Peak Plasma Levels of mtDNA Serve as a Predictive Biomarker for COVID-19 in-Hospital Mortality.

Several predictive biomarkers for coronavirus disease (COVID-19)-associated mortality in critically ill patients have been described. Although mitochondrial DNA (mtDNA) is elevated in patients with COVID-19, the association with coagulation function and its predictive power for mortality is unclear. Accordingly, this study investigates the predictive power of mtDNA for in-hospital mortality in critically ill patients with COVID-19, and whether combining it with thromboelastographic parameters can increase its predictive performance. This prospective explorative study included 29 patients with COVID-19 and 29 healthy matched controls. mtDNA encoding for NADH dehydrogenase 1 (ND1) was quantified using a quantitative polymerase chain reaction analysis, while coagulation function was evaluated using thromboelastometry and impedance aggregometry. Receiver operating characteristic (ROC) curves were used for the prediction of in-hospital mortality. Within the first 24 h, the plasma levels of mtDNA peaked significantly (controls: 65 (28-119) copies/µL; patients: 281 (110-805) at t0, 403 (168-1937) at t24, and 467 (188-952) copies/µL at t72; controls vs. patients: p = 0.02 at t0, p = 0.03 at t24, and p = 0.44 at t72). The mtDNA levels at t24 showed an excellent predictive performance for in-hospital mortality (area under the ROC curve: 0.90 (0.75-0.90)), which could not be improved by the combination with thromboelastometric or aggregometric parameters. Critically ill patients with COVID-19 present an early increase in the plasma levels of ND1 mtDNA, lasting over 24 h. They also show impairments in platelet function and fibrinolysis, as well as hypercoagulability, but these do not correlate with the plasma levels of fibrinogen. The peak plasma levels of mtDNA can be used as a predictive biomarker for in-hospital mortality; however, the combination with coagulation parameters does not improve the predictive validity.

Hypertension and COVID-19: Updates from the era of vaccines and variants.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the pathogen responsible for coronavirus disease 2019 (COVID-19) has been a major cause of morbidity and mortality globally. Older age, and the presence of certain components of metabolic syndrome, including hypertension have been associated with increased risk for severe disease and death in COVID-19 patients. The role of antihypertensive agents in the pathogenesis of COVID-19 has been extensively studied since the onset of the pandemic. This review discusses the potential pathophysiologic interactions between hypertension and COVID-19 and provides an up-to-date information on the implications of newly emerging SARS-CoV-2 variants, and vaccines on patients with hypertension.

COVID-19 and chronic fatigue syndrome: An endocrine perspective.

Patients recovering from COVID-19 may have persistent debilitating symptoms requiring long term support through individually tailored cardiopulmonary and psychological rehabilitation programs. Clinicians need to be aware about the likely long-term complications and their diagnostic assessments to help identify any occult problems requiring additional help. Endocrinological evaluations should be considered as part of the armamentarium in the management of such individuals with diligent cognizance about the involvement of the hypothalamo-pituitary-adrenal (HPA) axis, adrenal and thyroid function. We here review the literature and potential pathophysiological mechanisms involved in and related to post COVID-19 symptoms with an emphasis on endocrine function.

Severe COVID-19 acute respiratory distress syndrome in an adult with single-ventricle physiology: a case report.

BACKGROUND: COVID-19 can induce acute respiratory distress syndrome (ARDS). In patients with congenital heart disease, established treatment strategies are often limited due to their unique cardiovascular anatomy and passive pulmonary perfusion. CASE PRESENTATION: We report the first case of an adult with single-ventricle physiology and bidirectional cavopulmonary shunt who suffered from severe COVID-19 ARDS. Treatment strategies were successfully adopted, and pulmonary vascular resistance was reduced, both medically and through prone positioning, leading to a favorable outcome. CONCLUSION: ARDS treatment strategies including ventilatory settings, prone positioning therapy and cannulation techniques for extracorporeal oxygenation must be adopted carefully considering the passive venous return in patients with single-ventricle physiology.

Climate Change and Obesity.

Global warming and the rising prevalence of obesity are well described challenges of current mankind. Most recently, the COVID-19 pandemic arose as a new challenge. We here attempt to delineate their relationship with each other from our perspective. Global greenhouse gas emissions from the burning of fossil fuels have exponentially increased since 1950. The main contributors to such greenhouse gas emissions are manufacturing and construction, transport, residential, commercial, agriculture, and land use change and forestry, combined with an increasing global population growth from 1 billion in 1800 to 7.8 billion in 2020 along with rising obesity rates since the 1980s. The current Covid-19 pandemic has caused some decline in greenhouse gas emissions by limiting mobility globally via repetitive lockdowns. Following multiple lockdowns, there was further increase in obesity in wealthier populations, malnutrition from hunger in poor populations and death from severe infection with Covid-19 and its virus variants. There is a bidirectional relationship between adiposity and global warming. With rising atmospheric air temperatures, people typically will have less adaptive thermogenesis and become less physically active, while they are producing a higher carbon footprint. To reduce obesity rates, one should be willing to learn more about the environmental impact, how to minimize consumption of energy generating carbon dioxide and other greenhouse gas emissions, and to reduce food waste. Diets lower in meat such as a Mediterranean diet, have been estimated to reduce greenhouse gas emissions by 72%, land use by 58%, and energy consumption by 52%.

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 patient clinical outcomes upon ICU admission remain inadequate. This study aimed to develop a machine learning model, based on retrospective & prospective clinical data, to stratify patient risk and predict ICU survival and outcomes. METHODS: A Germany-wide electronic registry was established to pseudonymously collect admission, therapeutic and discharge information of SARS-CoV-2 ICU patients retrospectively and prospectively. Machine learning approaches were evaluated for the accuracy and interpretability of predictions. The Explainable Boosting Machine approach was selected as the most suitable method. Individual, non-linear shape functions for predictive parameters and parameter interactions are reported. RESULTS: 1039 patients were included in the Explainable Boosting Machine model, 596 patients retrospectively collected, and 443 patients prospectively collected. The model for prediction of general ICU outcome was shown to be more reliable to predict "survival". Age, inflammatory and thrombotic activity, and severity of ARDS at ICU admission were shown to be predictive of ICU survival. Patients' age, pulmonary dysfunction and transfer from an external institution were predictors for ECMO therapy. The interaction of patient age with D-dimer levels on admission and creatinine levels with SOFA score without GCS were predictors for renal replacement therapy. CONCLUSIONS: Using Explainable Boosting Machine analysis, we confirmed and weighed previously reported and identified novel predictors for outcome in critically ill COVID-19 patients. Using this strategy, predictive modeling of COVID-19 ICU patient outcomes can be performed overcoming the limitations of linear regression models. Trial registration "ClinicalTrials" (clinicaltrials.gov) under NCT04455451.