Comparison of post-COVID-19 symptoms in patients infected with the SARS-CoV-2 variants delta and omicron-results of the Cross-Sectoral Platform of the German National Pandemic Cohort Network (NAPKON-SUEP).

PURPOSE: The influence of new SARS-CoV-2 variants on the post-COVID-19 condition (PCC) remains unanswered. Therefore, we examined the prevalence and predictors of PCC-related symptoms in patients infected with the SARS-CoV-2 variants delta or omicron. METHODS: We compared prevalences and risk factors of acute and PCC-related symptoms three months after primary infection (3MFU) between delta- and omicron-infected patients from the Cross-Sectoral Platform of the German National Pandemic Cohort Network. Health-related quality of life (HrQoL) was determined by the EQ-5D-5L index score and trend groups were calculated to describe changes of HrQoL between different time points. RESULTS: We considered 758 patients for our analysis (delta: n = 341; omicron: n = 417). Compared with omicron patients, delta patients had a similar prevalence of PCC at the 3MFU (p = 0.354), whereby fatigue occurred most frequently (n = 256, 34%). HrQoL was comparable between the groups with the lowest EQ-5D-5L index score (0.75, 95% CI 0.73-0.78) at disease onset. While most patients (69%, n = 348) never showed a declined HrQoL, it deteriorated substantially in 37 patients (7%) from the acute phase to the 3MFU of which 27 were infected with omicron. CONCLUSION: With quality-controlled data from a multicenter cohort, we showed that PCC is an equally common challenge for patients infected with the SARS-CoV-2 variants delta and omicron at least for the German population. Developing the EQ-5D-5L index score trend groups showed that over two thirds of patients did not experience any restrictions in their HrQoL due to or after the SARS-CoV-2 infection at the 3MFU. CLINICAL TRAIL REGISTRATION: The cohort is registered at ClinicalTrials.gov since February 24, 2021 (Identifier: NCT04768998).

The AI ethics of digital COVID-19 diagnosis and their legal, medical, technological, and operational managerial implications.

The COVID-19 pandemic has given rise to a broad range of research from fields alongside and beyond the core concerns of infectiology, epidemiology, and immunology. One significant subset of this work centers on machine learning-based approaches to supporting medical decision-making around COVID-19 diagnosis. To date, various challenges, including IT issues, have meant that, notwithstanding this strand of research on digital diagnosis of COVID-19, the actual use of these methods in medical facilities remains incipient at best, despite their potential to relieve pressure on scarce medical resources, prevent instances of infection, and help manage the difficulties and unpredictabilities surrounding the emergence of new mutations. The reasons behind this research-application gap are manifold and may imply an interdisciplinary dimension. We argue that the discipline of AI ethics can provide a framework for interdisciplinary discussion and create a roadmap for the application of digital COVID-19 diagnosis, taking into account all disciplinary stakeholders involved. This article proposes such an ethical framework for the practical use of digital COVID-19 diagnosis, considering legal, medical, operational managerial, and technological aspects of the issue in accordance with our diverse research backgrounds and noting the potential of the approach we set out here to guide future research.

Influence of artificial intelligence on the diagnostic performance of endoscopists in the assessment of Barrett's esophagus: a tandem randomized and video trial.

BACKGROUND: This study evaluated the effect of an artificial intelligence (AI)-based clinical decision support system on the performance and diagnostic confidence of endoscopists in their assessment of Barrett's esophagus (BE). METHODS: 96 standardized endoscopy videos were assessed by 22 endoscopists with varying degrees of BE experience from 12 centers. Assessment was randomized into two video sets: group A (review first without AI and second with AI) and group B (review first with AI and second without AI). Endoscopists were required to evaluate each video for the presence of Barrett's esophagus-related neoplasia (BERN) and then decide on a spot for a targeted biopsy. After the second assessment, they were allowed to change their clinical decision and confidence level. RESULTS: AI had a stand-alone sensitivity, specificity, and accuracy of 92.2%, 68.9%, and 81.3%, respectively. Without AI, BE experts had an overall sensitivity, specificity, and accuracy of 83.3%, 58.1%, and 71.5%, respectively. With AI, BE nonexperts showed a significant improvement in sensitivity and specificity when videos were assessed a second time with AI (sensitivity 69.8% [95%CI 65.2%-74.2%] to 78.0% [95%CI 74.0%-82.0%]; specificity 67.3% [95%CI 62.5%-72.2%] to 72.7% [95%CI 68.2%-77.3%]). In addition, the diagnostic confidence of BE nonexperts improved significantly with AI. CONCLUSION: BE nonexperts benefitted significantly from additional AI. BE experts and nonexperts remained significantly below the stand-alone performance of AI, suggesting that there may be other factors influencing endoscopists' decisions to follow or discard AI advice.

