The Trail Making test: a study of its ability to predict falls in the acute neurological in-patient population

OBJECTIVE: To determine whether tests of cognitive function and patient-reported outcome measures of motor function can be used to create a machine learning-based predictive tool for falls. DESIGN: Prospective cohort study. SETTING: Tertiary neurological and neurosurgical center. SUBJECTS: In all, 337 in-patients receiving neurosurgical, neurological, or neurorehabilitation-based care. MAIN MEASURES: Binary (Y/N) for falling during the in-patient episode, the Trail Making Test (a measure of attention and executive function) and the Walk-12 (a patient-reported measure of physical function). RESULTS: The principal outcome was a fall during the in-patient stay ( n = 54). The Trail test was identified as the best predictor of falls. Moreover, addition of other variables, did not improve the prediction (Wilcoxon signed-rank P < 0.001). Classical linear statistical modeling methods were then compared with more recent machine learning based strategies, for example, random forests, neural networks, support vector machines. The random forest was the best modeling strategy when utilizing just the Trail Making Test data (Wilcoxon signed-rank P < 0.001) with 68% (± 7.7) sensitivity, and 90% (± 2.3) specificity. CONCLUSION: This study identifies a simple yet powerful machine learning (Random Forest) based predictive model for an in-patient neurological population, utilizing a single neuropsychological test of cognitive function, the Trail Making test

T1-Weighted Intensity Increase After a Single Administration of a Linear Gadolinium-Based Contrast Agent in Multiple Sclerosis

Purpose Through analysis of T1-weighted (T1w) images this study investigated gadolinium (Gd) deposition in the brain after administration of a linear (gadopentetic acid) and a cyclic (gadoteric acid) gadolinium-based contrast agent (GBCA) in patients with multiple sclerosis (MS), a disorder frequently requiring magnetic resonance imaging (MRI) scans over years. Methods A total of 3233 T1w images (unenhanced with respect to the same scanning session) of 881 MS patients were retrospectively analyzed. After spatial normalization and intensity scaling using a sphere within the pons, differences of all pairs of subsequent scans were calculated and attributed to either linear (nx202f;= 2718) or cyclic (nx202f;= 385) or no GBCA (nx202f;= 130) according to the first scan. Regional analyses were performed, focusing on the dentate nucleus, and whole brain analyses. By 1-sample t-tests, signal intensity increases within conditions were searched for;conditions were compared by 2-sample t-tests. Furthermore, recent hypotheses on the reversibility of GBCA deposition were tested. Results In the dentate nucleus, a significant increase was observed only after administration of linear GBCA even after a single GBCA administration. This increase differed significantly (px202f;< 0.001) from the other conditions (cyclic and no GBCA). Whole brain analyses revealed T1w signal increases only after administration of linear GBCA within two regions, the dentate nucleus and globus pallidus. Additional analyses did not indicate any decline of Gd deposition in the brain. Conclusion The data point towards Gd deposition in the brain after administration of linear GBCA even after a single administration