Development and validation of a scoring system for advanced colorectal neoplasm in young Korean subjects less than age 50 years

BACKGROUND/AIMS: Colorectal cancer incidence among patients aged ≤50 years is increasing. This study aimed to develop and validate an advanced colorectal neoplasm (ACRN) screening model for young adults aged <50 years in Korea. METHODS: This retrospective cross-sectional study included 59,575 consecutive asymptomatic Koreans who underwent screening colonoscopy between 2003 and 2012 at a single comprehensive health care center. Young Adult Colorectal Screening (YCS) score was developed as an optimized risk stratification model for ACRN using multivariate analysis and was internally validated. The predictive power and diagnostic performance of YCS score was compared with those of Asia-Pacific Colorectal Screening (APCS) and Korean Colorectal Screening (KCS) scores. RESULTS: 41,702 and 17,873 subjects were randomly allocated into the derivation and validation cohorts, respectively, by examination year. ACRN prevalence was 0.9% in both cohorts. YCS score comprised sex, age, alcohol, smoking, obesity, glucose metabolism abnormality, and family history of CRC, with score ranges of 0 to 10. In the validation cohort, ACRN prevalence was 0.6% in the low-risk tier (score, 0–4), 1.5% in the moderate-risk tier (score, 5–7), and 3.4% in the high-risk tier (score, 8–10). ACRN risk increased 2.5-fold (95% confidence interval [CI], 1.8–3.4) in the moderate-risk tier and 5.8-fold (95% CI, 3.4–9.8) in the high-risk tier compared with the low-risk tier. YCS score identified better balanced accuracy (53.9%) than APCS (51.5%) and KCS (50.7%) scores and had relatively good discriminative power (area under the curve=0.660). CONCLUSIONS: YCS score based on clinical and laboratory risk factors was clinically effective and beneficial for predicting ACRN risk and targeting screening colonoscopy in adults aged <50 years.

The Scope of Practice for Registered Nurses in 64 South Korean Laws

PURPOSE: The role of registered nurses is expanding in scope as the healthcare paradigm shifts from acute, hospital-based care to community and population-based care. Given this paradigm shift, this study explores the legal aspects of the role of a registered nurse.METHODS: We used document analysis for extracting laws and legal orders related to nursing from the entirety of Korean law. Using textualism approach, we examined the contents utilizing a framework that was developed based on the role classification of community nurses by Clark in this study.RESULTS: A total of 119 items related to nursing were derived from 64 laws. Of these, 71.4 % can be performed by people in multiple types of occupations including nurses. As a result of analyzing required qualifications, 45.4% of 119 items required additional qualifications besides registered nurse license. Analysis of workplace and activity type demonstrated that 26.1% of the 119 items were related to medical institutions, with nurses performing mostly “Client-oriented role.” More than half (68.9%) were non-medical institutions, with nurses performing mostly “Delivery-oriented role.” Some, however, did not stipulate the nurse's roles clearly.CONCLUSION: Therefore, to match the enhanced scope and responsibilities of registered nurses and to appropriately recognize, guide, and hold these nurses accountable, laws and policy must reflect these changes. In doing so, these updated laws and policies will ultimately serve as a basis for improving the quality and safety of nursing services.

The Scope of Practice for Registered Nurses in 64 South Korean Laws

Purpose@#The role of registered nurses is expanding in scope as the healthcare paradigm shifts from acute, hospital-based care to community and population-based care. Given this paradigm shift, this study explores the legal aspects of the role of a registered nurse. @*Methods@#We used document analysis for extracting laws and legal orders related to nursing from the entirety of Korean law. Using textualism approach, we examined the contents utilizing a framework that was developed based on the role classification of community nurses by Clark in this study. @*Results@#A total of 119 items related to nursing were derived from 64 laws. Of these, 71.4 % can be performed by people in multiple types of occupations including nurses. As a result of analyzing required qualifications, 45.4% of 119 items required additional qualifications besides registered nurse license. Analysis of workplace and activity type demonstrated that 26.1% of the 119 items were related to medical institutions, with nurses performing mostly “Client-oriented role.” More than half (68.9%) were non-medical institutions, with nurses performing mostly “Delivery-oriented role.” Some, however, did not stipulate the nurse’s roles clearly. @*Conclusion@#Therefore, to match the enhanced scope and responsibilities of registered nurses and to appropriately recognize, guide, and hold these nurses accountable, laws and policy must reflect these changes. In doing so, these updated laws and policies will ultimately serve as a basis for improving the quality and safety of nursing services.

HisCoM-GGI: Software for Hierarchical Structural Component Analysis of Gene-Gene Interactions

Gene-gene interaction (GGI) analysis is known to play an important role in explaining missing heritability. Many previous studies have already proposed software to analyze GGI, but most methods focus on a binary phenotype in a case-control design. In this study, we developed “Hierarchical structural CoMponent analysis of Gene-Gene Interactions” (HisCoM-GGI) software for GGI analysis with a continuous phenotype. The HisCoM-GGI method considers hierarchical structural relationships between genes and single nucleotide polymorphisms (SNPs), enabling both gene-level and SNP-level interaction analysis in a single model. Furthermore, this software accepts various types of genomic data and supports data management and multithreading to improve the efficiency of genome-wide association study data analysis. We expect that HisCoM-GGI software will provide advanced accessibility to researchers in genetic interaction studies and a more effective way to understand biological mechanisms of complex diseases.

Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the “large p and small n” problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

Study on Factors Associated with the Rise in Grade of Nursing Management Fee among Korean Hospitals / 보건행정학회지

BACKGROUND: The purpose of this study was to analyze the increase in Grade of Nursing Management Fee of medical institutions and establish a reasonable government policy by examining which factors affect the increase of nurse staffing. METHODS: Analyzing data collected from the Health Insurance Review & Assessment Service resource management department with targets of 1,104 medical institutions. The study period was 5 years from June 30, 2008 to June 30, 2013. SAS ver. 9.2 (SAS Institute Inc., Cary, NC, USA) was used for statistical analysis. The data was analyzed by a chi-square test and also conducted muiltivariate logistic regression analyses for variables of basic characteristics, human resource characteristics, and material resources. RESULTS: Adjusted odds ratio (AOR) of the rise in Grade of Nursing Management Fee among other hospitals compared to hospitals owned by government or universities was 0.264. The AOR in hospitals established after November 2006 compared to those before June 1995 was 2.383. The AOR in Gangwon, Chungcheng South, and Jeolla South Provinces compared to Seoul was 0.084, 0.036, and 0.194, respectively. The AOR in hospitals with more than 6.75 specialists per 100 beds compared to those with less than 6.75 specialists per 100 beds was 7.514. The AOR in hospitals with more than 17.48 nurse per 100 beds compared to those with less than 17.48 nurse per 100 beds was 3.300. The AOR in hospitals with 50% to 75% bed utilization, 75% to 90% bed utilization and more than 90% bed utilization compared to those with less than 50% bed utilization was 5.428, 9.884, and 10.699, respectively. The AOR in hospitals with one magnetic resonance imaging (MRI) and more than two MRI compared to those with no MRI was 2.018 and 2.942, respectively. CONCLUSION: This result has showed policies to induce the rise in Grade of Nursing Management Fee among old hospitals and the incentive system for local medical institutions are needed. Also we need to develop a governmental policy for medium-small hospitals with low operation rate of beds and insufficient medical personnel and number of equipment in hospitals.