Perspectives and awareness of endoscopy healthcare professionals on sustainable practices in gastrointestinal endoscopy: results of the LEAFGREEN survey.

BACKGROUND: Gastrointestinal (GI) endoscopy is one of healthcare's main contributors to climate change. We aimed to assess healthcare professionals' attitudes and the perceived barriers to implementation of sustainable GI endoscopy. METHODS: The LEAFGREEN web-based survey was a cross-sectional study conducted by the European Society of Gastrointestinal Endoscopy (ESGE) Green Endoscopy Working Group. The questionnaire comprised 39 questions divided into five sections (respondent demographics; climate change and sustainability beliefs; waste and resource management; single-use endoscopes and accessories; education and research). The survey was available via email to all active members of the ESGE and the European Society of Gastroenterology and Endoscopy Nurses and Associates (ESGENA) in March 2023. RESULTS: 407 respondents participated in the survey (11% response rate). Most participants (86%) agreed climate change is real and anthropogenic, but one-third did not consider GI endoscopy to be a significant contributor to climate change. Improvement in the appropriateness of endoscopic procedures (41%) and reduction in single-use accessories (34%) were considered the most important strategies to reduce the environmental impact of GI endoscopy. Respondents deemed lack of institutional support and knowledge from staff to be the main barriers to sustainable endoscopy. Strategies to reduce unnecessary GI endoscopic procedures and comparative studies of single-use versus reusable accessories were identified as research priorities. CONCLUSIONS: In this survey, ESGE and ESGENA members acknowledge climate change as a major threat to humanity. Further improvement in sustainability beliefs and professional attitudes, reduction in inappropriate GI endoscopy, and rational use of single-use accessories and endoscopes are critically required.

Covid-19 triage in the emergency department 2.0: how analytics and AI transform a human-made algorithm for the prediction of clinical pathways.

The Covid-19 pandemic has pushed many hospitals to their capacity limits. Therefore, a triage of patients has been discussed controversially primarily through an ethical perspective. The term triage contains many aspects such as urgency of treatment, severity of the disease and pre-existing conditions, access to critical care, or the classification of patients regarding subsequent clinical pathways starting from the emergency department. The determination of the pathways is important not only for patient care, but also for capacity planning in hospitals. We examine the performance of a human-made triage algorithm for clinical pathways which is considered a guideline for emergency departments in Germany based on a large multicenter dataset with over 4,000 European Covid-19 patients from the LEOSS registry. We find an accuracy of 28 percent and approximately 15 percent sensitivity for the ward class. The results serve as a benchmark for our extensions including an additional category of palliative care as a new label, analytics, AI, XAI, and interactive techniques. We find significant potential of analytics and AI in Covid-19 triage regarding accuracy, sensitivity, and other performance metrics whilst our interactive human-AI algorithm shows superior performance with approximately 73 percent accuracy and up to 76 percent sensitivity. The results are independent of the data preparation process regarding the imputation of missing values or grouping of comorbidities. In addition, we find that the consideration of an additional label palliative care does not improve the results.

Evaluation of a single-use gastroscope in patients presenting with suspected upper gastrointestinal hemorrhage: a pilot feasibility study (One-Scope I).

BACKGROUND : Outbreaks of multidrug-resistant bacteria due to contaminated duodenoscopes and infection risks during the COVID-19 pandemic have driven the development of single-use endoscopes. The first single-use gastroscope is now available in Europe. Besides waste disposal and cost issues, the infection risk and performance remain unclear. We aimed to evaluate a single-use gastroscope in patients with signs of upper gastrointestinal bleeding. METHODS : 20 consecutive patients presenting with clinical signs of upper gastrointestinal bleeding between October and November 2022 were included in this case series. The primary aim was technical success, defined as access to the descending duodenum and adequate assessment of the upper gastrointestinal tract for the presence of a bleeding site. RESULTS : The primary aim was achieved in 19/20 patients (95 %). The bleeding site was identified in 18 patients. A therapeutic intervention was performed in six patients (two cap-mounted clips, one standard hemostatic clip, two variceal band ligations, one hemostatic powder, two adrenaline injections); technical and clinical success were achieved in all six patients. Two crossovers to a standard gastroscope occurred. CONCLUSIONS : Use of single-use gastroscopes may be feasible for patients presenting for urgent endoscopic evaluation and treatment of upper gastrointestinal bleeding.

