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.

A survey of preferences for respiratory support in the intensive care unit for patients with acute hypoxaemic respiratory failure.

BACKGROUND: When caring for mechanically ventilated adults with acute hypoxaemic respiratory failure (AHRF), clinicians are faced with an uncertain choice between ventilator modes allowing for spontaneous breaths or ventilation fully controlled by the ventilator. The preferences of clinicians managing such patients, and what motivates their choice of ventilator mode, are largely unknown. To better understand how clinicians' preferences may impact the choice of ventilatory support for patients with AHRF, we issued a survey to an international network of intensive care unit (ICU) researchers. METHODS: We distributed an online survey with 32 broadly similar and interlinked questions on how clinicians prioritise spontaneous or controlled ventilation in invasively ventilated patients with AHRF of different severity, and which factors determine their choice. RESULTS: The survey was distributed to 1337 recipients in 12 countries. Of these, 415 (31%) completed the survey either fully (52%) or partially (48%). Most respondents were identified as medical specialists (87%) or physicians in training (11%). Modes allowing for spontaneous ventilation were considered preferable in mild AHRF, with controlled ventilation considered as progressively more important in moderate and severe AHRF. Among respondents there was strong support (90%) for a randomised clinical trial comparing spontaneous with controlled ventilation in patients with moderate AHRF. CONCLUSIONS: The responses from this international survey suggest that there is clinical equipoise for the preferred ventilator mode in patients with AHRF of moderate severity. We found strong support for a randomised trial comparing modes of ventilation in patients with moderate AHRF.

Intravenous vitamin C therapy in adult patients with sepsis: A rapid practice guideline.

BACKGROUND: This Rapid Practice Guideline provides an evidence-based recommendation to address the question: in adults with sepsis or septic shock, should we recommend using or not using intravenous vitamin C therapy? METHODS: The panel included 21 experts from 16 countries and used a strict policy for potential financial and intellectual conflicts of interest. Methodological support was provided by the Guidelines in Intensive Care, Development, and Evaluation (GUIDE) group. Based on an updated systematic review, and the grading of recommendations, assessment, development, and evaluation approach, we evaluated the certainty of evidence and developed recommendations using the evidence-to-decision framework. We conducted an electronic vote, requiring >80% agreement among the panel for a recommendation to be adopted. RESULTS: At longest follow-up, 90 days, intravenous vitamin C probably does not substantially impact (relative risk 1.05, 95% confidence interval [CI] 0.94 to 1.17; absolute risk difference 1.8%, 95% CI -2.2 to 6.2; 6 trials, n = 2148, moderate certainty). Effects of vitamin C on mortality at earlier timepoints was of low or very low certainty due to risk of bias of the included studies and significant heterogeneity between study results. Few adverse events were reported with the use of vitamin C. The panel did not identify any major differences in other outcomes, including duration of mechanical ventilation, ventilator free days, hospital or intensive care unit length of stay, acute kidney injury, need for renal replacement therapy. Vitamin C may result in a slight reduction in duration of vasopressor support (MD -18.9 h, 95% CI -26.5 to -11.4; 21 trials, n = 2661, low certainty); but may not reduce sequential organ failure assessment scores (MD -0.69, 95% CI -1.55 to 0.71; 24 trials, n = 4002, low certainty). The panel judged the undesirable consequences of using IV vitamin C to probably outweigh the desirable consequences, and therefore issued a conditional recommendation against using IV vitamin C therapy in sepsis. CONCLUSIONS: The panel suggests against use of intravenous vitamin C in adult patients with sepsis, beyond that of standard nutritional supplementation. Small and single center trials on this topic should be discouraged.

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 categorize 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 single-cell RNA sequencing 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, visualization 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. AVAILABILITY AND IMPLEMENTATION: GeoWaVe is available as part of the CytoCluster package https://github.com/burtonrj/CytoCluster and published on the Python Package Index https://pypi.org/project/cytocluster. Benchmarking data described are available from https://doi.org/10.5281/zenodo.7134723. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.