Diaphragm ultrasound in patients with prolonged weaning from mechanical ventilation.

Background: Several publications have examined diaphragmatic ultrasound using two-dimensional (2D) parameters in the context of weaning from mechanical ventilation (MV) and extubation. However, the studied cohorts had rather short duration of ventilation. Examinations on patients with prolonged weaning after long-term ventilation were missing.

Novel architecture for gated recurrent unit autoencoder trained on time series from electronic health records enables detection of ICU patient subgroups.

Electronic health records (EHRs) are used in hospitals to store diagnoses, clinician notes, examinations, lab results, and interventions for each patient. Grouping patients into distinct subsets, for example, via clustering, may enable the discovery of unknown disease patterns or comorbidities, which could eventually lead to better treatment through personalized medicine. Patient data derived from EHRs is heterogeneous and temporally irregular.

Computational Simulation of Virtual Patients Reduces Dataset Bias and Improves Machine Learning-Based Detection of ARDS from Noisy Heterogeneous ICU Datasets.

Machine learning (ML) technologies that leverage large-scale patient data are promising tools predicting disease evolution in individual patients. However, the limited generalizability of ML models developed on single-center datasets, and their unproven performance in real-world settings, remain significant constraints to their widespread adoption in clinical practice. One approach to tackle this issue is to base learning on large multi-center datasets.

Application of convex hull analysis for the evaluation of data heterogeneity between patient populations of different origin and implications of hospital bias in downstream machine-learning-based data processing: A comparison of 4 critical-care patient datasets.

Machine learning (ML) models are developed on a learning dataset covering only a small part of the data of interest. If model predictions are accurate for the learning dataset but fail for unseen data then generalization error is considered high. This problem manifests itself within all major sub-fields of ML but is especially relevant in medical applications.

A training strategy for hybrid models to break the curse of dimensionality.

Mechanistic/data-driven hybrid modeling is a key approach when the mechanistic details of the processes at hand are not sufficiently well understood, but also inferring a model purely from data is too complex. By the integration of first principles into a data-driven approach, hybrid modeling promises a feasible data demand alongside extrapolation. In this work, we introduce a learning strategy for tree-structured hybrid models to perform a binary classification task.

Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients.

Objective: The attitudes about the usage of artificial intelligence in healthcare are controversial. Unlike the perception of healthcare professionals, the attitudes of patients and their companions have been of less interest so far. In this study, we aimed to investigate the perception of artificial intelligence in healthcare among this highly relevant group along with the influence of digital affinity and sociodemographic factors.

Haemoglobin value and red blood cell transfusions in prolonged weaning from mechanical ventilation: a retrospective observational study.

Introduction: The role of haemoglobin (Hb) value and red blood cell (RBC) transfusions in prolonged weaning from mechanical ventilation (MV) is still controversial. Pathophysiological considerations recommend a not too restrictive transfusion strategy, whereas adverse effects of transfusions are reported. We aimed to investigate the association between Hb value, RBC transfusion and clinical outcome of patients undergoing prolonged weaning from MV.

Speckle tracking ultrasonography as a new tool to assess diaphragmatic function: a feasibility study.

A reliable method of measuring diaphragmatic function at the bedside is still lacking. Widely used two-dimensional (2D) ultrasonographic measurements, such as diaphragm excursion, diaphragm thickness, and fractional thickening (FT) have failed to show clear correlations with diaphragmatic function. A reason for this is that 2D ultrasonographic measurements, like FT, are merely able to measure the deformation of muscular diaphragmatic tissue in the transverse direction, while longitudinal measurements in the direction of contracting muscle fibres are not possible.

Biometric covariates and outcome in COVID-19 patients: are we looking close enough?

Background: The impact of biometric covariates on risk for adverse outcomes of COVID-19 disease was assessed by numerous observational studies on unstratified cohorts, which show great heterogeneity. However, multilevel evaluations to find possible complex, e.g.

Algorithmic surveillance of ICU patients with acute respiratory distress syndrome (ASIC): protocol for a multicentre stepped-wedge cluster randomised quality improvement strategy.

Introduction: The acute respiratory distress syndrome (ARDS) is a highly relevant entity in critical care with mortality rates of 40%. Despite extensive scientific efforts, outcome-relevant therapeutic measures are still insufficiently practised at the bedside. Thus, there is a clear need to adhere to early diagnosis and sufficient therapy in ARDS, assuring lower mortality and multiple organ failure.