Clinical Prediction Model Development and Validation for the Detection of Newborn Sepsis, Diagnostic Research Protocol.

Background: Neonatal sepsis is a leading cause of sickness and death in the entire world. Diagnosis is usually difficult because of the nonspecific clinical symptoms and the paucity of laboratory diagnostics in many low- and middle-income nations (LMICs). Clinical prediction models may increase diagnostic precision and rationalize the use of antibiotics in neonatal facilities, which could lead to a decrease in antimicrobial resistance and better neonatal outcomes. Early detection of newborn sepsis is critical to prevent serious consequences and reduce the need for unneeded drugs. Objective: The aim is to develop and validate a clinical prediction model for the detection of newborn sepsis. Methods: A cross-sectional study based on an institution will be carried out. The sample size was determined by assuming 10 events per predictor, based on this assumption, the total sample sizes were 467. Data will be collected using a structured checklist through chart review. Data will be coded, inputted, and analyzed using R statistical programming language version 4.0.4 after being entered into Epidata version 3.02 and further processed and analyzed. Bivariable logistic regression will be done to identify the relationship between each predictor and neonatal sepsis. In a multivariable logistic regression model, significant factors (P< 0.05) will be kept, while variables with (P< 0.25) from the bivariable analysis will be added. By calculating the area under the ROC curve (discrimination) and the calibration plot (calibration), respectively, the model's accuracy and goodness of fit will be evaluated.

Time to first optimal glycemic control and its predictors among type 1 diabetic children in Bahir Dar city public referral hospitals, North West Ethiopia: a retrospective follow up study.

BACKGROUND: Recognizing the level of glycemic control of a client is an important measure/tool to prevent acquiring complications and risk of death from diabetes. However, the other most important variable, which is the time that the patient stayed in that poor glycemic level before reaching optimal glycemic control, has not been studied so far. Therefore, this study aim to estimate time to first optimal glycemic control and identify predictors among type 1 diabetic children in Bahir Dar city public referral hospitals, Northwest, Ethiopia, 2021. METHODS: A Retrospective cohort study was conducted at Bahir Dar city public referral hospitals among a randomly selected sample of 385 patients with type 1 diabetes who were on follow up from January 1, 2016 to February30, 2021.Data were collected by using a data abstraction tool and then entered into Epi-data version 4.6 and exported into STATA 14.0 statistical software. Descriptive statistics, Kaplan Meier plots and median survival times, Log-rank test and Cox-proportional hazard regression were used for reporting the findings of this study. After performing Cox-proportional hazard regression, model goodness-of-fit and assumptions were checked. Finally, the association between independent variables and time to first optimal glycemic control in months was assessed using the multivariable Cox Proportional Hazard model and variables with a p-value < 0.05 were considered as statistically significant. RESULTS: Median survival time to first optimal glycemic control among type 1 diabetic clients was 8 months (95%CI: 6.9-8.9). The first optimal glycemic achievement rate was 8.2 (95%CI: 7.2-9.2) per 100 person/month observation. Factors that affect time to first optimal glycemic control were age > 10-14 years (AHR = 0.32;95%CI = 0.19-0.55), increased weight (AHR = 0.96;95%CI = 0.94-0.99), having primary care giver (AHR = 2.09;95%CI = 1.39-3.13), insulin dose (AHR = 1.05;95%CI = 1.03-1.08), duration of diabetes ≥4 years (AHR = 0.64;95%CI = 0.44-0.94), adherence to diabetic care (AHR = 9.72;95%CI = 6.09-15.51), carbohydrate counting (AHR = 2.43;95%CI = 1.12-5.26), and comorbidity (AHR = 0.72;95%CI = 0.53-0.98). CONCLUSION: The median survival time to first optimal glycemic control in this study was long. Age, weight, primary care giver, insulin dose, duration of diabetes, adherence, and carbohydrate counting, including history of comorbidity were determinant factors. Giving attention for overweight and comorbid illness prevention, increasing either the dose or frequency of insulin during initial treatment; counseling parent (for both the mother and father) about adherence to diabetic care focusing on insulin drugs and how to audit their children's diet as prescription helps to reduce the length of glycemic control.

Incidence and predictors of initial antiretroviral therapy regimen change among children in public health facilities of Bahir Dar City, Northwest Ethiopia, 2021: multicenter retrospective follow-up study.

BACKGROUND: The inconsistent use of antiretroviral therapy can lead to the risk of cross-resistance between drugs. This reduces subsequent antiretroviral drug options. The burden of initial antiretroviral therapy ranges from 11.3% in South Africa to 71.8% in Malaysia. There is evidence that it is important to maintain children's initial antiretroviral therapy regimens. However, the incidence and predictive factors of initial antiretroviral therapy regimen changes in the research context are still unknown in the study setting. So, the study was aimed to assess incidence and predictors of initial antiretroviral therapy regimen changes among children in public health facilities of Bahir Dar city. METHODS: A retrospective follow-up study was conducted in 485 children who received antiretroviral therapy between January 1, 2011 and December 30, 2020. These children were selected using simple random sampling techniques. The data were entered by Epi data 3.1 and the analysis was completed by STATA 14.0. The missing data was treated with multiple imputation method. The data were also summarized by median or mean, interquartile range or standard deviation, proportion and frequency. The survival time was determined using the Kaplan Meier curve. The Cox Proportional Hazard model was fitted to identify predictors of initial antiretroviral therapy regimen change. The global and Shoenfeld graphical proportional hazard tests were checked. Any statistical test was considered significant at P-value < 0.05. Finally, the data were presented in the form of tables, graphics and text. RESULT: Among the 459 study participants, 315 of them underwent initial regimen changes during the study accumulation period. The shortest and longest follow up time of the study were 1 month and 118 months, respectively. The overall incidence rate of initial regimen change was 1.85, 95% CI (1.66-2.07) per 100 person-month observation and the median follow up time of 49 (IQR 45, 53) months. The independent predictors of initial regimen changes were poor adherence (AHR = 1.49, 95%CI [1.16, 1.92]), NVP based regimen (AHR = 1.45, 95%CI [1.15, 1.84]) comparing to EFV based regimen, LPVr based regimen (AHR = 0.22, 95%CI: (0.07, 0.70)) comparing to EFV based regimen, history of tuberculosis (AHR = 1.59, 95%CI [1.14, 2.23]) and being male (AHR = 1.28, 95%CI [1.02, 1.60]). CONCLUSIONS AND RECOMMENDATIONS: In this study, the incidence of initial regimen change was high. The risk of initial regimen change would be increased by being male, poor adherence, having history of tuberculosis and NVP based initial regimen. Therefore, strengthening the health care providers' adherence counseling capability, strengthening tuberculosis screening and prevention strategies and care of initial regimen type choice needs attention in the HIV/AIDS care and treatment programs.