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

Retrospective cohort study to devise a treatment decision score predicting adverse 24-month radiological activity in early multiple sclerosis

Background: Multiple sclerosis (MS) is a chronic neuroinflammatory disease affecting about 2.8 million people worldwide. Disease course after the most common diagnoses of relapsing-remitting multiple sclerosis (RRMS) and clinically isolated syndrome (CIS) is highly variable and cannot be reliably predicted. This impairs early personalized treatment decisions. Objectives: The main objective of this study was to algorithmically support clinical decision-making regarding the options of early platform medication or no immediate treatment of patients with early RRMS and CIS. Design: Retrospective monocentric cohort study within the Data Integration for Future Medicine (DIFUTURE) Consortium. Methods: Multiple data sources of routine clinical, imaging and laboratory data derived from a large and deeply characterized cohort of patients with MS were integrated to conduct a retrospective study to create and internally validate a treatment decision score [Multiple Sclerosis Treatment Decision Score (MS-TDS)] through model-based random forests (RFs). The MS-TDS predicts the probability of no new or enlarging lesions in cerebral magnetic resonance images (cMRIs) between 6 and 24 months after the first cMRI. Results: Data from 65 predictors collected for 475 patients between 2008 and 2017 were included. No medication and platform medication were administered to 277 (58.3%) and 198 (41.7%) patients. The MS-TDS predicted individual outcomes with a cross-validated area under the receiver operating characteristics curve (AUROC) of 0.624. The respective RF prediction model provides patient-specific MS-TDS and probabilities of treatment success. The latter may increase by 5–20% for half of the patients if the treatment considered superior by the MS-TDS is used. Conclusion: Routine clinical data from multiple sources can be successfully integrated to build prediction models to support treatment decision-making. In this study, the resulting MS-TDS estimates individualized treatment success probabilities that can identify patients who benefit from early platform medication. External validation of the MS-TDS is required, and a prospective study is currently being conducted. In addition, the clinical relevance of the MS-TDS needs to be established

Association of retinal vessel pathology and brain atrophy in relapsing-remitting multiple sclerosis

BackgroundOptical coherence tomography angiography (OCTA) allows non-invasive assessment of retinal vessel structures. Thinning and loss of retinal vessels is evident in eyes of patients with multiple sclerosis (MS) and might be associated with a proinflammatory disease phenotype and worse prognosis. We investigated whether changes of the retinal vasculature are linked to brain atrophy and disability in MS.Material and methodsThis study includes one longitudinal observational cohort (n=79) of patients with relapsing-remitting MS. Patients underwent annual assessment of the expanded disability status scale (EDSS), timed 25-foot walk, symbol digit modalities test (SDMT), retinal optical coherence tomography (OCT), OCTA, and brain MRI during a follow-up duration of at least 20 months. We investigated intra-individual associations between changes in the retinal architecture, vasculature, brain atrophy and disability. Eyes with a history of optic neuritis (ON) were excluded.ResultsWe included 79 patients with a median disease duration of 12 (interquartile range 2 - 49) months and a median EDSS of 1.0 (0 - 2.0). Longitudinal retinal axonal and ganglion cell loss were linked to grey matter atrophy, cortical atrophy, and volume loss of the putamen. We observed an association between vessel loss of the superficial vascular complex (SVC) and both grey and white matter atrophy. Both observations were independent of retinal ganglion cell loss. Moreover, patients with worsening of the EDSS and SDMT revealed a pronounced longitudinal rarefication of the SVC and the deep vascular complex.DiscussionON-independent narrowing of the retinal vasculature might be linked to brain atrophy and disability in MS. Our findings suggest that retinal OCTA might be a new tool for monitoring neurodegeneration during MS

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 (n = 2718) or cyclic (n = 385) or no GBCA (n = 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 (p &amp;lt; 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

Percentage brain volume change in multiple sclerosis mainly reflects white matter and cortical volume

Abstract Brain atrophy in multiple sclerosis (MS), as measured by percentage brain volume change (PBVC) from brain magnetic resonance imaging (MRI), has been established as an outcome parameter in clinical trials. It is unknown to what extent volume changes within different brain tissue compartments contribute to PBVC. We analyzed pairs of MRI scans (at least 6 months apart) in 600 patients with relapsing–remitting MS. Multiple regression revealed that PBVC mainly reflects volume loss of white and cortical gray matter, while deep gray matter and white matter lesions were less represented. Our findings aid the interpretation of PBVC changes in MS

Prognostic value of white matter lesion shrinking in early multiple sclerosis: An intuitive or naïve notion?

Background and purpose New or enlarging T2-hyperintense white matter lesions (WML) are associated with clinical disease progression in multiple sclerosis (MS). The prognostic value of WML shrinking is unclear. Assuming that waning of acute inflammation and repair processes would be the main drivers of WML shrinking, we aimed to assess the prognostic value of WML shrinking in early MS. Methods We retrospectively analyzed a cohort of 144 early MS patients with three brain MRI scans at baseline and after 1 and 3 years available. All patients were therapy naive at baseline and 70.5% of them treated with disease modifying drugs at year 1. We determined the volume of WML shrinking between MRI scans, total WML volumes, number of gadolinium-enhancing and new WML, white matter (WM) and gray matter volumes at each MRI scan. Clinical disability was measured by Expanded Disability Status Scale. We performed the correlation analyses of WML shrinking with other MRI parameters and clinical outcome. Results White matter lesions shrinking was highly variable between patients and correlated with the initial number of gadolinium-enhancing WML and with WM volume decrease. WML shrinking was not associated with clinical outcome. Conclusion We found no indication of a prognostic value of WML shrinking in early MS patients. WML shrinking seems to be related to waning of acute inflammation

Somatosensory evoked potentials and magnetic resonance imaging of the central nervous system in early multiple sclerosis

Background Somatosensory evoked potentials (SSEP) are still broadly used, although not explicitly recommended, for the diagnostic work-up of suspected multiple sclerosis (MS). Objective To relate disability, SSEP, and lesions on T2-weighted magnetic resonance imaging (MRI) in patients with early MS. Methods In this monocentric retrospective study, we analyzed a cohort of patients with relapsing-remitting MS or clinically isolated syndrome, with a maximum disease duration of two years, as well as with available data on the score at the expanded disability status scale (EDSS), on SSEP, on whole spinal cord (SC) MRI, and on brain MRI. Results Complete data of 161 patients were available. Tibial nerve SSEP (tSSEP) were less frequently abnormal than SC MRI (22% vs. 68%, p < 0.001). However, higher EDSS scores were significantly associated with abnormal tSSEP (median, 2.0 vs. 1.0;p = 0.001) but not with abnormal SC MRI (i.e., at least one lesion;median, 1.5 vs. 1.5;p = 0.7). Of the 35 patients with abnormal tSSEP, 32 had lesions on SC MRI, and 2 had corresponding lesions on brain MRI. Conclusion Compared to tSSEP, SC MRI is the more sensitive diagnostic biomarker regarding SC involvement. In early MS, lesions as detectable by T2-weighted MRI are the main driver of abnormal tSSEP. However, tSSEP were more closely associated with disability, which is compatible with a potential role of tSSEP as prognostic biomarker in complementation of MRI

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