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

• Published in: Clinical rehabilitation 2018-10, Vol.32 (10), p.1396-1405
• Main Authors: Mateen, Bilal Akhter, Bussas, Matthias, Doogan, Catherine, Waller, Denise, Saverino, Alessia, Király, Franz J, Playford, E Diane
• Format: Article
• Language: English
• Subjects: Accident prevention; Accidental Falls - prevention & control; Aged; Artificial intelligence; Cognition; Cognitive ability; Cognitive functioning; Cohort analysis; Cohort Studies; Executive Function; Falls; Female; Humans; Male; Middle Aged; Models, Statistical; Motor ability; Nervous System Diseases - physiopathology; Nervous System Diseases - rehabilitation; Neural networks; Neurological disorders; Neuropsychological assessment; Neuropsychological Tests; Physical ability; Prediction models; Prospective Studies; Trail Making Test; Walking
• ISSN: 0269-2155; 1477-0873
• DOI: 10.1177/0269215518771127

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.