Prevalence of autonomic dysfunction among pre-dialysis chronic kidney disease patients in a tertiary hospital, South East Nigeria

Background: Autonomic dysfunction (AD) has been recognized as an important contributor to the poor outcome in chronic kidney disease (CKD) patients. Several studies have reported abnormalities in heart rate variability (HRV) among these patients. Objectives: To determine the prevalence of Autonomic Dysfunction (AD) in pre-dialysis Chronic Kidney Disease (CKD) patients in a tertiary hospital in South East Nigeria.Methods: A cross sectional study of eighty chronic kidney disease patients attending the renal unit out-patient in the University of Nigeria Teaching Hospital (UNTH) Enugu was carried out. Forty subjects, drawn randomly, who had no kidney disease served as control. Autonomic function was assessed with non – invasive cardiovascular tests including, measurement of resting tachycardia, orthostatic hypotension, heart rate response (HRR) to standing test, heart rate response to Vasalva manoeuvre and heart rate response to respiration. Results: With the battery of 5 tests used to assess AD, the frequency of autonomic dysfunction in pre-dialysis chronic kidney disease patients was 51.3% compared to 7.5% in the control group. Heart rate response to standing was the most sensitive test to detect AD in this group of subjects.  Conclusion: AD is a common problem among pre-dialysis CKD patients in Nigeria.  Keywords: Prevalence, autonomic dysfunction, pre-dialysis

Prevalence of autonomic dysfunction among pre-dialysis chronic kidney disease patients in a tertiary hospital, South East Nigeria

Background: Autonomic dysfunction (AD) has been recognized as an important contributor to the poor outcome in chronic kidney disease (CKD) patients. Several studies have reported abnormalities in heart rate variability (HRV) among these patients. Objectives: To determine the prevalence of Autonomic Dysfunction (AD) in pre-dialysis Chronic Kidney Disease (CKD) patients in a tertiary hospital in South East Nigeria. Methods: A cross sectional study of eighty chronic kidney disease patients attending the renal unit out-patient in the University of Nigeria Teaching Hospital (UNTH) Enugu was carried out. Forty subjects, drawn randomly, who had no kidney disease served as control. Autonomic function was assessed with non \u2013 invasive cardiovascular tests including, measurement of resting tachycardia, orthostatic hypotension, heart rate response (HRR) to standing test, heart rate response to Vasalva manoeuvre and heart rate response to respiration. Results: With the battery of 5 tests used to assess AD, the frequency of autonomic dysfunction in pre-dialysis chronic kidney disease patients was 51.3% compared to 7.5% in the control group. Heart rate response to standing was the most sensitive test to detect AD in this group of subjects. Conclusion: AD is a common problem among pre-dialysis CKD patients in Nigeria

Prevalence of CKD-MBD in pre-dialysis patients using biochemical markers in Enugu, South-East Nigeria

Background: As kidney function declines, there is a progressive deterioration in mineral homeostasis with disruption of normal serum and tissue concentration of phosphorus and calcium, and changes in circulating levels of hormones-parathyroid hormone (PTH), calcitriol (1,25(OH)2 D), and Fibroblast growth factor-23 (FGF-23). Objective: This study was aimed at determining the prevalence of markers of CKD-MBD in pre-dialysis patients. Methods: We evaluated consecutively 168 subjects made up of 85 CKD patients and 83 healthy controls, who were attending the renal clinics and medical outpatient of University of Nigeria Teaching Hospital, Enugu. GFR was estimated and serum calcium, phosphorus, alkaline phosphatase, PTH, and 25(OH) D levels assayed. Results: The prevalence of various mineral bone disease abnormalities were 70% hyper-phosphatemia, 85% hyper-parathyroidism, and 100% low levels of 25 (OH) D among the patients. Estimated GFR correlated negatively with both serum phosphorus, and PTH. Age of the patients ranged from18-76 years with a male to female ratio of 1.7:1. Chronic Glomerulonephritis (CGN), hypertension and diabetes mellitus caused CKD in 75% of the patients. There was no significant decrease in serum calcium levels of patients compared to controls. The patients did not have pathologically raised alkaline phosphatase, although their mean level was significantly higher than that of the control group. Conclusion: Low 25 (OH) D levels (insufficiency/deficiency), hyperparathyroidism, and hyper-phosphatemia were the obvious markers of CKD-MBD in our pre-dialysis patients. These should be evaluated at presentation in these patients

Organ Donation and Transplantation in Sub-Saharan Africa: Opportunities and Challenges

Sub-Saharan Africa (SSA), occupying about 80% of the African continent is a heterogeneous region with estimated population of 1.1 billion people in 47 countries. Most belong to the low resource countries (LRCs). The high prevalence of end-organ diseases of kidney, liver, lung and heart makes provision of organ donation and transplantation necessary. Although kidney and heart transplantations were performed in South Africa in the 1960s, transplant activity in SSA lags behind the developed world. Peculiar challenges militating against successful development of transplant programmes include high cost of treatment, low GDP of most countries, inadequate infrastructural and institutional support, absence of subsidy, poor knowledge of the disease condition, poor accessibility to health-care facilities, religious and trado-cultural practices. Many people in the region patronize alternative healthcare as first choice. Opportunities that if harnessed may alter the unfavorable landscape are: implementation of the 2007 WHO Regional Consultation recommendations for establishment of national legal framework and self-sufficient organ donation/transplantation in each country and adoption of their 2020 proposed actions for organ/transplantation for member states, national registries with sharing of data with GODT, prevention of transplant commercialization and tourism. Additionally, adapting some aspects of proven successful models in LRCs will improve transplantation programmes in SSA

Machine learning model (RG-DMML) and ensemble algorithm for prediction of students’ retention and graduation in education

Automated prediction of students' retention and graduation in education using advanced analytical methods such as artificial intelligence (AI), has recently attracted the attention of educators, both in theory and in practice. Whereas invaluable insights and theories for measuring and testing the topic have been proposed, most of the existing methods do not technically highlight the non-trivial factors behind the renowned challenges and attrition. To this effect, by making use of two categories of data collected in a higher education setting about students (i) retention (n = 52262) and (ii) graduation (n = 53639); this study proposes a machine learning model - RG-DMML (retention and graduation data mining and machine learning) and ensemble algorithm for prediction of students' retention and graduation status in education. This was done by training and testing key features that are technically deemed suitable for measuring the constructs (retention and graduation), such as (i) the Average grade of the previous high school, and (ii) the Entry/admission score. The proposed model (RG-DMML) is designed based on the cross industry standard process for data mining (CRISP-DM) methodology, implemented using supervised machine learning technique such as K-Nearest Neighbor (KNN), and validated using the k-fold cross-validation method. The results show that the executed model and algorithm based on the Bagging method and 10-fold cross-validation are efficient and effective for predicting the student's retention and graduation status, with Precision (retention = 0.909, graduation = 0.822), Recall (retention = 1.000, graduation = 0.957), Accuracy (retention = 0.909, graduation = 0.817), F1-Score (retention = 0.952, graduation = 0.885) showing significant high accuracy levels or performance rate, and low Error-rate (retention = 0.090, graduation = 0.182), respectively. In addition, by considering the individual features selected through the Wrapper method in predicting the outputs, the proposed model proved more effective for predicting the students' retention status in comparison to the graduation data. The implications of the models' output and factors that impact the effective prediction or identification of at-risk students, e.g., for timely intervention, counselling, decision-making, and sustainable educational practice are empirically discussed in the study