Data Resource Profile: The Cancer Public Library Database in South Korea.

This paper provides a comprehensive overview of the Cancer Public Library Database (CPLD), established under the Korean Clinical Data Utilization for Research Excellence project (K-CURE). The CPLD links data from four major population-based public sources: the Korea National Cancer Incidence Database in the Korea Central Cancer Registry, cause-of-death data in Statistics Korea, the National Health Information Database in the National Health Insurance Service, and the National Health Insurance Research Database in the Health Insurance Review & Assessment Service. These databases are linked using an encrypted resident registration number. The CPLD, established in 2022 and updated annually, comprises 1,983,499 men and women newly diagnosed with cancer between 2012 and 2019. It contains data on cancer registration and death, demographics, medical claims, general health checkups, and national cancer screening. The most common cancers among men in the CPLD were stomach (16.1%), lung (14.0%), colorectal (13.3%), prostate (9.6%), and liver (9.3%) cancers. The most common cancers among women were thyroid (20.4%), breast (16.6%), colorectal (9.0%), stomach (7.8%), and lung (6.2%) cancers. Among them, 571,285 died between 2012 and 2020 owing to cancer (89.2%) or other causes (10.8%). Upon approval, the CPLD is accessible to researchers through the K-CURE portal. The CPLD is a unique resource for diverse cancer research to investigate medical use before a cancer diagnosis, during initial diagnosis and treatment, and long-term follow-up. This offers expanded insight into healthcare delivery across the cancer continuum, from screening to end-of-life care.

Association between levels of radon and bioaerosols (bacteria and fungi) by living conditions.

This study aimed to measure the levels of airborne radon Rn and bioaerosols - culturable airborne bacteria (CAB) and culturable airborne fungi (CAF)-in South Korea's residential environments, considering living conditions such as the number of ventilations, number of windows, floors, temperature, and relative humidity. . The range of Rn levels was 0.43-7.439 pCi/L with a median of 0.70 pCi/L. The CAB levels were 239-488 colony-forming unit (CFU)/m3 with a median of 309 CFU/m3, and CAF levels were 174-366 CFU/m3 with a median of 233 CFU/m3. Thus, this study found that semi-basement residential indoor environments negatively affected Rn and bioaerosol levels, and living in such residences resulted in high health condition scores on the bad side. Given the correlation between airborne Rn and bioaerosol levels, further large-scale studies are needed to identify more reliable and representative of research.

Can computed tomography scanning in adults lead to an increased risk of thyroid cancer? A nationwide nested case-control study.

OBJECTIVE: To evaluate the association between computed tomography (CT) scanning and newly diagnosed thyroid cancer cases in relation to the confounding effect of the healthcare utilization rate. METHODS: This nested case-control study used the Korean National Health Insurance Service-National Sample Cohort 2002-2015: 3557 adult thyroid cancer cases were matched to 17,785 controls by age, sex, and diagnosis date. Odds ratios (ORs) were estimated for thyroid cancer associated with cumulative exposure to CT scanning > 3 years before cancer diagnosis. Changes in estimated ORs with and without adjustment for outpatient visit frequency were investigated. RESULTS: ORs for newly diagnosed thyroid cancer increased according to the higher number of total CT scans and thyroid-exposing CT scans (CT scans of the head, neck, or chest compartment; OR and 95% confidence interval [CI], 1.09 [1.03-1.16] and 1.28 [1.05-1.57], respectively). ORs for thyroid cancer increased according to higher outpatient visit frequency. The association between thyroid cancer incidence and CT scans became insignificant when outpatient visit frequency was adjusted in the models (OR [95% CI], 1.03 [0.97-1.10]: total CT scans, 1.14 [0.93-1.41]: thyroid-exposing CT scans). Subgroup analyses stratified by age, sex, and history of other malignancies did not reveal independent associations between CT scanning and thyroid cancer. CONCLUSIONS: The high incidence of thyroid cancer in adults exposed to ionizing radiation during CT scanning can be largely explained by the confounding effect of the healthcare utilization rate. These effects should be considered to avoid overestimation of the CT scanning-associated risk of thyroid cancer. KEY POINTS: • Studies indicate that diagnostic imaging using low-ionizing radiation may increase risks for thyroid cancer in adults. • Our findings suggest that the risk for radiation-induced thyroid cancer following CT scanning in adults may have been overestimated in observational studies due to medical surveillance-related biases.

