ObjectivesOur objective was to evaluate the effectiveness of peer-led self-management education in improving glycaemic control in patients with type 2 diabetes in a low-income country (Mali).MethodsWe conducted an open-label randomised controlled trial. A total of 151 adults (76% women, mean age 52.5) with type 2 diabetes (HbA1c≥8%), treated in the diabetes consultation units of two secondary health centres in Bamako, were allocated to peer-led structured patient education (n = 76) or conventional care alone (n = 75). The intervention group received 1 year of culturally tailored structured patient education (3 courses of 4 sessions) delivered in the community by five trained peer educators. Both groups underwent conventional diabetes monitoring and follow-up. Primary outcome was the mean absolute change in HbA1c from baseline to 12 months.Results177 education sessions were delivered to the intervention group. Patient attrition was 8%. From baseline to 12 months, the decrease in HbA1c levels was 1.05% (SD = 2.0; CI95%: 1.54;-0.56) in the intervention group compared with 0.15% (SD = 1.7; CI95%: -0.56; 0.26) in the control group, p = 0.006. Mean BMI change was -1.65 kg/m2 (SD = 2.5; CI95%: -2.25; -1.06) in the intervention group and +0.05 kg/m2 (SD = 3.2; CI95%: -0.71; 0.81) in the control group, p = 0.0005. Mean waist circumference decreased by 3.34 cm (SD = 9.3; CI95%: -5.56;-1.13) in the intervention group and increased by 2.65 cm (SD = 10.3; CI95%: 0.20; 5.09) in the control group, p = 0.0003.ConclusionsPeer-led structured patient education delivered over 1 year to patients with poorly controlled type 2 diabetes in Mali yielded substantial improvements in glycaemic control and anthropometric parameters. This is of importance for the scaling up of efficient interventions in low-resource settings in the future.Trial registrationClinicalTrials.gov NCT01485913

The epidemiology of meconium aspiration syndrome (MAS) in term neonates is described in a population-based retrospective study of data recorded for all births from 2000 to 2007 in a French region (Burgundy). Of the 132 884 eligible term newborns, the rate of meconium-stained amniotic fluid (MSAF) was 7.93%. The prevalence of severe MAS was 0.067% in the overall population. MAS rate was 0.11% at 37-38 weeks of gestation (WG), 0.20% at 39–41 WG, and 0.49% at 42-43 WG. Factors independently associated with severe MAS were identified by a case-control study, that is, thick meconium amniotic fluid, fetal tachycardia, Apgar score ≤3 at 1 minute, and birth in a level III facility. Our results confirm the high prevalence of MSAF after 37 WG but also show the low frequency of severe MAS in a period corresponding to the new international recommendations on the management of birth with MSAF.

Multivariable regression models are widely used in medical literature for the purpose of diagnosis or prediction. Conventionally, the adequacy of these models is assessed using metrics of diagnostic performances such as sensitivity and specificity, which fail to account for clinical utility of a specific model. Decision curve analysis (DCA) is a widely used method to measure this utility. In this framework, a clinical judgment of the relative value of benefits (treating a true positive case) and harms (treating a false positive case) associated with prediction models is made. As such, the preferences of patients or policy-makers are accounted for by using a metric called threshold probability. A decision analytic measure called net benefit is then calculated for each possible threshold probability, which puts benefits and harms on the same scale. The article is a technical note on how to perform DCA in R environment. The decision curve is depicted with the system. Correction for overfitting is done via either bootstrap or cross-validation. Confidence interval and P values for the comparison of two models are calculated using bootstrap method. Furthermore, we describe a method for computing area under net benefit for the comparison of two models. The average deviation about the probability threshold (ADAPT), which is a more recently developed index to measure the utility of a prediction model, is also introduced in this article.

Future studies should delineate more precisely the respective contribution of gestational age, maternal complication and induced delivery in the prognosis of infants born between 33 and 39 weeks gestation.

BackgroundThe benefits of cardiac surgery are sometimes difficult to predict and the decision to operate on a given individual is complex. Machine Learning and Decision Curve Analysis (DCA) are recent methods developed to create and evaluate prediction models.Methods and findingWe conducted a retrospective cohort study using a prospective collected database from December 2005 to December 2012, from a cardiac surgical center at University Hospital. The different models of prediction of mortality in-hospital after elective cardiac surgery, including EuroSCORE II, a logistic regression model and a machine learning model, were compared by ROC and DCA. Of the 6,520 patients having elective cardiac surgery with cardiopulmonary bypass, 6.3% died. Mean age was 63.4 years old (standard deviation 14.4), and mean EuroSCORE II was 3.7 (4.8) %. The area under ROC curve (IC95%) for the machine learning model (0.795 (0.755–0.834)) was significantly higher than EuroSCORE II or the logistic regression model (respectively, 0.737 (0.691–0.783) and 0.742 (0.698–0.785), p < 0.0001). Decision Curve Analysis showed that the machine learning model, in this monocentric study, has a greater benefit whatever the probability threshold.ConclusionsAccording to ROC and DCA, machine learning model is more accurate in predicting mortality after elective cardiac surgery than EuroSCORE II. These results confirm the use of machine learning methods in the field of medical prediction.