A precise performance-based reimbursement model for the multi-centre NAPKON cohorts - development and evaluation.

Fair allocation of funding in multi-centre clinical studies is challenging. Models commonly used in Germany - the case fees ("fixed-rate model", FRM) and up-front staffing and consumables ("up-front allocation model", UFAM) lack transparency and fail to suitably accommodate variations in centre performance. We developed a performance-based reimbursement model (PBRM) with automated calculation of conducted activities and applied it to the cohorts of the National Pandemic Cohort Network (NAPKON) within the Network of University Medicine (NUM). The study protocol activities, which were derived from data management systems, underwent validation through standardized quality checks by multiple stakeholders. The PBRM output (first funding period) was compared among centres and cohorts, and the cost-efficiency of the models was evaluated. Cases per centre varied from one to 164. The mean case reimbursement differed among the cohorts (1173.21€ [95% CI 645.68-1700.73] to 3863.43€ [95% CI 1468.89-6257.96]) and centres and mostly fell short of the expected amount. Model comparisons revealed higher cost-efficiency of the PBRM compared to FRM and UFAM, especially for low recruitment outliers. In conclusion, we have developed a reimbursement model that is transparent, accurate, and flexible. In multi-centre collaborations where heterogeneity between centres is expected, a PBRM could be used as a model to address performance discrepancies.Trial registration: https://clinicaltrials.gov/ct2/show/NCT04768998 ; https://clinicaltrials.gov/ct2/show/NCT04747366 ; https://clinicaltrials.gov/ct2/show/NCT04679584 .

Headache severity in patients with post COVID-19 condition: a case-control study.

Post COVID-19 conditions (PCC) present with a wide range of symptoms. Headache is one of the most frequently reported neurological symptoms by patients with PCC. We aimed to assess the prevalence of headache in patients with PCC who attended the Post-COVIDLMU outpatient department at LMU University Hospital in Munich. We hypothesized that headaches occur more frequently in patients with PCC than in the control group. Patients answered a questionnaire containing sociodemographic characteristics, their current symptoms, and prior psychiatric and somatic diagnoses, the WHO Quality of Life assessment (WHOQOL-BREF), 9-item Patient Health Questionnaire (PHQ-9), and the Fatigue Severity Scale (FSS). 188 PCC patients were included in this study and compared to a control group of patients with a history of COVID-19 or a different infectious disease - but no consecutive post-infectious condition (nc=27). 115 (61%) of our PCC patients were female. The median age was 41 years. 60 (32%, p = 0.001) had a pre-existing psychiatric diagnosis. PCC was associated with worse outcomes in all four domains of the WHOQOL-BREF (p < 0.001), high levels of fatigue (FSS; p < 0.001), and a higher likeliness for symptoms of depression (PHQ-9; p < 0.001). We were able to confirm that psychiatric disorders are more frequently associated with headaches in PCC patients. Headache should be assessed and treated in the context of PCC not only by neurologists but by multi-professional teams and regarding all PCC symptoms.

Characterization of cognitive symptoms in post COVID-19 patients.

Cognitive symptoms (CS) belong to the most common manifestations of the Post COVID-19 (PC) condition. We sought to objectify CS in PC patients using routine diagnostic assessments: neurocognitive testing (NCT) and brain imaging (BI). Further, we investigated possible associations of CS with patient reported outcomes (PROs), and risk factors for developing CS. Clinical data and PROs of 315 PC patients were assessed at a mean of 6 months after SARS-CoV-2 infection. 231 (73.3%) patients reported any sort of CS. Among them, 78 underwent NCT and 55 received BI. In NCT, the cognitive domains most affected were the working memory, attention, and concentration. Nonetheless, pathological thresholds were exceeded only in few cases. Neurocognitive performance did not differ significantly between patients complaining of severe (n = 26) versus non-severe (n = 52) CS. BI findings were abnormal in 8 (14.5%) cases with CS but were most likely not related to PC. Patients reporting high severity of CS scored worse in the PHQ-9, FSS, WHOQOL-BREF, were more likely to report impaired sleep, and had a higher prevalence of psychiatric diagnoses. Overall, NCT could confirm mild impairment in some but not all PC patients with CS, while BI studies were abnormal in only few cases. CS severity did not affect NCT results, but severe CS were associated with symptoms of depression (PHQ-9), fatigue (FSS), reduced quality of life (WHOQOL-BREF) and higher prevalence of psychiatric illnesses. These findings support the importance of NCT, BI, and neuro-psychological assessment in the work-up of PC patients reporting CS. TRIAL REGISTRATION: Trial registration number and date of registration: DRKS00030974, 22 Dec 2022, retrospectively registered.

