BackgroundThe number of medical studies performed through online surveys has increased dramatically in recent years. Despite their numerous advantages (eg, sample size, facilitated access to individuals presenting stigmatizing issues), selection bias may exist in online surveys. However, evidence on the representativeness of self-selected samples in online studies is patchy.ObjectiveOur objective was to explore the representativeness of a self-selected sample of online gamers using online players’ virtual characters (avatars).MethodsAll avatars belonged to individuals playing World of Warcraft (WoW), currently the most widely used online game. Avatars’ characteristics were defined using various games’ scores, reported on the WoW’s official website, and two self-selected samples from previous studies were compared with a randomly selected sample of avatars.ResultsWe used scores linked to 1240 avatars (762 from the self-selected samples and 478 from the random sample). The two self-selected samples of avatars had higher scores on most of the assessed variables (except for guild membership and exploration). Furthermore, some guilds were overrepresented in the self-selected samples.ConclusionsOur results suggest that more proficient players or players more involved in the game may be more likely to participate in online surveys. Caution is needed in the interpretation of studies based on online surveys that used a self-selection recruitment procedure. Epidemiological evidence on the reduced representativeness of sample of online surveys is warranted.

Background The COVID-19 pandemic has led to surges of patients presenting to emergency departments (ED) and potentially overwhelming health systems. Objective This study aimed to assess the predictive accuracy of host biomarkers at clinical presentation to the ED for adverse outcome. Methods Prospective observational study of PCR-confirmed COVID-19 patients in the ED of a Swiss hospital. Concentrations of inflammatory and endothelial dysfunction biomarkers were determined at clinical presentation. We evaluated the accuracy of clinical signs and these biomarkers in predicting 30-day intubation/mortality, and oxygen requirement by calculating the area under the receiver operating characteristic curve (AUROC) and by classification and regression tree analysis. Results Of 76 COVID-19 patients included, 24 were outpatients or hospitalized without oxygen requirement, 35 hospitalized with oxygen requirement and 17 intubated/died. We found that soluble triggering receptor expressed on myeloid cells (sTREM-1) had the best prognostic accuracy for 30-day intubation/mortality (AUROC 0.86; 95% CI 0.77-0.95) and interleukin-6 (IL-6) measured at presentation to the ED had the best accuracy for 30-day oxygen requirement (AUROC 0.84; 95% CI 0.74-0.94) .An algorithm based on respiratory rate and sTREM-1 predicted 30-day intubation/mortality with 94% sensitivity and 0.1 NLR. An IL-6-based algorithm had 98% sensitivity and 0.04 negative likelihood ratio (NLR) for 30-day oxygen requirement. Conclusion sTREM-1 and IL-6 concentrations in COVID-19 in the ED have good predictive accuracy for intubation/mortality and oxygen requirement. sTREM-1- and IL-6-based algorithms are highly sensitive to identify patients with adverse outcome and could serve as early triage tools.

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. Supplementary Information The online version contains supplementary material available at 10.1007/s11606-021-07242-1.

Quality of smartphone apps related to panic: smartphone apps have a growing role in health care. This study assessed the quality of English-language apps for panic disorder (PD) and compared paid and free apps. Keywords related to PD were entered into the Google Play Store search engine. Apps were assessed using the following quality indicators: accountability, interactivity, self-help score (the potential of smartphone apps to help users in daily life), and evidence-based content quality. The Brief DISCERN score and the criteria of the “Health on the Net” label were also used as content quality indicators as well as the number of downloads. Of 247 apps identified, 52 met all inclusion criteria. The content quality and self-help scores of these PD apps were poor. None of the assessed indicators were associated with payment status or number of downloads. Multiple linear regressions showed that the Brief DISCERN score significantly predicted the content quality and self-help scores. Poor content quality and self-help scores of PD smartphone apps highlight the gap between their technological potential and the overall quality of available products.