How does SARS-CoV-2 targets the elderly patients? A review on potential mechanisms increasing disease severity

Importance: Among COVID-19 cases, especially the (frail) elderly show a high number of severe infections, hospital admissions, complications, and death. The highest mortality is found between 80 and 89 years old. Why do these patients have a higher risk of severe COVID-19? In this narrative review we address potential mechanisms regarding viral transmission, physical reserve and the immune system, increasing the severity of this infection in elderly patients. Observations: First, the spread of COVID-19 may be enhanced in elderly patients. Viral shedding may be increased, and early identification may be complicated due to atypical disease presentation and limited testing capacity. Applying hygiene and quarantine measures, especially in patients with cognitive disorders including dementia, can be challenging. Additionally, elderly patients have a decreased cardiorespiratory reserve and are more likely to have co-morbidity including atherosclerosis, rendering them more susceptible to complications. The aging innate and adaptive immune system is weakened, while there is a pro-inflammatory tendency. The effects of SARS-CoV-2 on the immune system on cytokine production and T-cells, further seem to aggravate this pro-inflammatory tendency, especially in patients with cardiovascular comorbidity, increasing disease severity. Conclusions and relevance: The combination of all factors mentio

A study protocol of external validation of eight COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting

BACKGROUND: The COVID-19 pandemic has a large impact worldwide and is known to particularly affect the older population. This paper outlines the protocol for external validation of prognostic models predicting mortality risk after presentation with COVID-19 in the older population. These prognostic models were originally developed in an adult population and will be validated in an older population (≥ 70 years of age) in three healthcare settings: the hospital setting, the primary care setting, and the nursing home setting.METHODS: Based on a living systematic review of COVID-19 prediction models, we identified eight prognostic models predicting the risk of mortality in adults with a COVID-19 infection (five COVID-19 specific models: GAL-COVID-19 mortality, 4C Mortality Score, NEWS2 + model, Xie model, and Wang clinical model and three pre-existing prognostic scores: APACHE-II, CURB65, SOFA). These eight models will be validated in six different cohorts of the Dutch older population (three hospital cohorts, two primary care cohorts, and a nursing home cohort). All prognostic models will be validated in a hospital setting while the GAL-COVID-19 mortality model will be validated in hospital, primary care, and nursing home settings. The study will include individuals ≥ 70 years of age with a highly suspected or PCR-confirmed COVID-19 infection from March 2020 to December 2020 (and up to December 2021 in a sensitivity analysis). The predictive performance will be evaluated in terms of discrimination, calibration, and decision curves for each of the prognostic models in each cohort individually. For prognostic models with indications of miscalibration, an intercept update will be performed after which predictive performance will be re-evaluated.DISCUSSION: Insight into the performance of existing prognostic models in one of the most vulnerable populations clarifies the extent to which tailoring of COVID-19 prognostic models is needed when models are applied to the older population. Such insight will be important for possible future waves of the COVID-19 pandemic or future pandemics.</p

Dutch Robotics 2011 adult-size team description

This document presents the 2011 edition of the team Dutch Robotics from The Netherlands. Our team gathers three Dutch technical universities, namely Delft University of Technology, Eindhoven University of Technology and University of Twente, and the commercial company Philips. We contribute an adult-size humanoid robot TUlip, which is designed based on theory of the limit cycle walking developed in our earlier research. The key of our theory is that stable periodic walking gaits can be achieved even without high-bandwidth robot position control. Our control approach is based on simultaneous position and force control. For accurate force control, we make use of the Series Elastic Actuation. The control software of TUlip is based on the Darmstadt’s RoboFrame, and it runs on a PC104 computer with Linux Xenomai. The vision system consists of two wide-angle cameras, each interfaced with a dedicated Blackfin processor running vision algorithms, and a wireless networking interface

