Metabolic syndrome among Nigerians with type 2 diabetes mellitus: a comparative study of the diagnostic criteria.

BackgroundMetabolic syndrome is associated with increased cardiovascular death. The objectives of this study were to find the frequency of metabolic syndrome among Nigerians with type 2 diabetes and to compare the modified NCEP ATP III criteria and the IDF criteria MethodsThe study involved 134 participants. Sixty-seven were cases with type 2 diabetes while the rest were the controls without type 2 diabetes. Ethical approval was granted by the institution’s ethics review committee. Anthropometric, clinical and laboratory parameters were obtained using standard protocols. Data were analyzed with SPSS version 22. Means were compared with Student’s t test while proportions were compared with the Pearson’s chi square. Point biserial correlation was used to determine the association between the dichotomous variables and interval variables. Agreement between the criteria was tested with the Cohen’s kappa test.ResultsType 2 diabetes was associated with a higher prevalence of hypertension and truncal obesity. The frequency of metabolic syndrome was lower with the IDF criteria compared with the modified NCEP criteria (65.7% vs 71.6%). Although there was a strong agreement between the IDF and the modified NCEP criteria (κ=0.862; p<0.0001) yet, the IDF criteria missed 8.3% of diabetic individuals diagnosed with metabolic syndrome by the modified NCEP criteria. Cardiovascular risk is better predicted when the modified NCEP criteria were used to diagnose metabolic syndrome.ConclusionMetabolic syndrome is very common among Nigerians with type 2 diabetes and it is better diagnosed with the modified NCEP ATP III criteria.

An Efficient Time-Variant Reliability Analysis Method with Mixed Uncertainties

In practical engineering, due to the lack of information, it is impossible to accurately determine the distribution of all variables. Therefore, time-variant reliability problems with both random and interval variables may be encountered. However, this kind of problem usually involves a complex multilevel nested optimization problem, which leads to a substantial computational burden, and it is difficult to meet the requirements of complex engineering problem analysis. This study proposes a decoupling strategy to efficiently analyze the time-variant reliability based on the mixed uncertainty model. The interval variables are treated with independent random variables that are uniformly distributed in their respective intervals. Then the time-variant reliability-equivalent model, containing only random variables, is established, to avoid multi-layer nesting optimization. The stochastic process is first discretized to obtain several static limit state functions at different times. The time-variant reliability problem is changed into the conventional time-invariant system reliability problem. First order reliability analysis method (FORM) is used to analyze the reliability of each time. Thus, an efficient and robust convergence hybrid time-variant reliability calculation algorithm is proposed based on the equivalent model. Finally, numerical examples shows the effectiveness of the proposed method.

A Time-Variant Reliability Analysis Method Based on the Stochastic Process Discretization under Random and Interval Variables

In practical engineering, it is a cost-consuming problem to consider the time-variant reliability of both random variables and interval variables, which usually requires a lot of calculation. Therefore, a time-variant reliability analysis approach with hybrid uncertain variables is proposed in this paper. In the design period, the stochastic process is discretized into random variables. Simultaneously, the original random variables and the discrete random variables are converted into independent normal variables, and the interval variables are changed into standard variables. Then it is transformed into a hybrid reliability problem of static series system. At different times, the limited state functions are linearized at the most probable point (MPP) and at the most unfavorable point (MUP). The transformed static system reliability problem with hybrid uncertain variables can be solved effectively by introducing random variables. To solve the double-loop nested optimization in the hybrid reliability calculation, an effective iterative method is proposed. Two numerical examples and an engineering example demonstrate the validity of the present approach.

Modified PIRO (predisposition, insult, response, organ dysfunction) severity score as a predictor for mortality of children with pneumonia in Hasan Sadikin Hospital, Bandung, Indonesia

Background: Clinical manifestations for pneumonia vary from mild to severe. The PIRO model (predisposition, insult, response, organ dysfunction) was used as scoring system to determine severity of sepsis and pneumonia in adult patients. The PIRO model was modified for sorting the severity of pneumonia in children and predicting its risk of mortality.Methods: An ambispective cohort study of pneumonia patients aged 1 month ≤18 years admitted over the period from May to September 2020. Data were collected from history, physical examination, laboratory examination, and chest radiography. Based on bivariate analysis (p<0.05 and relative risk (RR) with 95% confidence interval), variables of each PIRO component that were significant for mortality were assigned a value of 1. The cut-off score for predictor of mortality was calculated using the receiver operating characteristics (ROC) curve. Stratify the score into three degrees of risk based on interquartile range. Score ≤Q1 is categorized as low risk; Q1-Q3 is categorized as moderate risk; and score >Q3 is categorized as high risk.Results: Out of the 80 subjects subjects enrolled, 6 months-5 years was the largest age group (56.3%). The observed mortality was 15/80 (18.8%). The modified PIRO severity score was compiled from significant variable of predisposition (malnutrition), insult (chest radiograph), response (hypoxemia, hypotension, CRP >0.5 mg/dL, PCT >0.5 ng/dL) and organ dysfunction, with range of score 0-7. Score >3 as a cutoff point score for predictor of mortality with AUC 0.919 (95% CI 0.836–0.968), sensitivity of 80%, and specificity of 84.62%. Subjects with score >3 have RR of 10.544 compared to those with score ≤3. The stratification of score level was low (≤2), moderate (3-4), and high (5-7). The mortality levels were 0%, 46.7%, and 53.3%, respectively.Conclusions: Modified PIRO severity score can be used as a sorting tool and predictor of mortality risk in children with pneumonia. This score can also be used to select candidates for intensive care, especially in health facilities with limited intensive care capacity.