Performance of the VitaPCR rapid molecular test for SARS-CoV-2 screening at hospital admission.

OBJECTIVE: To evaluate the diagnostic accuracy of rapid VitaPCR™ (Credo) assay as screening test in emergency department (ED) patients prior to transfer or medical interventions. METHODS: In this prospective study 6642 oropharyngeal swabs from nonpreselected ED patients were tested for SARS-CoV-2 with (1) extraction-free VitaPCR and (2) extraction-based reference assays (Aptima®, cobas®, Xpert®Xpress). RESULTS: The median TAT of VitaPCR was 47 minutes (IQR: 38-59), while reference assays required 6.2 hours (IQR: 4.4-13.3). VitaPCR's sensitivity, specificity, PPV and NPV was 77.9%, 99.9%, 97.9%, and 98.9% in relation to Hologic Panther TMA; 78.3%, 99.8%, 96.4%, and 98.5% compared to Roche cobas6800 PCR; 71.2%, 99.2%, 94.9%, and 94.3% using Cepheid GeneXpert PCR as reference. CONCLUSION: High-sensitivity testing is needed to limit nosocomial spread and identify asymptomatic COVID-19 patients. However, time advantage of the VitaPCR must be weighed against its significantly lower sensitivity, especially when used in high-risk environments such as hospitals.

Diagnostic Value of Adding Magnifying Chromoendoscopy to Magnifying Narrow-Band Imaging Endoscopy for Colorectal Polyps.

BACKGROUND & AIMS: This study examined the additional value of magnifying chromoendoscopy (MCE) on magnifying narrow-band imaging endoscopy (M-NBI) in the optical diagnosis of colorectal polyps. METHODS: A multicenter prospective study was conducted at 9 facilities in Japan and Germany. Patients with colorectal polyps scheduled for resection were included. Optical diagnosis was performed by M-NBI first, followed by MCE. Both diagnoses were made in real time. MCE was performed on all type 2B lesions classified according to the Japan NBI Expert Team classification and other lesions at the discretion of endoscopists. The diagnostic accuracy and confidence of M-NBI and MCE for colorectal cancer (CRC) with deep invasion (≥T1b) were compared on the basis of histologic findings after resection. RESULTS: In total, 1173 lesions were included between February 2018 and December 2020, with 654 (5 hyperplastic polyp/sessile serrated lesion, 162 low-grade dysplasia, 403 high-grade dysplasia, 97 T1 CRCs, and 32 ≥T2 CRCs) examined using MCE after M-NBI. In the diagnostic accuracy for predicting CRC with deep invasion, sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for M-NBI were 63.1%, 94.2%, 61.6%, 94.5%, and 90.2%, respectively, and for MCE they were 77.4%, 93.2%, 62.5%, 96.5%, and 91.1%, respectively. The sensitivity was significantly higher in MCE (P < .001). However, these additional values were limited to lesions with low confidence in M-NBI or the ones diagnosed as ≥T1b CRC by M-NBI. CONCLUSIONS: In this multicenter prospective study, we demonstrated the additional value of MCE on M-NBI. We suggest that additional MCE be recommended for lesions with low confidence or the ones diagnosed as ≥T1b CRC. Trials registry number: UMIN000031129.

Detection of duodenal villous atrophy on endoscopic images using a deep learning algorithm.

