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Start date: 2010Subject: search.filters.subject.Shared Frailty

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    Prevalence & Risk factors of Patent Ductus Arteriosus (PDA) in Preterm Neonates: Evidence from Survival & Shared Frailty Model
    (Mekelle University, 2023-11-28) Nigus Gebreab
    Background: The ductus arteriosus is a leftover fetal artery connecting the main body artery (aorta) and the main lung artery (pulmonary artery). The ductus allows blood to detour away from the lungs before birth. Every baby is born with a ductus arteriosus. After birth, the opening is no longer needed and it usually narrows and closes within the first few days. Patent ductus arteriosus (PDA), resulted when this artery remains open (patent) after birth, is a heart problem that occurs soon after birth in some babies. Its human and economic loss is too much in general if it is not treated at all or treated early. Objective: The overall objective of this study was to assess prevalence of PDA in preterm neonates; their survival time (time to death) & associate risk factors. Methods: To address the study objective, a secondary data from Health Facilities of Mekelle City was collected from 125 preterm neonates who initiated their follow up between December 2019 and December 2020. The Cox PH model with parametric shared frailty distribution where hospitals (health facilities) preterm neonates treated used as a clustering effect in the models. The gamma and inverse Gaussian shared frailty distributions with Exponential, Weibull and log-logistic baseline models was employed to analyze risk factors associated with age at circumcision using socio-economic and demographic factors. All the fitted models were compared by using AIC and BIC values from actual dataset. Results: A total of 125 children were seen at the Health Facilities of Mekelle City during the study period. The result revealed that about 20% of preterm neonates were exposed to PDA while the remaining were not. The AIC value for the three baseline distributions (Exponential, Weibull, and Gompertz) of PH model was found 173.6275, 174.6895, and 175.6086 and the BIC value of those baseline distributions for the same model was also found 216.0522, 219.9425, and 220.8616 respectively. The AIC value for the three baseline distributions for gamma shared frailty model was found 172.5497, 176.6895, and 176.6896 (the same value with Weibull) and BIC value of those baseline distributions for the same model also found 220.8805, 224.7708, and 224.7708 (the same with Weibull) respectively. Based on AIC and BIC values from simulation experiment and graphical evidences, Gamma shared frailty model, with the exponential baseline preferred when compared with other models. The clustering effect (the hospital effect) was significant for modeling the determinants of time-to-death preterm neonates with PDA. The estimated value of ix | P a g e theta (θ) which is a measure of contribution of a frailty component to the model was 1.1056 and a chi-square value of 0.003722 with one degree of freedom resulted a p-value of 0.0027. Based on the result of Gamma shared frailty model with the exponential baseline, gestational age, birth weight, place of delivery at home, maternal history with HIV, and treatment with paracetamol were found to be the most significant risk factors of the outcome variable, survival time (time to death from PDA). The hazard ratio and 95% Confidence interval of gestational age and birth weight was also 2.0742 with CI (0.5905259, 0.9013781) and 2.7191 with CI (1.000007, 1.000614) that yielded a p-value of 0.003 and 0.045 respectively. The hazard ratio and 95% CI for the covariates place of delivery at home, maternal history with HIV, and treatment with paracetamol were 5.1852 with CI (0.7416363, 5.1268725), 2.139 and (2.417719, 263.6325), and 42.0056 with (-3881.608, 3846.564) which yielded a p-value of 0.0092, and 0.007 respectively. They have also a prevalence rate of a unit (for place of delivery at home and maternal history with HIV) and 0.13 for treatment with paracetamol (No). The overall prevalence rate was also yielded 0.32. Conclusions: The model suggested that there is a strong evidence of heterogeneity among health facilities where the preterm neonates were treated. From the candidate models, Gamma shared frailty model, with the exponential baseline was an appropriate model for predicting the PDA data. There was a frailty effect on the survival of the preterm neonates that arises due to differences in the distribution of hospitals of the neonates. The risk factors place of delivery at home, maternal history with HIV, gestational age (week), Birth weight, and treatment with paracetamol were statistically significant for the survival of preterm neonates whereas the other risk factors were not statistically significant. The frailty component had also a significant effect to the model
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    Shared Frailty Model in Survival Analysis of Time to Discharge Dynamics for Myocardial Infarction Adult Patients at Ayder Comprehensive Specialized Referral Hospital (Jan 1, 2018 - Dec 31, 2020)
