Comparison of clinical outcomes in hospitalized patients with COVID-19 or non-COVID-19 community-acquired pneumonia in a prospective observational cohort study.

PURPOSE: Coronavirus disease 2019 (COVID-19) and non-COVID-19 community-acquired pneumonia (NC-CAP) often result in hospitalization with considerable risks of mortality, ICU treatment, and long-term morbidity. A comparative analysis of clinical outcomes in COVID-19 CAP (C-CAP) and NC-CAP may improve clinical management. METHODS: Using prospectively collected CAPNETZ study data (January 2017 to June 2021, 35 study centers), we conducted a comprehensive analysis of clinical outcomes including in-hospital death, ICU treatment, length of hospital stay (LOHS), 180-day survival, and post-discharge re-hospitalization rate. Logistic regression models were used to examine group differences between C-CAP and NC-CAP patients and associations with patient demography, recruitment period, comorbidity, and treatment. RESULTS: Among 1368 patients (C-CAP: n = 344; NC-CAP: n = 1024), C-CAP showed elevated adjusted probabilities for in-hospital death (aOR 4.48 [95% CI 2.38-8.53]) and ICU treatment (aOR 8.08 [95% CI 5.31-12.52]) compared to NC-CAP. C-CAP patients were at increased risk of LOHS over seven days (aOR 1.88 [95% CI 1.47-2.42]). Although ICU patients had similar in-hospital mortality risk, C-CAP was associated with length of ICU stay over seven days (aOR 3.59 [95% CI 1.65-8.38]). Recruitment period influenced outcomes in C-CAP but not in NC-CAP. During follow-up, C-CAP was linked to a reduced risk of re-hospitalization and mortality post-discharge (aOR 0.43 [95% CI 0.27-0.70]). CONCLUSION: Distinct clinical trajectories of C-CAP and NC-CAP underscore the need for adapted management to avoid acute and long-term morbidity and mortality amid the evolving landscape of CAP pathogens.

Clinical evaluation of a web-based personalized recommendation system with electronic health record interface to optimize healthcare resources during SARS-CoV-2 surges.

During the SARS-CoV-2 pandemic, the German healthcare system faced challenges of efficiently allocating testing resources. To address this, we developed an open-source personalized recommendation system (PRS) called "CovApp". The PRS utilized a questionnaire to estimate the risk of infection, provided personalized recommendations such as testing, self-isolation, or quarantine, and featured QR code data transmission to electronic health records. The PRS served up to 2.5 million monthly users and received 67,000 backlinks from 1800 domains. We clinically evaluated the PRS at the SARS-CoV-2 testing facility at Charité and observed a 21.7% increase in patient throughput per hour and a 22.5% increase in patients per day. Patients using the PRS were twice as likely to belong to the High Risk group eligible for testing (18.6% vs. 8.9%, p < 0.0001), indicating successful compliance with CovApp's recommendations. CovApp served as a digital bridge between the population and medical staff and significantly improved testing efficiency. As an open-source platform, CovApp can be readily customized to address emerging public health crises. Further, given the EHR interface, the app is of great utility for other applications in clinical settings.

Opposing Roles of Blood-Borne Monocytes and Tissue-Resident Macrophages in Limbal Stem Cell Damage after Ocular Injury.

Limbal stem cell (LSC) deficiency is a frequent and severe complication after chemical injury to the eye. Previous studies have assumed this is mediated directly by the caustic agent. Here we show that LSC damage occurs through immune cell mediators, even without direct injury to LSCs. In particular, pH elevation in the anterior chamber (AC) causes acute uveal stress, the release of inflammatory cytokines at the basal limbal tissue, and subsequent LSC damage and death. Peripheral C-C chemokine receptor type 2 positive/CX3C motif chemokine receptor 1 negative (CCR2+ CX3CR1-) monocytes are the key mediators of LSC damage through the upregulation of tumor necrosis factor-alpha (TNF-α) at the limbus. In contrast to peripherally derived monocytes, CX3CR1+ CCR2- tissue-resident macrophages have a protective role, and their depletion prior to injury exacerbates LSC loss and increases LSC vulnerability to TNF-α-mediated apoptosis independently of CCR2+ cell infiltration into the tissue. Consistently, repopulation of the tissue by new resident macrophages not only restores the protective M2-like phenotype of macrophages but also suppresses LSC loss after exposure to inflammatory signals. These findings may have clinical implications in patients with LSC loss after chemical burns or due to other inflammatory conditions.

