Reduced Olfactory Bulb Volume Accompanies Smelling Dysfunction After Mild SARS-CoV-2 Infection: The Hamburg City Health Study COVID Program

BackgroundDespite its high prevalence, the determinants of smelling impairment in COVID-19 remain opaque. Olfactory bulb volumetry has been previously established as a promising surrogate marker of smelling function in multiple otorhinolaryngological diseases. In this work, we aimed to elucidate the correspondence between olfactory bulb volume and the clinical trajectory of COVID-19-related smelling impairment. Therefore, we conducted a large-scale magnetic resonance imaging (MRI)-based investigation of individuals recovered from mainly mild to moderate COVID-19. MethodsData of 233 COVID-19 convalescents from the Hamburg City Health Study COVID Program were analyzed. Upon recruitment, patients underwent cranial MR imaging and assessment of neuropsychological testing. Automated olfactory bulb volumetry was performed on T2-weighted MR imaging data. Olfactory function was assessed longitudinally after recruitment and at follow-up via a structured questionnaire. Follow-up assessment included quantitative olfactometric testing with Sniffin Sticks. Group comparisons of olfactory bulb volume and olfactometric scores were performed between individuals with and without smelling impairment. The associations of olfactory bulb volume and neuropsychological as well as olfactometric scores were assessed via multiple linear regression. ResultsLongitudinal assessment demonstrated a declining prevalence of olfactory dysfunction from 67.6% at acute infection, 21.0% at baseline examination (on average 8.31 {+/-} 2.77 months post infection) and 17.5% at follow-up (21.8 {+/-} 3.61 months post infection). Participants with post-acute olfactory dysfunction had a significantly lower olfactory bulb volume [mm3] at scan-time than normally smelling individuals (mean {+/-} SD, baseline: 40.76 {+/-} 13.08 vs. 46.74 {+/-} 13.66, f=4.07, p=0.046; follow-up: 40.45 {+/-} 12.59 vs. 46.55 {+/-} 13.76, f=4.50, p=0.036). Olfactory bulb volume successfully predicted olfactometric scores at follow-up (rsp = 0.154, p = 0.025). Performance in neuropsychological testing was not significantly associated with the olfactory bulb volume. ConclusionsOur work demonstrates the association of smelling dysfunction and olfactory bulb integrity in a sample of individuals recovered from mainly mild to moderate COVID-19. Olfactory bulb volume was demonstrably lower in individuals with sustained smelling impairment and predicted smelling function longitudinally. Collectively, our results highlight olfactory bulb volume as a surrogate marker that may inform diagnosis and guide rehabilitation strategies in COVID-19.

Brain imaging and neuropsychological assessment of individuals recovered from mild to moderate SARS-CoV-2 infection

As SARS-CoV-2 infections have been shown to affect the central nervous system, the investigation of associated alterations of brain structure and neuropsychological sequelae is crucial to help address future health care needs. Therefore, we performed a comprehensive neuroimaging and neuropsychological assessment of 223 non-vaccinated individuals recovered from a mild to moderate SARS-CoV-2 infection (100 female/123 male, age [years], mean {+/-} SD, 55.54 {+/-} 7.07; median 9.7 months after infection) in comparison with 223 matched controls (93 female/130 male, 55.74 {+/-} 6.60) within the framework of the Hamburg City Health Study. Primary study outcomes were advanced diffusion magnetic resonance imaging (MRI) measures of white matter microstructure, cortical thickness, white matter hyperintensity load and neuropsychological test scores. Among all 11 MRI markers tested, significant differences were found in global measures of mean diffusivity and extracellular free-water which were elevated in the white matter of post-SARS-CoV-2 individuals comparing to matched controls (free-water: 0.148 {+/-} 0.018 vs. 0.142 {+/-} 0.017, P<.001; mean diffusivity [10-3 mm2/s]: 0.747 {+/-} 0.021 vs. 0.740 {+/-} 0.020, P<.001). Group classification accuracy based on diffusion imaging markers was up to 80%. Neuropsychological test scores did not significantly differ between groups. Collectively, our findings suggest that subtle changes in white matter extracellular water content last beyond the acute infection with SARS-CoV-2. However, in our sample, a mild to moderate SARS-CoV-2 infection was not associated with neuropsychological deficits, significant changes in cortical structure or vascular lesions several months after recovery. External validation of our findings and longitudinal follow-up investigations are needed. Significance statementIn this case-control study, we demonstrate that non-vaccinated individuals recovered from a mild to moderate SARS-CoV-2 infection show significant alterations of the cerebral white matter identified by diffusion weighted imaging, such as global increases in extracellular free-water and mean diffusivity. Despite the observed brain white matter alterations in this sample, a mild to moderate SARS-CoV-2 infection was not associated with worse cognitive functions within the first year after recovery. Collectively, our findings indicate the presence of a prolonged neuroinflammatory response to the initial viral infection. Further longitudinal research is necessary to elucidate the link between brain alterations and clinical features of post-SARS-CoV-2 individuals.

Sex- and Gender-specific Risk Factors of Post-COVID-19 Syndrome: A Population-based Cohort Study in Switzerland

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.

Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings

Objective@#To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. @*Materials and Methods@#All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients’ needs for intensive care (yeso) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. @*Results@#While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79–0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77–0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85–0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66–0.88). @*Conclusion@#Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings

Objective@#To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management. @*Materials and Methods@#All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients’ needs for intensive care (yeso) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans. @*Results@#While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79–0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77–0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85–0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66–0.88). @*Conclusion@#Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.