Machine learning predicts mortality based on analysis of ventilation parameters of critically ill patients: multi-centre validation

Background Mechanical Ventilation (MV) is a complex and central treatment process in the care of critically ill patients. It influences acid–base balance and can also cause prognostically relevant biotrauma by generating forces and liberating reactive oxygen species, negatively affecting outcomes. In this work we evaluate the use of a Recurrent Neural Network (RNN) modelling to predict outcomes of mechanically ventilated patients, using standard mechanical ventilation parameters. Methods We performed our analysis on VENTILA dataset, an observational, prospective, international, multi-centre study, performed to investigate the effect of baseline characteristics and management changes over time on the all-cause mortality rate in mechanically ventilated patients in ICU. Our cohort includes 12,596 adult patients older than 18, associated with 12,755 distinct admissions in ICUs across 37 countries and receiving invasive and non-invasive mechanical ventilation. We carry out four different analysis. Initially we select typical mechanical ventilation parameters and evaluate the machine learning model on both, the overall cohort and a subgroup of patients admitted with respiratory disorders. Furthermore, we carry out sensitivity analysis to evaluate whether inclusion of variables related to the function of other organs, improve the predictive performance of the model for both the overall cohort as well as the subgroup of patients with respiratory disorders. Results Predictive performance of RNN-based model was higher with Area Under the Receiver Operating Characteristic (ROC) Curve (AUC) of 0.72 (± 0.01) and Average Precision (AP) of 0.57 (± 0.01) in comparison to RF and LR for the overall patient dataset. Higher predictive performance was recorded in the subgroup of patients admitted with respiratory disorders with AUC of 0.75 (± 0.02) and AP of 0.65 (± 0.03). Inclusion of function of other organs further improved the performance to AUC of 0.79 (± 0.01) and AP 0.68 (± 0.02) for the overall patient dataset and AUC of 0.79 (± 0.01) and AP 0.72 (± 0.02) for the subgroup with respiratory disorders. Conclusion The RNN-based model demonstrated better performance than RF and LR in patients in mechanical ventilation and its subgroup admitted with respiratory disorders. Clinical studies are needed to evaluate whether it impacts decision-making and patient outcomes

Non-Modifiable factors as moderators of the relationship between physical activity and brain volume: A Cross-Sectional UK Biobank Study

Background:Previous research suggests physical activity attenuates grey and white matter loss; however, there appears to be individual variability in this effect. Understanding factors that can influence the relationship between physical activity and brain volume may enable prediction of individual response. Objective:The current study examined the relationship between objectively-measured physical activity and brain volume; and whether this relationship is moderated by age, sex, or a priori candidate genetic factors, brain-derived neurotrophic factor (BDNF) Val66Met, or apolipoprotein (APOE) ɛ4 allele carriage. Methods:Data from 10,083 men and women (50 years and over) of the UK Biobank were used to examine the study objectives. All participants underwent a magnetic resonance imaging scan to quantify grey and white matter volumes, physical activity monitoring via actigraphy, and genotyping. Results:Physical activity was associated with total grey matter volume, total white matter volume, and right hippocampal volume. Only males had an association between higher physical activity levels and greater cortical grey matter volume, total grey matter volume, and right hippocampal volume. Age moderated the relationship between physical activity and white matter volume. Conclusion:Our results indicate that in males, but not females, an association exists between objectively-measured physical activity and grey matter volume. Age may also play a role in impacting the relationship between physical activity and brain volume. Future research should evaluate longitudinal brain volumetrics to better understand the nature of age and sex-effects on the physical activity and brain volume relationship

The relationship between physical activity, apolipoprotein E ε4 carriage, and brain health

Background Neuronal hyperexcitability and hypersynchrony have been described as key features of neurophysiological dysfunctions in the Alzheimer’s disease (AD) continuum. Conversely, physical activity (PA) has been associated with improved brain health and reduced AD risk. However, there is controversy regarding whether AD genetic risk (in terms of APOE ε4 carriage) modulates these relationships. The utilization of multiple outcome measures within one sample may strengthen our understanding of this complex phenomenon. Method The relationship between PA and functional connectivity (FC) was examined in a sample of 107 healthy older adults using magnetoencephalography. Additionally, we explored whether ε4 carriage modulates this association. The correlation between FC and brain structural integrity, cognition, and mood was also investigated. Results A relationship between higher PA and decreased FC (hyposynchrony) in the left temporal lobe was observed among all individuals (across the whole sample, in ε4 carriers, and in ε4 non-carriers), but its effects manifest differently according to genetic risk. In ε4 carriers, we report an association between this region-specific FC profile and preserved brain structure (greater gray matter volumes and higher integrity of white matter tracts). In this group, decreased FC also correlated with reduced anxiety levels. In ε4 non-carriers, this profile is associated with improved cognition (working and episodic memory). Conclusions PA could mitigate the increase in FC (hypersynchronization) that characterizes preclinical AD, being beneficial for all individuals, especially ε4 carriers