Systematic review and meta-analysis of the pharmacokinetics of benznidazole in the treatment of Chagas disease

Chagas disease is a neglected parasitic illness affecting approximately 8 million people, predominantly in Latin America. Benznidazole is the drug of choice for treatment, although its availability has been limited. A paucity of knowledge of the pharmacokinetic properties of this drug has contributed to its limited availability in several jurisdictions. The objective of this study was to conduct a systematic literature review and a Bayesian meta-analysis of pharmacokinetic studies to improve estimates of the basic pharmacokinetic properties of benznidazole. A systematic search of the Embase, Medline, LILACS, and SciELO (Scientific Electronic Library Online) databases was conducted. Eligible studies reported patient-level data from single-100-mg-dose pharmacokinetic evaluations of benznidazole in adults or otherwise provided data relevant to the estimation of pharmacokinetic parameters which could be derived from such studies. A Bayesian hierarchical model was used for analysis. Secondary data (i.e., data from studies that did not include patient-level, single-100-mg-dose data) were used for the generation of empirical priors for the Bayesian analysis. The systematic search identified nine studies for inclusion. Nine pharmacokinetic parameters were estimated, including the area under the concentration-time curve (AUC), the maximum concentration of drug in plasma (Cmax), the time to Cmax, the elimination rate constant (kel), the absorption rate constant (Ka), the absorption and elimination half-lives, the apparent oral clearance, and the apparent oral volume of distribution. The results showed consistency across studies. AUC and Cmax were 51.31 mg · h/liter (95% credible interval [CrI], 45.01, 60.28 mg · h/liter) and 2.19 mg/liter (95% CrI, 2.06, 2.33 mg/liter), respectively. Ka and kel were 1.16 h-1 (95% CrI, 0.59, 1.76 h-1) and 0.052 h-1 (95% CrI, 0.045, 0.059 h-1), respectively, with the corresponding absorption and elimination half-lives being 0.60 h (95% CrI, 0.38, 1.11 h) and 13.27 h (95% CrI, 11.79, 15.42 h), respectively. The oral clearance and volume of distribution were 2.04 liters/h (95% CrI, 1.77, 2.32 liters/h) and 39.19 liters (95% CrI, 36.58, 42.17 liters), respectively. A Bayesian meta-analysis was used to improve the estimates of the standard pharmacokinetic parameters of benznidazole. These data can inform clinicians and policy makers as access to this drug increases.Fil: Wiens, Matthew O.. University of British Columbia; CanadáFil: Kanters, Steve. Precision Global Health;Fil: Mills, Edward. Mc Master University; CanadáFil: Peregrina Lucano, Alejandro A.. Universidad de Guadalajara; MéxicoFil: Gold, Silvia. Fundación Mundo Sano; ArgentinaFil: Ayers, Dieter. Precision Global Health;Fil: Ferrero, Luis. Fundación Mundo Sano; ArgentinaFil: Krolewiecki, Alejandro Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Patología Experimental. Universidad Nacional de Salta. Facultad de Ciencias de la Salud. Instituto de Patología Experimental; Argentin

Selecting candidate predictor variables for the modelling of post-discharge mortality from sepsis: a protocol development project

Background: Post-discharge mortality is a frequent but poorly recognized contributor to child mortality in resource limited countries. The identification of children at high risk for post-discharge mortality is a critically important first step in addressing this problem.Objectives: The objective of this project was to determine the variables most likely to be associated with post-discharge mortality which are to be included in a prediction modelling study.Methods: A two-round modified Delphi process was completed for the review of a priori selected variables and selection of new variables. Variables were evaluated on relevance according to (1) prediction (2) availability (3) cost and (4) time required for measurement. Participants included experts in a variety of relevant fields.Results: During the first round of the modified Delphi process, 23 experts evaluated 17 variables. Forty further variables were suggested and were reviewed during the second round by 12 experts. During the second round 16 additional variables were evaluated. Thirty unique variables were compiled for use in the prediction modelling study.Conclusion: A systematic approach was utilized to generate an optimal list of candidate predictor variables for the incorporation into a study on prediction of pediatric post-discharge mortality in a resource poor setting.Keywords: Candidate predictor variables, pediatrics, prediction, post-discharge mortality, sepsi

Prognostic algorithms for post-discharge readmission and mortality among mother-infant dyads: an observational study protocol

