BackgroundDiarrhoea is the second leading cause of child mortality worldwide. Low- and middle-income countries are particularly burdened with this both preventable and treatable condition. Targeted interventions include the provision of safe water, the use of sanitation facilities and hygiene education, but are implemented with varying local success.ObjectiveTo determine the prevalence of and factors associated with diarrhoea in children under five years of age in rural Burundi.DesignA cross-sectional survey was conducted among 551 rural households in northwestern Burundi. Areas of inquiry included 1) socio-demographic information, 2) diarrhoea period prevalence and treatment, 3) behaviour and knowledge, 4) socio-economic indicators, 5) access to water and water chain as well as 6) sanitation and personal/children's hygiene.ResultsA total of 903 children were enrolled. The overall diarrhoea prevalence was 32.6%. Forty-six per cent (n=255) of households collected drinking water from improved water sources and only 3% (n=17) had access to improved sanitation. We found a lower prevalence of diarrhoea in children whose primary caretakers received hygiene education (17.9%), boiled water prior to its utilisation (19.4%) and were aged 40 or older (17.9%). Diarrhoea was associated with factors such as the mother's age being less than 25 and the conviction that diarrhoea could not be prevented. No gender differences were detected regarding diarrhoea prevalence or the caretaker's decision to treat.ConclusionsDiarrhoea prevalence can be reduced through hygiene education and point-of use household water treatment such as boiling. In order to maximise the impact on children's health in the given rural setting, future interventions must assure systematic and regular hygiene education at the household and community level.

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Background There are several tools for primary prevention (e.g. Framingham, ESC) that can be used to predict mortality risk in healthy individuals. However, only a few scores have been validated to predict outcome in patients with cardiovascular disease. One of these instruments is the REACH (REduction of Atherothrombosis for Continued Health) score. The ESC guideline for stable coronary artery disease (CAD) places a clear emphasis on carrying out risk stratification before using invasive treatment. Recent studies have revealed a prognostic value of serum hs-cTnI in patients with stable CAD. Purpose The aim of this study was to evaluate the prognostic information provided by hs-cTnI in stable high-risk CAD patients. Methods Between 2011 and 2014, consecutive stable patients with suspected CAD undergoing coronary angiography were included in the study. Data from a 4-year follow-up was obtained; the study endpoint was defined as all-cause mortality. Serum hs-cTnI was measured before angiography using a high-sensitivity assay. Results A total of 3,742 patients were included, of whom 2,274 (60.1%) had confirmed CAD. Patients with an estimated annual mortality rate above 3% using the REACH score were defined as having high risk (n=996 in the low-risk group, n=1,278 in the high-risk cohort). Patients with higher risk were more often male (81.5% vs. 69.2%, p<0.001), were older (mean age 73.2±8.1 y vs. 63±9.4 y), and had more cardiovascular risk factors (diabetes mellitus (DM) 43.5% vs. 13.7%, p<0.001; arterial hypertension 90.8% vs. 86%, p<0.001). Median hs-cTnI was elevated in high-risk patients (6.9 ng/L [IQR 1–3: 3.8–14.8 ng/L] vs. 3 ng/L [IQR 1–3: 1.7–5.9 ng/L]; p<0.001). A total of 298 patients (23.3%) died in the high-risk group compared with 74 patients (7.4%) in the low-risk group. Log(hs-cTnI) was found to be a risk factor based on regression analysis including age, gender, DM, arterial hypertension and the REACH score (OR 2.02 [95% CI 1.61–2.54]). The area under the ROC of hs-cTnI for predicting all-cause mortality was 0.69 (95% CI 0.66–0.72) for hs-cTnI and 0.72 (95% CI 0.69–0.72) for the REACH score. There was a correlation between hs-cTnI and the REACH score (Spearman correlation 0.458; p<0.001). In patients at low risk, the best cut-off for hs-cTnI was 3 ng/L, and for high-risk patients 8.25 ng/L was the best threshold value. Using low REACH score and low hs-cTnI levels, it was possible to identify patients at very low risk with a mortality rate below 3.4% in a follow-up of 48 months. It was also feasible to determine patients at very high risk in the group of patients who were already at high risk using the hs-cTnI cut-off (mortality 15.2% vs. 33.7%). Conclusion Hs-cTnI was found to be an independent risk factor in low- as well as high-risk patients. Hs-cTnI levels correlate with the REACH risk score. Moreover, it was possible to separate patients at very high and very low risk by combining REACH score and hs-cTnI.

