BackgroundIn individuals infected with HIV, hematological abnormalities are common and are associated with increased risk of disease progression and death. However, the profile of hematological abnormalities in HIV infected adult patients is not known in Ethiopia. Thus, the aim of this study was to assess the hematological manifestations of HIV infection and to identify the factors associated with cytopenias in both HAART and HAART naïve HIV infected adult patients in Ethiopia.MethodWe conducted a cross-sectional quantitative study of HIV-infected adult patients attending the ART follow-up clinic of Jimma University Specialized Hospital in Jimma, Ethiopia, from July 2012 to September 2012. We used a structured questionnaire to collect socio-demographic and clinical information. After interviewing, 4 ml of venous blood was drawn from each study subject for hematologic and immunologic parameters.ResultThe prevalence of anemia, leucopenia, thrombocytopenia and lymphopenia among the study individuals were 51.5%, 13%, 11.1% and 5% respectively. Presence of opportunistic infection (p = 0.001), use of CPT (p = 0.04) and CD4 count < 200 cells/μl (p = 0.002) were associated with an increased risk of anemia.ConclusionHematologic abnormalities were common in HIV infected adult patients. Of the cytopenias anemia was the most common. Use of CPT was independently associated with increased risk of anemia and leucopenia. Therefore, large scale and longitudinal studies, giving emphasis on the association of CPT and cytopenia, are recommended to strengthen and explore the problem in depth.

Introduction The prevalence of end-stage renal disease has raised the need for renal replacement therapy over recent decades. Even though a kidney transplant offers an improved quality of life and lower cost of care than dialysis, graft failure is possible after transplantation. Hence, this study aimed to predict the risk of graft failure among post-transplant recipients in Ethiopia using the selected machine learning prediction models. Methodology The data was extracted from the retrospective cohort of kidney transplant recipients at the Ethiopian National Kidney Transplantation Center from September 2015 to February 2022. In response to the imbalanced nature of the data, we performed hyperparameter tuning, probability threshold moving, tree-based ensemble learning, stacking ensemble learning, and probability calibrations to improve the prediction results. Merit-based selected probabilistic (logistic regression, naive Bayes, and artificial neural network) and tree-based ensemble (random forest, bagged tree, and stochastic gradient boosting) models were applied. Model comparison was performed in terms of discrimination and calibration performance. The best-performing model was then used to predict the risk of graft failure. Results A total of 278 completed cases were analyzed, with 21 graft failures and 3 events per predictor. Of these, 74.8% are male, and 25.2% are female, with a median age of 37. From the comparison of models at the individual level, the bagged tree and random forest have top and equal discrimination performance (AUC-ROC = 0.84). In contrast, the random forest has the best calibration performance (brier score = 0.045). Under testing the individual model as a meta-learner for stacking ensemble learning, the result of stochastic gradient boosting as a meta-learner has the top discrimination (AUC-ROC = 0.88) and calibration (brier score = 0.048) performance. Regarding feature importance, chronic rejection, blood urea nitrogen, number of post-transplant admissions, phosphorus level, acute rejection, and urological complications are the top predictors of graft failure. Conclusions Bagging, boosting, and stacking, with probability calibration, are good choices for clinical risk predictions working on imbalanced data. The data-driven probability threshold is more beneficial than the natural threshold of 0.5 to improve the prediction result from imbalanced data. Integrating various techniques in a systematic framework is a smart strategy to improve prediction results from imbalanced data. It is recommended for clinical experts in kidney transplantation to use the final calibrated model as a decision support system to predict the risk of graft failure for individual patients.

Despite the fact that dialysis treatment is useful in End Stage Renal Diseases (ESRD) patients for prolonging their life, the occurrence of Bone Mineral Disease (BMD) associated with an abnormal levels of serum calcium (Ca) and phosphorus (P) has been increasing, and deaths remain high. The risks in the derangement of serum Ca and P levels on BMD in patients undergoing hemodialysis (HD) have not been consistently studied in Ethiopia. Thus, our study aimed to assess serum Ca, P, and Parathyroid Hormone (PTH) levels, risks on BMD, and it's level of control in patients undergoing HD and compared the value with clinical practice guidelines of the Kidney Disease Outcomes Quality Initiative (K/DOQI). This study was a hospital-based quantitative retrospective study conducted on patients who had Ca, P, and PTH measurements from the medical record and assessed 56 patients (March 2018 to February 2022) in the dialysis center. The collected data was analyzed using SPSS software: Versions 22 computes variables by correlation and regression analysis. The finding of the study showed that patients average Ca level was 7.21, which is fairly low, and the value has decreased with the increase in hemodialysis frequency, and their relationship signified as 80.1%. In contrast to this, elevated P and PTH values were beyond the recommended KDOQ's P and PTH values. The level of P and PTH have increased with the increased HD frequency and demonstrated a very strong positive relationship signified with 84.2% and 71.2% with a frequency of hemodialysis, respectively. The increased disturbance of serum Ca, P and PTH levels noted in patients with HD in the dialysis center which appear to increase the prevalence of abnormalities of mineral metabolism in patients, and these adverse outcomes results in full-blown high-risk patients linked with metabolic bone diseases unless the conditions are aggressively managed.