IntroductionIn low-income country settings, the first six weeks after birth remain a critical period of vulnerability for both mother and newborn. Despite recommendations for routine follow-up after delivery and facility discharge, few mothers and newborns receive guideline recommended care during this period. Prediction modelling of post-delivery outcomes has the potential to improve outcomes for both mother and newborn by identifying high-risk dyads, improving risk communication, and informing a patient-centered approach to postnatal care interventions. This study aims to derive post-discharge risk prediction algorithms that identify mother-newborn dyads who are at risk of re-admission or death in the first six weeks after delivery at a health facility.MethodsThis prospective observational study will enroll 7,000 mother-newborn dyads from two regional referral hospitals in southwestern and eastern Uganda. Women and adolescent girls aged 12 and above delivering singletons and twins at the study hospitals will be eligible to participate. Candidate predictor variables will be collected prospectively by research nurses. Outcomes will be captured six weeks following delivery through a follow-up phone call, or an in-person visit if not reachable by phone. Two separate sets of prediction models will be built, one set of models for newborn outcomes and one set for maternal outcomes. Derivation of models will be based on optimization of the area under the receiver operator curve (AUROC) and specificity using an elastic net regression modelling approach. Internal validation will be conducted using 10-fold cross-validation. Our focus will be on the development of parsimonious models (5–10 predictor variables) with high sensitivity (>80%). AUROC, sensitivity, and specificity will be reported for each model, along with positive and negative predictive values.DiscussionThe current recommendations for routine postnatal care are largely absent of benefit to most mothers and newborns due to poor adherence. Data-driven improvements to postnatal care can facilitate a more patient-centered approach to such care. Increasing digitization of facility care across low-income settings can further facilitate the integration of prediction algorithms as decision support tools for routine care, leading to improved quality and efficiency. Such strategies are urgently required to improve newborn and maternal postnatal outcomes. Clinical trial registrationhttps://clinicaltrials.gov/, identifier (NCT05730387)

Predictor variables for post-discharge mortality modelling in infants: a protocol development project

Background: Over two-thirds of the five million annual deaths in children under five occur in infants, mostly in developing countries and many after hospital discharge. However, there is a lack of understanding of which children are at higher risk based on early clinical predictors. Early identification of vulnerable infants at high-risk for death post-discharge is important in order to craft interventional programs. Objectives: To determine potential predictor variables for post-discharge mortality in infants less than one year of age who are likely to die after discharge from health facilities in the developing world. Methods: A two-round modified Delphi process was conducted, wherein a panel of experts evaluated variables selected from a systematic literature review. Variables were evaluated based on (1) predictive value, (2) measurement reliability, (3) availability, and (4) applicability in low-resource settings. Results: In the first round, 18 experts evaluated 37 candidate variables and suggested 26 additional variables. Twenty-seven variables derived from those suggested in the first round were evaluated by 17 experts during the second round. A final total of 55 candidate variables were retained. Conclusion: A systematic approach yielded 55 candidate predictor variables to use in devising predictive models for post-discharge mortality in infants in a low-resource setting

Selecting candidate predictor variables for the modelling of post-discharge mortality from sepsis: a protocol development project.

Background: Post-discharge mortality is a frequent but poorly recognized contributor to child mortality in resource limited countries. The identification of children at high risk for post-discharge mortality is a critically important first step in addressing this problem. Objectives: The objective of this project was to determine the variables most likely to be associated with post-discharge mortality which are to be included in a prediction modelling study. Methods: A two-round modified Delphi process was completed for the review of a priori selected variables and selection of new variables. Variables were evaluated on relevance according to (1) prediction (2) availability (3) cost and (4) time required for measurement. Participants included experts in a variety of relevant fields. Results: During the first round of the modified Delphi process, 23 experts evaluated 17 variables. Forty further variables were suggested and were reviewed during the second round by 12 experts. During the second round 16 additional variables were evaluated. Thirty unique variables were compiled for use in the prediction modelling study. Conclusion: A systematic approach was utilized to generate an optimal list of candidate predictor variables for the incorporation into a study on prediction of pediatric post-discharge mortality in a resource poor setting

Individual, maternal and household risk factors for anaemia among young children in sub-Saharan Africa: a cross-sectional study

