Point-of-care lung ultrasonography for early identification of mild COVID-19: a prospective cohort of outpatients in a Swiss screening center.

OBJECTIVES: Early identification of SARS-CoV-2 infection is important to guide quarantine and reduce transmission. This study evaluates the diagnostic performance of lung ultrasound (LUS), an affordable, consumable-free point-of-care tool, for COVID-19 screening. DESIGN, SETTING AND PARTICIPANTS: This prospective observational cohort included adults presenting with cough and/or dyspnoea at a SARS-CoV-2 screening centre of Lausanne University Hospital between 31 March and 8 May 2020. INTERVENTIONS: Investigators recorded standardised LUS images and videos in 10 lung zones per patient. Two blinded independent experts reviewed LUS recording and classified abnormal findings according to prespecified criteria to investigate their predictive value to diagnose SARS-CoV-2 infection according to PCR on nasopharyngeal swabs (COVID-19 positive vs COVID-19 negative). PRIMARY AND SECONDARY OUTCOME MEASURES: We finally combined LUS and clinical findings to derive a multivariate logistic regression diagnostic score. RESULTS: Of 134 included patients, 23% (n=30/134) were COVID-19 positive and 77% (n=103/134) were COVID-19 negative; 85%, (n=114/134) cases were previously healthy healthcare workers presenting within 2-5 days of symptom onset (IQR). Abnormal LUS findings were significantly more frequent in COVID-19 positive compared with COVID-19 negative (45% vs 26%, p=0.045) and mostly consisted of focal pathologic B lines. Combining clinical findings in a multivariate logistic regression score had an area under the receiver operating curve of 80.3% to detect COVID-19, and slightly improved to 84.5% with the addition of LUS features. CONCLUSIONS: COVID-19-positive patients are significantly more likely to have lung pathology by LUS. However, LUS has an insufficient sensitivity and is not an appropriate screening tool in outpatients. LUS only adds little value to clinical features alone.

Lessons from COVID-19 syndromic surveillance through emergency department activity: a prospective time series study from western Switzerland.

OBJECTIVE: We aimed to assess if emergency department (ED) syndromic surveillance during the first and second waves of the COVID-19 outbreak could have improved our surveillance system. DESIGN AND SETTINGS: We did an observational study using aggregated data from the ED of a university hospital and public health authorities in western Switzerland. PARTICIPANTS: All patients admitted to the ED were included. PRIMARY OUTCOME MEASURE: The main outcome was intensive care unit (ICU) occupancy. We used time series methods for ED syndromic surveillance (influenza-like syndrome, droplet isolation) and usual indicators from public health authorities (new cases, proportion of positive tests in the population). RESULTS: Based on 37 319 ED visits during the COVID-19 outbreak, 1421 ED visits (3.8%) were positive for SARS-CoV-2. Patients with influenza-like syndrome or droplet isolation in the ED showed a similar correlation to ICU occupancy as confirmed cases in the general population, with a time lag of approximately 13 days (0.73, 95% CI 0.64 to 0.80; 0.79, 95% CI 0.71 to 0.86; and 0.76, 95% CI 0.67 to 0.83, respectively). The proportion of positive tests in the population showed the best correlation with ICU occupancy (0.95, 95% CI 0.85 to 0.96). CONCLUSION: ED syndromic surveillance is an effective tool to detect and monitor a COVID-19 outbreak and to predict hospital resource needs. It would have allowed to anticipate ICU occupancy by 13 days, including significant aberration detection at the beginning of the second wave.

Post­COVID­19 Syndrome in Outpatients: a Cohort Study.

