49 resultados para Sensitivity Analysis
Resumo:
No reports testing the efficacy of the use of the QT/RR ratio <1/2 for detecting a normal QTc interval were found in the literature. The objective of the present study was to determine if a QT/RR ratio <=1/2 can be considered to be equal to the normal QTc and to compare the QT and QTc measured and calculated clinically and by a computerized electrocardiograph. Ratios (140 QT/RR) of 28 successive electrocardiograms obtained from 28 consecutive patients in a tertiary level teaching hospital were analyzed clinically by 5 independent observers and by a computerized electrocardiograph. The QT/RR ratio provided 56% sensitivity and 78% specificity, with an area under the receiver operator characteristic curve of 75.8% (95%CI: 0.68 to 0.84). The divergence in QT and QTc interval measurements between clinical and computerized evaluation were 0.01 ± 0.03 s (95%CI: 0.04-0.02) and 0.01 ± 0.04 s (95%CI: -0.05-0.03), respectively. The QT and QTc values measured clinically and by a computerized electrocardiograph were similar. The QT/RR ratio <=1/2 was not a satisfactory index for QTc evaluation because it could not predict a normal QTc value.
Resumo:
The present study was designed to compare the homeostasis model assessment (HOMA) and quantitative insulin sensitivity check index (QUICKI) with data from forearm metabolic studies of healthy individuals and of subjects in various pathological states. Fifty-five healthy individuals and 112 patients in various pathological states, including type 2 diabetes mellitus, essential hypertension and others, were studied after an overnight fast and for 3 h after ingestion of 75 g of glucose, by HOMA, QUICKI and the forearm technique to estimate muscle uptake of glucose combined with indirect calorimetry (oxidative and non-oxidative glucose metabolism). The patients showed increased HOMA (1.88 ± 0.14 vs 1.13 ± 0.10 pmol/l x mmol/l) and insulin/glucose (I/G) index (1.058.9 ± 340.9 vs 518.6 ± 70.7 pmol/l x (mg/100 ml forearm)-1), and decreased QUICKI (0.36 ± 0.004 vs 0.39 ± 0.006 (µU/ml + mg/dl)-1) compared with the healthy individuals. Analysis of the data for the group as a whole (patients and healthy individuals) showed that the estimate of insulin resistance by HOMA was correlated with data obtained in the forearm metabolic studies (glucose uptake: r = -0.16, P = 0.04; non-oxidative glucose metabolism: r = -0.20. P = 0.01, and I/G index: r = 0.17, P = 0.03). The comparison of QUICKI with data of the forearm metabolic studies showed significant correlation between QUICKI and non-oxidative glucose metabolism (r = 0.17, P = 0.03) or I/G index (r = -0.37, P < 0.0001). The HOMA and QUICKI are good estimates of insulin sensitivity as data derived from forearm metabolic studies involving direct measurements of insulin action on muscle glucose metabolism.
Resumo:
The present study examined the distribution of hepatitis C virus (HCV) genotypes and subtypes in a hemodialysis population in Goiás State, Central Brazil, and evaluated the efficiency of two genotyping methods: line probe assay (LiPA) based on the 5' noncoding region and nucleotide sequencing of the nonstructural 5B (NS5B) region of the genome. A total of 1095 sera were tested for HCV RNA by RT-nested PCR of the 5' noncoding region. The LiPA assay was able to genotype all 131 HCV RNA-positive samples. Genotypes 1 (92.4%) and 3 (7.6%) were found. Subtype 1a (65.7%) was the most prevalent, followed by subtypes 1b (26.7%) and 3a (7.6%). Direct nucleotide sequencing of 340 bp from the NS5B region was performed in 106 samples. The phylogenetic tree showed that 98 sequences (92.4%) were classified as genotype 1, subtypes 1a (72.6%) and 1b (19.8%), and 8 sequences (7.6%) as subtype 3a. The two genotyping methods gave concordant results within HCV genotypes and subtypes in 100 and 96.2% of cases, respectively. Only four samples presented discrepant results, with LiPA not distinguishing subtypes 1a and 1b. Therefore, HCV genotype 1 (subtype 1a) is predominant in hemodialysis patients in Central Brazil. By using sequence analysis of the NS5B region as a reference standard method for HCV genotyping, we found that LiPA was efficient at the genotype level, although some discrepant results were observed at the subtype level (sensitivity of 96.1% for subtype 1a and 95.2% for subtype 1b). Thus, analysis of the NS5B region permitted better discrimination between HCV subtypes, as required in epidemiological investigations.
Resumo:
High saturated and trans fatty acid intake, the typical dietary pattern of Western populations, favors a proinflammatory status that contributes to generating insulin resistance (IR). We examined whether the consumption of these fatty acids was associated with IR and inflammatory markers. In this cross-sectional study, 127 non-diabetic individuals were allocated to a group without IR and 56 to another with IR, defined as homeostasis model assessment-IR (HOMA-IR) >2.71. Diet was assessed using 24-h food recalls. Multiple linear regression was employed to test independent associations with HOMA-IR. The IR group presented worse anthropometric, biochemical and inflammatory profiles. Energy intake was correlated with abdominal circumference and inversely with adiponectin concentrations (r = -0.227, P = 0.002), while saturated fat intake correlated with inflammatory markers and trans fat with HOMA-IR (r = 0.160, P = 0.030). Abdominal circumference was associated with HOMA-IR (r = 0.430, P < 0.001). In multiple analysis, HOMA-IR remained associated with trans fat intake (β = 1.416, P = 0.039) and body mass index (β = 0.390, P < 0.001), and was also inversely associated with adiponectin (β = -1.637, P = 0.004). Inclusion of other nutrients (saturated fat and added sugar) or other inflammatory markers (IL-6 and CRP) into the models did not modify these associations. Our study supports that trans fat intake impairs insulin sensitivity. The hypothesis that its effect could depend on transcription factors, resulting in expression of proinflammatory genes, was not corroborated. We speculate that trans fat interferes predominantly with insulin signaling via intracellular kinases, which alter insulin receptor substrates.