BACKGROUND AND AIMS: Celiac disease with its endoscopic manifestation of villous atrophy (VA) is underdiagnosed worldwide. The application of artificial intelligence (AI) for the macroscopic detection of VA at routine EGD may improve diagnostic performance. METHODS: A dataset of 858 endoscopic images of 182 patients with VA and 846 images from 323 patients with normal duodenal mucosa was collected and used to train a ResNet18 deep learning model to detect VA. An external dataset was used to test the algorithm, in addition to 6 fellows and 4 board-certified gastroenterologists. Fellows could consult the AI algorithm's result during the test. From their consultation distribution, a stratification of test images into "easy" and "difficult" was performed and used for classified performance measurement. RESULTS: External validation of the AI algorithm yielded values of 90%, 76%, and 84% for sensitivity, specificity, and accuracy, respectively. Fellows scored corresponding values of 63%, 72%, and 67% and experts scored 72%, 69%, and 71%, respectively. AI consultation significantly improved all trainee performance statistics. Although fellows and experts showed significantly lower performance for difficult images, the performance of the AI algorithm was stable. CONCLUSIONS: In this study, an AI algorithm outperformed endoscopy fellows and experts in the detection of VA on endoscopic still images. AI decision support significantly improved the performance of nonexpert endoscopists. The stable performance on difficult images suggests a further positive add-on effect in challenging cases.

Factors associated with an increased risk of SARS-CoV-2 infection in healthcare workers in aerosol-generating disciplines.

BACKGROUND: Healthcare workers (HCWs) are at a high risk of SARS-CoV-2 infection due to exposure to potentially infectious material, especially during aerosol-generating procedures (AGP). We aimed to investigate risk factors for SARS-CoV-2 infection among HCWs in medical disciplines with AGP. METHODS: A nationwide questionnaire-based study in private practices and hospital settings was conducted between 12/16/2020 and 01/24/2021. Data on SARS-CoV-2 infections among HCWs and potential risk factors of infection were investigated. RESULTS: 2070 healthcare facilities with 25113 employees were included in the study. The overall infection rate among HCWs was 4.7%. Multivariate analysis showed that regions with higher incidence rates had a significantly increased risk of infection. Furthermore, hospital setting and HCWs in gastrointestinal endoscopy (GIE) had more than double the risk of infection (OR 2.63; 95% CI 2.50-2.82, p<0.01 and OR 2.35; 95% CI 2.25-2.50, p<0.01). For medical facilities who treated confirmed SARS-CoV-2 cases, there was a tendency towards higher risk of infection (OR 1.39; 95% CI 1.11-1.63, p=0.068). CONCLUSION: Both factors within and outside medical facilities appear to be associated with an increased risk of infection among HCWs. Therefore, GIE and healthcare delivery setting were related to increased infection rates. Regions with higher SARS-CoV-2 incidence rates were also significantly associated with increased risk of infection.

Impact of age and gender on lymphocyte subset counts in patients with COVID-19.

In symptomatic patients with acute Coronavirus disease 2019 (COVID-19), lymphocytopenia is one of the most prominent laboratory findings. However, to date age and gender have not been considered in assessment of COVID-19-related cell count alterations. In this study, the impact of COVID-19 as well as age and gender on a large variety of lymphocyte subsets was analyzed in 33 COVID-19 patients and compared with cell counts in 50 healthy humans. We confirm that cell counts of total lymphocytes, B, NK, cytotoxic and helper T cells are reduced in patients with severe COVID-19, and this tendency was observed in patients with moderate COVID-19. Decreased cell counts were also found in all subsets of these cell types, except for CD4+ and CD8+ effector memory RA+ (EMRA) and terminal effector CD8+ cells. In multivariate analysis however, we show that in addition to COVID-19, there is an age-dependent reduction of total, central memory (CM), and early CD8+ cell subsets, as well as naïve, CM, and regulatory CD4+ cell subsets. Remarkably, reduced naïve CD8+ cell counts could be attributed to age alone, and not to COVID-19. By contrast, decreases in other subsets could be largely attributed to COVID-19, and only partly to age. In addition to COVID-19, male gender was a major factor influencing lower counts of CD3+ and CD4+ lymphocyte numbers. Our study confirms that cell counts of lymphocytes and their subsets are reduced in patients with COVID-19, but that age and gender must be considered when interpreting the altered cell counts.

One year of COVID-19 pandemic: Health care workers' infection rates and economical burden in medical facilities for oral and maxillofacial surgery.

