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AimsHeart transplantation may represent a particular risk factor for severe coronavirus infectious disease 2019 (COVID-19) due to chronic immunosuppression and frequent comorbidities. We conducted a nation-wide survey of all heart transplant centers in Germany presenting the clinical characteristics of heart transplant recipients with COVID-19 during the first months of the pandemic in Germany.Methods and resultsA multicenter survey of all heart transplant centers in Germany evaluating the current status of COVID-19 among adult heart transplant recipients was performed. A total of 21 heart transplant patients with COVID-19 was reported to the transplant centers during the first months of the pandemic in Germany. Mean patient age was 58.6 ± 12.3 years and 81.0% were male. Comorbidities included arterial hypertension (71.4%), dyslipidemia (71.4%), diabetes mellitus (33.3%), chronic kidney failure requiring dialysis (28.6%) and chronic-obstructive lung disease/asthma (19.0%). Most patients received an immunosuppressive drug regimen consisting of a calcineurin inhibitor (71.4%), mycophenolate mofetil (85.7%) and steroids (71.4%). Eight of 21 patients (38.1%) displayed a severe course needing invasive mechanical ventilation. Those patients showed a high mortality (87.5%) which was associated with right ventricular dysfunction (62.5% vs. 7.7%; p = 0.014), arrhythmias (50.0% vs. none; p = 0.012), and thromboembolic events (50.0% vs. none; p = 0.012). Elevated high-sensitivity cardiac troponin T- and N-terminal prohormone of brain natriuretic peptide were significantly associated with the severe form of COVID-19 (p = 0.017 and p < 0.001, respectively).ConclusionSevere course of COVID-19 was frequent in heart transplanted patients. High mortality was associated with right ventricular dysfunction, arrhythmias, thromboembolic events, and markedly elevated cardiac biomarkers.

Background: Staphylococcus aureus bacteremia (SAB) is of great clinical relevance, as it is the most common type of bacteremia. Several studies show that the quality of care and thus the outcome can be positively influenced by the involvement of infectious disease specialists and structured programs like Antimicrobial Stewardship (AMS). In 2020, the SARS-CoV-2 pandemic occurred, which dominated the healthcare system and global events during this time. At the same time, a standard operational procedure (SOP) for SAB quality management (SABQM) was introduced in a German maximum-care hospital with 500 beds. Additionally, voluntary AMS team consultations were introduced in June 2021. This work addresses whether the introduction of SABQM has led to an improvement in the quality of care for SAB, despite the possible negative influences of the pandemic. Methods: Retrospective statistical analyses were conducted on all 145 cases coded as SAB at this hospital during the “pre-pandemic” period (2017 to 2019, 75 cases) and the pandemic period (2020 to 2022, 70 cases). Population parameters and quality management parameters were extracted from the clinical patient documentation. In a first analysis, the SARS-CoV-2 status served as a discriminatory parameter to determine its influence on the quality of care within the “pandemic period”. In a second analysis, the period served as a discriminatory parameter to determine its influence on the quality of care. In a third analysis, the use of AMS team consultation served as a discriminatory parameter to determine its influence on the quality of care in a subgroup of 42 cases from June 2021 to 2022. Results: The SARS-CoV-2 status had no influence on the population parameters or the quality management parameters. Between both analyzed periods, there was an improvement in the quality management parameters, with statistically significant higher rates of follow-up blood cultures, transthoracic echocardiography and adequate antibiotic therapy. AMS team consultation led to a relevant, but not statistically significant improvement in the quality management indicators. Conclusions: An SOP for SABQM leads to an improvement in the quality of care, even under the possible negative influences of a pandemic. AMS team consultations further strengthen this positive influence, even if this is not statistically significant due to the small number of cases in the subgroup analyzed.

