Machine Learning in Falls Prediction; A cognition-based predictor of falls for the acute neurological in-patient population

Background Information: Falls are associated with high direct and indirect costs, and significant morbidity and mortality for patients. Pathological falls are usually a result of a compromised motor system, and/or cognition. Very little research has been conducted on predicting falls based on this premise. Aims: To demonstrate that cognitive and motor tests can be used to create a robust predictive tool for falls. Methods: Three tests of attention and executive function (Stroop, Trail Making, and Semantic Fluency), a measure of physical function (Walk-12), a series of questions (concerning recent falls, surgery and physical function) and demographic information were collected from a cohort of 323 patients at a tertiary neurological center. The principal outcome was a fall during the in-patient stay (n = 54). Data-driven, predictive modelling was employed to identify the statistical modelling strategies which are most accurate in predicting falls, and which yield the most parsimonious models of clinical relevance. Results: The Trail test was identified as the best predictor of falls. Moreover, addition of any others variables, to the results of the Trail test did not improve the prediction (Wilcoxon signed-rank p < .001). The best statistical strategy for predicting falls was the random forest (Wilcoxon signed-rank p < .001), based solely on results of the Trail test. Tuning of the model results in the following optimized values: 68% (+- 7.7) sensitivity, 90% (+- 2.3) specificity, with a positive predictive value of 60%, when the relevant data is available. Conclusion: Predictive modelling has identified a simple yet powerful machine learning prediction strategy based on a single clinical test, the Trail test. Predictive evaluation shows this strategy to be robust, suggesting predictive modelling and machine learning as the standard for future predictive tools

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

A qualitative description of falls in a neuro-rehabilitation unit : the use of a standardised fall report including the International Classification of Functioning (ICF) to describe activities and environmental factors

PURPOSE: Falls are a recognised problem for people with long-term neurological conditions but less is known about fall risk in young adults. This study describes fallers' and falls' characteristics in adults less than 60 years old, in a neuro-rehabilitation unit. METHODS: This single-centre, longitudinal, observational study included 114 consecutive admissions to a UK neuro-rehabilitation unit over 20 months. The demographic and clinical characteristics of eligible patients included age, sex, diagnosis, hospital length of stay and the Functional Independence Measure (FIM). Falls were recorded prospectively in a fall report, using the activities and environmental domains of the International Classification of Functioning (ICF). RESULTS: A total of 34 (30%) patients reported a fall, with 50% experiencing more than one fall. The majority of falls (60%) occurred during the first 2 weeks, during day-time (90%) and during mobile activities (70%). Overall, falls rate (95% confidence interval) was 1.33 (1.04 to 1.67) per 100 d of patient hospital stay. Factors associated with increased falls included becoming a walker during admission or being cognitively impaired. There were no serious fall-related injuries. CONCLUSION: The first 2 weeks of admission is a high risk time for fallers, in particular those who become walkers or are cognitively impaired. Prevention policies should be put in place based on fall characteristics. Implications for Rehabilitation The ICF is a valuable instrument for describing subject and environmental factors during a fall-event. Falls are frequent events but do not usually cause serious injuries during inpatient rehabilitation. There is an increased fall risk for subjects with cognitive impairments or those relearning how to walk. KEYWORDS: Cognitive; International Classification of Functioning; fall risk; mobility; rehabilitatio

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

<p>Supplemental material, Tables for The Trail Making test: a study of its ability to predict falls in the acute neurological in-patient population by Bilal Akhter Mateen, Matthias Bussas, Catherine Doogan, Denise Waller, Alessia Saverino, Franz J Király and E Diane Playford in Clinical Rehabilitation</p

Figure_2 – Supplemental material for The Trail Making test: a study of its ability to predict falls in the acute neurological in-patient population

<p>Supplemental material, Figure_2 for The Trail Making test: a study of its ability to predict falls in the acute neurological in-patient population by Bilal Akhter Mateen, Matthias Bussas, Catherine Doogan, Denise Waller, Alessia Saverino, Franz J Király and E Diane Playford in Clinical Rehabilitation</p

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

<p>Supplemental material, Appendix for The Trail Making test: a study of its ability to predict falls in the acute neurological in-patient population by Bilal Akhter Mateen, Matthias Bussas, Catherine Doogan, Denise Waller, Alessia Saverino, Franz J Király and E Diane Playford in Clinical Rehabilitation</p