Application of Machine Learning to Identify Clinically Meaningful Risk Group for Osteoporosis in Individuals Under the Recommended Age for Dual-Energy X-Ray Absorptiometry.

Dual-energy X-ray absorptiometry (DXA) is the gold standard for diagnosing osteoporosis; it is generally recommended in men ≥ 70 and women ≥ 65 years old. Therefore, assessment of clinical risk factors for osteoporosis is very important in individuals under the recommended age for DXA. Here, we examine the diagnostic performance of machine learning-based prediction models for osteoporosis in individuals under the recommended age for DXA examination. Data of 2210 men aged 50-69 and 1099 women aged 50-64 obtained from the Korea National Health and Nutrition Examination Survey IV-V were analyzed. Extreme gradient boosting (XGBoost) was used to find relevant clinical features and applied to three machine learning models: XGBoost, logistic regression, and a multilayer perceptron. For the prediction of osteoporosis, the XGBoost model using the top 20 features extracted from XGBoost showed the most reliable performance with area under the receiver operating characteristic curve (AUROC) of 0.73 and 0.79 in men and women, respectively. We compared the diagnostic accuracy of the Shapley additive explanation values based on a risk-score model obtained from XGBoost and conventional osteoporosis risk assessment tools for prediction of osteoporosis using optimal cut-off values for each model. We observed that a cut-off risk score of ≥ 28 in men and ≥ 47 in women was optimal to classify a positive screening for osteoporosis (an AUROC of 0.86 in men and 0.91 in women). The XGBoost-based osteoporosis-prediction model outperformed conventional risk assessment tools. Therefore, machine learning-based prediction models are a more suitable option than conventional risk assessment methods for screening osteoporosis in individuals under the recommended age for DXA examination.

Impact of image compression on deep learning-based mammogram classification.

Image compression is used in several clinical organizations to help address the overhead associated with medical imaging. These methods reduce file size by using a compact representation of the original image. This study aimed to analyze the impact of image compression on the performance of deep learning-based models in classifying mammograms as "malignant"-cases that lead to a cancer diagnosis and treatment-or "normal" and "benign," non-malignant cases that do not require immediate medical intervention. In this retrospective study, 9111 unique mammograms-5672 normal, 1686 benign, and 1754 malignant cases were collected from the National Cancer Center in the Republic of Korea. Image compression was applied to mammograms with compression ratios (CRs) ranging from 15 to 11 K. Convolutional neural networks (CNNs) with three convolutional layers and three fully-connected layers were trained using these images to classify a mammogram as malignant or not malignant across a range of CRs using five-fold cross-validation. Models trained on images with maximum CRs of 5 K had an average area under the receiver operating characteristic curve (AUROC) of 0.87 and area under the precision-recall curve (AUPRC) of 0.75 across the five folds and compression ratios. For images compressed with CRs of 10 K and 11 K, model performance decreased (average 0.79 in AUROC and 0.49 in AUPRC). Upon generating saliency maps that visualize the areas each model views as significant for prediction, models trained on less compressed (CR < = 5 K) images had maps encapsulating a radiologist's label, while models trained on images with higher amounts of compression had maps that missed the ground truth completely. In addition, base ResNet18 models pre-trained on ImageNet and trained using compressed mammograms did not show performance improvements over our CNN model, with AUROC and AUPRC values ranging from 0.77 to 0.87 and 0.52 to 0.71 respectively when trained and tested on images with maximum CRs of 5 K. This paper finds that while training models on images with increased the robustness of the models when tested on compressed data, moderate image compression did not substantially impact the classification performance of DL-based models.

Prediction of Prolonged Length of Hospital Stay After Cancer Surgery Using Machine Learning on Electronic Health Records: Retrospective Cross-sectional Study.