Persistent immune abnormalities discriminate post-COVID syndrome from convalescence.

BACKGROUND: Innate lymphoid cells (ILCs) are key organizers of tissue immune responses and regulate tissue development, repair, and pathology. Persistent clinical sequelae beyond 12 weeks following acute COVID-19 disease, named post-COVID syndrome (PCS), are increasingly recognized in convalescent individuals. ILCs have been associated with the severity of COVID-19 symptoms but their role in the development of PCS remains poorly defined. METHODS AND RESULTS: Here, we used multiparametric immune phenotyping, finding expanded circulating ILC precursors (ILCPs) and concurrent decreased group 2 innate lymphoid cells (ILC2s) in PCS patients compared to well-matched convalescent control groups at > 3 months after infection or healthy controls. Patients with PCS showed elevated expression of chemokines and cytokines associated with trafficking of immune cells (CCL19/MIP-3b, FLT3-ligand), endothelial inflammation and repair (CXCL1, EGF, RANTES, IL-1RA, PDGF-AA). CONCLUSION: These results define immunological parameters associated with PCS and might help find biomarkers and disease-relevant therapeutic strategies.

Multidisciplinary rehabilitation with a focus on physiotherapy in patients with Post Covid19 condition: an observational pilot study.

There is a lack of interventions that treat the Post-Covid-19 Condition (PCC) itself. Accordingly, treatment guidelines recommend physiotherapy interventions to alleviate symptoms and enhance functioning. In cases where unimodal treatments prove ineffective, non-organ-specific multidisciplinary bio-psycho-social rehabilitation (MBR) programs are a suitable option. In a pilot observational study with assessments at the entry and end of treatment we aimed to evaluate the feasibility of a 3-week day clinic MBR program and explore its effects on physical functioning in PCC patients with fatigue and reduced physical capacity. Patient selection was based on an interdisciplinary assessment involving a physician, a psychologist and a physiotherapist. Feasibility was determined based on full participation (≥ 8 of 9 days) and maintenance of stable endurance in the 6-Minute Walk Test (6MWT). From 37 patients included in the study, 33 completed the MBR (mean age: 43 ± 12 years, 73% female). Four patients discontinued the MBR, with two of them having reported deterioration of PCC symptoms. The 6MWT showed a numerical improvement from 501 ± 97 m to 512 ± 87 m, although it did not reach statistical significance. These results support the feasibility of outpatient MBR with a focus on active physiotherapy interventions in PCC patients with fatigue. This study aligns with previous research supporting the effectiveness of physiotherapy and rehabilitation in PCC patients. However, further research is needed to address possible different treatment responses and varying treatment approaches in subgroups of PCC patients.

All eyes on PCS: analysis of the retinal microvasculature in patients with post-COVID syndrome-study protocol of a 1 year prospective case-control study.

Since widespread vaccination against COVID-19, the development of effective antiviral drugs, and the decreasing number of patients with COVID-19 in intensive care, the risk from SARS-CoV-2 infection appears less threatening. However, studies show that a significant number of patients suffer from long-term sequelae, even months after SARS-CoV-2 infection. The so-called post-COVID syndrome (PCS) often presents a diagnostic and treatment challenge for physicians. This study protocol describes the "All Eyes on PCS" study, which aims to investigate the retinal microvasculature in PCS patients and COVID-19-recovered patients to provide new insights into the pathophysiology of PCS. "All Eyes on PCS" is a prospective, case-control study with the primary objective of detecting endothelial dysfunction (ED) in patients with PCS. Therefore, we intend to recruit patients with PCS, fully SARS-CoV-2-infection-recovered (CR) participants, and SARS-CoV-2-infection-naïve (CN) participants. Baseline measurements will include: (1) patient-specific characteristics, (2) biochemistry, (3) retinal vessel analysis (RVA), (4) survey questionnaires as patient-reported outcomes measurements (PROMs), (5) optical coherence tomography (OCT), OCT angiography (OCTA), and adaptive optics (AO), (6) blood pressure recordings, (7) handgrip strength test. After 6 months, baseline measurements will be repeated in the PCS cohort, and after 1 year, a telephone query will be conducted to assess residual symptoms and treatment needs. The aim of this study is to gain insight into the pathophysiology of PCS and to provide an objective biomarker for diagnosis and treatment, while also creating a comprehensive clinical database of PCS patients.ClinicalTrials.gov Identifier: NCT05635552; Date: 2.12.2022.