PurposeKnowledge regarding patients’ clinical condition at severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection is sparse. Data in the international, multicenter Lean European Open Survey on SARS-CoV-2-Infected Patients (LEOSS) cohort study may enhance the understanding of COVID-19.MethodsSociodemographic and clinical characteristics of SARS-CoV-2-infected patients, enrolled in the LEOSS cohort study between March 16, 2020, and May 14, 2020, were analyzed. Associations between baseline characteristics and clinical stages at diagnosis (uncomplicated vs. complicated) were assessed using logistic regression models.ResultsWe included 2155 patients, 59.7% (1,287/2,155) were male; the most common age category was 66–85 years (39.6%; 500/2,155). The primary COVID-19 diagnosis was made in 35.0% (755/2,155) during complicated clinical stages. A significant univariate association between age; sex; body mass index; smoking; diabetes; cardiovascular, pulmonary, neurological, and kidney diseases; ACE inhibitor therapy; statin intake and an increased risk for complicated clinical stages of COVID-19 at diagnosis was found. Multivariable analysis revealed that advanced age [46–65 years: adjusted odds ratio (aOR): 1.73, 95% CI 1.25–2.42, p = 0.001; 66–85 years: aOR 1.93, 95% CI 1.36–2.74, p < 0.001; > 85 years: aOR 2.38, 95% CI 1.49–3.81, p < 0.001 vs. individuals aged 26–45 years], male sex (aOR 1.23, 95% CI 1.01–1.50, p = 0.040), cardiovascular disease (aOR 1.37, 95% CI 1.09–1.72, p = 0.007), and diabetes (aOR 1.33, 95% CI 1.04–1.69, p = 0.023) were associated with complicated stages of COVID-19 at diagnosis.ConclusionThe LEOSS cohort identified age, cardiovascular disease, diabetes and male sex as risk factors for complicated disease stages at SARS-CoV-2 diagnosis, thus confirming previous data. Further data regarding outcomes of the natural course of COVID-19 and the influence of treatment are required.

Resistance to multiple antiretroviral drugs among people living with HIV (PLWH) can result in a high pill burden, causing toxicity and drug interactions. Thus, the goal is to simplify treatment regimens while maintaining effectiveness. However, former resistance analysis data may not be current or complete. The use of proviral DNA genotyping may assist in selecting appropriate treatment options. A retrospective study was carried out on individuals belonging to the Cologne HIV cohort with a resistance history to two or more antiretroviral (ARV) classes and on non-standard antiretroviral therapy (ART). Patients required former viral RNA and a recent proviral DNA resistance test to be available prior to the switch to ART. Potential discrepancies between resistance test results obtained through RNA and proviral DNA methods and the consequent virological and clinical outcomes following ART adjustments were analyzed. Out of 1250 patients, 35 were eligible for inclusion in this study. The median length of known HIV infection was 27 years, and the median duration of ART was 22 years. Of the 35 participants, 16 had received all five ARV classes. Based on proviral DNA genotyping results, ART was simplified in 17 patients. At the last follow-up examination after changing therapy, 15 patients had HIV RNA <50 copies/mL (median 202 days, range 21–636). The mean number of pills per day decreased from eight to three, and the median intake frequency decreased from two to one time/day (ranges 1–2). Our study supports the use of proviral DNA genotyping as a safe strategy for switching to simplified ART regimens. However, the lack of extensive research on the advantages of proviral DNA genotyping makes it challenging to fully assess its benefits in terms of treatment selection.