BACKGROUND: Postoperative length of stay is a key indicator in the management of medical resources and an indirect predictor of the incidence of surgical complications and the degree of recovery of the patient after cancer surgery. Recently, machine learning has been used to predict complex medical outcomes, such as prolonged length of hospital stay, using extensive medical information. OBJECTIVE: The objective of this study was to develop a prediction model for prolonged length of stay after cancer surgery using a machine learning approach. METHODS: In our retrospective study, electronic health records (EHRs) from 42,751 patients who underwent primary surgery for 17 types of cancer between January 1, 2000, and December 31, 2017, were sourced from a single cancer center. The EHRs included numerous variables such as surgical factors, cancer factors, underlying diseases, functional laboratory assessments, general assessments, medications, and social factors. To predict prolonged length of stay after cancer surgery, we employed extreme gradient boosting classifier, multilayer perceptron, and logistic regression models. Prolonged postoperative length of stay for cancer was defined as bed-days of the group of patients who accounted for the top 50% of the distribution of bed-days by cancer type. RESULTS: In the prediction of prolonged length of stay after cancer surgery, extreme gradient boosting classifier models demonstrated excellent performance for kidney and bladder cancer surgeries (area under the receiver operating characteristic curve [AUC] >0.85). A moderate performance (AUC 0.70-0.85) was observed for stomach, breast, colon, thyroid, prostate, cervix uteri, corpus uteri, and oral cancers. For stomach, breast, colon, thyroid, and lung cancers, with more than 4000 cases each, the extreme gradient boosting classifier model showed slightly better performance than the logistic regression model, although the logistic regression model also performed adequately. We identified risk variables for the prediction of prolonged postoperative length of stay for each type of cancer, and the importance of the variables differed depending on the cancer type. After we added operative time to the models trained on preoperative factors, the models generally outperformed the corresponding models using only preoperative variables. CONCLUSIONS: A machine learning approach using EHRs may improve the prediction of prolonged length of hospital stay after primary cancer surgery. This algorithm may help to provide a more effective allocation of medical resources in cancer surgery.

Risk Factors of Undiagnosed Diabetes Mellitus among Korean Adults: A National Cross-Sectional Study Using the KNHANES Data.

In this cross-sectional study, we investigated the baseline risk factors of diabetes mellitus (DM) in patients with undiagnosed DM (UDM). We utilized the Korean National Health and Nutrition Examination Survey (KNHANES) 2010-2017 data. Data regarding the participants' demographic characteristics, health status, health determinants, healthcare accessibility, and laboratory tests were gathered to explore the differences between the DM, UDM, and without-DM groups. Among the 64,759 individuals who participated in the KNHANES 2010-2017, 32,611 individuals aged ≥20 years with fasting plasma glucose levels of <100 or ≥126 mg/dL were selected. The odds ratios (ORs) regarding family history of diabetes and the performance of national health and cancer screening tests were lower in the UDM group than in the DM group (adjusted OR: 0.54; 95% confidence interval (CI): 0.43, 0.66; adjusted OR: 0.74; 95% CI: 0.62, 0.89; adjusted OR: 0.71; 95% CI: 0.60, 0.85). The ORs of hypertension and obesity were higher in the UDM group than in the DM group (adjusted OR: 1.32; 95% CI: 1.06, 1.64; adjusted OR: 1.80; 95% CI: 1.37, 2.36, respectively). Patients with UDM were more likely to be exposed to DM-related risk factors than those with and without DM. Public health interventions to prevent UDM development are necessary.

Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting.