Short- and long-term T cell and antibody responses following dexamethasone treatment in COVID-19.

BACKGROUNDAfter its introduction as standard-of-care for severe COVID-19, dexamethasone has been administered to a large number of patients globally. Detailed knowledge of its impact on the cellular and humoral immune response to SARS-CoV-2 remains scarce.METHODSWe included immunocompetent individuals with (a) mild COVID-19, (b) severe COVID-19 before introduction of dexamethasone treatment, and (c) severe COVID-19 infection treated with dexamethasone from prospective observational cohort studies at Charité-Universitätsmedizin Berlin, Germany. We analyzed SARS-CoV-2 spike-reactive T cells, spike-specific IgG titers, and serum neutralizing activity against B.1.1.7 and B.1.617.2 in samples ranging from 2 weeks to 6 months after infection. We also analyzed BA.2 neutralization in sera after booster immunization.RESULTSPatients with severe COVID-19 and dexamethasone treatment had lower T cell and antibody responses to SARS-CoV-2 compared with patients without dexamethasone treatment in the early phase of disease, which converged in both groups before 6 months after infection and also after immunization. Patients with mild COVID-19 had comparatively lower T cell and antibody responses than patients with severe disease, including a lower response to booster immunization during convalescence.CONCLUSIONDexamethasone treatment was associated with a short-term reduction in T cell and antibody responses in severe COVID-19 when compared with the nontreated group, but this difference evened out 6 months after infection. We confirm higher cellular and humoral immune responses in patients after severe versus mild COVID-19 and the concept of improved hybrid immunity upon immunization.FUNDINGBerlin Institute of Health, German Federal Ministry of Education, and German Federal Institute for Drugs and Medical Devices.

A deep-learning algorithm to classify skin lesions from mpox virus infection.

Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbreak. To enable earlier detection of MPXV infection, we developed an image-based deep convolutional neural network (named MPXV-CNN) for the identification of the characteristic skin lesions caused by MPXV. We assembled a dataset of 139,198 skin lesion images, split into training/validation and testing cohorts, comprising non-MPXV images (n = 138,522) from eight dermatological repositories and MPXV images (n = 676) from the scientific literature, news articles, social media and a prospective cohort of the Stanford University Medical Center (n = 63 images from 12 patients, all male). In the validation and testing cohorts, the sensitivity of the MPXV-CNN was 0.83 and 0.91, the specificity was 0.965 and 0.898 and the area under the curve was 0.967 and 0.966, respectively. In the prospective cohort, the sensitivity was 0.89. The classification performance of the MPXV-CNN was robust across various skin tones and body regions. To facilitate the usage of the algorithm, we developed a web-based app by which the MPXV-CNN can be accessed for patient guidance. The capability of the MPXV-CNN for identifying MPXV lesions has the potential to aid in MPXV outbreak mitigation.

The industry impact of the American Medical Association's Digital Medicine Payment Advisory Group (DMPAG).

Digital medicine interventions are currently transforming health care and have created new efficiencies in the delivery process. The business model along with physician payment models are crucial drivers for the adoption of innovations. In the U.S., physician payment is mostly codified in the Current Procedural Terminology (CPT). Until recently, CPT codes related to digital medicine activities were mainly limited to telephone services. To embrace the evolving implementation of the various modalities of digital medicine, the American Medical Association (AMA) determined that a more comprehensive codeset is needed. Thus, the Digital Medicine Payment Advisory Group (DMPAG) was initiated in late 2016. Since then, the DMPAG has achieved a significant and measurable impact on digital medicine intervention adoption by introducing CPT codes for remote physiologic monitoring, remote therapeutic monitoring, artificial intelligence, and other digital innovations.

