RESUMEN
Aims: The differentiation of left ventricular (LV) hypertrophic phenotypes is challenging in patients with normal ejection fraction (EF). The myocardial contraction fraction (MCF) is a simple dimensionless index useful for specifically identifying cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) when calculated by cardiac magnetic resonance. The purpose of this study was to evaluate the value of MCF measured by three-dimensional automated, machine-learning-based LV chamber metrics (dynamic heart model [DHM]) for the discrimination of different forms of hypertrophic phenotypes. Methods and Results: We analyzed the DHM LV metrics of patients with CA (n = 10), hypertrophic cardiomyopathy (HCM, n = 36), isolated hypertension (IH, n = 87), and 54 healthy controls. MCF was calculated by dividing LV stroke volume by LV myocardial volume. Compared with controls (median 61.95%, interquartile range 55.43-67.79%), mean values for MCF were significantly reduced in HCM-48.55% (43.46-54.86% p < 0.001)-and CA-40.92% (36.68-46.84% p < 0.002)-but not in IH-59.35% (53.22-64.93% p < 0.7). MCF showed a weak correlation with EF in the overall cohort (R2 = 0.136) and the four study subgroups (healthy adults, R2 = 0.039 IH, R2 = 0.089; HCM, R2 = 0.225; CA, R2 = 0.102). ROC analyses showed that MCF could differentiate between healthy adults and HCM (sensitivity 75.9%, specificity 77.8%, AUC 0.814) and between healthy adults and CA (sensitivity 87.0%, specificity 100%, AUC 0.959). The best cut-off values were 55.3% and 52.8%. Conclusions: The easily derived quantification of MCF by DHM can refine our echocardiographic discrimination capacity in patients with hypertrophic phenotype and normal EF. It should be added to the diagnostic workup of these patients.
RESUMEN
Cognitive Fatigue (CF) is an important confound impacting cognitive performance. How CF is triggered and what are the features that make a cognitive effort perceived as exhausting remain unclear. In the theoretical framework of the Time-based Resource-sharing (TBRS) model (Barrouillet et al., 2004), we hypothesized that CF is an outcome of increased cognitive load due to constrained time to process ongoing cognitive demands. We tested this cognitive load-related CF hypothesis across 2 experiments manipulating both task complexity and cognitive load induced by the processing time interval. To do so, we used the TloadDback paradigm, a working memory dual task in which high and low cognitive load levels can be individually adjusted. In Experiment 1, participants were administered a high cognitive load (HCL, short processing time interval) and a low cognitive load (LCL, large processing time interval) conditions while complexity of the task was kept constant (1-back dual task). In Experiment 2, two tasks featuring different levels of complexity were both administered at the individual's maximal processing speed capacity for each task (i.e., short processing time interval). Results disclosed higher CF in the HCL than in the LCL condition in Experiment 1. On the contrary, in Experiment 2 similar levels of CF were obtained for different levels of task complexity when processing time interval was individually adjusted to induce a HCL condition. Altogether, our results indicate that processing time-related cognitive load eventually leads to the subjective feeling of CF, and to a decrease in alertness. In this framework, we propose that the development of CF can be envisioned as the result of sustained cognitive demands irrespective of task complexity.
