GeoWaVe: Geometric median clustering with weighted voting for ensemble clustering of cytometry data

Motivation Clustering is an unsupervised method for identifying structure in unlabelled data. In the context of cytometry, it is typically used to categorise cells into subpopulations of similar phenotypes. However, clustering is greatly dependent on hyperparameters and the data to which it is applied as each algorithm makes different assumptions and generates a different ‘view’ of the dataset. As such, the choice of clustering algorithm can significantly influence results, and there is often not one preferred method but different insights to be obtained from different methods. To overcome these limitations, consensus approaches are needed that directly address the effect of competing algorithms. To the best of our knowledge, consensus clustering algorithms designed specifically for the analysis of cytometry data are lacking. Results We present a novel ensemble clustering methodology based on geometric median clustering with weighted voting (GeoWaVe). Compared to graph ensemble clustering methods that have gained popularity in scRNA-seq analysis, GeoWaVe performed favourably on different sets of high-dimensional mass and flow cytometry data. Our findings provide proof of concept for the power of consensus methods to make the analysis, visualisation and interpretation of cytometry data more robust and reproducible. The wide availability of ensemble clustering methods is likely to have a profound impact on our understanding of cellular responses, clinical conditions, and therapeutic and diagnostic options

Conventional and unconventional T cell responses contribute to the prediction of clinical outcome and causative bacterial pathogen in sepsis patients

Sepsis is characterised by a dysfunctional host response to infection culminating in life-threatening organ failure that requires complex patient management and rapid intervention. Timely diagnosis of the underlying cause of sepsis is crucial, and identifying those at risk of complications and death is imperative for triaging treatment and resource allocation. Here, we explored the potential of explainable machine learning models to predict mortality and causative pathogen in sepsis patients. By using a modelling pipeline employing multiple feature selection algorithms, we demonstrate the feasibility to identify integrative patterns from clinical parameters, plasma biomarkers and extensive phenotyping of blood immune cells. Whilst no single variable had sufficient predictive power, models that combined five and more features showed a macro area under the curve (AUC) of 0.85 to predict 90 day mortality after sepsis diagnosis, and a macro AUC of 0.86 to discriminate between Gram-positive and Gram-negative bacterial infections. Parameters associated with the cellular immune response contributed the most to models predictive of 90 day mortality, most notably, the proportion of T cells among PBMCs, together with expression of CXCR3 by CD4+ T cells and CD25 by mucosal-associated invariant T (MAIT) cells. Frequencies of Vδ2+ γδ T cells had the most profound impact on the prediction of Gram-negative infections, alongside other T cell-related variables and total neutrophil count. Overall, our findings highlight the added value of measuring the proportion and activation patterns of conventional and unconventional T cells in the blood of sepsis patients in combination with other immunological, biochemical and clinical parameters

Prophylactic Melatonin for Delirium in Intensive Care (Pro-MEDIC): Study protocol for a randomised controlled trial

Background: Delirium is an acute state of brain dysfunction characterised by fluctuating inattention and cognitive disturbances, usually due to illness. It occurs commonly in the intensive care unit (ICU), and it is associated with greater morbidity and mortality. It is likely that disturbances of sleep and of the day-night cycle play a significant role. Melatonin is a naturally occurring, safe and cheap hormone that can be administered to improve sleep. The main aim of this trial will be to determine whether prophylactic melatonin administered to critically ill adults, when compared with placebo, decreases the rate of delirium. Methods: This trial will be a multi-centre, randomised, placebo-controlled study conducted in closed ICUs in Australia. Our aim is to enrol 850 adult patients with an expected ICU length of stay (LOS) of 72h or more. Eligible patients for whom there is consent will be randomised to receive melatonin 4mg enterally or placebo in a 1:1 ratio according to a computer-generated randomisation list, stratified by site. The study drug will be indistinguishable from placebo. Patients, doctors, nurses, investigators and statisticians will be blinded. Melatonin or placebo will be administered once per day at 21:00 until ICU discharge or 14days after enrolment, whichever occurs first. Trained staff will assess patients twice daily to determine the presence or absence of delirium using the Confusion Assessment Method for the ICU score. Data will also be collected on demographics, the overall prevalence of delirium, duration and severity of delirium, sleep quality, participation in physiotherapy sessions, ICU and hospital LOS, morbidity and mortality, and healthcare costs. A subgroup of 100 patients will undergo polysomnographic testing to further evaluate the quality of sleep. Discussion: Delirium is a significant issue in ICU because of its frequency and associated poorer outcomes. This trial will be the largest evaluation of melatonin as a prophylactic agent to prevent delirium in the critically ill population. This study will also provide one of the largest series of polysomnographic testing done in ICU. Trial registration: Australian New Zealand Clinical Trial Registry (ANZCTR) number: ACTRN12616000436471. Registered on 20 December 2015