Dutch Robotics 2010 adult-size team description

This document presents the 2010 edition of the team Dutch Robotics from The Netherlands. Our team gathers three Dutch technical universities, namely Delft University of Technology, Eindhoven University of Technology and University of Twente, and the commercial company Philips. We contribute an adult-size humanoid robot TUlip, which is designed based on theory of the limit cycle walking developed in our earlier research. The key of our theory is that stable periodic walking gaits can be achieved even without high-bandwidth robot position control. Our control approach is based on simultaneous position and force control. For accurate force control, we make use of the Series Elastic Actuation. The control software of TUlip is based on the Darmstadt’s RoboFrame, and it runs on a PC104 computer with Linux Xenomai. The vision system consists of two wide-angle cameras, each interfaced with a dedicated Blackfin processor running vision algorithms, and a wireless networking interface

A study protocol of external validation of eight COVID-19 prognostic models for predicting mortality risk in older populations in a hospital, primary care, and nursing home setting

BACKGROUND: The COVID-19 pandemic has a large impact worldwide and is known to particularly affect the older population. This paper outlines the protocol for external validation of prognostic models predicting mortality risk after presentation with COVID-19 in the older population. These prognostic models were originally developed in an adult population and will be validated in an older population (≥ 70 years of age) in three healthcare settings: the hospital setting, the primary care setting, and the nursing home setting.METHODS: Based on a living systematic review of COVID-19 prediction models, we identified eight prognostic models predicting the risk of mortality in adults with a COVID-19 infection (five COVID-19 specific models: GAL-COVID-19 mortality, 4C Mortality Score, NEWS2 + model, Xie model, and Wang clinical model and three pre-existing prognostic scores: APACHE-II, CURB65, SOFA). These eight models will be validated in six different cohorts of the Dutch older population (three hospital cohorts, two primary care cohorts, and a nursing home cohort). All prognostic models will be validated in a hospital setting while the GAL-COVID-19 mortality model will be validated in hospital, primary care, and nursing home settings. The study will include individuals ≥ 70 years of age with a highly suspected or PCR-confirmed COVID-19 infection from March 2020 to December 2020 (and up to December 2021 in a sensitivity analysis). The predictive performance will be evaluated in terms of discrimination, calibration, and decision curves for each of the prognostic models in each cohort individually. For prognostic models with indications of miscalibration, an intercept update will be performed after which predictive performance will be re-evaluated.DISCUSSION: Insight into the performance of existing prognostic models in one of the most vulnerable populations clarifies the extent to which tailoring of COVID-19 prognostic models is needed when models are applied to the older population. Such insight will be important for possible future waves of the COVID-19 pandemic or future pandemics.</p

The association of inflammatory markers with frailty and in-hospital mortality in older COVID-19 patients

INTRODUCTION: During the COVID19 pandemic, older patients hospitalized for COVID-19 exhibited an increased mortality risk compared to younger patients. While ageing is associated with compromised immune responses and frailty, their contributions and interplay remain understudied. This study investigated the association between inflammatory markers and mortality and potential modification by frailty among older patients hospitalized for COVID-19. METHODS: Data were from three multicenter Dutch cohorts (COVID-OLD, CliniCo, Covid-Predict). Patients were 70 years or older, hospitalized for COVID-19and categorized into three frailty groups: fit (Clinical frailty score (CFS) 1-3), pre-frail (CFS 4-5), and frail (CFS 6-9). Immunological markers (lymphocyte count, neutrophil count, C-reactive protein, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and systemic inflammation index (SII)) were measured at baseline. Associations with in hospital mortality were examined using logistic regression. RESULTS: A total of 1697 patients were included from COVID-OLD, 656 from Covid-Predict, and 574 from CliniCo. The median age was 79, 77, and 78 years for each cohort. Hospital mortality rates were 33 %, 27 % and 39 % in the three cohorts, respectively. A lower CRP was associated with a higher frailty score in all three cohorts (all p 0.05). CONCLUSION: While frailty is a significant factor in determining overall outcomes in older patients, our study suggests that the elevated risk of mortality in older patients with frailty compared to fit patients is likely not explained by difference in inflammatory responses

Optimising fluid therapy in intensive care: Hypo- versus hypervolaemia

This dissertation focuses on the optimisation of fluid therapy in the intensive care unit (ICU) to prevent both hypo- and hypervolaemia. The general aim is to investigate whether fluid responsiveness can be predicted using less- or even non-invasive haemodynamic parameters, and secondly, what effect the type of fluid can have on diuresis and blood pH