BACKGROUND: Despite excellent prediction performance, noninterpretability has undermined the value of applying deep-learning algorithms in clinical practice. To overcome this limitation, attention mechanism has been introduced to clinical research as an explanatory modeling method. However, potential limitations of using this attractive method have not been clarified to clinical researchers. Furthermore, there has been a lack of introductory information explaining attention mechanisms to clinical researchers. OBJECTIVE: The aim of this study was to introduce the basic concepts and design approaches of attention mechanisms. In addition, we aimed to empirically assess the potential limitations of current attention mechanisms in terms of prediction and interpretability performance. METHODS: First, the basic concepts and several key considerations regarding attention mechanisms were identified. Second, four approaches to attention mechanisms were suggested according to a two-dimensional framework based on the degrees of freedom and uncertainty awareness. Third, the prediction performance, probability reliability, concentration of variable importance, consistency of attention results, and generalizability of attention results to conventional statistics were assessed in the diabetic classification modeling setting. Fourth, the potential limitations of attention mechanisms were considered. RESULTS: Prediction performance was very high for all models. Probability reliability was high in models with uncertainty awareness. Variable importance was concentrated in several variables when uncertainty awareness was not considered. The consistency of attention results was high when uncertainty awareness was considered. The generalizability of attention results to conventional statistics was poor regardless of the modeling approach. CONCLUSIONS: The attention mechanism is an attractive technique with potential to be very promising in the future. However, it may not yet be desirable to rely on this method to assess variable importance in clinical settings. Therefore, along with theoretical studies enhancing attention mechanisms, more empirical studies investigating potential limitations should be encouraged.

Screening Model for Estimating Undiagnosed Diabetes among People with a Family History of Diabetes Mellitus: A KNHANES-Based Study.

A screening model for estimating undiagnosed diabetes mellitus (UDM) is important for early medical care. There is minimal research and a serious lack of screening models for people with a family history of diabetes (FHD), especially one which incorporates gender characteristics. Therefore, the primary objective of our study was to develop a screening model for estimating UDM among people with FHD and enable its validation. We used data from the Korean National Health and Nutrition Examination Survey (KNHANES). KNAHNES (2010-2016) was used as a developmental cohort (n = 5939) and was then evaluated in a validation cohort (n = 1047) KNHANES (2017). We developed the screening model for UDM in male (SMM), female (SMF), and male and female combined (SMP) with FHD using backward stepwise logistic regression analysis. The SMM and SMF showed an appropriate performance (area under curve (AUC) = 76.2% and 77.9%) compared with SMP (AUC = 72.9%) in the validation cohort. Consequently, simple screening models were developed and validated, for the estimation of UDM among patients in the FHD group, which is expected to reduce the burden on the national health care system.

Comparison of FOLFIRINOX and Gemcitabine Plus Nab-paclitaxel for Treatment of Metastatic Pancreatic Cancer: Using Korean Pancreatic Cancer (K-PaC) Registry.

OBJECTIVE: By using the Korean Pancreatic Cancer (K-PaC) registry, we compared the clinical outcomes of FOLFIRINOX (FFX) and gemcitabine plus nab-paclitaxel (GNP) in patients with metastatic pancreatic cancer (MPC). METHODS: We constructed a web-based database of 3748 anonymized patients diagnosed with pancreatic ductal adenocarcinoma. MPC patients who received first-line FFX or GNP were enrolled. Overall survival (OS), progression-free survival, grade III to IV toxicity, and cross-over treatment were analyzed. RESULTS: A total of 413 patients (232 vs. 181, FFX vs. GNP; all data are presented in this sequence) were eligible. Median age was 63 years (60 vs. 69 y) with 43% (39% vs. 47%) comprising female individuals. The major metastatic sites were the liver (64%), peritoneum (25%), and distant lymph nodes (18%). The median OS was 11.5 versus 12.7 months (hazard ratio [HR]=0.87, 95% confidence interval [CI]: 0.68-1.12, P=0.286), and median progression-free survival was 7.5 versus 8.1 months (HR=0.92, 95% CI: 0.70-1.20, P=0.517), respectively. The frequency of grade III to IV febrile neutropenia was higher in the FFX group (18% vs. 11%, P=0.040), and that of peripheral neuropathy was higher in the GNP group (8% vs. 14%, P=0.046). The chance to receive second-line chemotherapy was higher in the GNP group (45% vs. 56%, P=0.036). In the cross-over treatment, the median OS of the FFX-GNP group (n=43) and the GNP-FFX group (n=47) was 16.8 versus 17.7 months (HR=0.79, 95% CI: 0.44-1.41, P=0.425). CONCLUSIONS: FFX and GNP showed similar efficacy and comparable toxicity in MPC patients. Although the GNP group had a higher chance to receive second-line chemotherapy, they did not have improved overall survival.

