Artificial neural network to classify cognitive impairment using gait and clinical variables

Combining gait and clinical variables could increase the accuracy of identifying cognitive impairment (CI) in geriatric patients. We aimed to classify geriatric patients with and without CI based on clinical variables, gait, or a combination of clinical and gait variables, using two machine learning methods, Random Forest (RF) and Artificial Neural Network (ANN). The most accurate classification model examined how interactions between clinical and gait variables would improve classification accuracy and determine the contributions of key variables. Based on Minimal Mental State Examination (MMSE) scores, 131 geriatric patients were divided into a cognitive impaired and a cognitively healthy (CH) group. From 3D accelerometer data collected during 3 min of walking at a habitual speed, we computed 23 dynamic gait variables. In conclusion, an ANN model incorporating the interaction between clinical and gait variables classified geriatric patients with an accuracy of 96%, an area of the receiver operating characteristic curve of 0.95, and a model validation score of 0.97 (F1) based on their clinical status. Machine learning analyses of gait and clinical variables can inform geriatricians about the diagnosis of geriatric patients’ cognitive status.</p

Artificial neural network to classify cognitive impairment using gait and clinical variables

Combining gait and clinical variables could increase the accuracy of identifying cognitive impairment (CI) in geriatric patients. We aimed to classify geriatric patients with and without CI based on clinical variables, gait, or a combination of clinical and gait variables, using two machine learning methods, Random Forest (RF) and Artificial Neural Network (ANN). The most accurate classification model examined how interactions between clinical and gait variables would improve classification accuracy and determine the contributions of key variables. Based on Minimal Mental State Examination (MMSE) scores, 131 geriatric patients were divided into a cognitive impaired and a cognitively healthy (CH) group. From 3D accelerometer data collected during 3 min of walking at a habitual speed, we computed 23 dynamic gait variables. In conclusion, an ANN model incorporating the interaction between clinical and gait variables classified geriatric patients with an accuracy of 96%, an area of the receiver operating characteristic curve of 0.95, and a model validation score of 0.97 (F1) based on their clinical status. Machine learning analyses of gait and clinical variables can inform geriatricians about the diagnosis of geriatric patients’ cognitive status

ABCB1 genotypes and haplotypes in patients with dementia and age-matched non-demented control patients

Amyloid β is an in vitro substrate for P-glycoprotein (P-gp), an efflux pump at the blood brain barrier (BBB). The Multi Drug Resistance (ABCB1) gene, encoding for P-gp, is highly polymorphic and this may result in a changed function of P-gp and may possibly interfere with the pathogenesis of Alzheimer's disease. This study investigates to what extent ABCB1 Single Nucleotide Polymorphisms (SNPs; C1236T in exon 12, G2677T/A in exon 21 and C3435T in exon 26) and inferred haplotypes exist in an elderly population and if these SNPs and haplotypes differ between patients with dementia and age-matched non-demented control patients. ABCB1 genotype, allele and haplotype frequencies were neither significantly different between patients with dementia and age-matched controls, nor between subgroups of different types of dementia nor age-matched controls. This study shows ABCB1 genotype frequencies to be comparable with described younger populations. To our knowledge this is the first study on ABCB1 genotypes in dementia. ABCB1 genotypes are presently not useful as a biomarker for dementia, as they were not significantly different between demented patients and age-matched control subjects

The detection of age groups by dynamic gait outcomes using machine learning approaches

Prevalence of gait impairments increases with age and is associated with mobility decline, fall risk and loss of independence. For geriatric patients, the risk of having gait disorders is even higher. Consequently, gait assessment in the clinics has become increasingly important. The purpose of the present study was to classify healthy young-middle aged, older adults and geriatric patients based on dynamic gait outcomes. Classification performance of three supervised machine learning methods was compared. From trunk 3D-accelerations of 239 subjects obtained during walking, 23 dynamic gait outcomes were calculated. Kernel Principal Component Analysis (KPCA) was applied for dimensionality reduction of the data for Support Vector Machine (SVM) classification. Random Forest (RF) and Artificial Neural Network (ANN) were applied to the 23 gait outcomes without prior data reduction. Classification accuracy of SVM was 89%, RF accuracy was 73%, and ANN accuracy was 90%. Gait outcomes that significantly contributed to classification included: Root Mean Square (Anterior-Posterior, Vertical), Cross Entropy (Medio-Lateral, Vertical), Lyapunov Exponent (Vertical), step regularity (Vertical) and gait speed. ANN is preferable due to the automated data reduction and significant gait outcome identification. For clinicians, these gait outcomes could be used for diagnosing subjects with mobility disabilities, fall risk and to monitor interventions. (This work was supported by Keep Control project, funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 721577.