Magnitude and determinants of excess total, age-specific and sex-specific all-cause mortality in 24 countries worldwide during 2020 and 2021: results on the impact of the COVID-19 pandemic from the C-MOR project.

INTRODUCTION: To examine the impact of the COVID-19 pandemic on mortality, we estimated excess all-cause mortality in 24 countries for 2020 and 2021, overall and stratified by sex and age. METHODS: Total, age-specific and sex-specific weekly all-cause mortality was collected for 2015-2021 and excess mortality for 2020 and 2021 was calculated by comparing weekly 2020 and 2021 age-standardised mortality rates against expected mortality, estimated based on historical data (2015-2019), accounting for seasonality, and long-term and short-term trends. Age-specific weekly excess mortality was similarly calculated using crude mortality rates. The association of country and pandemic-related variables with excess mortality was investigated using simple and multilevel regression models. RESULTS: Excess cumulative mortality for both 2020 and 2021 was found in Austria, Brazil, Belgium, Cyprus, England and Wales, Estonia, France, Georgia, Greece, Israel, Italy, Kazakhstan, Mauritius, Northern Ireland, Norway, Peru, Poland, Slovenia, Spain, Sweden, Ukraine, and the USA. Australia and Denmark experienced excess mortality only in 2021. Mauritius demonstrated a statistically significant decrease in all-cause mortality during both years. Weekly incidence of COVID-19 was significantly positively associated with excess mortality for both years, but the positive association was attenuated in 2021 as percentage of the population fully vaccinated increased. Stringency index of control measures was positively and negatively associated with excess mortality in 2020 and 2021, respectively. CONCLUSION: This study provides evidence of substantial excess mortality in most countries investigated during the first 2 years of the pandemic and suggests that COVID-19 incidence, stringency of control measures and vaccination rates interacted in determining the magnitude of excess mortality

Long-term outcomes of bilateral lobar lung transplantation

OBJECTIVES: Lobar lung transplantation is an option that provides the possibility of transplanting an urgent listed recipient of small size with a size-mismatched donor lung by surgically reducing the size of the donor lung. We report our short- and long-term results with bilateral lobar lung transplantation (BLLT) and compare it with the long-term outcomes of our cohort. METHODS: Retrospective analyses of 75 lung transplant recipients who received downsized lungs with a special focus on 23 recipients with BLLT performed since January 2000. Postoperative surgical complications, lung function tests, late complications and survival were analyzed. The decision to perform lobar transplantation was considered during allocation and finally decided prior to implantation. RESULTS: Cystic fibrosis was the most common indication (43.5%) followed by pulmonary fibrosis (35%). Median age at transplantation was 41 (range 13–66) years. Fifteen were females. Nineteen of the transplantations (83%) were done with extracorporeal membrane oxygenation (ECMO) support; 3 of them were already on ECMO prior to transplantation. There was no 30-day or in-hospital mortality. No bronchial complications occurred. The most common early complication was haematothorax (39%), which required surgical intervention. The rate of postoperative atrial arrhythmias was 30%. Forced expiratory volumes in 1 s (% predicted) at 1 and 2 years were 76 ± 23 and 76 ± 22, respectively (mean ± standard deviation). By 2-year follow-up, bronchiolitis obliterans syndrome was documented in 3 patients with a median follow-up of 1457 days. Overall survivals at 1 and 5 years were 82 ± 8 and 64 ± 11%, respectively and were comparable with those of 219 other recipients who received bilateral lung transplantation during the same period (log rank test, P = 0.56). CONCLUSIONS: This study demonstrates that BLLT has short- and long-term outcomes comparable with those of standard bilateral lung transplantation. The limitation of lung transplantation due to size-mismatch, particularly in smaller recipients, could be overcome by utilizing lobar lung transplantation