BackgroundWhile the short-term symptoms of post-COVID syndromes (PCS) are well-known, the long-term clinical characteristics, risk factors and outcomes of PCS remain unclear. Moreover, there is ongoing discussion about the effectiveness of post-infection vaccination against severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) to aid in PCS recovery.MethodsIn this longitudinal and observational case-control study we aimed at identifying long-term PCS courses and evaluating the effects of post-infection vaccinations on PCS recovery. Individuals with initial mild COVID-19 were followed for a period of 15 months after primary infection. We assessed PCS outcomes, distinct symptom clusters (SC), and SARS-CoV-2 immunoglobulin G (IgG) levels in patients who received SARS-CoV-2 vaccination, as well as those who did not. To identify potential associating factors with PCS, we used binomial regression models and reported the results as odds ratios (OR) with 95% confidence intervals (95%CI).ResultsOut of 958 patients, follow-up data at 15 month after infection was obtained for 222 (23.2%) outpatients. Of those individuals, 36.5% (81/222) and 31.1% (69/222) were identified to have PCS at month 10 and 15, respectively. Fatigue and dyspnea (SC2) rather than anosmia and ageusia (SC1) constituted PCS at month 15. SARS-CoV-2 IgG levels were equally distributed over time among age groups, sex, and absence/presence of PCS. Of the 222 patients, 77.0% (171/222) were vaccinated between 10- and 15-months post-infection, but vaccination did not affect PCS recovery at month 15. 26.3% of unvaccinated and 25.8% of vaccinated outpatients improved from PCS (p= .9646). Baseline headache (SC4) and diarrhoea (SC5) were risk factors for PCS at months 10 and 15 (SC4: OR 1.85 (95%CI 1.04-3.26), p=.0390; SC5: OR 3.27(95%CI 1.54-6.64), p=.0009).ConclusionBased on the specific symptoms of PCS our findings show a shift in the pattern of recovery. We found no effect of SARS-CoV-2 vaccination on PCS recovery and recommend further studies to identify predicting biomarkers and targeted PCS therapeutics.

Background The coronavirus disease 2019 (COVID-19) pandemic is driven by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which has led to an enormous burden on patient morbidity and mortality. The renin–angiotensin–aldosterone system (RAAS) plays a significant role in various pulmonary diseases. Since SARS-CoV-2 utilizes the angiotensin-converting enzyme (ACE)2 receptor to exert its virulence and pathogenicity, the RAAS is of particular importance in COVID 19. Methods Our preliminary study investigates retrospectively the influence of selected ACE-polymorphisms (I/D location at intron 16 in the B-coding sequence (rs4646994) and A-240T (rs 4291) at the A-promoter) as well as ACE1 and ACE2 serum levels on disease severity and the inflammatory response in inpatients and outpatients with COVID-19. Results Our study included 96 outpatients and 88 inpatients (65.9% male, mean age 60 years) with COVID-19 from April to December 2020 in four locations in Germany. Of the hospitalized patients, 88.6% participants were moderately ill (n = 78, 64% male, median age 60 years), and 11.4% participants were severely ill or deceased (n = 10, 90% male, median age 71 years). We found no polymorphism-related difference in disease, in age distribution, time to hospitalization and time of hospitalization for the inpatient group. ACE1 serum levels were significantly increased in the DD compared to the II polymorphism and in the TT compared to the AA polymorphism. There was no significant difference in ACE 1 serum levels l between moderately ill and severely ill patients. However, participants requiring oxygen supplementation had significantly elevated ACE1 levels compared to participants not requiring oxygen, with no difference in ACE2 levels whereas females had significantly higher ACE2 levels. Conclusions Although there were no differences in the distribution of ACE polymorphisms in disease severity, we found increased proinflammatory regulation of the RAAS in patients with oxygen demand and increased serum ACE2 levels in women, indicating a possible enhanced anti-inflammatory immune response. Clinical trial registration : PreBiSeCov: German Clinical Trials Register, DRKS-ID: DRKS00021591, Registered on 27th April 2020.

Background/Objectives: Antimicrobial resistance is a global threat to safe health care, and a reduction in antibiotic consumption seems to be an appropriate preventive measure. In Germany, the reporting of hospital antibiotics consumption to an independent institution is only voluntary. Although a high level of willingness to improve can be assumed in the case of participation, the median consumptions of reporting hospitals change only slightly. This study examines the question of whether the logistical consumption figures adequately reflect real consumption, and if not, how to optimize the use of logistical data for clinical decisions. Methods: Four selected wards were analyzed during six months. A retrospective analysis of patient case files was performed to receive “prescribed daily doses” (PDDs). These were compared to “defined daily doses” (DDDs) from logistical data. Additional inventories were performed to calculated stored antibiotics. Antibiotics goods flows were presented via waterfall diagrams to identify logistic patterns that could explain PDD/DDD quotients. Antimicrobial stewardship (AMS) quality indicators were analyzed to give advice for optimized clinical AMS measures. Results: The total PDD/DDD quotient was 0.69. Four logistical patterns were identified. Optimized prophylaxis, AMS consultations and reevaluation of therapy seem to be the most useful measures to reduce PDDs. Conclusions: If AMS programs rely solely on DDDs, measures cannot be optimal. A complete consideration of antibiotic goods flows supports clinical decisions, but is very costly in terms of data collection. The consideration of logistical data can help to identify areas of focus for AMS programs. Therefore, specialists of antibiotics logistics should complement clinical AMS teams.