Substantial differences in perception of disease severity between post COVID-19 patients, internists, and psychiatrists or psychologists: the Health Perception Gap and its clinical implications.

Patient-reported outcome measures (PROMs) such as the Numeric Pain Rating Scale (NPRS) or Likert scales addressing various domains of health are important tools to assess disease severity in Post COVID-19 (PC) patients. By design, they are subjective in nature and prone to bias. Our findings reveal substantial differences in the perception of disease severity between patients (PAT), their attending internists (INT) and psychiatrists/psychologists (PSY). Patients rated almost all aspects of their health worse than INT or PSY. Most of the differences were statistically highly significant. The presence of fatigue and mood disorders correlated negatively with health perception. The physical health section of the WHO Quality of Life Assessment (WHOQoL-BREF) and Karnofsky index correlated positively with overall and mental health ratings by PAT and INT. Health ratings by neither PAT, PSY nor INT were associated with the number of abnormal findings in diagnostic procedures. This study highlights how strongly perceptions of disease severity diverge between PC patients and attending medical staff. Imprecise communication, different experiences regarding health and disease, and confounding psychological factors may explain these observations. Discrepancies in disease perception threaten patient-physician relationships and pose strong confounders in clinical studies. Established scores (e.g., WHOQoL-BREF, Karnofsky index) may represent an approach to overcome these discrepancies. Physicians and psychologists noting harsh differences between a patient's and their own perception of the patient's health should apply screening tools for mood disorders (i.e., PHQ-9, WHOQoL-BREF), psychosomatic symptom burden (SSD-12, FCV-19) and consider further psychological evaluation. An interdisciplinary approach to PC patients remains imperative. Trial Registration Number & Date of Registration: DRKS00030974, 22 Dec 2022, retrospectively registered.

Persistent endothelial dysfunction in post-COVID-19 syndrome and its associations with symptom severity and chronic inflammation.

BACKGROUND: Post-COVID-19 syndrome (PCS) is a lingering disease with ongoing symptoms such as fatigue and cognitive impairment resulting in a high impact on the daily life of patients. Understanding the pathophysiology of PCS is a public health priority, as it still poses a diagnostic and treatment challenge for physicians. METHODS: In this prospective observational cohort study, we analyzed the retinal microcirculation using Retinal Vessel Analysis (RVA) in a cohort of patients with PCS and compared it to an age- and gender-matched healthy cohort (n = 41, matched out of n = 204). MEASUREMENTS AND MAIN RESULTS: PCS patients exhibit persistent endothelial dysfunction (ED), as indicated by significantly lower venular flicker-induced dilation (vFID; 3.42% ± 1.77% vs. 4.64% ± 2.59%; p = 0.02), narrower central retinal artery equivalent (CRAE; 178.1 [167.5-190.2] vs. 189.1 [179.4-197.2], p = 0.01) and lower arteriolar-venular ratio (AVR; (0.84 [0.8-0.9] vs. 0.88 [0.8-0.9], p = 0.007). When combining AVR and vFID, predicted scores reached good ability to discriminate groups (area under the curve: 0.75). Higher PCS severity scores correlated with lower AVR (R = - 0.37 p = 0.017). The association of microvascular changes with PCS severity were amplified in PCS patients exhibiting higher levels of inflammatory parameters. CONCLUSION: Our results demonstrate that prolonged endothelial dysfunction is a hallmark of PCS, and impairments of the microcirculation seem to explain ongoing symptoms in patients. As potential therapies for PCS emerge, RVA parameters may become relevant as clinical biomarkers for diagnosis and therapy management. TRIAL REGISTRATION: This study was previously registered at ClinicalTrials ("All Eyes on PCS-Analysis of the Retinal Microvasculature in Patients with Post-COVID-19 Syndrome". NCT05635552. https://clinicaltrials.gov/ct2/show/NCT05635552 ). Persistent endothelial dysfunction in post-COVID-19 syndrome. Acute SARS-CoV-2 infection indirectly or directly causes endotheliitis in patients. N = 41 PCS patients were recruited and retinal vessel analysis was performed to assess microvascular endothelial function. Images of SVA and DVA are illustrative for RVA data analysis. For each PCS patient and healthy cohort, venular vessel diameter of the three measurement cycles was calculated and plotted on a diameter-time curve. Patients exhibited reduced flicker-induced dilation in veins (vFID) measured by dynamic vessel analysis (DVA) and lower central retinal arteriolar equivalent (CRAE) and arteriolar-venular ratio (AVR) and a tendency towards higher central retinal venular equivalent (CRVE) when compared to SARS-CoV-2 infection naïve participants. Created with BioRender.com.