The aim of this study was to create an overview on the COVID-associated burdens faced by the oral and maxillofacial surgery (OMS) workforce during 1 year of the pandemic. OMS hospitals and private practices nationwide were surveyed regarding health care worker (HCW) screening, infection status, pre-interventional testing, personal protective equipment (PPE), and economic impact. Participants were recruited via the German Society for Oral and Maxillofacial Surgery. A total of 11 hospitals (416 employees) and 55 private practices (744 employees) participated. The HCW infection rate was significantly higher in private practices than in clinics (4.7% vs. 1.4%, p<0.01), although most infections in HCW occurred in private environment (hospitals 88.2%, private practice 66.7%). Pre-interventional testing was performed significantly less for outpatients in private practices than in hospitals (90.7% vs. 36.4%, p<0.01). Polymerase chain reaction (PCR) was used significantly more for inpatients in hospitals than in private practices (100.0% vs. 27.3%, p<0.01). FFP2/3 use rose significantly in hospitals (0% in second quarter vs. 46% in fourth quarter, p<0.05) and private practices (15% in second quarter vs. 38% in fourth quarter, p<0.01). The decrease in procedures (≤50%) was significantly higher in hospitals than in private practices (90.9% vs. 40.0%, p<0.01). Despite higher infection rates in private practices, declining procedures and revenue affected hospitals more. Future COVID-related measures must adjust the infrastructure especially for hospitals to prevent further straining of staff and finances.

Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm.

In this study, we aimed to develop an artificial intelligence clinical decision support solution to mitigate operator-dependent limitations during complex endoscopic procedures such as endoscopic submucosal dissection and peroral endoscopic myotomy, for example, bleeding and perforation. A DeepLabv3-based model was trained to delineate vessels, tissue structures and instruments on endoscopic still images from such procedures. The mean cross-validated Intersection over Union and Dice Score were 63% and 76%, respectively. Applied to standardised video clips from third-space endoscopic procedures, the algorithm showed a mean vessel detection rate of 85% with a false-positive rate of 0.75/min. These performance statistics suggest a potential clinical benefit for procedure safety, time and also training.

The German National Pandemic Cohort Network (NAPKON): rationale, study design and baseline characteristics.

The German government initiated the Network University Medicine (NUM) in early 2020 to improve national research activities on the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic. To this end, 36 German Academic Medical Centers started to collaborate on 13 projects, with the largest being the National Pandemic Cohort Network (NAPKON). The NAPKON's goal is creating the most comprehensive Coronavirus Disease 2019 (COVID-19) cohort in Germany. Within NAPKON, adult and pediatric patients are observed in three complementary cohort platforms (Cross-Sectoral, High-Resolution and Population-Based) from the initial infection until up to three years of follow-up. Study procedures comprise comprehensive clinical and imaging diagnostics, quality-of-life assessment, patient-reported outcomes and biosampling. The three cohort platforms build on four infrastructure core units (Interaction, Biosampling, Epidemiology, and Integration) and collaborations with NUM projects. Key components of the data capture, regulatory, and data privacy are based on the German Centre for Cardiovascular Research. By April 01, 2022, 34 university and 40 non-university hospitals have enrolled 5298 patients with local data quality reviews performed on 4727 (89%). 47% were female, the median age was 52 (IQR 36-62-) and 50 pediatric cases were included. 44% of patients were hospitalized, 15% admitted to an intensive care unit, and 12% of patients deceased while enrolled. 8845 visits with biosampling in 4349 patients were conducted by April 03, 2022. In this overview article, we summarize NAPKON's design, relevant milestones including first study population characteristics, and outline the potential of NAPKON for German and international research activities.Trial registration https://clinicaltrials.gov/ct2/show/NCT04768998 . https://clinicaltrials.gov/ct2/show/NCT04747366 . https://clinicaltrials.gov/ct2/show/NCT04679584.

Hospitalized patients dying with SARS-CoV-2 infection-An analysis of patient characteristics and management in ICU and general ward of the LEOSS registry.