Next-Generation Sequencing-Based Cancer Panel Data Conversion Using International Standards to Implement a Clinical Next-Generation Sequencing Research System: Single-Institution Study.

BACKGROUND: The analytical capacity and speed of next-generation sequencing (NGS) technology have been improved. Many genetic variants associated with various diseases have been discovered using NGS. Therefore, applying NGS to clinical practice results in precision or personalized medicine. However, as clinical sequencing reports in electronic health records (EHRs) are not structured according to recommended standards, clinical decision support systems have not been fully utilized. In addition, integrating genomic data with clinical data for translational research remains a great challenge. OBJECTIVE: To apply international standards to clinical sequencing reports and to develop a clinical research information system to integrate standardized genomic data with clinical data. METHODS: We applied the recently published ISO/TS 20428 standard to 367 clinical sequencing reports generated by panel (91 genes) sequencing in EHRs and implemented a clinical NGS research system by extending the clinical data warehouse to integrate the necessary clinical data for each patient. We also developed a user interface with a clinical research portal and an NGS result viewer. RESULTS: A single clinical sequencing report with 28 items was restructured into four database tables and 49 entities. As a result, 367 patients' clinical sequencing data were connected with clinical data in EHRs, such as diagnosis, surgery, and death information. This system can support the development of cohort or case-control datasets as well. CONCLUSIONS: The standardized clinical sequencing data are not only for clinical practice and could be further applied to translational research.

Why Do Data Users Say Health Care Data Are Difficult to Use? A Cross-Sectional Survey Study.

BACKGROUND: There has been significant effort in attempting to use health care data. However, laws that protect patients' privacy have restricted data use because health care data contain sensitive information. Thus, discussions on privacy laws now focus on the active use of health care data beyond protection. However, current literature does not clarify the obstacles that make data usage and deidentification processes difficult or elaborate on users' needs for data linking from practical perspectives. OBJECTIVE: The objective of this study is to investigate (1) the current status of data use in each medical area, (2) institutional efforts and difficulties in deidentification processes, and (3) users' data linking needs. METHODS: We conducted a cross-sectional online survey. To recruit people who have used health care data, we publicized the promotion campaign and sent official documents to an academic society encouraging participation in the online survey. RESULTS: In total, 128 participants responded to the online survey; 10 participants were excluded for either inconsistent responses or lack of demand for health care data. Finally, 118 participants' responses were analyzed. The majority of participants worked in general hospitals or universities (62/118, 52.5% and 51/118, 43.2%, respectively, multiple-choice answers). More than half of participants responded that they have a need for clinical data (82/118, 69.5%) and public data (76/118, 64.4%). Furthermore, 85.6% (101/118) of respondents conducted deidentification measures when using data, and they considered rigid social culture as an obstacle for deidentification (28/101, 27.7%). In addition, they required data linking (98/118, 83.1%), and they noted deregulation and data standardization to allow access to health care data linking (33/98, 33.7% and 38/98, 38.8%, respectively). There were no significant differences in the proportion of responded data needs and linking in groups that used health care data for either public purposes or commercial purposes. CONCLUSIONS: This study provides a cross-sectional view from a practical, user-oriented perspective on the kinds of data users want to utilize, efforts and difficulties in deidentification processes, and the needs for data linking. Most users want to use clinical and public data, and most participants conduct deidentification processes and express a desire to conduct data linking. Our study confirmed that they noted regulation as a primary obstacle whether their purpose is commercial or public. A legal system based on both data utilization and data protection needs is required.

The Korea Cancer Big Data Platform (K-CBP) for Cancer Research.