BACKGROUND: After mild COVID-19, some outpatients experience persistent symptoms. However, data are scarce and prospective studies are urgently needed. OBJECTIVES: To characterize the post-COVID-19 syndrome after mild COVID-19 and identify predictors. PARTICIPANTS: Outpatients with symptoms suggestive of COVID-19 with (1) PCR-confirmed COVID-19 (COVID-positive) or (2) SARS-CoV-2 negative PCR (COVID-negative). DESIGN: Monocentric cohort study with prospective phone interview between more than 3 months to 10 months after initial visit to the emergency department and outpatient clinics. MAIN MEASURES: Data of the initial visits were extracted from the electronic medical file. Predefined persistent symptoms were assessed through a structured phone interview. Associations between long-term symptoms and PCR results, as well as predictors of persistent symptoms among COVID-positive, were evaluated by multivariate logistic regression adjusted for age, gender, smoking, comorbidities, and timing of the survey. KEY RESULTS: The study population consisted of 418 COVID-positive and 89 COVID-negative patients, mostly young adults (median age of 41 versus 36 years in COVID-positive and COVID-negative, respectively; p = 0.020) and healthcare workers (67% versus 82%; p = 0.006). Median time between the initial visit and the phone survey was 150 days in COVID-positive and 242 days in COVID-negative patients. Persistent symptoms were reported by 223 (53%) COVID-positive and 33 (37%) COVID-negative patients (p = 0.006) and proportions were stable among the periods of the phone interviews. Overall, 21% COVID-positive and 15% COVID-negative patients (p = 0.182) attended care for this purpose. Four surveyed symptoms were independently associated with COVID-19: fatigue (adjusted odds ratio 2.14, 95% CI 1.04-4.41), smell/taste disorder (26.5, 3.46-202), dyspnea (2.81, 1.10-7.16), and memory impairment (5.71, 1.53-21.3). Among COVID-positive, female gender (1.67, 1.09-2.56) and overweight/obesity (1.67, 1.10-2.56) were predictors of persistent symptoms. CONCLUSIONS: More than half of COVID-positive outpatients report persistent symptoms up to 10 months after a mild disease. Only 4 of 14 symptoms were associated with COVID-19 status. The symptoms and predictors of the post-COVID-19 syndrome need further characterization as this condition places a significant burden on society.

Size and duration of COVID-19 clusters go along with a high SARS-CoV-2 viral load: A spatio-temporal investigation in Vaud state, Switzerland.

To understand the geographical and temporal spread of SARS-CoV-2 during the first documented wave of infection in the state of Vaud, Switzerland, we analyzed clusters of positive cases using the precise residential location of 33,651 individuals tested (RT-PCR) between January 10 and June 30, 2020. We used a prospective Poisson space-time scan statistic (SaTScan) and a Modified Space-Time Density-Based Spatial Clustering of Application with Noise (MST-DBSCAN) to identify both space-time and transmission clusters, and estimated cluster duration, transmission behavior (emergence, growth, reduction, etc.) and relative risk. For each cluster, we computed the number of individuals, the median age of individuals and their viral load. Among the 1684 space-time clusters identified, 457 (27.1%) were significant (p ≤ 0.05), such that they harbored a higher relative risk of infection within the cluster than compared to regions outside the cluster. Clusters lasted a median of 11 days (IQR 7-13) and included a median of 12 individuals per cluster (IQR 5-20). The majority of significant clusters (n = 260; 56.9%) had at least one person with an extremely high viral load (>1 billion copies/ml). Those clusters were considerably larger (median of 17 infected individuals, p < 0.001) than clusters with individuals showing a viral load below 1 million copies/ml (median of three infected individuals). The highest viral loads were found in clusters with the lowest average age group considered in the investigation, while clusters with the highest average age had low to middle viral load. In 20 significant clusters, the viral load of the three first cases was below 100,000 copies/ml, suggesting that subjects with fewer than 100,000 copies/ml may still be contagious. Notably, the dynamics of transmission clusters made it possible to identify three diffusion zones, which predominantly differentiated between rural and urban areas, the latter being more prone to persistence and expansion, which may result in the emergence of new clusters nearby. The use of geographic information is key for public health decision makers in mitigating the spread of the SARS-CoV-2 virus. This study suggests that early localization of clusters may help implement targeted protective measures limiting the spread of the virus.