Anticholinergic and Sedative Medications and Dynamic Gait Parameters in Older Patients

BACKGROUND: Anticholinergic and sedative medications are associated with poorer physical function in older age. Gait and physical function have traditionally been assessed with the time needed to execute objective function tests. Accelerometer-based gait parameters provide a precise capturing of gait dynamics and patterns and as such have added value. OBJECTIVES: This study examined the associations between cumulative exposure to anticholinergic and sedative medications and gait dimensions as assessed with accelerometer-based dynamic gait parameters. METHODS: Data were collected from outpatients of a diagnostic geriatric day clinic who underwent a comprehensive geriatric assessment (CGA). Cumulative exposure to anticholinergic and sedative medications was quantified with the Drug Burden Index (DBI), a linear additive pharmacological dose-response model. From a total of 22 dynamic gait parameters, the gait dimensions 'Regularity', 'Complexity', 'Stability', 'Pace', and 'Postural Control' were derived using factor analysis (and standardized total scores for these dimensions were calculated accordingly). Data were analyzed with multivariable linear regression analysis, in which adjustment was made for the covariates age, gender, body mass index (BMI), Mini Mental State Examination (MMSE) score, Charlson Comorbidity Index (CCI) including dementia, and number of medications not included in the DBI. RESULTS: A total of 184 patients participated, whose mean age was 79.8 years (± SD 5.8), of whom 110 (60%) were women and of whom 88 (48%) had polypharmacy (i.e., received treatment with ≥5 medications). Of the 893 medications that were prescribed in total, 157 medications (17.6%) had anticholinergic and/or sedative properties. Of the patients, 100 (54%) had no exposure (DBI = 0), 42 (23%) had moderate exposure (0 > DBI ≤ 1), while another 42 (23%) had high exposure (DBI >1) to anticholinergic and sedative medications. Findings showed that high cumulative exposure to anticholinergic and sedative medications was related with poorer function on the Regularity and Pace dimensions. Furthermore, moderate and high exposure were associated with poorer function on the Complexity dimension. CONCLUSIONS: These findings show that in older patients with comorbidities, cumulative anticholinergic and sedative exposure is associated with poorer function on multiple gait dimensions

The Cross-Cultural Dementia Screening (CCD):A new neuropsychological screening instrument for dementia in elderly immigrants

Objective: Currently, approximately 3.9% of the European population are non-EU citizens, and a large part of these people are from "non-Western" societies, such as Turkey and Morocco. For various reasons, the incidence of dementia in this group is expected to increase. However, cognitive testing is challenging due to language barriers and low education and/or illiteracy. The newly developed Cross-Cultural Dementia Screening (CCD) can be administered without an interpreter. It contains three subtests that assess memory, mental speed, and executive function. We hypothesized the CCD to be a culture-fair test that could discriminate between demented patients and cognitively healthy controls. Method: To test this hypothesis, 54 patients who had probable dementia were recruited via memory clinics. Controls (N = 1625) were recruited via their general practitioners. All patients and controls were aged 55 years and older and of six different self-defined ethnicities (Dutch, Turkish, Moroccan-Arabic, Moroccan-Berber, Surinamese-Creole, and Surinamese-Hindustani). Exclusion criteria included current or previous conditions that affect cognitive functioning. Results: There were performance differences between the ethnic groups, but these disappeared after correcting for age and education differences between the groups, which supports our central hypothesis that the CCD is a culture-fair test. Receiver-operating characteristic (ROC) and logistic regression analyses showed that the CCD has high predictive validity for dementia (sensitivity: 85%; specificity: 89%). Discussion: The CCD is a sensitive and culture-fair neuropsychological instrument for dementia screening in low-educated immigrant populations.</p

Long-term exposure to anticholinergic and sedative medications and cognitive and physical function in later life