Critical assessment of transformer-based AI models for German clinical notes.

Objective: Healthcare data such as clinical notes are primarily recorded in an unstructured manner. If adequately translated into structured data, they can be utilized for health economics and set the groundwork for better individualized patient care. To structure clinical notes, deep-learning methods, particularly transformer-based models like Bidirectional Encoder Representations from Transformers (BERT), have recently received much attention. Currently, biomedical applications are primarily focused on the English language. While general-purpose German-language models such as GermanBERT and GottBERT have been published, adaptations for biomedical data are unavailable. This study evaluated the suitability of existing and novel transformer-based models for the German biomedical and clinical domain. Materials and Methods: We used 8 transformer-based models and pre-trained 3 new models on a newly generated biomedical corpus, and systematically compared them with each other. We annotated a new dataset of clinical notes and used it with 4 other corpora (BRONCO150, CLEF eHealth 2019 Task 1, GGPONC, and JSynCC) to perform named entity recognition (NER) and document classification tasks. Results: General-purpose language models can be used effectively for biomedical and clinical natural language processing (NLP) tasks, still, our newly trained BioGottBERT model outperformed GottBERT on both clinical NER tasks. However, training new biomedical models from scratch proved ineffective. Discussion: The domain-adaptation strategy's potential is currently limited due to a lack of pre-training data. Since general-purpose language models are only marginally inferior to domain-specific models, both options are suitable for developing German-language biomedical applications. Conclusion: General-purpose language models perform remarkably well on biomedical and clinical NLP tasks. If larger corpora become available in the future, domain-adapting these models may improve performances.

[Evidence of positive care effects by digital health apps-methodological challenges and approaches]. / Nachweis positiver Versorgungseffekte von digitalen Gesundheitsanwendungen ­ methodische Herausforderungen und Lösungsansätze.

Since 2020, digital health applications (DiGA) can be prescribed at the expense of the German statutory health insurance (SHI) system after undergoing an approval procedure by the Federal Institute for Drugs and Medical Devices (BfArM). DiGA can be approved provisionally for 1 year (with the option of extension) or permanently. The latter is dependent on scientific evidence of a positive effect on care, which can be a medical benefit or a patient-relevant structural and procedural improvement in care. However, it is apparent that the investigation of DiGA in scientific studies is challenging, as they are often complex interventions whose success also includes user and prescriber factors. In addition, health services research data underpinning the benefits of DiGA are lacking to date. In the current article, methodological considerations for DiGA research are presented, and a selection of internal medicine DiGAs is used to critically discuss current research practice.

Functional limitations 12 months after SARS-CoV-2 infection correlate with initial disease severity: An observational study of cardiopulmonary exercise capacity testing in COVID-19 convalescents.

BACKGROUND: Cardiopulmonary Exercise Testing (CPET) provides a comprehensive assessment of pulmonary, cardiovascular and musculosceletal function. Reduced CPET performance could be an indicator for chronic morbidity after COVID-19. METHODS: Patients ≥18 years with confirmed PCR positive SARS-CoV-2 infection were offered to participate in a prospective observational study of clinical course and outcomes of COVID-19. 54 patients completed CPET, questionnaires on respiratory quality of life and performed pulmonary function tests 12 months after SARS-CoV-2 infection. RESULTS: At 12 months after SARS-CoV-2 infection, 46.3% of participants had a peak performance and 33.3% a peak oxygen uptake of <80% of the predicted values, respectively. Further impairments were observed in diffusion capacity and ventilatory efficiency. Functional limitations were particularly pronounced in patients after invasive mechanical ventilation and extracorporeal membrane oxygenation treatment. Ventilatory capacity was reduced <80% of predicted values in 55.6% of participants, independent from initial clinical severity. Patient reported dyspnea and respiratory quality of life after COVID-19 correlated with CPET performance and parameters of gas exchange. Risk factors for reduced CPET performance 12 months after COVID-19 were prior intensive care treatment (OR 5.58, p = 0.004), SGRQ outcome >25 points (OR 3.48, p = 0.03) and reduced DLCO (OR 3.01, p = 0.054). CONCLUSIONS: Functional limitations causing chronic morbidity in COVID-19 survivors persist over 12 months after SARS-CoV-2 infection. These limitations were particularly seen in parameters of overall performance and gas exchange resulting from muscular deconditioning and lung parenchymal changes. Patient reported reduced respiratory quality of life was a risk factor for adverse CPET performance.

