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Abstract HIV/AIDS is one of the most devastating infectious diseases affecting humankind all over the world and its impact goes beyond public health problems. This study was conducted to investigate the joint predictors of hemoglobin level and time to default from treatment for adult clients living with HIV/AIDS under HAART at the University of Gondar Comprehensive and Specialized Hospital, North-west Ethiopia. The study was conducted using a retrospective cohort design from the medical records of 403 randomly selected adult clients living with HIV whose follow-ups were from September 2015 to March 2022. Hemoglobin level was projected using Sahli’s acid-hematin method. Hence, the hemoglobin tube was filled with N/10 hydrochloric acid up to 2 g % marking and the graduated tube was placed in Sahli’s hemoglobin meter. The blood samples were collected using the finger-pick method, considering 22 G disposable needles. The health staff did this. From a total of 403 adult patients living with HIV/AIDS included in the current study, about 44.2% defaulted from therapy. The overall mean and median estimated survival time of adult clients under study were 44.3 and 42 months respectively. The patient’s lymphocyte count (AHR = 0.7498, 95% CI: (0.7411: 0.7587), p-value

Abstract Introduction Cardiovascular diseases are a class of heart and blood vessel-related illnesses. In Sub-Saharan Africa, including Ethiopia, preventable heart disease continues to be a significant factor, contrasting with its presence in developed nations. Therefore, the objective of the study was to assess the prevalence of death due to cardiac disease and its risk factors among heart patients in Ethiopia. Methods The current investigation included all cardiac patients who had cardiac surgery in the country between 2012 and 2023. A total of 1520 individuals were participated in the study. Data collection took place between February 2022 and January 2023. The study design was a retrospective cohort since the study track back patients’ chart since 2012. Machine learning algorithms were applied for data analysis. For machine learning algorithms comparison, lift and AUC was applied. Results From all possible algorithms, logistic algorithm at 90%/10% was the best fit since it produces the maximum AUC value. In addition, based on the lift value of 3.33, it can be concluded that the logistic regression algorithm was performing well and providing substantial improvement over random selection. From the logistic regression machine learning algorithms, age, saturated oxygen, ejection fraction, duration of cardiac center stays after surgery, waiting time to surgery, hemoglobin, and creatinine were significant predictors of death. Conclusion Some of the predictors for the death of cardiac disease patients are identified as such special attention should be given to aged patients, for patients waiting for long periods of time to get surgery, lower saturated oxygen, higher creatinine value, lower ejection fraction and for patients with lower hemoglobin values.

Abstract Introduction Living in poverty, especially in low-income countries, are more affected by cardiovascular disease. Unlike the developed countries, it remains a significant cause of preventable heart disease in the Sub-Saharan region, including Ethiopia. According to the Ethiopian Ministry of Health statement, around 40,000 cardiac patients have been waiting for surgery in Ethiopia since September 2020. There is insufficient information about long-term cardiac patients’ post-survival after cardiac surgery in Ethiopia. Therefore, the main objective of the current study was to determine the long-term post-cardiac surgery patients’ survival status in Ethiopia. Methods All patients attended from 2012 to 2023 throughout the country were included in the current study. The total number of participants was 1520 heart disease patients. The data collection procedure was conducted from February 2022- January 2023. Machine learning algorithms were applied. Gompertz regression was used also for the multivariable analysis report. Results From possible machine learning models, random survival forest were preferred. It emphasizes, the most important variable for clinical prediction was SPO2, Age, time to surgery waiting time, and creatinine value and it accounts, 42.55%, 25.17%,11.82%, and 12.19% respectively. From the Gompertz regression, lower saturated oxygen, higher age, lower ejection fraction, short period of cardiac center stays after surgery, prolonged waiting time to surgery, and creating value were statistically significant predictors of death outcome for post-cardiac surgery patients’ survival in Ethiopia. Conclusion Some of the risk factors for the death of post-cardiac surgery patients are identified in the current investigation. Particular attention should be given to patients with prolonged waiting times and aged patients. Since there were only two fully active cardiac centers in Ethiopia it is far from an adequate number of centers for more than 120 million population, therefore, the study highly recommended to increase the number of cardiac centers that serve as cardiac surgery in Ethiopia.