Detecting HIV associated neurocognitive disorders (HAND) using neurocognitive assessment test in Uganda

A research report submitted to the Faculty of Health Sciences, University of Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Epidemiology. August, 2017.Background: HIV associated neurocognitive disorders (HAND), are a well-established consequence of HIV infection yet there is a lack of normative data required for diagnosis in Sub-Saharan Africa. Screening tools such as the International HIV dementia scale (IHDS) that are routinely used in the Sub-Saharan African region have questionable validity. This study investigates the use of the neuropsychological test battery in the detection of HAND in the absence of normative data. Further, the construct validity of the IHDS in the detection of HAND in the Ugandan context is examined. Methods Secondary data from a longitudinal Mental Health study carried out in Uganda were analysed. Information from a total of 1121 patients who underwent neuropsychological assessment in the main study qualified for the present study. A descriptive analysis of the neuropsychological performance of the study participants was conducted. To assess the relationship between demographic factors and the neurocognitive test scores of the neuropsychological test battery, multiple linear regression models were fitted. To determine how well the neuropsychological test battery predicted the IHDS score, a receiver-operating curve (ROC) analysis was conducted. The construct validity of the IHDS in detecting HAND in the Ugandan population was then assessed using ROC analysis and published normative data. Results The total study population was 1,121 participants, with the majority being female (66.3%) while almost 62% had only primary school education. The mean age of the study participants was 35.0±9.3 years. Using the IHDS, 73.3% of the HIV infected patients were identified to be at risk of developing HIV associated dementia (HAD). Using the Frascati criteria and published normative data, only 9.1% of the HIV infected patients had HAND. Ageing, being female, having a lower socio-economic score and having lower levels of education were identified as predictors for poor neurocognitive performance. Poor performance in the neurocognitive measures to assess gross and fine motor function was directly proportional to poor performance in the IHDS (score ≥10 points). Better performance in the neurocognitive measures to assess verbal leaning/working memory and attention/working memory was directly proportional to poor performance in the IHDS (score ≥10 points). The neurocognitive tests discriminated modestly between patients at risk of developing HAD and those that were not at risk of developing HAD (sensitivity=64.62%; specificity=66.67%). At the recommended cut-off score of 10 points, the IHDS had poor ability to identify patients with HAND (sensitivity=34.54%) and a high ability to identify patients without HAND (specificity=90.74%). At a cut-off point of 7 points, the IHDS discriminated modestly between patients with HAND and those without (sensitivity=65.66%; specificity=58.52%). Conclusion The neuropsychological test battery used in the present study discriminated modestly among HIV patients at risk of developing HIV associated dementia and those that were not at risk of developing dementia. In the Ugandan population, the construct validity of the IHDS in the diagnosis of HAND was poor. Further work is required to produce an algorithm to detect HAND in the absence of normative data. This includes an inclusion of important clinical biomarkers, exploration of further demographic confounders as well strengthening of the HAND diagnostic criteria using the neuropsychological test battery.LG201

Does stroke location predict walk speed response to gait rehabilitation?

Objectives Recovery of independent ambulation after stroke is a major goal. However, which rehabilitation regimen best benefits each individual is unknown and decisions are currently made on a subjective basis. Predictors of response to specific therapies would guide the type of therapy most appropriate for each patient. Although lesion topography is a strong predictor of upper limb response, walking involves more distributed functions. Earlier studies that assessed the cortico-spinal tract (CST) were negative, suggesting other structures may be important. Experimental Design: The relationship between lesion topography and response of walking speed to standard rehabilitation was assessed in 50 adult-onset patients using both volumetric measurement of CST lesion load and voxel-based lesion–symptom mapping (VLSM) to assess non-CST structures. Two functional mobility scales, the functional ambulation category (FAC) and the modified rivermead mobility index (MRMI) were also administered. Performance measures were obtained both at entry into the study (3–42 days post-stroke) and at the end of a 6-week course of therapy. Baseline score, age, time since stroke onset and white matter hyperintensities score were included as nuisance covariates in regression models. Principal Observations: CST damage independently predicted response to therapy for FAC and MRMI, but not for walk speed. However, using VLSM the latter was predicted by damage to the putamen, insula, external capsule and neighbouring white matter. Conclusions Walk speed response to rehabilitation was affected by damage involving the putamen and neighbouring structures but not the CST, while the latter had modest but significant impact on everyday functions of general mobility and gait