BACKGROUND: COVID-19 is a severe disease with a high need for intensive care treatment and a high mortality rate in hospitalized patients. The objective of this study was to describe and compare the clinical characteristics and the management of patients dying with SARS-CoV-2 infection in the acute medical and intensive care setting. METHODS: Descriptive analysis of dying patients enrolled in the Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS), a non-interventional cohort study, between March 18 and November 18, 2020. Symptoms, comorbidities and management of patients, including palliative care involvement, were compared between general ward and intensive care unit (ICU) by univariate analysis. RESULTS: 580/4310 (13%) SARS-CoV-2 infected patients died. Among 580 patients 67% were treated on ICU and 33% on a general ward. The spectrum of comorbidities and symptoms was broad with more comorbidities (≥ four comorbidities: 52% versus 25%) and a higher age distribution (>65 years: 98% versus 70%) in patients on the general ward. 69% of patients were in an at least complicated phase at diagnosis of the SARS-CoV-2 infection with a higher proportion of patients in a critical phase or dying the day of diagnosis treated on ICU (36% versus 11%). While most patients admitted to ICU came from home (71%), patients treated on the general ward came likewise from home and nursing home (44% respectively) and were more frequently on palliative care before admission (29% versus 7%). A palliative care team was involved in dying patients in 15%. Personal contacts were limited but more often documented in patients treated on ICU (68% versus 47%). CONCLUSION: Patients dying with SARS-CoV-2 infection suffer from high symptom burden and often deteriorate early with a demand for ICU treatment. Therefor a demand for palliative care expertise with early involvement seems to exist.

An artificial intelligence algorithm is highly accurate for detecting endoscopic features of eosinophilic esophagitis.

The endoscopic features associated with eosinophilic esophagitis (EoE) may be missed during routine endoscopy. We aimed to develop and evaluate an Artificial Intelligence (AI) algorithm for detecting and quantifying the endoscopic features of EoE in white light images, supplemented by the EoE Endoscopic Reference Score (EREFS). An AI algorithm (AI-EoE) was constructed and trained to differentiate between EoE and normal esophagus using endoscopic white light images extracted from the database of the University Hospital Augsburg. In addition to binary classification, a second algorithm was trained with specific auxiliary branches for each EREFS feature (AI-EoE-EREFS). The AI algorithms were evaluated on an external data set from the University of North Carolina, Chapel Hill (UNC), and compared with the performance of human endoscopists with varying levels of experience. The overall sensitivity, specificity, and accuracy of AI-EoE were 0.93 for all measures, while the AUC was 0.986. With additional auxiliary branches for the EREFS categories, the AI algorithm (AI-EoE-EREFS) performance improved to 0.96, 0.94, 0.95, and 0.992 for sensitivity, specificity, accuracy, and AUC, respectively. AI-EoE and AI-EoE-EREFS performed significantly better than endoscopy beginners and senior fellows on the same set of images. An AI algorithm can be trained to detect and quantify endoscopic features of EoE with excellent performance scores. The addition of the EREFS criteria improved the performance of the AI algorithm, which performed significantly better than endoscopists with a lower or medium experience level.

Artificial Intelligence-Based Detection of Pneumonia in Chest Radiographs.

Artificial intelligence is gaining increasing relevance in the field of radiology. This study retrospectively evaluates how a commercially available deep learning algorithm can detect pneumonia in chest radiographs (CR) in emergency departments. The chest radiographs of 948 patients with dyspnea between 3 February and 8 May 2020, as well as 15 October and 15 December 2020, were used. A deep learning algorithm was used to identify opacifications associated with pneumonia, and the performance was evaluated by using ROC analysis, sensitivity, specificity, PPV and NPV. Two radiologists assessed all enrolled images for pulmonal infection patterns as the reference standard. If consolidations or opacifications were present, the radiologists classified the pulmonal findings regarding a possible COVID-19 infection because of the ongoing pandemic. The AUROC value of the deep learning algorithm reached 0.923 when detecting pneumonia in chest radiographs with a sensitivity of 95.4%, specificity of 66.0%, PPV of 80.2% and NPV of 90.8%. The detection of COVID-19 pneumonia in CR by radiologists was achieved with a sensitivity of 50.6% and a specificity of 73%. The deep learning algorithm proved to be an excellent tool for detecting pneumonia in chest radiographs. Thus, the assessment of suspicious chest radiographs can be purposefully supported, shortening the turnaround time for reporting relevant findings and aiding early triage.

Obesity and Impaired Metabolic Health Increase Risk of COVID-19-Related Mortality in Young and Middle-Aged Adults to the Level Observed in Older People: The LEOSS Registry.

