Frequency of secondary dyslipidemia in obese children

Ulrike Korsten-Reck1, Katrin Kromeyer-Hauschild2, Katrin Korsten1, Manfred W Baumstark1, Hans-H Dickhuth1, Aloys Berg11Department of Rehabilitative and Preventive Sports Medicine, University Medical Center, University of Freiburg, 79106 Freiburg, Germany; 2Institute of Human Genetics and Anthropology, Friedrich-Schiller-University Jena, 07740 Jena, GermanyObjective: This paper reports the frequency, type, and degree of dyslipidemia in obese children before therapeutic intervention. The relationships between lipid values and weight status, as well as lipid values and physical fitness, of these children were also investigated.Design and methods: The initial examination of the Freiburg Intervention Trial for Obese Children (FITOC) measured the values of triglycerides (TG), total cholesterol (C), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C) in 546 obese children aged 7–12 (body mass index [BMI] > 97th percentile), and compared these values with those of the age- and sex-specific reference group in the Lipid Research Clinics Population Studies Data Book (LRC). Four groups were selected according to the following scheme: A, Normolipidemia; B, Hyper-LDL-cholesterolemia alone; C, Hypo-HDL-C + hypertriglyceridemia; D, Combined hyperlipidemia = Hyper-LDL-C + hypertriglyceridemia. Body mass index, BMI-SDS (corrected BMI), and physical performance in watt/kg body weight were measured.Results: A total of 45.8% of the overweight children showed an abnormal lipid profile. Ten percent of the children had high LDL-C levels (group B), while 15% had increased LDL-C and increased TG (group D) (higher prevalence in boys). In 18.9% we found increased TG, combined with decreased HDL-C values (group C).Conclusion: Obese children are at risk of dyslipoproteinemia and related diseases. Children with the highest BMI-SDS and lowest physical fitness have the lowest HDL-C values and increased TG, indicating a higher risk for the metabolic syndrome.Keywords: atherosclerotic risk, childhood, dyslipidemia, obesit

Tracebook : a dynamic checklist support system

It has recently been demonstrated that checklist scan enable significant improvements to patient safety. However, their clinical acceptance is significantly lower than expected. This is due to the lack of good support systems. Specifically, support systems are too static: this holds for paper-based support as well as for electronic systems that digitize paper-based support naively. Both approaches are independent from clinical process and clinical context. In this paper, we propose a process-oriented and context-aware dynamic checklist support system: Tracebook. This system supports the execution of complex clinical processes and rules involving data from Electronic Medical Record systems. Workflow activities and forms are specific to individual patients based on clinical rules and they are dispatched to the right user automatically based on a process model. Besides describing the Tracebook functionality in general, this paper demonstrates the support system specifically on an example application that we are preparing for a controlled clinical evaluation. At last we discuss the difference between Tracebook and other support systems which also rely on a checklist format

DCCSS:a meta-model for dynamic clinical checklist support systems

Clinical safety checklists receive much research attention since they can reduce medical errors and improve patient safety. Computerized checklist support systems are also being developed actively. Such systems should individualize checklists based on information from the patient’s medical record while also considering the context of the clinical workflows. Unfortunately, the form definitions, database queries and workflow definitions related to dynamic checklists are too often hard-coded in the source code of the support systems. This increases the cognitive effort for the clinical stakeholders in the design process, it complicates the sharing of dynamic checklist definitions as well as the interoperability with other information systems. In this paper, we address these issues by contributing the DCCSS meta-model which enables the model-based development of dynamic checklist support systems. DCCSS was designed as an incremental extension of standard meta-models, which enables the reuse of generic model editors in a novel setting. In particular, DCCSS integrates the Business Process Model and Notation (BPMN) and the Guideline Interchange Format (GLIF), which represent best of breed languages for clinical workflow modeling and clinical rule modeling respectively. We also demonstrate one of the use cases where DCCSS has already been applied in a clinical setting

Risicoscreening van geriatrische patienten bij ziekenhuisopname met een clinical rule op basis van het HARM-onderzoek

Risicoscreening van de geriatrische patient bij opname met behulp van een clinical rule op basis van de reslutaten van het HARM-onderzoek

Novel ultrasound contrast agent dilution method for the assessment of ventricular ejection fraction

Aims: Left ventricular (LV) ejection fraction is an important determinant of prognosis in heart failure. We evaluated the accuracy of a novel algorithm for LV ejection fraction quantification based on indicator dilution curve (IDC) principles using ultrasound contrast as indicator, and compared the results with contrast enhanced biplane LV ejection fraction assessment. Method: A diluted ultrasound contrast bolus (SonoVue®) was injected intravenously in 31 patients (19 male, age 65 ± 11) with known or suspected heart disease. A total of 68 recordings were made. The developed algorithm used the left atrium and LV IDC for LV ejection fraction measurement. Biplane enhanced LV ejection fraction measurements with pure ultrasound contrast (SonoVue®) were determined in multiple four- and two-chamber recordings as reference. Results: The mean LV ejection fraction measured by biplane and IDC method was 33 ± 17% and 35 ± 18%, respectively. A correlation coefficient r = 0.93 was observed between the two methods. Bland–Altman analysis demonstrated a slight LV ejection fraction overestimation with IDC (mean 1.9 ± 6.3%). Conclusion: A new fast method for LV ejection fraction assessment based on IDC principles is described and comparison with contrast enhanced biplane LV ejection fraction quantification shows accurate results