How to exclude pulmonary embolism in patients hospitalized with COVID-19: a comparison of predictive scores.

BACKGROUND: Pulmonary embolism (PE) is an important complication of Coronavirus disease 2019 (COVID-19). COVID-19 is associated with respiratory impairment and a pro-coagulative state, rendering PE more likely and difficult to recognize. Several decision algorithms relying on clinical features and D-dimer have been established. High prevalence of PE and elevated Ddimer in patients with COVID-19 might impair the performance of common decision algorithms. Here, we aimed to validate and compare five common decision algorithms implementing age adjusted Ddimer, the GENEVA, and Wells scores as well as the PEGeD- and YEARS-algorithms in patients hospitalized with COVID-19. METHODS: In this single center study, we included patients who were admitted to our tertiary care hospital in the COVID-19 Registry of the LMU Munich. We retrospectively selected patients who received a computed tomography pulmonary angiogram (CTPA) or pulmonary ventilation/perfusion scintigraphy (V/Q) for suspected PE. The performances of five commonly used diagnostic algorithms (age-adjusted D-dimer, GENEVA score, PEGeD-algorithm, Wells score, and YEARS-algorithm) were compared. RESULTS: We identified 413 patients with suspected PE who received a CTPA or V/Q confirming 62 PEs (15%). Among them, 358 patients with 48 PEs (13%) could be evaluated for performance of all algorithms. Patients with PE were older and their overall outcome was worse compared to patients without PE. Of the above five diagnostic algorithms, the PEGeD- and YEARS-algorithms performed best, reducing diagnostic imaging by 14% and 15% respectively with a sensitivity of 95.7% and 95.6%. The GENEVA score was able to reduce CTPA or V/Q by 32.2% but suffered from a low sensitivity (78.6%). Age-adjusted D-dimer and Wells score could not significantly reduce diagnostic imaging. CONCLUSION: The PEGeD- and YEARS-algorithms outperformed other tested decision algorithms and worked well in patients admitted with COVID-19. These findings need independent validation in a prospective study.

Validation of the SACOV-19 score for identifying patients at risk of complicated or more severe COVID-19: a prospective study.

PURPOSE: Identification of patients at risk of complicated or more severe COVID-19 is of pivotal importance, since these patients might require monitoring, antiviral treatment, and hospitalization. In this study, we prospectively evaluated the SACOV-19 score for its ability to predict complicated or more severe COVID-19. METHODS: In this prospective multicenter study, we included 124 adult patients with acute COVID-19 in three German hospitals, who were diagnosed in an early, uncomplicated stage of COVID-19 within 72 h of inclusion. We determined the SACOV-19 score at baseline and performed a follow-up at 30 days. RESULTS: The SACOV-19 score's AUC was 0.816. At a cutoff of > 3, it predicted deterioration to complicated or more severe COVID-19 with a sensitivity of 94% and a specificity of 55%. It performed significantly better in predicting complicated COVID-19 than the random tree-based SACOV-19 predictive model, the CURB-65, 4C mortality, or qCSI scores. CONCLUSION: The SACOV-19 score is a feasible tool to aid decision making in acute COVID-19.

Consistency as a Data Quality Measure for German Corona Consensus Items Mapped from National Pandemic Cohort Network Data Collections.