The effects of orthopedic pathologies on the prevalence of hip osteoarthritis

Osteoarthritis (OA) is a degenerative joint disease that is a leading cause of disability among aging adults. In the U.S., many individuals living with total hip arthroplasties attribute OA as the cause. Because the majority of anthropological OA research excludes pathological individuals (i.e., individuals with systemic disease, traumatic injuries, or arthroplasties), little is known about how prostheses and pathologies impact OA. This project adds to the research surrounding OA by investigating its relationship with age, disease, and prostheses. The proximal femora of 186 African- and European-American individuals (21-95 years old) from the Edmonds Orthopedic Pathology Collection (National Museum of Health and Medicine; Armed Forces Institute of Pathology) were analyzed. These individuals were grouped into three cohorts: non-disease; disease; and previous injury/prosthesis. Jurmain’s (1990) method was used to score OA, using an ordinal fourpoint scale to categorize OA changes as: none/slight; moderate; severe; and ankylosis. Results show that osteoarthritic hip changes are positively correlated with age and presence of a prosthesis, and that systemic diseases, such as cancer, increase the likelihood of OA in an individual. Results from Chi-square tests, exploratory data analysis, and ordinal logistic regression show that there is a statistically significant relationship (p<0.000) between degree of OA, age, recorded disease, and evidence of previous injury or prostheses. In contrast with the expectation that different populations would exhibit different patterns of OA, no sex or ancestry effects are observed. These results will help researchers better understand the etiology and contemporary risk factors of OA, as well as contribute data to OA research on an underrepresented sample

Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data

Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future

Computer-vision based method for quantifying rising from chair in Parkinson's disease patients

BACKGROUND: The ability to arise from a sitting to a standing position is often impaired in Parkinson's disease (PD). This impairment is associated with an increased risk of falling, and higher risk of dementia. We propose a novel approach to estimate Movement Disorder Society Unified PD Rating Scale (MDS-UPDRS) ratings for “item 3.9” (arising from chair) using a computer vision-based method, whereby we use clinically informed reasoning to engineer a small number of informative features from high dimensional markerless pose estimation data. METHODS: We analysed 447 videos collected via the KELVIN-PD™ platform, recorded in clinical settings at multiple sites, using commercially available mobile smart devices. Each video showed an examination for item 3.9 of the MDS-UPDRS and had an associated severity rating from a trained clinician on the 5-point scale (0, 1, 2, 3 or 4). The deep learning library OpenPose was used to extract pose estimation key points from each frame of the videos, resulting in time-series signals for each key point. From these signals, features were extracted which capture relevant characteristics of the movement; velocity variation, smoothness, whether the patient used their hands to push themselves up, how stooped the patient was while sitting and how upright the patient was when fully standing. These features were used to train an ordinal classification system (with one class for each of the possible ratings on the UPDRS), based on a series of random forest classifiers. RESULTS: The UPDRS ratings estimated by this system, using leave-one-out cross validation, corresponded exactly to the ratings made by clinicians in 79% of videos, and were within one of those made by clinicians in 100% of cases. The system was able to distinguish normal from Parkinsonian movement with a sensitivity of 62.8% and a specificity of 90.3%. Analysis of misclassified examples highlighted the potential of the system to detect potentially mislabelled data. CONCLUSION: We show that our computer-vision based method can accurately quantify PD patients’ ability to perform the arising from chair action. As far as we are aware this is the first study estimating scores for item 3.9 of the MDS-UPDRS from singular monocular video. This approach can help prevent human error by identifying unusual clinician ratings, and provides promise for such a system being used routinely for clinical assessments, either locally or remotely, with potential for use as stratification and outcome measures in clinical trials