Background Acute myocardial infarction (MI) is associated with high morbidity and mortality. A robust differentiation between type 1 and type 2 MI (T1/T2MI) has prognostic and therapeutic implications. We investigated whether serial high-sensitivity cardiac troponin I measurements could reliably discriminate T1MI from T2MI in patients presenting with a non-ST elevation myocardial infarction (NSTEMI). Methods We used data from a prospective acute coronary syndrome biomarker registry of patients with suspected MI that presented at or were transferred to one of two study centres. Here, we analysed an unselected group of 265 NSTEMI patients (67.2% males). Blood was drawn on admission and after 3 hours. High-sensitivity troponin I (hs-cTnI) was measured in frozen samples by a technician blinded to patient characteristics. T1MI or T2MI was defined as the gold-standard study diagnosis by two independent cardiologists based on all available data according to the Third Universal Definition of MI. Results A diagnosis of T2MI was made in 55 patients (20.8%) in the NSTEMI cohort. T2MI patients did not differ from T1MI patients regarding age, gender, traditional risk factors, or percentage of those with a history of coronary artery disease. Median baseline hs-cTnI levels were higher in T1MI (436.25; IQR 63.7–1918.8 ng/L) than in T2MI patients (48.4; IQR 11.7–305.9 ng/L; p<0.001). Absolute change in hs-cTnI concentration between 0 and 3 h was greater in T1MI than in T2MI patients with Dhs-cTnI 93.6 ng/L (IQR 13.5–815.3 ng/L) vs. 20.4 ng/L (IQR 2.5–106.5 ng/L) (p<0.001). hs-cTnI yielded an area under the receiver operator characteristics (AUROC) curve for identifying T2MI at baseline of 0.71 (IQR 0.64–0.79) and after 3 h of 0.7 (IQR 0.61–0.78).Dhs-cTnI was associated with an AUROC of 0.68 (IQR 0.6–0.76). Regarding a rule-out approach, Youden-optimized cut-offs for hs-cTnI at baseline as well as for the absolute change in hs-cTnI concentration were calculated (186.5 ng/L; 154.4 ng/L). Use of these two criteria yielded a sensitivity of 89% (78–96%) and a negative predictive value of 95% (89–98%) to exclude T2MI. 49 of 55 T2MI patients would have been ruled out using this algorithm. Conclusion Our data show that hs-cTnI concentrations differ between patients presenting with T1 and T2MI. The concentration of hs-cTnI and its change over time has the potential to rule out T2MI and therefore to identify patients who might benefit from an early invasive management. The differentiation between T1MI and T2MI by using hs-cTnI is nevertheless challenging, and further research on specific algorithms is needed. Acknowledgement/Funding 3German Center for Cardiovascular Research (DZHK), Partnersite Rhein Main, Bad Nauheim, Germany

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Introduction and aim Vitamin D deficiency is associated with an adverse prognosis in patients with coronary artery disease (CAD). Decreased levels of vitamin D are associated with low sunshine exposure, resulting in seasonal variations of vitamin D. The aims of this study were to investigate the influence of different specific weather conditions on vitamin D levels and to explore a possible improvement of risk stratification by vitamin D levels in stable patients with CAD using meteorological data. Methods The study population consists of two independent cohorts of stable patients undergoing coronary angiography with suspected or known CAD: as derivation cohort, the ongoing biomarker registry BioPROSPECTIVE with n=1,766 enrolled patients between 2010 and 2013 (median age 70.1 yrs; 30.8% females); and as validation cohort, the Ludwigshafen Risk and Cardiovascular Health (LURIC) Study with n=3,299 patients (median age 63.5 yrs; 30.3% females). In the derivation cohort 235 (13.3%) patients were known to be deceased by 08/2018. In the validation cohort 760 (23.0%) patients died within a median follow-up time of 7.75 years. 25-OH vitamin D levels were measured by commercial assays. Vitamin D deficiency was defined as 25-OH vitamin D levels ≤20 ng/mL. Daily averaged data on six weather conditions of the 180 days prior to enrolment were collected for each patient from the weather station located closest to the respective study centre. Using air pressure, precipitation height, sunshine duration, temperature, relative humidity, and vapour pressure a weather model was constructed that significantly correlated with vitamin D levels (r=0.37; p<0.001). Results In the derivation cohort, median vitamin D levels were lower in non-survivors (13.3 [9.65–19.65] ng/mL) than in survivors (15.70 [10.7–22.65] ng/mL; p<0.001). Vitamin D predicted all-cause mortality with an area under the receiver operator characteristic curve (AUROC) of 0.576 (CI: 0.54–0.62). Adding the weather model to vitamin D significantly improved the AUROC to 0.601 (CI: 0.56–0.64; p=0.031). The vitamin D/weather model combination enhanced the prognostic value of the ESC SCORE to predict mortality (AUROC=0.571 [CI: 0.53–0.61] vs. 0.628 [CI: 0.59–0.67]; p=0.004). Comparable results were observed in the validation cohort. Here, vitamin D deficiency predicted mortality with a hazard ratio (HR) of 1.89 (CI: 1.59–2.26) after adjustment for ESC SCORE. Adding the weather model improved this HR to 1.92 (1.62–2.32). Reclassification analyses support the additive prognostic information of weather conditions with a continuous net reclassification improvement of 0.114 ([0.033–0.194]; p=0.006) if adding the weather model to vitamin D as base model for predicting mortality. Conclusions Different weather conditions show a significant impact on vitamin D levels in stable patients. Adding data on weather conditions improve the risk stratification by vitamin D for predicting mortality in stable CAD patients. Acknowledgement/Funding The study is financially supported by the Kerckhoff Heart Research Institute (KHFI) and the German Center for Cardiovascular Research (DZHK).