PurposeWhile 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.MethodsWe 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).ResultsThe 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.ConclusionWe present a machine learning-based predictor model and a clinical score for identifying patients at risk of developing advanced COVID-19.

PurposeThe ongoing pandemic caused by the novel severe acute respiratory coronavirus 2 (SARS-CoV-2) has stressed health systems worldwide. Patients with chronic kidney disease (CKD) seem to be more prone to a severe course of coronavirus disease (COVID-19) due to comorbidities and an altered immune system. The study’s aim was to identify factors predicting mortality among SARS-CoV-2-infected patients with CKD.MethodsWe analyzed 2817 SARS-CoV-2-infected patients enrolled in the Lean European Open Survey on SARS-CoV-2-infected patients and identified 426 patients with pre-existing CKD. Group comparisons were performed via Chi-squared test. Using univariate and multivariable logistic regression, predictive factors for mortality were identified.ResultsComparative analyses to patients without CKD revealed a higher mortality (140/426, 32.9% versus 354/2391, 14.8%). Higher age could be confirmed as a demographic predictor for mortality in CKD patients (> 85 years compared to 15–65 years, adjusted odds ratio (aOR) 6.49, 95% CI 1.27–33.20, p = 0.025). We further identified markedly elevated lactate dehydrogenase (> 2 × upper limit of normal, aOR 23.21, 95% CI 3.66–147.11, p < 0.001), thrombocytopenia (< 120,000/µl, aOR 11.66, 95% CI 2.49–54.70, p = 0.002), anemia (Hb < 10 g/dl, aOR 3.21, 95% CI 1.17–8.82, p = 0.024), and C-reactive protein (≥ 30 mg/l, aOR 3.44, 95% CI 1.13–10.45, p = 0.029) as predictors, while renal replacement therapy was not related to mortality (aOR 1.15, 95% CI 0.68–1.93, p = 0.611).ConclusionThe identified predictors include routinely measured and universally available parameters. Their assessment might facilitate risk stratification in this highly vulnerable cohort as early as at initial medical evaluation for SARS-CoV-2.

•A simple approach to use the hand vein pattern as a tool for identification is introduced.•A standardised grid system consisting of six lines and four sectors is applied on the dorsum of the hands.•This approach can be a simple and non-costly tool for the analysis of hand vein patterns. In a case of child pornography, only the dorsum of the offender's hand was clearly visible. After identification of a suspect, the question arose of whether and how it is possible to identify or exclude the suspect as perpetrator according to the morphology of the hand vein pattern. A simple approach to use the hand vein pattern in crime suspects as a tool for identification was tested. In this study, the hand vein patterns of 30 study participants were analysed from conventional frames on videography. A standardised grid system consisting of six lines and four sectors was applied on the dorsum of the hands. Vein branchings within the sectors and line crossings of the veins were counted, leading to a total of 11 variables for each hand. A positive identification of each of the 30 test participants was possible for each hand when taking only the first five variables into account. A random overlapping prediction was obtained by statistically simulating hand vein patterns of different numbers of persons using this sample. Considering the hand vein frequencies in this sample, the results indicate that the chance for two persons having the same pattern is smaller than 1:1000. It can be concluded that the introduced grid system approach can be an appropriate simple and non-costly tool for the analysis of the pattern of hand veins for identification purposes.