Objective: Anaemia affects the majority of children in sub-Saharan Africa (SSA). Previous studies of risk factors for anaemia have been limited by sample size, geography and the association of many risk factors with poverty. In order to measure the relative impact of individual, maternal and household risk factors for anaemia in young children, we analysed data from all SSA countries that performed haemoglobin (Hb) testing in the Demographic and Health Surveys. Design and setting This cross-sectional study pooled household-level data from the most recent Demographic and Health Surveys conducted in 27 SSA between 2008 and 2014. Participants: 96 804 children age 6–59 months. Results: The prevalence of childhood anaemia (defined as Hb <11 g/dL) across the region was 59.9%, ranging from 23.7% in Rwanda to 87.9% in Burkina Faso. In multivariable regression models, older age, female sex, greater wealth, fewer household members, greater height-for-age, older maternal age, higher maternal body mass index, current maternal pregnancy and higher maternal Hb, and absence of recent fever were associated with higher Hb in tested children. Demographic, socioeconomic factors, family structure, water/sanitation, growth, maternal health and recent illnesses were significantly associated with the presence of childhood anaemia. These risk factor groups explain a significant fraction of anaemia (ranging from 1.0% to 16.7%) at the population level. Conclusions: The findings from our analysis of risk factors for anaemia in SSA underscore the importance of family and socioeconomic context in childhood anaemia. These data highlight the need for integrated programmes that address the multifactorial nature of childhood anaemia

Individual, maternal and household risk factors for anaemia among young children in sub-Saharan Africa: a cross-sectional study

Objective: Anaemia affects the majority of children in sub-Saharan Africa (SSA). Previous studies of risk factors for anaemia have been limited by sample size, geography and the association of many risk factors with poverty. In order to measure the relative impact of individual, maternal and household risk factors for anaemia in young children, we analysed data from all SSA countries that performed haemoglobin (Hb) testing in the Demographic and Health Surveys. Design and setting This cross-sectional study pooled household-level data from the most recent Demographic and Health Surveys conducted in 27 SSA between 2008 and 2014. Participants: 96 804 children age 6–59 months. Results: The prevalence of childhood anaemia (defined as Hb <11 g/dL) across the region was 59.9%, ranging from 23.7% in Rwanda to 87.9% in Burkina Faso. In multivariable regression models, older age, female sex, greater wealth, fewer household members, greater height-for-age, older maternal age, higher maternal body mass index, current maternal pregnancy and higher maternal Hb, and absence of recent fever were associated with higher Hb in tested children. Demographic, socioeconomic factors, family structure, water/sanitation, growth, maternal health and recent illnesses were significantly associated with the presence of childhood anaemia. These risk factor groups explain a significant fraction of anaemia (ranging from 1.0% to 16.7%) at the population level. Conclusions: The findings from our analysis of risk factors for anaemia in SSA underscore the importance of family and socioeconomic context in childhood anaemia. These data highlight the need for integrated programmes that address the multifactorial nature of childhood anaemia

World Health Organization Danger Signs to predict bacterial sepsis in young infants: A pragmatic cohort study

Bacterial sepsis is generally a major concern in ill infants. To help triaging decisions by front-line health workers in these situations, the World Health Organization (WHO) has developed danger signs (DS). The objective of this study was to evaluate the extent to which nine DS predict bacterial sepsis in young infants presenting with suspected sepsis in a low-income country setting. The study pragmatically evaluated nine DS in infants younger than 3 months with suspected sepsis in a regional hospital in Lilongwe, Malawi, between June 2018 and April 2020. Main outcomes were positive blood or cerebrospinal fluid (CSF) cultures for neonatal pathogens, and mortality. Among 401 infants (gestational age [mean ± SD]: 37.1±3.3 weeks, birth weight 2865±785 grams), 41 had positive blood or CSF cultures for a neonatal pathogen. In-hospital mortality occurred in 9.7% of infants overall (N = 39/401), of which 61.5% (24/39) occurred within 48 hours of admission. Mortality was higher in infants with bacterial sepsis compared to other infants (22.0% [9/41] versus 8.3% [30/360]; p = 0.005). All DS were associated with mortality except for temperature instability and tachypnea, whereas none of the DS were significantly associated with bacterial sepsis, except for “unable to feed” (OR 2.25; 95%CI: 1.17–4.44; p = 0.017). The number of DS predicted mortality (OR: 1.75; 95%CI: 1.43–2.17; p<0.001; AUC: 0.756), but was marginally associated with positive cultures with a neonatal pathogen (OR 1.22; 95%CI: 1.00–1.49; p = 0.046; AUC: 0.743). The association between number of DS and mortality remained significant after adjusting for admission weight, the only statistically significant co-variable (OR 1.75 [95% CI: 1.39–2.23]; p<0.001). Considering all positive cultures including potential bacterial contaminants resulted a non-significant association between number of DS and sepsis (OR 1.09 [95% CI: 0.93–1.28]; p = 0.273). In conclusion, this study shows that DS were strongly associated with death, but were marginally associated with culture-positive pathogen sepsis in a regional hospital setting. These data imply that the incidence of bacterial sepsis and attributable mortality in infants in LMIC settings may be inaccurately estimated based on clinical signs alone