain SRSOs or subphenotypes. Conclusions: We identified distinct PASC subphenotypes and highlight that although most symptoms progressively resolve, specific PASC subpopulations are impacted by either high burden of constitutional symptoms or persistent olfactory/gustatory dysfunction, requiring prospective identification and targeted preventive or therapeutic interventions.

Prognostic value of bedside lung ultrasound score in patients with COVID-19 (preprint)

Background: Bedside lung ultrasound (LUS) has emerged as a useful and noninvasive tool to detect lung involvement and monitor changes in patients with coronavirus disease 2019 (COVID-19). However, the clinical significance of the LUS score in patients with COVID-19 remains unknown. We aimed to investigate the prognostic value of the LUS score in patients with COVID-19. Methods: : The LUS protocol consisted of 12 scanning zones and was performed in 280 consecutive patients with COVID-19. The LUS score based on B-lines, lung consolidation and pleural line abnormalities was evaluated. Results: : The median time from admission to LUS examinations was 7 days (interquartile range [IQR] 3-10). Patients in the highest LUS score group were more likely to have a lower lymphocyte percentage (LYM%); higher levels of D-dimer, C-reactive protein, hypersensitive troponin I and creatine kinase muscle-brain; more invasive mechanical ventilation therapy; higher incidence of ARDS; and higher mortality than patients in the lowest LUS score group. After a median follow-up of 14 days [IQR, 10-20 days], 37 patients developed ARDS, and 13 died. Patients with adverse outcomes presented a higher rate of bilateral involvement; more involved zones and B-lines, pleural line abnormalities and consolidation; and a higher LUS score than event-free survivors. The Cox models adding the LUS score as a continuous variable (hazard ratio [HR]: 1.05, 95% confidence intervals [CI]: 1.02～1.08; P < 0.001; Akaike Information Criterion [AIC] =272; C-index = 0.903) or as a categorical variable (HR: 10.76, 95% CI: 2.75～42.05; P = 0.001; AIC =272; C-index = 0.902) were found to predict poor outcomes more accurately than the basic model (AIC =286; C-index = 0.866). An LUS score cut-off >12 predicted adverse outcomes with a specificity and sensitivity of 90.5% and 91.9%, respectively. Conclusions: : The LUS score devised by our group performs well at predicting adverse outcomes in patients with COVID-19 and is important for risk stratification in COVID-19 patients.

Prognostic value of bedside lung ultrasound-score in patients with COVID-19 (preprint)

Background: Bedside lung ultrasound (LUS) has emerged as a useful and non-invasive tool to detect lung involvement and monitor changes in patients with coronavirus disease 2019(COVID-19). While the clinical significance of LUS-score in patients with COVID-19 remains unknown. We aimed to investigate the prognostic value of LUS-score in patients with COVID-19.MethodsLUS protocol consisted of 12 scanning zones and was performed in 280 consecutive patients with COVID-19. LUS-score based on B-lines, pleural line abnormalities and lung consolidation was evaluated. The primary outcome was a combination of severe acute respiratory distress syndrome (ARDS), and mortality.ResultsCompared with patients in the lowest LUS-score group, those in the highest LUS-score group were more likely to have a lower lymphocyte%, higher levels of D-dimer, C-reactive protein, hypersensitive troponin I and creatine kinase muscle-brain, more invasive mechanical ventilation therapy, higher incidence of ARDS, and higher mortality. After a median follow-up of 14 days, 37 patients progressed to the poor outcome. Compared with event-free survivors, patients with adverse event presented higher rate of bilateral involved, more involved zones and B-lines, pleural lines abnormalities and consolidation, and higher LUS-score. The Cox models adding LUS-score as a continuous variable (hazard ratio [HR]: 1.05, 95% confidence intervals [CI]: 1.02~1.08; P < 0.001; Akaike Information Criterion [AIC] =272; C-index = 0.903) or as a categorical variable (HR: 10.76, 95% CI: 2.75~42.05; P = 0.001; AIC =272; C-index = 0.902) were found to predict poor outcome more accurately than the basic model (AIC =286; C-index = 0.866). LUS-score cutoff >12 would predict adverse events with specificity and sensitivity of 90.5% and 91.9%, respectively.ConclusionsLUS-score is a powerful predictor of adverse events in patients with COVID-19, and is important for risk stratification in COVID-19 patients.