Data warehousing is the most important technology to address recent advances in precision medicine. However, a generic clinical data warehouse does not address unstructured and insufficient data. In precision medicine, it is essential to develop a platform that can collect and utilize data. Data were collected from electronic medical records, genomic sequences, tumor biopsy specimens, and national cancer control initiative databases in the National Cancer Center (NCC), Korea. Data were de-identified and stored in a safe and independent space. Unstructured clinical data were standardized and incorporated into cancer registries and linked to cancer genome sequences and tumor biopsy specimens. Finally, national cancer control initiative data from the public domain were independently organized and linked to cancer registries. We constructed a system for integrating and providing various cancer data called the Korea Cancer Big Data Platform (K-CBP). Although the K-CBP could be used for cancer research, the legal and regulatory aspects of data distribution and usage need to be addressed first. Nonetheless, the system will continue collecting data from cancer-related resources that will hopefully facilitate precision-based research.

Validation of the American Joint Committee on Cancer 8th edition staging system for the pancreatic ductal adenocarcinoma.

BACKGROUND & AIMS: The American Joint Commission on Cancer (AJCC) 8th edition staging system for pancreatic ductal adenocarcinoma (PDA) contains several significant changes. This study aimed to validate the AJCC 8th edition staging system of PDA. METHODS: We analyzed patients with resected PDA between 2001 and 2017 using the Korean Pancreatic Cancer (K-PaC) registry. Overall survival (OS) was estimated using the Kaplan-Meier survival curves and compared via the log-rank test. RESULTS: In total, 701 resected PDA patients were identified. During a median follow-up of 24.5 months, the median OS was 21.7 months. Meanwhile, the median OS of each stage according to the AJCC 8th edition was 73.5 months (stage IA), 41.9 months (stage IB), 24.2 months (stage IIA), 18.3 months (stage IIB), and 16.8 months (stage III). However, the new N-category (pN1 vs. pN2) did not subdivide prognosis, although the lymph node ratio (i.e., the ratio of the number of LN involved to the number of examined LN) did. Although pT3 and pN2 belong under stage III, pN2 has a significantly longer median OS than pT3 (16.9 months vs 11.2 months; p < 0.01). CONCLUSION: The AJCC 8th edition staging system appropriately stratifies the prognosis of PDA patients. However, the cutoff of the N-category is not statistically valid, and the new stage III includes a heterogeneous category (pN2 and pT4). Therefore, we propose that stage III be divided into stage IIIA (Tany N2 M0) and stage IIIB (T4 Nany M0).

Service-Oriented Security Framework for Remote Medical Services in the Internet of Things Environment.

OBJECTIVES: Remote medical services have been expanding globally, and this is expansion is steadily increasing. It has had many positive effects, including medical access convenience, timeliness of service, and cost reduction. The speed of research and development in remote medical technology has been gradually accelerating. Therefore, it is expected to expand to enable various high-tech information and communications technology (ICT)-based remote medical services. However, the current state lacks an appropriate security framework that can resolve security issues centered on the Internet of things (IoT) environment that will be utilized significantly in telemedicine. METHODS: This study developed a medical service-oriented frame work for secure remote medical services, possessing flexibility regarding new service and security elements through its service-oriented structure. First, the common architecture of remote medical services is defined. Next medical-oriented secu rity threats and requirements within the IoT environment are identified. Finally, we propose a "service-oriented security frame work for remote medical services" based on previous work and requirements for secure remote medical services in the IoT. RESULTS: The proposed framework is a secure framework based on service-oriented cases in the medical environment. A com parative analysis focusing on the security elements (confidentiality, integrity, availability, privacy) was conducted, and the analysis results demonstrate the security of the proposed framework for remote medical services with IoT. CONCLUSIONS: The proposed framework is service-oriented structure. It can support dynamic security elements in accordance with demands related to new remote medical services which will be diversely generated in the IoT environment. We anticipate that it will enable secure services to be provided that can guarantee confidentiality, integrity, and availability for all, including patients, non-patients, and medical staff.

Implementation of hospital examination reservation system using data mining technique.

