BACKGROUND/AIMS: Liver stiffness (LS) was assessed using transient elastography, and the enhanced liver fibrosis (ELF) test was performed to accurately assess fibrotic burden. We validated the LS-ELF algorithm and investigated whether the sequential LS-ELF algorithm performs better than concurrent combination of these analyses in chronic hepatitis B (CHB) patients. METHODS: Between 2009 and 2013, 222 CHB patients who underwent liver biopsy (LB), as well as LS measurement and the ELF test, were enrolled. RESULTS: Advanced fibrosis (≥F3) and cirrhosis (F4) were identified in 141 (63.6%) and 118 (53.2%) patients, respectively. Areas under receiver operating characteristic curve for LS predictions of ≥F3 (0.887 vs 0.703) and F4 (0.853 vs 0.706) were significantly higher than the ELF test (all p < 0.001). Based on the LS-ELF algorithm, 60.4% to 71.6% and 55.7% to 66.3% of patients could have avoided LB to exclude ≥F3 and F4, respectively, whereas 68.0% to 78.7% and 63.5% to 66.1% of patients could have avoided LB to confirm ≥F3 and F4, respectively. When confirmation and exclusion strategies were applied simultaneously, 69.4% to 72.5% and 60.8% to 65.3% of patients could have avoided LB and been diagnosed as ≥F3 and F4, respectively. The proportion of patients who correctly avoided LB for the prediction of ≥F3 (69.4% to 72.5% vs 42.3% to 59.0%) and F4 (60.8% to 65.3% vs 23.9% to 49.5%) based on the sequential LS-ELF algorithm was significantly higher than the concurrent combination (all p < 0.05). CONCLUSIONS: The sequential LS-ELF algorithm conferred a greater probability of avoiding LB in CHB patients to diagnose advanced fibrosis and cirrhosis, and this test performed significantly better than the concurrent combination.
Biopsy ; Elasticity Imaging Techniques* ; Fibrosis ; Hepatitis B ; Hepatitis B, Chronic* ; Hepatitis, Chronic* ; Humans ; Liver Cirrhosis* ; Liver* ; ROC Curve

Clinical prediction models are developed to calculate estimates of the probability of the presence/occurrence or future course of a particular prognostic or diagnostic outcome from multiple clinical or non-clinical parameters. Radiologic imaging techniques are being developed for accurate detection and early diagnosis of disease, which will eventually affect patient outcomes. Hence, results obtained by radiological means, especially diagnostic imaging, are frequently incorporated into a clinical prediction model as important predictive parameters, and the performance of the prediction model may improve in both diagnostic and prognostic settings. This article explains in a conceptual manner the overall process of developing and validating a clinical prediction model involving radiological parameters in relation to the study design and statistical methods. Collection of a raw dataset; selection of an appropriate statistical model; predictor selection; evaluation of model performance using a calibration plot, Hosmer-Lemeshow test and c-index; internal and external validation; comparison of different models using c-index, net reclassification improvement, and integrated discrimination improvement; and a method to create an easy-to-use prediction score system will be addressed. This article may serve as a practical methodological reference for clinical researchers.
Calibration ; Dataset ; Diagnosis ; Diagnostic Imaging ; Discrimination (Psychology) ; Early Diagnosis ; Humans ; Methods* ; Models, Statistical ; Prognosis

PURPOSE: The aim of the present study was to investigate associations between the renin gene (REN) and the risk of essential hypertension and blood pressure (BP) levels in Koreans. MATERIALS AND METHODS: To outline the functional role of a single nucleotide polymorphism in the transcription of the REN gene, we conducted a case-control study of 1975 individuals: 646 hypertension (HT) patients and 1329 ethnically and age-matched normotensive subjects. RESULTS: Logistic regression analysis indicated that the genotypes AA/AG were strongly associated with risk of HT (odds ratio, 1.493; 95% confidence interval, 1.069-2.086, p=0.018) in female subjects. The genotypes AA/AG also showed significant association with higher blood pressure levels, both systolic and diastolic, in postmenopausal HT women (p=0.003 and p=0.017, respectively). Analysis of the promoter containing rs6682082 revealed a 2.4+/-0.01-fold higher activity in the A variant promoter than the G variant promoter, suggesting that rs6682082 is itself a functional variant. CONCLUSION: We suggest that the A allele of rs6682082 is a positive genetic marker for predisposition to essential hypertension and high BP in Korean women and may be mediated through the transcriptional activation of REN.
Alleles ; Asian Continental Ancestry Group/*genetics ; Blood Pressure/*genetics ; Case-Control Studies ; Diastole/genetics ; Female ; Gene Frequency ; *Genetic Association Studies ; *Genetic Predisposition to Disease ; Humans ; Hypertension/*genetics/*physiopathology ; Luciferases/metabolism ; Middle Aged ; Polymorphism, Single Nucleotide/*genetics ; Promoter Regions, Genetic/genetics ; Renin/*genetics ; Republic of Korea ; Risk Factors ; Systole/genetics ; Transfection