Advanced age, followed by male sex, by far poses the greatest risk for severe COVID-19. An unresolved question is the extent to which modifiable comorbidities increase the risk of COVID-19-related mortality among younger patients, in whom COVID-19-related hospitalization strongly increased in 2021. A total of 3,163 patients with SARS-COV-2 diagnosis in the Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) cohort were studied. LEOSS is a European non-interventional multi-center cohort study established in March 2020 to investigate the epidemiology and clinical course of SARS-CoV-2 infection. Data from hospitalized patients and those who received ambulatory care, with a positive SARS-CoV-2 test, were included in the study. An additive effect of obesity, diabetes and hypertension on the risk of mortality was observed, which was particularly strong in young and middle-aged patients. Compared to young and middle-aged (18-55 years) patients without obesity, diabetes and hypertension (non-obese and metabolically healthy; n = 593), young and middle-aged adult patients with all three risk parameters (obese and metabolically unhealthy; n = 31) had a similar adjusted increased risk of mortality [OR 7.42 (95% CI 1.55-27.3)] as older (56-75 years) non-obese and metabolically healthy patients [n = 339; OR 8.21 (95% CI 4.10-18.3)]. Furthermore, increased CRP levels explained part of the elevated risk of COVID-19-related mortality with age, specifically in the absence of obesity and impaired metabolic health. In conclusion, the modifiable risk factors obesity, diabetes and hypertension increase the risk of COVID-19-related mortality in young and middle-aged patients to the level of risk observed in advanced age.

A Simulation-Based Cost-Effectiveness Analysis of Severe Acute Respiratory Syndrome Coronavirus 2 Infection Prevention Strategies for Visitors of Healthcare Institutions.

OBJECTIVES: The aim is to quantitatively evaluate different infection prevention strategies in the context of hospital visitor management during pandemics and to provide a decision support system for strategic and operational decisions based on this evaluation. METHODS: A simulation-based cost-effectiveness analysis is applied to the data of a university hospital in Southern Germany and published COVID-19 research. The performance of different hospital visitor management strategies is evaluated by several decision-theoretic methods with varying objective functions. RESULTS: Appropriate visitor restrictions and infection prevention measures can reduce additional infections and costs caused by visitors of healthcare institutions by >90%. The risk of transmission of severe acute respiratory syndrome coronavirus 2 by visitors of terminal care (ie, palliative care) patients can be reduced almost to 0 if appropriate infection prevention measures are implemented. Antigen tests do not seem to be beneficial from both a cost and an effectiveness perspective. CONCLUSIONS: Hospital visitor management is crucial and effectively prevents infections while maintaining cost-effectiveness. For terminal care patients, visitor restrictions can be omitted if appropriate infection prevention measures are taken. Antigen testing plays a subordinate role, except in the case of a pure focus on additional infections caused by visitors of healthcare institutions. We provide decision support to authorities and hospital visitor managers to identify appropriate visitor restriction and infection prevention strategies for specific local conditions, incidence rates, and objectives.

All-cause mortality and disease progression in SARS-CoV-2-infected patients with or without antibiotic therapy: an analysis of the LEOSS cohort.

PURPOSE: Reported antibiotic use in coronavirus disease 2019 (COVID-19) is far higher than the actual rate of reported bacterial co- and superinfection. A better understanding of antibiotic therapy in COVID-19 is necessary. METHODS: 6457 SARS-CoV-2-infected cases, documented from March 18, 2020, until February 16, 2021, in the LEOSS cohort were analyzed. As primary endpoint, the correlation between any antibiotic treatment and all-cause mortality/progression to the next more advanced phase of disease was calculated for adult patients in the complicated phase of disease and procalcitonin (PCT) ≤ 0.5 ng/ml. The analysis took the confounders gender, age, and comorbidities into account. RESULTS: Three thousand, six hundred twenty-seven cases matched all inclusion criteria for analyses. For the primary endpoint, antibiotic treatment was not correlated with lower all-cause mortality or progression to the next more advanced (critical) phase (n = 996) (both p > 0.05). For the secondary endpoints, patients in the uncomplicated phase (n = 1195), regardless of PCT level, had no lower all-cause mortality and did not progress less to the next more advanced (complicated) phase when treated with antibiotics (p > 0.05). Patients in the complicated phase with PCT > 0.5 ng/ml and antibiotic treatment (n = 286) had a significantly increased all-cause mortality (p = 0.029) but no significantly different probability of progression to the critical phase (p > 0.05). CONCLUSION: In this cohort, antibiotics in SARS-CoV-2-infected patients were not associated with positive effects on all-cause mortality or disease progression. Additional studies are needed. Advice of local antibiotic stewardship- (ABS-) teams and local educational campaigns should be sought to improve rational antibiotic use in COVID-19 patients.