Traumatic brain injury (TBI) results in a variable degree of cerebral atrophy that is not always related to cognitive measures across studies. However, the use of different methods for examining atrophy may be a reason why differences exist. The purpose of this manuscript was to examine the predictive utility of seven magnetic resonance imaging (MRI)-derived brain volume or indices of atrophy for a large cohort of TBI patients (n = 65). The seven quantitative MRI (qMRI) measures included uncorrected whole brain volume, brain volume corrected by total intracranial volume, brain volume corrected by the ratio of the individual TICV by group TICV, a ventricle to brain ratio, total ventricular volume, ventricular volume corrected by TICV, and a direct measure of parenchymal volume loss. Results demonstrated that the various qMRI measures were highly interrelated and that corrected measures proved to be the most robust measures related to neuropsychological performance. Similar to an earlier study that examined cerebral atrophy in aging and dementia, these results suggest that a single corrected brain volume measure is all that is necessary in studies examining global MRI indicators of cerebral atrophy in relationship to cognitive function making additional measures of global atrophy redundant and unnecessary.

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Background Phase space is a mechanical systems approach and large-scale data representation of an object in 3-dimensional space. Whether such techniques can be applied to predict left ventricular pressures non-invasively and at the point-of-care is unknown. Objective This study prospectively validated a phase space machine-learned approach based on a novel electro-mechanical pulse wave method of data collection through orthogonal voltage gradient (OVG) and photoplethysmography (PPG) for the prediction of elevated left ventricular end diastolic pressure (LVEDP). Methods Consecutive outpatients across 15 US-based healthcare centers with symptoms suggestive of coronary artery disease were enrolled at the time of elective cardiac catheterization and underwent OVG and PPG data acquisition immediately prior to angiography with signals paired with LVEDP (IDENTIFY; NCT #03864081). The primary objective was to validate a ML algorithm for prediction of elevated LVEDP using a definition of ≥25 mmHg (study cohort) and normal LVEDP ≤ 12 mmHg (control cohort), using AUC as the measure of diagnostic accuracy. Secondary objectives included performance of the ML predictor in a propensity matched cohort (age and gender) and performance for an elevated LVEDP across a spectrum of comparative LVEDP (<12 through 24 at 1 mmHg increments). Features were extracted from the OVG and PPG datasets and were analyzed using machine-learning approaches. Results The study cohort consisted of 684 subjects stratified into three LVEDP categories, ≤12 mmHg (N = 258), LVEDP 13–24 mmHg (N = 347), and LVEDP ≥25 mmHg (N = 79). Testing of the ML predictor demonstrated an AUC of 0.81 (95% CI 0.76–0.86) for the prediction of an elevated LVEDP with a sensitivity of 82% and specificity of 68%, respectively. Among a propensity matched cohort (N = 79) the ML predictor demonstrated a similar result AUC 0.79 (95% CI: 0.72–0.8). Using a constant definition of elevated LVEDP and varying the lower threshold across LVEDP the ML predictor demonstrated and AUC ranging from 0.79–0.82. Conclusion The phase space ML analysis provides a robust prediction for an elevated LVEDP at the point-of-care. These data suggest a potential role for an OVG and PPG derived electro-mechanical pulse wave strategy to determine if LVEDP is elevated in patients with symptoms suggestive of cardiac disease.