BACKGROUND/AIMS: The aim of this study was to evaluate the estimated glomerular filtration rate (eGFR) during telbivudine (LdT) versus entecavir (ETV) treatment in chronic hepatitis B (CHB) patients with underlying comorbidities such as diabetes mellitus (DM), hypertension, and cirrhosis. METHODS: From 2010 to 2012, 116 CHB patients treated with LdT and 578 treated with ETV were compared in this real-practice cohort. The mean changes in eGFR (Modification of Diet in Renal Disease [MDRD] formula) from baseline to months 6, 12, and 18 were analyzed using a linear mixed model. RESULTS: In LdT-treated patients, the mean eGFR increased by 7.6% at month 18 compared with the eGFR at baseline (MDRD formula in mL/min/1.73 m2). However, in ETV-treated patients, the mean eGFR decreased by 4.1% at month 18 compared with the eGFR at baseline. In the LdT-treated patients with DM, hypertension, cirrhosis or low eGFR <90 mL/min/1.73 m2, the mean eGFR showed a steady improvement, whereas the mean eGFR was reduced in the same subgroups of ETV-treated patients. CONCLUSIONS: The eGFR gradually increased over time during LdT treatment, especially in patients with mild abnormal eGFR at baseline, and in those with DM, hypertension, and cirrhosis, whereas a reduction in eGFR was seen with ETV treatment.
Adult ; Antiviral Agents/*administration & dosage ; Diabetes Complications ; Diabetes Mellitus ; Drug Administration Schedule ; Female ; Fibrosis/complications ; Glomerular Filtration Rate/*drug effects ; Guanine/administration & dosage/*analogs & derivatives ; Hepatitis B, Chronic/complications/*drug therapy/physiopathology ; Humans ; Hypertension/complications ; Linear Models ; Male ; Middle Aged ; Thymidine/administration & dosage/*analogs & derivatives ; Time Factors ; Treatment Outcome

PURPOSE: To do nursing research effectively requires an understanding of fundamental principles of statistical methods. In this article, some key statistical methods which are commonly used in nursing research are identified and summarized. METHODS: Ninety-two original articles from the Journal of Korean Academy of Nursing Administration were reviewed. Statistical methods were classified and summarized for usage in research and occurrence of common errors. RESULTS: Among the original articles reviewed, 58 statistical usages contained errors. Most errors were found in linear regression analysis, Pearson correlation analysis, and chi-square test. From the detection of statistical errors in usage, suggestions for appropriate statistical methods were made. CONCLUSION: In order to improve validity of original articles in the Journal of Korean Academy of Nursing Administration, clearly stated statistical usage and close editorial attention to statistical methods are needed. Understanding statistical methods is part of the process that researchers must use to determine both quality and usefulness of the research. Research findings will be used to guide nursing practice and reduce uncertainty in decision making. However, to understand how to interpret research results, it is important to be able to understand basic statistical concepts. Researchers should also choose statistical methods that match their purposes.
Decision Making ; Linear Models ; Nursing Research ; Uncertainty

OBJECTIVES: The mixture-of-experts (ME) network uses a modular type of neural network architecture optimized for supervised learning. This model has been applied to a variety of areas related to pattern classification and regression. In this research, we applied a ME model to classify hidden subgroups and test its significance by measuring the stiffness of the liver as associated with the development of liver cirrhosis. METHODS: The data used in this study was based on transient elastography (Fibroscan) by Kim et al. We enrolled 228 HBsAg-positive patients whose liver stiffness was measured by the Fibroscan system during six months. Statistical analysis was performed by R-2.13.0. RESULTS: A classical logistic regression model together with an expert model was used to describe and classify hidden subgroups. The performance of the proposed model was evaluated in terms of the classification accuracy, and the results confirmed that the proposed ME model has some potential in detecting liver cirrhosis. CONCLUSIONS: This method can be used as an important diagnostic decision support mechanism to assist physicians in the diagnosis of liver cirrhosis in patients.
Elasticity Imaging Techniques ; Humans ; Learning ; Liver ; Liver Cirrhosis ; Logistic Models