[Practical use of digital health applications (DiGA) in internal medicine]. / Praktische Anwendung digitaler Gesundheitsanwendungen (DiGA) in der Inneren Medizin.

Since 2020 physicians can prescribe digital health applications (DiGA), also colloquially known as apps on prescription, which are reimbursed by the statutory health insurance when they are approved by the Federal Institute for Drugs and Medical Devices (BfArM) and are included in the DiGA Ordinance. Currently, there is one approved DiGA (indication obesity) for internal medicine. There are many questions on the practical use of the DiGA, ranging from the prescription, the effectiveness, the complexities and reimbursement as well as the liability risks. The DiGA are innovative new means, which maybe support internal medicine physicians in the diagnostics and treatment in the future. The benefits in this field of indications are limited by unclarified issues, especially on the prescription practice and the currently low number of DiGA available in internal medicine.

Severity of respiratory failure and computed chest tomography in acute COVID-19 correlates with pulmonary function and respiratory symptoms after infection with SARS-CoV-2: An observational longitudinal study over 12 months.

INTRODUCTION: Prospective and longitudinal data on pulmonary injury over one year after acute coronavirus disease 2019 (COVID-19) are sparse. We aim to determine reductions in pulmonary function and respiratory related quality of life up to 12 months after acute COVID-19. METHODS: Patients with acute COVID-19 were enrolled into an ongoing single-centre, prospective observational study and prospectively examined 6 weeks, 3, 6 and 12 months after onset of COVID-19 symptoms. Chest CT-scans, pulmonary function and symptoms assessed by St. Georges Respiratory Questionnaire were used to evaluate respiratory limitations. Patients were stratified according to severity of acute COVID-19. RESULTS: Median age of all patients was 57 years, 37.8% were female. Higher age, male sex and higher BMI were associated with acute-COVID-19 severity (p < 0.0001, 0.001 and 0.004 respectively). Also, pulmonary restriction and reduced carbon monoxide diffusion capacity was associated with disease severity. In patients with restriction and impaired diffusion capacity, FVC improved over 12 months from 61.32 to 71.82, TLC from 68.92 to 76.95, DLCO from 60.18 to 68.98 and KCO from 81.28 to 87.80 (percent predicted values; p = 0.002, 0.045, 0.0002 and 0.0005). The CT-score of lung involvement in the acute phase was associated with restriction and reduction in diffusion capacity in follow-up. Respiratory symptoms improved for patients in higher severity groups during follow-up, but not for patients with initially mild disease. CONCLUSION: Severity of respiratory failure during COVID-19 correlates with the degree of pulmonary function impairment and respiratory quality of life in the year after acute infection.

A proteomic survival predictor for COVID-19 patients in intensive care.

Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Additional tools are also needed to monitor treatment, including experimental therapies in clinical trials. Comprehensively capturing human physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index, and APACHE II score showed limited performance in predicting the COVID-19 outcome. Instead, the quantification of 321 plasma protein groups at 349 timepoints in 50 critically ill patients receiving invasive mechanical ventilation revealed 14 proteins that showed trajectories different between survivors and non-survivors. A predictor trained on proteomic measurements obtained at the first time point at maximum treatment level (i.e. WHO grade 7), which was weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81). We tested the established predictor on an independent validation cohort (AUROC 1.0). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that plasma proteomics can give rise to prognostic predictors substantially outperforming current prognostic markers in intensive care.

