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BackgroundWomen are overrepresented amongst individuals suffering from post-acute sequelae of SARS-CoV-2 infection (PASC). Biological (sex) as well as sociocultural (gender) differences between women and men might account for this imbalance, yet their impact on PASC is unknown. Methods and FindingsBy using Bayesian models comprising >200 co-variates, we assessed the impact of social context in addition to biological data on PASC in a multi-centre prospective cohort study of 2927 (46% women) individuals in Switzerland. Women more often reported at least one persistent symptom than men (43.5% vs 32.0% of men, p<0.001) six (IQR 5-9) months after SARS-CoV-2 infection. Adjusted models showed that women with personality traits stereotypically attributed to women were most often affected by PASC (OR 2.50[1.25-4.98], p<0.001), in particular when they were living alone (OR 1.84[1.25-2.74]), had an increased stress level (OR 1.06[1.03-1.09]), had undergone higher education (OR 1.30[1.08-1.54]), preferred pre-pandemic physical greeting over verbal greeting (OR 1.71[1.44-2.03]), and had experienced an increased number of symptoms during index infection (OR 1.27[1.22-1.33]). ConclusionBesides gender- and sex-sensitive biological parameters, sociocultural variables play an important role in producing sex differences in PASC. Our results indicate that predictor variables of PASC can be easily identified without extensive diagnostic testing and are targets of interventions aiming at stress coping and social support.
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Objectives While superinfections are associated with unfavourable disease course, their impact on clinical outcomes in critically ill COVID-19 patients remains largely unknown. We aimed to investigate the burden of superinfections in COVID-19 patients. Methods In this prospective single centre cohort study in an intensive care setting patients aged [≥] 18 years with COVID-19 acute respiratory distress syndrome were assessed for concomitant microbial infections by longitudinal analysis of tracheobronchial secretions, bronchoalveolar lavages and blood. Our primary outcome was ventilator-free survival on day 28 in patients with and without clinically relevant superinfection. Further outcomes included the association of superinfection with ICU length of stay, incidence of bacteremia, viral reactivations, and fungal colonization. Results In 45 critically ill COVID-19 patients, we identified 19 patients with superinfections (42.2%) by longitudinal analysis of 433 TBS, 35 BAL and 455 blood samples, respectively. On average, superinfections were detected on day 10 after ICU admission. The most frequently isolated clinically relevant bacteria were Enterobacteriaceae, Streptococcus pneumoniae, and Pseudomonas aeruginosa. Ventilator-free survival was substantially lower in patients with superinfection (subhazard ratio 0.37, 95%-CI 0.15-0.90, p=0.028). Patients with pulmonary superinfections more often had bacteraemia, virus reactivations, yeast colonization, and needed ICU treatment for a significantly longer time. Conclusions The detection of superinfections was frequent and associated with reduced ventilator-free survival. Despite empirical antibiotic therapy, superinfections lead to an extended ICU stay in COVID 19 patients. Longitudinal microbiological sampling in COVID-19 patients could allow targeted antimicrobial therapy, and therefore minimize the use of broad-spectrum and reserve antibiotics.
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Advances in medical technology and IT infrastructure have led to increased availability of continuous patient data that allows investigation of the longitudinal progression of novel and known diseases in unprecedented detail. However, to accurately describe any underlying pathophysiology with longitudinal data, the individual patient trajectories have to be synchronized based on temporal markers. In this study, we use longitudinal data from 28 critically ill ICU COVID-19 patients to compare the commonly used alignment markers "onset of symptoms", "hospital admission" and "ICU admission" with a novel objective method based on the peak value of inflammatory marker C-reactive protein (CRP). By applying our CRP-based method to align the progression of neutrophils and lymphocytes, we were able to define a pathophysiological window that allowed further risk stratification in our COVID-19 patient cohort. Our data highlights that proper synchronization of patient data is crucial to differentiate severity subgroups and to allow reliable interpatient comparisons.