    (Mekelle University, 2024-11-28) Gebrewahd Tewelemedhin
    Background: MI, commonly known as heart attack, happens when a blood clot obstructs the coronary arteries, resulting in decreased oxygen and nutrient supply to the heart muscle. MI continues to be a significant cause of morbidity and mortality globally, with variations in the time-to-discharge dynamics among patients. Understanding the time to discharge is crucial for optimizing patient care and resource allocation, particularly in settings with limited healthcare resources like Ayder Comprehensive Specialized Referral Hospital. Objective: The overall objective of this study was to investigate and gain a comprehensive understanding of the time to discharge dynamics in MI patients at Ayder Comprehensive Specialized Referral Hospital, using a survival analysis with a shared frailty model. Methods: To fulfill the study goal, secondary data from Ayder Comprehensive Specialized Referral Hospital was collected from 206 MI patients who initiated their follow-up between January 2018 and December 2020. K-M curves used to compare the survival curve for categorical variables and the univariable analysed used Cox regression model to select variable which were included in the multivariable analysis. The Cox PH model with a parametric shared frailty distribution was utilized, with the follow-up site where treatment was administered serving as a clustering effect in the models. The study employed gamma and inverse Gaussian shared frailty distributions alongside Exponential, Weibull, and log-logistic baseline models to analyze the risk factors associated with survival time until discharge, considering socioeconomic and demographic factors. All fitted models were compared using the AIC and BIC values derived from the actual dataset. Results: Of the 206 patients were seen at Ayder Comprehensive Specialized Referral Hospital during the study period. The results revealed that approximately 54% experienced the event, while 46% did not experience it by the end of the follow-up period. The AIC values for the three baseline distributions (Exponential, Weibull, and Gompertz) of the PH model were found to be 370.8967, 90.3539, and 96.2921, respectively. The corresponding BIC values for those baseline distributions were 424.1427, 146.9278, and 152.8661, respectively. The AIC values for the three baseline distributions for the Gamma shared frailty model were found to be 375.4022, 89.2839, and 98.5131, with the BIC values for the same model found to be 431.9761, 148.9278, and ix | P a g e By: G/wahd T. 155.4568, respectively. Based on the AIC and BIC values from the simulation experiment and graphical evidence, the Gamma shared frailty model with the Weibull baseline was preferred when compared to other models. The clustering effect (follow-up site) was found to be significant for modeling the risk factors of time-to-discharge patients with MI. The estimated value of theta (θ), which measures the contribution of a frailty component to the model, was 1.1056. A chi-square value of 0.00372 with one degree of freedom resulted in a p-value of 0.0031. Based on the results of the Gamma shared frailty model with the Weibull baseline, the follow-up site at the medical ward, obesity (BMI > 30), age (in years), weight, diabetes mellitus, family history of MI, uncontrolled blood pressure, high cholesterol levels, and male gender were identified as the most significant risk factors for the outcome variable, survival time to discharge. The hazard ratio and 95% confidence interval for patient age and weight were 0.9844 (CI [1.0105, 1.5116]) and 1.0101 (CI [0.0193, 0.9879]) with p-values of 0.002 and 0.019, respectively. The covariates, including follow-up site at the Medical ward, BMI with obesity (BMI>30), diabetes mellitus, family history of MI, uncontrolled blood pressure, high cholesterol levels, and male gender, exhibited hazard ratios of 2.4868 (CI [0.4281, 1.4369]), 2.3445 (CI [0.3901, 2.1253]), 2.7563 (CI [0.582, 1.5858]), 3.7139 (CI [0.0152, 1.3031]), 1.0726 (CI [0.3823, 1.8024]), 1.7318 (CI [0.3385, 1.3835]), and 4.1012 (CI [0.0110, 1.1967]), respectively, with associated p-values of 0.018, 0.001, 0.003, 0.021, 0.026, 0.013, and 0.001, indicating their respective impacts on the study outcomes. Conclusions & Recommendation: The model suggested that there is a strong evidence of heterogeneity among follow up sites where the MI patients were treated. From the candidate models, Weibull-gamma shared frailty model was an appropriate model for the MI dataset. There was a frailty effect on the survival of the MI patients that arises due to differences in the distribution of follow up sites. The risk factors follow-up site at the medical ward, being obese, age(in years), weight, diabetes mellitus, family history of MI, uncontrolled blood pressure, high cholesterol levels, and male gender were statistically significant for the survival of MI patients whereas the other risk factors were not statistically significant. Health care providers must focus on high-risk MI patients, considering factors like age, obesity, uncontrolled blood pressure, diabetes, high cholesterol level, gender, and family history of MI.