SARS-CoV-2 infection triggers profibrotic macrophage responses and lung fibrosis.

COVID-19-induced "acute respiratory distress syndrome" (ARDS) is associated with prolonged respiratory failure and high mortality, but the mechanistic basis of lung injury remains incompletely understood. Here, we analyze pulmonary immune responses and lung pathology in two cohorts of patients with COVID-19 ARDS using functional single-cell genomics, immunohistology, and electron microscopy. We describe an accumulation of CD163-expressing monocyte-derived macrophages that acquired a profibrotic transcriptional phenotype during COVID-19 ARDS. Gene set enrichment and computational data integration revealed a significant similarity between COVID-19-associated macrophages and profibrotic macrophage populations identified in idiopathic pulmonary fibrosis. COVID-19 ARDS was associated with clinical, radiographic, histopathological, and ultrastructural hallmarks of pulmonary fibrosis. Exposure of human monocytes to SARS-CoV-2, but not influenza A virus or viral RNA analogs, was sufficient to induce a similar profibrotic phenotype in vitro. In conclusion, we demonstrate that SARS-CoV-2 triggers profibrotic macrophage responses and pronounced fibroproliferative ARDS.

A time-resolved proteomic and prognostic map of COVID-19.

COVID-19 is highly variable in its clinical presentation, ranging from asymptomatic infection to severe organ damage and death. We characterized the time-dependent progression of the disease in 139 COVID-19 inpatients by measuring 86 accredited diagnostic parameters, such as blood cell counts and enzyme activities, as well as untargeted plasma proteomes at 687 sampling points. We report an initial spike in a systemic inflammatory response, which is gradually alleviated and followed by a protein signature indicative of tissue repair, metabolic reconstitution, and immunomodulation. We identify prognostic marker signatures for devising risk-adapted treatment strategies and use machine learning to classify therapeutic needs. We show that the machine learning models based on the proteome are transferable to an independent cohort. Our study presents a map linking routinely used clinical diagnostic parameters to plasma proteomes and their dynamics in an infectious disease.

Clinical and virological characteristics of hospitalised COVID-19 patients in a German tertiary care centre during the first wave of the SARS-CoV-2 pandemic: a prospective observational study.

PURPOSE: Adequate patient allocation is pivotal for optimal resource management in strained healthcare systems, and requires detailed knowledge of clinical and virological disease trajectories. The purpose of this work was to identify risk factors associated with need for invasive mechanical ventilation (IMV), to analyse viral kinetics in patients with and without IMV and to provide a comprehensive description of clinical course. METHODS: A cohort of 168 hospitalised adult COVID-19 patients enrolled in a prospective observational study at a large European tertiary care centre was analysed. RESULTS: Forty-four per cent (71/161) of patients required invasive mechanical ventilation (IMV). Shorter duration of symptoms before admission (aOR 1.22 per day less, 95% CI 1.10-1.37, p < 0.01) and history of hypertension (aOR 5.55, 95% CI 2.00-16.82, p < 0.01) were associated with need for IMV. Patients on IMV had higher maximal concentrations, slower decline rates, and longer shedding of SARS-CoV-2 than non-IMV patients (33 days, IQR 26-46.75, vs 18 days, IQR 16-46.75, respectively, p < 0.01). Median duration of hospitalisation was 9 days (IQR 6-15.5) for non-IMV and 49.5 days (IQR 36.8-82.5) for IMV patients. CONCLUSIONS: Our results indicate a short duration of symptoms before admission as a risk factor for severe disease that merits further investigation and different viral load kinetics in severely affected patients. Median duration of hospitalisation of IMV patients was longer than described for acute respiratory distress syndrome unrelated to COVID-19.