OBJECTIVES: New methods for obtaining appropriate information for users have been attempted with the development of information technology and the Internet. Among such methods, the demand for systems and services that can improve patient satisfaction has increased in hospital care environments. METHODS: In this paper, we proposed the Hospital Exam Reservation System (HERS), which uses the data mining method. First, we focused on carrying clinical exam data and finding the optimal schedule for generating rules using the multi-examination pattern-mining algorithm. Then, HERS was applied by a rule master and recommending system with an exam log. Finally, HERS was designed as a user-friendly interface. RESULTS: HERS has been applied at the National Cancer Center in Korea since June 2014. As the number of scheduled exams increased, the time required to schedule more than a single condition decreased (from 398.67% to 168.67% and from 448.49% to 188.49%; p < 0.0001). As the number of tests increased, the difference between HERS and non-HERS increased (from 0.18 days to 0.81 days). CONCLUSIONS: It was possible to expand the efficiency of HERS studies using mining technology in not only exam reservations, but also the medical environment. The proposed system based on doctor prescription removes exams that were not executed in order to improve recommendation accuracy. In addition, we expect HERS to become an effective system in various medical environments.

Development of a document management system for the standardization of clinical laboratory documents.

BACKGROUND: Documentation is very important; a considerable number of documents exist for use in accreditation inspection. However, most laboratories do not effectively manage the processes of documentation, organization, and storage. The purpose of this study was to facilitate the establishment of a strategically effective and sustainably standardized document management system. METHODS: A document code formatting system was modified by comparing the document list data received from 3 major university hospitals. In addition, a questionnaire regarding document code standardization was created and sent to 268 institutes to establish document classifications and generate a standard coding scheme. A computerized document management system was developed. RESULTS: Only 32% (8 out of 25 institutes) answered that they were able to identify all of the document types and their numbers. In total, 76% of institutes (19 out of 25) answered that a systematic document management system was necessary. Disorganized document files were systemized by classifying them into 8 major groups according to their characteristics: patient test records (T), test quality control (Q), manuals (M), equipment and environment management (E), statistics (S), division administration (A), department administration (R), and others (X). CONCLUSIONS: Our documentation system may serve as a basis for the standardization of documents and the creation of a document management system for all hospital laboratories.

Single nucleotide polymorphisms in bone turnover-related genes in Koreans: ethnic differences in linkage disequilibrium and haplotype.

BACKGROUND: Osteoporosis is defined as the loss of bone mineral density that leads to bone fragility with aging. Population-based case-control studies have identified polymorphisms in many candidate genes that have been associated with bone mass maintenance or osteoporotic fracture. To investigate single nucleotide polymorphisms (SNPs) that are associated with osteoporosis, we examined the genetic variation among Koreans by analyzing 81 genes according to their function in bone formation and resorption during bone remodeling. METHODS: We resequenced all the exons, splice junctions and promoter regions of candidate osteoporosis genes using 24 unrelated Korean individuals. Using the common SNPs from our study and the HapMap database, a statistical analysis of deviation in heterozygosity depicted. RESULTS: We identified 942 variants, including 888 SNPs, 43 insertion/deletion polymorphisms, and 11 microsatellite markers. Of the SNPs, 557 (63%) had been previously identified and 331 (37%) were newly discovered in the Korean population. When compared SNPs in the Korean population with those in HapMap database, 1% (or less) of SNPs in the Japanese and Chinese subpopulations and 20% of those in Caucasian and African subpopulations were significantly differentiated from the Hardy-Weinberg expectations. In addition, an analysis of the genetic diversity showed that there were no significant differences among Korean, Han Chinese and Japanese populations, but African and Caucasian populations were significantly differentiated in selected genes. Nevertheless, in the detailed analysis of genetic properties, the LD and Haplotype block patterns among the five sub-populations were substantially different from one another. CONCLUSION: Through the resequencing of 81 osteoporosis candidate genes, 118 unknown SNPs with a minor allele frequency (MAF) > 0.05 were discovered in the Korean population. In addition, using the common SNPs between our study and HapMap, an analysis of genetic diversity and deviation in heterozygosity was performed and the polymorphisms of the above genes among the five populations were substantially differentiated from one another. Further studies of osteoporosis could utilize the polymorphisms identified in our data since they may have important implications for the selection of highly informative SNPs for future association studies.