Development of caesarean section prediction models: secondary analysis of a prospective cohort study in two sub-Saharan African countries.

BACKGROUND: Caesarean section is recommended in situations in which vaginal birth presents a greater likelihood of adverse maternal or perinatal outcomes than normal. However, it is associated with a higher risk of complications, especially when performed without a clear medical indication. Since labour attendants have no standardised clinical method to assist in this decision, statistical tools developed based on multiple labour variables may be an alternative. The objective of this paper was to develop and evaluate the accuracy of models for caesarean section prediction using maternal and foetal characteristics collected at admission and through labour. METHOD: This is a secondary analysis of the World Health Organization's Better Outcomes in Labour Difficulty prospective cohort study in two sub-Saharan African countries. Data were collected from women admitted for labour and childbirth in 13 hospitals in Nigeria as well as Uganda between 2014 and 2015. We applied logistic regression to develop different models to predict caesarean section, based on the time when intrapartum assessment was made. To evaluate discriminatory capacity of the various models, we calculated: area under the curve, diagnostic accuracy, positive predictive value, negative predictive value, sensitivity and specificity. RESULTS: A total of 8957 pregnant women with 12.67% of caesarean births were used for model development. The model based on labour admission characteristics showed an area under the curve of 78.70%, sensitivity of 63.20%, specificity of 78.68% and accuracy of 76.62%. On the other hand, the models that applied intrapartum assessments performed better, with an area under the curve of 93.66%, sensitivity of 80.12%, specificity of 89.26% and accuracy of 88.03%. CONCLUSION: It is possible to predict the likelihood of intrapartum caesarean section with high accuracy based on labour characteristics and events. However, the accuracy of this prediction is considerably higher when based on information obtained throughout the course of labour.

Factors associated with spontaneous HBsAg clearance in chronic hepatitis B patients followed at a university hospital.

INTRODUCTION: Few studies have evaluated the factors involved in the spontaneous HBsAg seroclearance in patients with chronic hepatitis B (HBV) followed up on a long-term basis from areas with a low prevalence of HBV infection. We aimed to determine the rate of spontaneous HBsAg seroclearance and the factors related to it in patients with chronic HBV infection followed up at the Hepatitis Outpatient Clinic of HCFMRP from 1992-2008. MATERIALS AND METHODS: A total of 548 patients with chronic HBV infection (366 with chronic hepatitis B and 182 inactive carriers) were followed for 15 years and 9 months with an annual measurement of HBV-DNA, ALT, AST and GGT (average of 4 annual determinations) and serology (HBsAg, HBeAg, Anti-HBeAg and Anti-HBsAg). RESULTS: Spontaneous HBsAg seroclearance occurred in 40 patients (7.3%) with a mean age of 46.0 ± 14.4 years, corresponding to an annual rate of 0.7%.The factors related to spontaneous HBsAg seroclearance were inactive carrier status (67.5 vs. 32.5%, p = 0.000191) and age of more than 40 years (p = 0.0007). There was no difference in the rate of spontaneous HBsAg seroclearance when comparing males and females (p = 0.383). Patients with spontaneous HBsAg seroclearance did not progress to more severe forms of the disease during follow-up. CONCLUSION: Spontaneous HBsAg seroclearance has a favorable long-term prognosis in patients with chronic HBV infection. HBsAg seroclearance occurred at rates compatible with low prevalence areas and was associated with low serum HBV-DNA levels and an age older than 40 years.

Different Estimation Procedures for the Parameters of the Extended Exponential Geometric Distribution for Medical Data.

We have considered different estimation procedures for the unknown parameters of the extended exponential geometric distribution. We introduce different types of estimators such as the maximum likelihood, method of moments, modified moments, L-moments, ordinary and weighted least squares, percentile, maximum product of spacings, and minimum distance estimators. The different estimators are compared by using extensive numerical simulations. We discovered that the maximum product of spacings estimator has the smallest mean square errors and mean relative estimates, nearest to one, for both parameters, proving to be the most efficient method compared to other methods. Combining these results with the good properties of the method such as consistency, asymptotic efficiency, normality, and invariance we conclude that the maximum product of spacings estimator is the best one for estimating the parameters of the extended exponential geometric distribution in comparison with its competitors. For the sake of illustration, we apply our proposed methodology in two important data sets, demonstrating that the EEG distribution is a simple alternative to be used for lifetime data.