BACKGROUND: As a national effort to better understand the current pandemic, three cohorts collect sociodemographic and clinical data from coronavirus disease 2019 (COVID-19) patients from different target populations within the German National Pandemic Cohort Network (NAPKON). Furthermore, the German Corona Consensus Dataset (GECCO) was introduced as a harmonized basic information model for COVID-19 patients in clinical routine. To compare the cohort data with other GECCO-based studies, data items are mapped to GECCO. As mapping from one information model to another is complex, an additional consistency evaluation of the mapped items is recommended to detect possible mapping issues or source data inconsistencies. OBJECTIVES: The goal of this work is to assure high consistency of research data mapped to the GECCO data model. In particular, it aims at identifying contradictions within interdependent GECCO data items of the German national COVID-19 cohorts to allow investigation of possible reasons for identified contradictions. We furthermore aim at enabling other researchers to easily perform data quality evaluation on GECCO-based datasets and adapt to similar data models. METHODS: All suitable data items from each of the three NAPKON cohorts are mapped to the GECCO items. A consistency assessment tool (dqGecco) is implemented, following the design of an existing quality assessment framework, retaining their-defined consistency taxonomies, including logical and empirical contradictions. Results of the assessment are verified independently on the primary data source. RESULTS: Our consistency assessment tool helped in correcting the mapping procedure and reveals remaining contradictory value combinations within COVID-19 symptoms, vital signs, and COVID-19 severity. Consistency rates differ between the different indicators and cohorts ranging from 95.84% up to 100%. CONCLUSION: An efficient and portable tool capable of discovering inconsistencies in the COVID-19 domain has been developed and applied to three different cohorts. As the GECCO dataset is employed in different platforms and studies, the tool can be directly applied there or adapted to similar information models.

Proteomics reveals antiviral host response and NETosis during acute COVID-19 in high-risk patients.

SARS-CoV-2 remains an acute threat to human health, endangering hospital capacities worldwide. Previous studies have aimed at informing pathophysiologic understanding and identification of disease indicators for risk assessment, monitoring, and therapeutic guidance. While findings start to emerge in the general population, observations in high-risk patients with complex pre-existing conditions are limited. We addressed the gap of existing knowledge with regard to a differentiated understanding of disease dynamics in SARS-CoV-2 infection while specifically considering disease stage and severity. We biomedically characterized quantitative proteomics in a hospitalized cohort of COVID-19 patients with mild to severe symptoms suffering from different (co)-morbidities in comparison to both healthy individuals and patients with non-COVID related inflammation. Deep clinical phenotyping enabled the identification of individual disease trajectories in COVID-19 patients. By the use of the individualized disease phase assignment, proteome analysis revealed a severity dependent general type-2-centered host response side-by-side with a disease specific antiviral immune reaction in early disease. The identification of phenomena such as neutrophil extracellular trap (NET) formation and a pro-coagulatory response characterizing severe disease was successfully validated in a second cohort. Together with the regulation of proteins related to SARS-CoV-2-specific symptoms identified by proteome screening, we not only confirmed results from previous studies but provide novel information for biomarker and therapy development.

[Post-COVIDLMU: implementation and evaluation of an interdisciplinary and cross-sectoral healthcare and research network for evidence-based treatment of patients with severe post-COVID syndrome]. / Post-COVIDLMU: Implementierung und Evaluierung eines interdisziplinären und sektorenübergreifenden Versorgungs- und Forschungsnetzwerks für eine evidenzgeleitete Behandlung von Patienten mit schwerem Post-COVID-Syndrom.

Post-COVIDLMU is an interdisciplinary and cross-sectoral healthcare and research network initiated by the Munich University Hospital. The focus is on the treatment and research of adult post-COVID cases with complex and severe symptoms. The treatment of this patient group is carried out with interdisciplinary and comprehensive involvement of numerous specialized clinics of the Munich University Hospital. In addition, the university treatment services cover modern telemedical consultation, interdisciplinary case conferences together with the option for participation of referring physicians as well as the possibility for patients to take part in the respective medical research studies on post-COVID syndrome. The Munich University Hospital acts in close cooperation with physicians in private practice as well as various rehabilitation institutions in Germany.

Prediction of COVID-19 deterioration in high-risk patients at diagnosis: an early warning score for advanced COVID-19 developed by machine learning.

