Validation of sTREM-1 and IL-6 based algorithms for outcome prediction of COVID-19.

BACKGROUND: A prospective observational cohort study of COVID-19 patients in a single Emergency Department (ED) showed that sTREM-1- and IL-6-based algorithms were highly predictive of adverse outcome (Van Singer et al. J Allergy Clin Immunol 2021). We aim to validate the performance of these algorithms at ED presentation. METHODS: This multicentric prospective observational study of PCR-confirmed COVID-19 adult patients was conducted in the ED of three Swiss hospitals. Data of the three centers were retrospectively completed and merged. We determined the predictive accuracy of the sTREM-1-based algorithm for 30-day intubation/mortality. We also determined the performance of the IL-6-based algorithm using data from one center for 30-day oxygen requirement. RESULTS: 373 patients were included in the validation cohort, 139 (37%) in Lausanne, 93 (25%) in St.Gallen and 141 (38%) in EOC. Overall, 18% (93/373) patients died or were intubated by day 30. In Lausanne, 66% (92/139) patients required oxygen by day 30. The predictive accuracy of sTREM-1 and IL-6 were similar compared to the derivation cohort. The sTREM-1-based algorithm confirmed excellent sensitivity (90% versus 100% in the derivation cohort) and negative predictive value (94% versus 100%) for 30-day intubation/mortality. The IL-6-based algorithm performance was acceptable with a sensitivity of 85% versus 98% in the derivation cohort and a negative predictive value of 60% versus 92%. CONCLUSION: The sTREM-1 algorithm demonstrated good reproducibility. A prospective randomized controlled trial, comparing outcomes with and without the algorithm, is necessary to assess its safety and impact on hospital and ICU admission rates. The IL-6 algorithm showed acceptable validity in a single center and need additional validation before widespread implementation.

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.

Factors Influencing the Implementation of Antimicrobial Stewardship in Primary Care: A Narrative Review.

Antimicrobial resistance (AMR) is directly driven by inappropriate use of antibiotics. Although the majority of antibiotics (an estimated 80%) are consumed in primary care settings, antimicrobial stewardship (AMS) activities in primary care remain underdeveloped and factors influencing their implementation are poorly understood. This can result in promising stewardship activities having little-to-no real-world impact. With this narrative review, we aim to identify and summarize peer-reviewed literature reporting on (1) the nature and impact of AMS interventions in primary care and (2) the individual and contextual factors influencing their implementation. Reported activities included AMS at different contextual levels (individual, collective and policy). AMS activities being often combined, it is difficult to evaluate them as stand-alone interventions. While some important individual and contextual factors were reported (difficulty to reach physicians leading to a low uptake of interventions, tight workflow of physicians requiring implementation of flexible and brief interventions and AMS as a unique opportunity to strengthen physician-patients relationship), this review identified a paucity of information in the literature about the factors that support or hinder implementation of AMS in primary care settings. In conclusion, identifying multilevel barriers and facilitators for AMS uptake is an essential step to explore before implementing primary care AMS interventions.

COVID-19 risk stratification algorithms based on sTREM-1 and IL-6 in emergency department.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has led to surges of patients presenting to emergency departments (EDs) and potentially overwhelming health systems. OBJECTIVE: We sought 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 and by classification and regression tree analysis. RESULTS: Of 76 included patients with COVID-19, 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 had the best prognostic accuracy for 30-day intubation/mortality (area under the receiver-operating characteristic curve, 0.86; 95% CI, 0.77-0.95) and IL-6 measured at presentation to the ED had the best accuracy for 30-day oxygen requirement (area under the receiver-operating characteristic curve, 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 negative likelihood ratio. An IL-6-based algorithm had 98% sensitivity and 0.04 negative likelihood ratio for 30-day oxygen requirement. CONCLUSIONS: 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.

Quality of Smartphone Apps Related to Alcohol Use Disorder.

BACKGROUND: Apps for smartphones are opening an important range of opportunities for improving the care of people with alcohol use disorders (AUDs). AIMS: This study aimed to evaluate the quality of English language apps for AUDs and to compare paid and free apps. The keywords 'alcohol', 'alcohol addiction', 'alcohol help' and 'stop drinking' were entered into the iTunes Store search engine. Apps were evaluated using a standardized assessment designed to rate the quality of apps in terms of accountability, interactivity, self-help score and evidence-based content. The Brief DISCERN score and the criteria of the 'Health on the Net' label were also used as content quality indicators. RESULTS AND CONCLUSIONS: Of the 137 unique apps identified, 52 met the inclusion criteria. Overall, the content quality and self-help scores of these AUD apps were poor. The main quality indicators were not linked to payment status. Multiple linear regressions showed that the Brief DISCERN score significantly predicted content quality. Poor content quality and self-help scores of AUD smartphone apps underline the gap between their potential promises and the overall quality of available products in stores. The quality indicators used in the present study may be used for further app developments.

Quality of Smartphone Apps Related to Panic Disorder.

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.

Does self-selection affect samples' representativeness in online surveys? An investigation in online video game research.

BACKGROUND: The 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. OBJECTIVE: Our objective was to explore the representativeness of a self-selected sample of online gamers using online players' virtual characters (avatars). METHODS: All 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. RESULTS: We 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. CONCLUSIONS: Our 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.