Rapid molecular detection of airway pathogens in lung transplant recipients.

BackgroundAirway infections are difficult to distinguish from acute rejection in lung transplant recipients. Traditional culture techniques take time that may delay treatment. We hypothesized that a rapid multiplex molecular assay could improve time to diagnosis and appropriate clinical decision making.MethodsIn a prospective observational study of recipients undergoing bronchoscopy, we assessed the BioFire® FilmArray® Pneumonia Panel (BFPP) in parallel to standard of care (SOC) diagnostics. Research clinicians performed shadow (research only) clinical decision making in real time. Time to report and interpretation were reported as median and interquartile ranges and compared by Wilcoxon signed-ranked test. Agreement was defined based on detection of any species targeted in the molecular assay.ResultsFor the 150 enrolled subjects, BFPP results were available 3.8 hours (IQR 2.8-5.1) following bronchoscopy, compared to 13 hours for viral SOC (IQR 10-34, P < .001) results and 48 hours for bacterial SOC (IQR 46-70, P < .001) results. Positive BFPP results were interpreted in 9 hours (IQR 5-20) following bronchoscopy, compared to 74 hours for SOC (IQR 37-110, P < .001). Assays agreed for 138 (92%) of the 150 subjects. Of 22 BFPP diagnoses, five (23%) resulted in a shadow antibiotic recommendation. Notable BFPP deficiencies included fungal species and H parainfluenzae, accounting for 15 (27%) and 13 (23%) of the 56 actionable SOC results, respectively.ConclusionsThis molecular diagnostic including bacterial targets has the potential to shorten time to diagnosis and augment current clinical decision making but cannot replace SOC culture methods

Natural killer cells activated through NKG2D mediate lung ischemia-reperfusion injury.

Pulmonary ischemia-reperfusion injury (IRI) is a clinical syndrome of acute lung injury that occurs after lung transplantation or remote organ ischemia. IRI causes early mortality and has no effective therapies. While NK cells are innate lymphocytes capable of recognizing injured cells, their roles in acute lung injury are incompletely understood. Here, we demonstrated that NK cells were increased in frequency and cytotoxicity in 2 different IRI mouse models. We showed that NK cells trafficked to the lung tissue from peripheral reservoirs and were more mature within lung tissue. Acute lung ischemia-reperfusion injury was blunted in a NK cell-deficient mouse strain but restored with adoptive transfer of NK cells. Mechanistically, NK cell NKG2D receptor ligands were induced on lung endothelial and epithelial cells following IRI, and antibody-mediated NK cell depletion or NKG2D stress receptor blockade abrogated acute lung injury. In human lung tissue, NK cells were increased at sites of ischemia-reperfusion injury and activated NK cells were increased in prospectively collected human bronchoalveolar lavage in subjects with severe IRI. These data support a causal role for recipient peripheral NK cells in pulmonary IRI via NK cell NKG2D receptor ligation. Therapies targeting NK cells may hold promise in acute lung injury

Natural killer cells activated through NKG2D mediate lung ischemia-reperfusion injury.

Pulmonary ischemia-reperfusion injury (IRI) is a clinical syndrome of acute lung injury that occurs after lung transplantation or remote organ ischemia. IRI causes early mortality and has no effective therapies. While NK cells are innate lymphocytes capable of recognizing injured cells, their roles in acute lung injury are incompletely understood. Here, we demonstrated that NK cells were increased in frequency and cytotoxicity in 2 different IRI mouse models. We showed that NK cells trafficked to the lung tissue from peripheral reservoirs and were more mature within lung tissue. Acute lung ischemia-reperfusion injury was blunted in a NK cell-deficient mouse strain but restored with adoptive transfer of NK cells. Mechanistically, NK cell NKG2D receptor ligands were induced on lung endothelial and epithelial cells following IRI, and antibody-mediated NK cell depletion or NKG2D stress receptor blockade abrogated acute lung injury. In human lung tissue, NK cells were increased at sites of ischemia-reperfusion injury and activated NK cells were increased in prospectively collected human bronchoalveolar lavage in subjects with severe IRI. These data support a causal role for recipient peripheral NK cells in pulmonary IRI via NK cell NKG2D receptor ligation. Therapies targeting NK cells may hold promise in acute lung injury

NKG2D receptor activation drives primary graft dysfunction severity and poor lung transplantation outcomes

Clinical outcomes after lung transplantation, a life-saving therapy for patients with end-stage lung diseases, are limited by primary graft dysfunction (PGD). PGD is an early form of acute lung injury with no specific pharmacologic therapies. Here, we present a large multicenter study of plasma and bronchoalveolar lavage (BAL) samples collected on the first posttransplant day, a critical time for investigations of immune pathways related to PGD. We demonstrated that ligands for NKG2D receptors were increased in the BAL from participants who developed severe PGD and were associated with increased time to extubation, prolonged intensive care unit length of stay, and poor peak lung function. Neutrophil extracellular traps (NETs) were increased in PGD and correlated with BAL TNF-α and IFN-γ cytokines. Mechanistically, we found that airway epithelial cell NKG2D ligands were increased following hypoxic challenge. NK cell killing of hypoxic airway epithelial cells was abrogated with NKG2D receptor blockade, and TNF-α and IFN-γ provoked neutrophils to release NETs in culture. These data support an aberrant NK cell/neutrophil axis in human PGD pathogenesis. Early measurement of stress ligands and blockade of the NKG2D receptor hold promise for risk stratification and management of PGD

Ecological validity of a deep learning algorithm to detect gait events from real-life walking bouts in mobility-limiting diseases

The clinical assessment of mobility, and walking specifically, is still mainly based on functional tests that lack ecological validity. Thanks to inertial measurement units (IMUs), gait analysis is shifting to unsupervised monitoring in naturalistic and unconstrained settings. However, the extraction of clinically relevant gait parameters from IMU data often depends on heuristics-based algorithms that rely on empirically determined thresholds. These were mainly validated on small cohorts in supervised settings.MethodsHere, a deep learning (DL) algorithm was developed and validated for gait event detection in a heterogeneous population of different mobility-limiting disease cohorts and a cohort of healthy adults. Participants wore pressure insoles and IMUs on both feet for 2.5 h in their habitual environment. The raw accelerometer and gyroscope data from both feet were used as input to a deep convolutional neural network, while reference timings for gait events were based on the combined IMU and pressure insoles data.Results and discussionThe results showed a high-detection performance for initial contacts (ICs) (recall: 98%, precision: 96%) and final contacts (FCs) (recall: 99%, precision: 94%) and a maximum median time error of −0.02 s for ICs and 0.03 s for FCs. Subsequently derived temporal gait parameters were in good agreement with a pressure insoles-based reference with a maximum mean difference of 0.07, −0.07, and <0.01 s for stance, swing, and stride time, respectively. Thus, the DL algorithm is considered successful in detecting gait events in ecologically valid environments across different mobility-limiting diseases

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BackgroundEstimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period.Methods22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution.FindingsGlobal all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations.InterpretationGlobal adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic