Nomogram to predict risk of neonatal mortality among preterm neonates admitted with sepsis at University of Gondar Comprehensive Specialized Hospital: risk prediction model development and validation.

BACKGROUND: Mortality in premature neonates is a global public health problem. In developing countries, nearly 50% of preterm births ends with death. Sepsis is one of the major causes of death in preterm neonates. Risk prediction model for mortality in preterm septic neonates helps for directing the decision making process made by clinicians. OBJECTIVE: We aimed to develop and validate nomogram for the prediction of neonatal mortality. Nomograms are tools which assist the clinical decision making process through early estimation of risks prompting early interventions. METHODS: A three year retrospective follow up study was conducted at University of Gondar Comprehensive Specialized Hospital and a total of 603 preterm neonates with sepsis were included. Data was collected using KoboCollect and analyzed using STATA version 16 and R version 4.2.1. Lasso regression was used to select the most potent predictors and to minimize the problem of overfitting. Nomogram was developed using multivariable binary logistic regression analysis. Model performance was evaluated using discrimination and calibration. Internal model validation was done using bootstrapping. Net benefit of the nomogram was assessed through decision curve analysis (DCA) to assess the clinical relevance of the model. RESULT: The nomogram was developed using nine predictors: gestational age, maternal history of premature rupture of membrane, hypoglycemia, respiratory distress syndrome, perinatal asphyxia, necrotizing enterocolitis, total bilirubin, platelet count and kangaroo-mother care. The model had discriminatory power of 96.7% (95% CI: 95.6, 97.9) and P-value of 0.165 in the calibration test before and after internal validation with brier score of 0.07. Based on the net benefit analysis the nomogram was found better than treat all and treat none conditions. CONCLUSION: The developed nomogram can be used for individualized mortality risk prediction with excellent performance, better net benefit and have been found to be useful in clinical practice with contribution in preterm neonatal mortality reduction by giving better emphasis for those at high risk.

Development and Validation of a Risk Prediction Model to Estimate the Risk of Stroke Among Hypertensive Patients in University of Gondar Comprehensive Specialized Hospital, Gondar, 2012 to 2022.

Background: A risk prediction model to predict the risk of stroke has been developed for hypertensive patients. However, the discriminating power is poor, and the predictors are not easily accessible in low-income countries. Therefore, developing a validated risk prediction model to estimate the risk of stroke could help physicians to choose optimal treatment and precisely estimate the risk of stroke. Objective: This study aims to develop and validate a risk prediction model to estimate the risk of stroke among hypertensive patients at the University of Gondar Comprehensive Specialized Hospital. Methods: A retrospective follow-up study was conducted among 743 hypertensive patients between September 01/2012 and January 31/2022. The participants were selected using a simple random sampling technique. Model performance was evaluated using discrimination, calibration, and Brier scores. Internal validity and clinical utility were evaluated using bootstrapping and a decision curve analysis. Results: Incidence of stroke was 31.4 per 1000 person-years (95% CI: 26.0, 37.7). Combinations of six predictors were selected for model development (sex, residence, baseline diastolic blood pressure, comorbidity, diabetes, and uncontrolled hypertension). In multivariable logistic regression, the discriminatory power of the model was 0.973 (95% CI: 0.959, 0.987). Calibration plot illustrated an overlap between the probabilities of the predicted and actual observed risks after 10,000 times bootstrap re-sampling, with a sensitivity of 92.79%, specificity 93.51%, and accuracy of 93.41%. The decision curve analysis demonstrated that the net benefit of the model was better than other intervention strategies, starting from the initial point. Conclusion: An internally validated, accurate prediction model was developed and visualized in a nomogram. The model is then changed to an offline mobile web-based application to facilitate clinical applicability. The authors recommend that other researchers eternally validate the model.

Geographic weighted regression analysis of hot spots of modern contraceptive utilization and its associated factors in Ethiopia.

BACKGROUND: Utilization of modern contraceptives is a common healthcare challenge in Ethiopia. Prevalence of modern contraception utilization is varying across different regions. Therefore, this study aimed to investigate Geographic weighted regression analysis of hotspots of modern contraceptive utilization and its associated factors in Ethiopia, using Ethiopian Demographic and Health Survey 2016 data. METHODS: Based on the 2016 Ethiopian Demographic Health Survey data, a total weighted sample of 8,673 women was included in this study. For the Geographic Weighted Regression analysis, Arc-GIS version 10.7 and SaTScan version 9.6, statistical software was used. Spatial regression was done to identify factors associated with the hotspots of modern contraceptive utilization and model comparison was carried out using adjusted R2 and AICc. Variables with a p-value < 0.25 in the bi-variable analysis were considered for the multivariable analysis. Multilevel robust Poisson regression analysis was fitted for associated factors since the prevalence of modern contraceptive was >10%. In the multilevel robust Poisson regression analysis, the adjusted prevalence ratio with the 95% confidence interval was reported to declare the statistical significance and strength of association. RESULT: The prevalence of modern contraceptive utilization in Ethiopia was 37.25% (95% CI: 36.23%, 38.27%). Most of the hotspot areas were located in Oromia and Amhara regions, followed by the SNNPR region and Addis Ababa City administration. Single Women, poor Women, and more fertility preference were significant predictors of hotspots areas of modern contraceptive utilization. In the multivariable multilevel robust Poisson regression analysis, Women aged 25-34 years (APR = 0.88, 95% CI: 0.79, 0.98), 35-49 years (APR = 0.71, 95% CI: 0.61, 0.83), married marital status (APR = 2.59, 95% CI: 2.18, 3.08), Others religions (APR = 0.76, 95% CI: 0.65, 0.89), number of children 1-4 (APR = 1.18, 95% CI: 1.02, 1.37), no more fertility preference (APR = 1.21, 95% CI: 1.11, 1.32), Afar, Somali, Harari, and Dire Dawa: (APR = 0.42, 95% CI: 0.27, 0.67), (APR = 0.06, 95% CI: 0.03, 0.12), (APR = 0.78, 95% CI: 0.62, 0.98), and (APR = 0.75, 95% CI: 0.58, 0.98), respectively. Amhara region (APR = 1.34, 95% CI: 1.13, 1.57), rural residence (APR = 0.80, 95% CI: 0.67, 0.95) High community wealth index (APR = 0.78, 95% CI: 0.67, 0.91) were significantly associated with modern contraceptive utilization. CONCLUSION AND RECOMMENDATION: There were significant spatial variations of factors affecting modern contraceptive use across regions in Ethiopia. Therefore, public health interventions targeting areas with low modern contraceptive utilization will help to increase modern contraception use considering significant factors at individual and community levels.The detailed map of modern contraceptive use cold spots among reproductive age group and its predictors could assist program planners and decision-makers to design targeted public health interventions.Government of Ethiopia must develop more geographic targeted strategies for improving socioeconomic status of women and availability & accessibility of health facilities in rural areas of the countries.

Development and validation of risk prediction model for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, 2005-2021.

BACKGROUND: Diabetic neuropathy is the most common complication in both Type-1 and Type-2 DM patients with more than one half of all patients developing nerve dysfunction in their lifetime. Although, risk prediction model was developed for diabetic neuropathy in developed countries, It is not applicable in clinical practice, due to poor data, methodological problems, inappropriately analyzed and reported. To date, no risk prediction model developed for diabetic neuropathy among DM in Ethiopia, Therefore, this study aimed prediction the risk of diabetic neuropathy among DM patients, used for guiding in clinical decision making for clinicians. OBJECTIVE: Development and validation of risk prediction model for diabetic neuropathy among diabetes mellitus patients at selected referral hospitals, in Amhara regional state Northwest Ethiopia, 2005-2021. METHODS: A retrospective follow up study was conducted with a total of 808 DM patients were enrolled from January 1,2005 to December 30,2021 at two selected referral hospitals in Amhara regional state. Multi-stage sampling techniques were used and the data was collected by checklist from medical records by Kobo collect and exported to STATA version-17 for analysis. Lasso method were used to select predictors and entered to multivariable logistic regression with P-value<0.05 was used for nomogram development. Model performance was assessed by AUC and calibration plot. Internal validation was done through bootstrapping method and decision curve analysis was performed to evaluate net benefit of model. RESULTS: The incidence proportion of diabetic neuropathy among DM patients was 21.29% (95% CI; 18.59, 24.25). In multivariable logistic regression glycemic control, other comorbidities, physical activity, hypertension, alcohol drinking, type of treatment, white blood cells and red blood cells count were statistically significant. Nomogram was developed, has discriminating power AUC; 73.2% (95% CI; 69.0%, 77.3%) and calibration test (P-value = 0.45). It was internally validated by bootstrapping method with discrimination performance 71.7 (95% CI; 67.2%, 75.9%). It had less optimism coefficient (0.015). To make nomogram accessible, mobile based tool were developed. In machine learning, classification and regression tree has discriminating performance of 70.2% (95% CI; 65.8%, 74.6%). The model had high net benefit at different threshold probabilities in both nomogram and classification and regression tree. CONCLUSION: The developed nomogram and decision tree, has good level of accuracy and well calibration, easily individualized prediction of diabetic neuropathy. Both models had added net benefit in clinical practice and to be clinically applicable mobile based tool were developed.