BACKGROUND AND OBJECTIVES : The common methods of genetic association analysis are sensitive to population stratification, which may easily lead to a spurious association result. We used a regression approach based for linkage disequilibrium to perform a high resolution genetic association analysis. SUBJECTS AND METHODS : We applied a regression approach that can increase the resolution of quantitative traits that are related with cardiovascular diseases. The population data was composed of 543 males and 876 females without cardiovascular diseases, and it was obtained from a cardiovascular genome center. We used information about linkage disequilibrium between the marker and trait locus, and we added the covariates to model their effects. RESULTS : We found that this regression approach has the merit of analyzing genetic association based on linkage disequilibrium. In the analysis of the male group, the total cholesterol was significantly in linkage disequilibrium with CETP3 (p=0.002), and triglyceride was significantly in linkage disequilibrium with ACE8 (p=0.037), APOA1-1 (p=0.031), APOA5-1 (p=0.001), APOA5-2 (p=0.001) and LIPC4 (p=0.022). HDL-cholesterol was significantly in linkage disequilibrium with ACE7 (p=0.002), ACE8 (p=0.008), ACE10 (p=0.003), APOA5-2 (p=0.022), and MTP1 (p=0.001). In the female group, total cholesterol was significantly associated with APOA5-1 (p=0.020), APOA5-2 (p=0.001), and LIPC1 (p=0.016), and triglyceride was significantly associated with APOA5-1 (p=0.009), APOA5-2 (p=0.001), and CETP5 (p=0.049). LDL-cholesterol was significantly associated with APOA5-2 (p=0.004), and HDL-cholesterol was significantly associated with LIPC1 (p=0.004). CONCLUSION : We used a regression-based method to perform high resolution linkage disequilibrium analysis of a quantitative trait locus that's associated with lipid profiles. This method of using a single marker, as applied in this paper, was well suited for analysis of genetic association. Because of the simplicity, the method can also be easily performed by routine statistical analysis software.
Cardiovascular Diseases ; Cholesterol ; Female ; Genome ; Humans ; Linkage Disequilibrium* ; Male ; Quantitative Trait Loci* ; Triglycerides

BACKGROUND AND OBJECTIVES: Analyzing the association between multiple SNPs and the disease outcomes will provide new insight into the disease's etiology. However, this presents an analytic difficulty due to the large number of SNPs and the complex relationships among them. We proposed using the mixed model approach to identify the significant multi-locus genotypes and the high-order gene-to-gene interactions. SUBJECTS AND METHODS: We described the mixed effects model and applied this approach to real world data. For the purpose of these analyses, we examine the association of four types of SNPs (AGT5, APOB, CETP3 and ACE6) with the lipid profiles and the measures related with cardiovascular disease. We used data from 672 healthy individuals (283 males and 389 females) who were without cardiovascular diseases. RESULTS: The results of our analysis suggested that there were significant random genotype patterns and genotype groups according to the gender effect on the lipid profiles. In other words, there was significant variability across the genotype groups because of the effect of gender on the lipid profiles. CONCLUSION: The mixed model approach provided a flexible statistical framework for controlling potential confounding variables and for identifying a significant genetic contributions that may come about through the effects of multi-locus genotypes or through an interaction between the genotype and environmental variables (e.g. gender) with the variations in quantitative traits (e.g. lipid profiles). There were significant genetic contributions to the variability in the lipid profiles, and these were explained by the 4 SNPs described in our real data.
Apolipoproteins B ; Cardiovascular Diseases ; Confounding Factors (Epidemiology) ; Genotype ; Humans ; Male ; Polymorphism, Single Nucleotide

BACKGROUND/AIMS: A diagnosis of gastroesophageal reflux disease (GERD) is based on the typical symptoms, such as acid regurgitation and heartburn. However, there is a very high inter-observer variation in the evaluation of GERD patients. METHODS: The endoscopic images of forty-two cases with reflux symptoms (2 still images and 15-second video images per case) were analyzed by 18 experienced endoscopists and 22 trainees. The findings were classified into the following: (1) 6 groups (modified LA classification: 4 LA groups, minimal, and normal), (2) erosinve and non-erosive, and (3) confluent erosive and others. The level of inter-observer variation is expressed as a kappa value. RESULTS: The level of inter-observer agreement of the 18 experienced endoscopists for classifying the patients into 6 groups was fairly low (kappa=0.364). However, when the findings were classified into the 2 groups suggested in the Genval workshop (NERD, A, or B versus C or D), the level of inter- observer agreement increased substantially (kappa=0.710). The kappa value of the 22 trainees for classifying the patients into 6 groups was 0.402. CONCLUSIONS: Modified LA classification with minimal change lesions showed a fairly low level of agreement. The problem caused by inter-observer variations decreased significantly when the findings were classified into two groups.
Classification ; Diagnosis* ; Education ; Gastroesophageal Reflux* ; Heartburn ; Humans ; Observer Variation*

Purpose: To proceed effectively with clinical research requires an understanding of the fundamental principles of study design and biostatistical methods. In this article, we identified and summarized basic clinical research designs and some of the key biostatistical methods that have been commonly used in clinical research. Materials and Methods: In an observational study, cross-sectional, case- control and Cohort designs were illustrated and compared. In a clinical trial study, parallel group design and cross-over designs were described according to their characteristics. Also, the biostatistical methods for their usages classified and summarized. Results: Understanding and evaluating research design are part of the process researchers must use to determine both the quality and usefulness of their research. Adequate applications to biostatistical methods are need; i.e., descriptive statistics, Student's t-test, ANOVA, nonparametrics, categorical data analysis, correlation and regression, and survival analysis. Conclusions: Research findings are used by clinical researcher to guide their practice and reduce their uncertainty in clinical decision making. However, to understand how to interpret research results, it is important to be able to understand basic statistical concepts and types of study design. Clinicians should also appropriately choose the biostatistical methods to suit their purposes.
Biostatistics ; Cohort Studies ; Cross-Over Studies ; Decision Making ; Observational Study ; Research Design* ; Statistics as Topic ; Uncertainty