PURPOSE: While more advanced COVID-19 necessitates medical interventions and hospitalization, patients with mild COVID-19 do not require this. Identifying patients at risk of progressing to advanced COVID-19 might guide treatment decisions, particularly for better prioritizing patients in need for hospitalization. METHODS: We developed a machine learning-based predictor for deriving a clinical score identifying patients with asymptomatic/mild COVID-19 at risk of progressing to advanced COVID-19. Clinical data from SARS-CoV-2 positive patients from the multicenter Lean European Open Survey on SARS-CoV-2 Infected Patients (LEOSS) were used for discovery (2020-03-16 to 2020-07-14) and validation (data from 2020-07-15 to 2021-02-16). RESULTS: The LEOSS dataset contains 473 baseline patient parameters measured at the first patient contact. After training the predictor model on a training dataset comprising 1233 patients, 20 of the 473 parameters were selected for the predictor model. From the predictor model, we delineated a composite predictive score (SACOV-19, Score for the prediction of an Advanced stage of COVID-19) with eleven variables. In the validation cohort (n = 2264 patients), we observed good prediction performance with an area under the curve (AUC) of 0.73 ± 0.01. Besides temperature, age, body mass index and smoking habit, variables indicating pulmonary involvement (respiration rate, oxygen saturation, dyspnea), inflammation (CRP, LDH, lymphocyte counts), and acute kidney injury at diagnosis were identified. For better interpretability, the predictor was translated into a web interface. CONCLUSION: We present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.

Genomic epidemiology reveals multiple introductions of SARS-CoV-2 followed by community and nosocomial spread, Germany, February to May 2020.

BackgroundIn the SARS-CoV-2 pandemic, viral genomes are available at unprecedented speed, but spatio-temporal bias in genome sequence sampling precludes phylogeographical inference without additional contextual data.AimWe applied genomic epidemiology to trace SARS-CoV-2 spread on an international, national and local level, to illustrate how transmission chains can be resolved to the level of a single event and single person using integrated sequence data and spatio-temporal metadata.MethodsWe investigated 289 COVID-19 cases at a university hospital in Munich, Germany, between 29 February and 27 May 2020. Using the ARTIC protocol, we obtained near full-length viral genomes from 174 SARS-CoV-2-positive respiratory samples. Phylogenetic analyses using the Auspice software were employed in combination with anamnestic reporting of travel history, interpersonal interactions and perceived high-risk exposures among patients and healthcare workers to characterise cluster outbreaks and establish likely scenarios and timelines of transmission.ResultsWe identified multiple independent introductions in the Munich Metropolitan Region during the first weeks of the first pandemic wave, mainly by travellers returning from popular skiing areas in the Alps. In these early weeks, the rate of presumable hospital-acquired infections among patients and in particular healthcare workers was high (9.6% and 54%, respectively) and we illustrated how transmission chains can be dissected at high resolution combining virus sequences and spatio-temporal networks of human interactions.ConclusionsEarly spread of SARS-CoV-2 in Europe was catalysed by superspreading events and regional hotspots during the winter holiday season. Genomic epidemiology can be employed to trace viral spread and inform effective containment strategies.

Dynamics of SARS-CoV-2 shedding in the respiratory tract depends on the severity of disease in COVID-19 patients.

A fraction of COVID-19 patients progress to a severe disease manifestation with respiratory failure and the necessity of mechanical ventilation. Identifying patients at risk is critical for optimised care and early therapeutic interventions. We investigated the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) shedding relative to disease severity.We analysed nasopharyngeal and tracheal shedding of SARS-CoV-2 in 92 patients with diagnosed COVID-19. Upon admission, standardised nasopharyngeal swab or sputum samples were collected. If patients were mechanically ventilated, endotracheal aspirate samples were additionally obtained. Viral shedding was quantified by real-time PCR detection of SARS-CoV-2 RNA.45% (41 out of 92) of COVID-19 patients had a severe disease course with the need for mechanical ventilation (severe group). At week 1, the initial viral shedding determined from nasopharyngeal swabs showed no significant difference between nonsevere and severe cases. At week 2, a difference could be observed as the viral shedding remained elevated in severely ill patients. A time-course of C-reactive protein, interleukin-6 and procalcitonin revealed an even more protracted inflammatory response following the delayed drop of virus shedding load in severely ill patients. A significant proportion (47.8%) of patients showed evidence of prolonged viral shedding (>17 days), which was associated with severe disease courses (73.2%).We report that viral shedding does not differ significantly between severe and nonsevere COVID-19 cases upon admission to the hospital. Elevated SARS-CoV-2 shedding in the second week of hospitalisation, a systemic inflammatory reaction peaking between the second and third week, and prolonged viral shedding are associated with a more severe disease course.