Background: Anticholinergic and sedative medications are frequently prescribed to older individuals. These medications are associated with short-term cognitive and physical impairment, but less is known about long-term associations. We therefore examined over twenty years whether cumulative exposure to these medications was related to poorer cognitive and physical functioning. Methods: Older adult participants of the Longitudinal Aging Study Amsterdam (LASA) were followed from 1992-2012. On 7 measurement occasions, cumulative exposure to anticholinergic and sedative medications was quantified with the Drug Burden Index (DBI), a linear additive pharmacological dose-response model. Cognitive functioning was assessed with the Mini Mental State Examination (MMSE), Alphabet Coding Task (ACT, 3 trials), Auditory Verbal Learning Test (AVLT, learning and retention condition), and Raven Colored Progressive Matrices (RCPM, 2 trials). Physical functioning was assessed with the Walking Test (WT), Cardigan Test (CT), Chair Stands Test (CST), Balance Test (BT), and self-reported Functional Independence (FI). Data were analyzed with linear mixed models adjusted for age, education, sex, living with a partner, BMI, depressive symptoms, co-morbidities (cardiovascular disease, diabetes, cancer, COPD, osteoarthritis, CNS diseases), and prescribed medications. Results: Longitudinal associations were found of the DBI with poorer cognitive functioning (less items correct on the 3 ACT trials, AVLT learning condition, and the 2 RCPM trials) and with poorer physical functioning (longer completion time on the CT, CST, and lower self-reported FI). Conclusions: This longitudinal analysis of data collected over 20 years, showed that higher long-term cumulative exposure to anticholinergic and sedative medications was associated with poorer cognitive and physical functioning

Gait characteristics and their discriminative power in geriatric patients with and without cognitive impairment

Abstract Background A detailed gait analysis (e.g., measures related to speed, self-affinity, stability, and variability) can help to unravel the underlying causes of gait dysfunction, and identify cognitive impairment. However, because geriatric patients present with multiple conditions that also affect gait, results from healthy old adults cannot easily be extrapolated to geriatric patients. Hence, we (1) quantified gait outcomes based on dynamical systems theory, and (2) determined their discriminative power in three groups: healthy old adults, geriatric patients with- and geriatric patients without cognitive impairment. Methods For the present cross-sectional study, 25 healthy old adults recruited from community (65 ± 5.5 years), and 70 geriatric patients with (n = 39) and without (n = 31) cognitive impairment from the geriatric dayclinic of the MC Slotervaart hospital in Amsterdam (80 ± 6.6 years) were included. Participants walked for 3 min during single- and dual-tasking at self-selected speed while 3D trunk accelerations were registered with an IPod touch G4. We quantified 23 gait outcomes that reflect multiple gait aspects. A multivariate model was built using Partial Least Square- Discriminant Analysis (PLS-DA) that best modelled participant group from gait outcomes. Results For single-task walking, the PLS-DA model consisted of 4 Latent Variables that explained 63 and 41% of the variance in gait outcomes and group, respectively. Outcomes related to speed, regularity, predictability, and stability of trunk accelerations revealed with the highest discriminative power (VIP > 1). A high proportion of healthy old adults (96 and 93% for single- and dual-task, respectively) was correctly classified based on the gait outcomes. The discrimination of geriatric patients with and without cognitive impairment was poor, with 57% (single-task) and 64% (dual-task) of the patients misclassified. Conclusions While geriatric patients vs. healthy old adults walked slower, and less regular, predictable, and stable, we found no differences in gait between geriatric patients with and without cognitive impairment. The effects of multiple comorbidities on geriatric patients’ gait possibly causes a ‘floor-effect’, with no room for further deterioration when patients develop cognitive impairment. An accurate identification of cognitive status thus necessitates a multifactorial approach

Associations between vertebral fractures, increased thoracic kyphosis, a flexed posture and falls in older adults:a prospective cohort study

Background: Vertebral fractures, an increased thoracic kyphosis and a flexed posture are associated with falls. However, this was not confirmed in prospective studies. We performed a prospective cohort study to investigate the association between vertebral fractures, increased thoracic kyphosis and/or flexed posture with future fall incidents in older adults within the next year. Methods: Patients were recruited at a geriatric outpatient clinic. Vertebral fractures were evaluated on lateral radiographs of the spine with the semi-quantitative method of Genant; the degree of thoracic kyphosis was assessed with the Cobb angle. The occiput-to-wall distance was used to determine a flexed posture. Self-reported falls were prospectively registered by monthly phone contact for the duration of 12 months. Results: Fifty-one older adults were included; mean age was 79 years (SD = 4.8). An increased thoracic kyphosis was independently associated with future falls (OR 2.13; 95% CI 1.10-4.51). Prevalent vertebral fractures had a trend towards significancy (OR 3.67; 95% CI 0.85-15.9). A flexed posture was not significantly associated with future falls. Conclusion: Older adults with an increased thoracic kyphosis are more likely to fall within the next year. We suggest clinical attention for underlying causes. Because patients with increased thoracic curvature of the spine might have underlying osteoporotic vertebral fractures, clinicians should be aware of the risk of a new fracture