The estimation of body mass from human skeletal remains

The ability to estimate body mass from human skeletal remains with a high degree of accuracy would be significant for the identification of identifying unknown individuals in a forensic anthropology context, documenting secular change in modern populations, and evaluating any prevalence in prehistoric populations. Modern research investigating body mass incorporates one of two models: morphometric and biomechanical. The morphometric model views the body as a cylinder, where weight estimates are gathered from extreme points such as the breadth of the pelvis. In contrast, the biomechanical model incorporates engineering principles and biology to understand the effects of mass on the human skeleton. Only the biomechanical model can accommodate extremes in body mass, such as those exhibited by modern populations. This study examined the accuracy of estimating body mass (obesity in particular) from human skeletal remains using a suite of traits shown to be significant in previous studies, including documented biomechanical analysis of obese individuals involving gait and sit-to-stand (STS) movements. It was hypothesized that using a combination of methods, body mass could be estimated with a high degree of accuracy. Using a large skeletal sample (n = 191), composed of male and females with documented age, weight, and height, the following three variables were examined: (1) the spinal manifestation of diffuse idiopathic skeletal hyperostosis (DISH), (2) osteoarthritis (OA) of the tibiae, and (3) external femoral dimensions. These were then subject to statistical tests. Spearman's rank-order correlation and Mann-Whitney U tests showed significant relationships between DISH and obesity in females (p<.05), but not for males. The presence and severity of OA of the medial condyles were also significantly related to BMI in females (p<.05). In males, the relationship between BMI and OA was only significant on the condyles of the right tibiae (p<.05). Finally, ANOVA and Pearson's product-moment correlation tests were performed to evaluate the cross-sectional dimensions of the femur. The effect of age, stature, and BMI were also examined. ANOVA results showed a significant effect between BMI and M-L cross-sectional dimensions among both sexes (p<.05). Initial Pearson's tests performed separately on males and females showed no significant correlations; however, after the sexes were pooled, small to moderate negative correlations between the M-L/A-P ratio along the diaphysis of the femur and BMI were found. Finally, multiple regression analyses were performed. The models for both sexes with all ten variables was statistically significant for BMI. The final accuracy rate was 78.48% for females and 84.37% for males. The primary goal of this study was to evaluate Moore's (2008) body mass estimation study. In this investigation, however, all dimensions of the femur were performed using an osteometric board and sliding calipers following the guidelines used by Agostini and Ross (2011). The results of this study paralleled many of the observations seen in previous studies, particularly the M-L lateral widening of the femur. Future research should continue to examine the relationship of DISH and OA with body mass, particularly regarding the varying manifestations between the sexes and confounding factors such as age

Identifying cow – level factors and farm characteristics associated with locomotion scores in dairy cows using cumulative link mixed models

Lameness is a tremendous problem in intensively managed dairy herds all over the world. It has been associated with considerable adverse effects on animal welfare and economic viability. The majority of studies have evaluated factors associated with gait disturbance by categorising cows into lame and non-lame. This procedure yet entails a loss of information and precision. In the present study, we extend the binomial response to five categories acknowledging the ordered categorical nature of locomotion assessments, which conserves a higher level of information. A cumulative link mixed modelling approach was used to identify factors associated with increasing locomotion scores. The analysis revealed that a low body condition, elevated somatic cell count, more severe hock lesions, increasing parity, absence of pasture access, and poor udder cleanliness were relevant variables associated with higher locomotion scores. Furthermore, distinct differences in the locomotion scores assigned were identified in regard to breed, observer, and season. Using locomotion scores rather than a dichotomised response variable uncovers more refined relationships between gait disturbances and associated factors. This will help to understand the intricate nature of gait disturbances in dairy cows more deeply