The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

The common data model (CDM) has found widespread application in healthcare studies, but its utilization in cancer research has been limited. This article describes the development and implementation strategy for Cancer Clinical Library Databases (CCLDs), which are standardized cancer-specific databases established under the Korea-Clinical Data Utilization Network for Research Excellence (K-CURE) project by the Korean Ministry of Health and Welfare. Fifteen leading hospitals and fourteen academic associations in Korea are engaged in constructing CCLDs for 10 primary cancer types. For each cancer type-specific CCLD, cancer data experts determine key clinical data items essential for cancer research, standardize these items across cancer types, and create a standardized schema. Comprehensive clinical records covering diagnosis, treatment, and outcomes, with annual updates, are collected for each cancer patient in the target population, and quality control is based on six-sigma standards. To protect patient privacy, CCLDs follow stringent data security guidelines by pseudonymizing personal identification information and operating within a closed analysis environment. Researchers can apply for access to CCLD data through the K-CURE portal, which is subject to Institutional Review Board and Data Review Board approval. The CCLD is considered a pioneering standardized cancer-specific database, significantly representing Korea’s cancer data. It is expected to overcome limitations of previous CDMs and provide a valuable resource for multicenter cancer research in Korea.

OBJECTIVES: This study investigated the associations between several obesity-related anthropometric indices and mortality in middle-aged and elderly populations to compare the indices’ predictive ability with that of the body mass index (BMI). METHODS: We analyzed data on 12 indices calculated from 19,805 community-based cohort participants (average age, 63.27 years; median follow-up, 13.49 years). Each index was calculated using directly measured values of height, weight, waist circumference (WC), and hip circumference (HC). We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for each index using Cox regression and evaluated mortality prediction with the Harrell concordance index (c-index). RESULTS: Adding anthropometric indices to the basic mortality model (c-index, 0.7723; 95% CI, 0.7647 to 0.7799) significantly increased the predictive power of BMI (c-index, 0.7735; 95% CI, 0.7659 to 0.7811), a body shape index (ABSI; c-index, 0.7735; 95% CI, 0.7659 to 0.7810), weight-adjusted waist index (WWI; c-index, 0.7731; 95% CI, 0.7656 to 0.7807), and waist to hip index (WHI; c-index, 0.7733; 95% CI, 0.7657 to 0.7809). The differences between the BMI model and the other 3 models were not statistically significant. CONCLUSIONS In predicting all-cause mortality, the ABSI, WWI, and WHI models based on WC or HC had stronger predictive power than conventional risk factors but were not significantly different from the BMI model.

OBJECTIVES: This study investigated the associations between several obesity-related anthropometric indices and mortality in middle-aged and elderly populations to compare the indices’ predictive ability with that of the body mass index (BMI). METHODS: We analyzed data on 12 indices calculated from 19,805 community-based cohort participants (average age, 63.27 years; median follow-up, 13.49 years). Each index was calculated using directly measured values of height, weight, waist circumference (WC), and hip circumference (HC). We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for each index using Cox regression and evaluated mortality prediction with the Harrell concordance index (c-index). RESULTS: Adding anthropometric indices to the basic mortality model (c-index, 0.7723; 95% CI, 0.7647 to 0.7799) significantly increased the predictive power of BMI (c-index, 0.7735; 95% CI, 0.7659 to 0.7811), a body shape index (ABSI; c-index, 0.7735; 95% CI, 0.7659 to 0.7810), weight-adjusted waist index (WWI; c-index, 0.7731; 95% CI, 0.7656 to 0.7807), and waist to hip index (WHI; c-index, 0.7733; 95% CI, 0.7657 to 0.7809). The differences between the BMI model and the other 3 models were not statistically significant. CONCLUSIONS In predicting all-cause mortality, the ABSI, WWI, and WHI models based on WC or HC had stronger predictive power than conventional risk factors but were not significantly different from the BMI model.

OBJECTIVES: This study investigated the associations between several obesity-related anthropometric indices and mortality in middle-aged and elderly populations to compare the indices’ predictive ability with that of the body mass index (BMI). METHODS: We analyzed data on 12 indices calculated from 19,805 community-based cohort participants (average age, 63.27 years; median follow-up, 13.49 years). Each index was calculated using directly measured values of height, weight, waist circumference (WC), and hip circumference (HC). We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for each index using Cox regression and evaluated mortality prediction with the Harrell concordance index (c-index). RESULTS: Adding anthropometric indices to the basic mortality model (c-index, 0.7723; 95% CI, 0.7647 to 0.7799) significantly increased the predictive power of BMI (c-index, 0.7735; 95% CI, 0.7659 to 0.7811), a body shape index (ABSI; c-index, 0.7735; 95% CI, 0.7659 to 0.7810), weight-adjusted waist index (WWI; c-index, 0.7731; 95% CI, 0.7656 to 0.7807), and waist to hip index (WHI; c-index, 0.7733; 95% CI, 0.7657 to 0.7809). The differences between the BMI model and the other 3 models were not statistically significant. CONCLUSIONS In predicting all-cause mortality, the ABSI, WWI, and WHI models based on WC or HC had stronger predictive power than conventional risk factors but were not significantly different from the BMI model.

OBJECTIVES: This study investigated the associations between several obesity-related anthropometric indices and mortality in middle-aged and elderly populations to compare the indices’ predictive ability with that of the body mass index (BMI). METHODS: We analyzed data on 12 indices calculated from 19,805 community-based cohort participants (average age, 63.27 years; median follow-up, 13.49 years). Each index was calculated using directly measured values of height, weight, waist circumference (WC), and hip circumference (HC). We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for each index using Cox regression and evaluated mortality prediction with the Harrell concordance index (c-index). RESULTS: Adding anthropometric indices to the basic mortality model (c-index, 0.7723; 95% CI, 0.7647 to 0.7799) significantly increased the predictive power of BMI (c-index, 0.7735; 95% CI, 0.7659 to 0.7811), a body shape index (ABSI; c-index, 0.7735; 95% CI, 0.7659 to 0.7810), weight-adjusted waist index (WWI; c-index, 0.7731; 95% CI, 0.7656 to 0.7807), and waist to hip index (WHI; c-index, 0.7733; 95% CI, 0.7657 to 0.7809). The differences between the BMI model and the other 3 models were not statistically significant. CONCLUSIONS In predicting all-cause mortality, the ABSI, WWI, and WHI models based on WC or HC had stronger predictive power than conventional risk factors but were not significantly different from the BMI model.

Aging has become a global problem, and the interest in healthy aging is growing. Healthy aging involves a focus on the maintenance of the function and well-being of elderly adults, rather than a specific disease. Thus, the management of frailty, which is an accumulated decline in function, is important for healthy aging. The adaptation method was used to develop clinical practice guidelines on frailty management that are applicable in primary care settings. The guidelines were developed in three phases: preparation (organization of committees and establishment of the scope of development), literature screening and evaluation (selection of the clinical practice guidelines to be adapted and evaluation of the guidelines using the Korean Appraisal of Guidelines for Research and Evaluation II tool), and confirmation of recommendations (three rounds of Delphi consensus and internal and external reviews). A total of 16 recommendations (five recommendations for diagnosis and assessment, 11 recommendations for intervention of frailty) were made through the guideline development process. These clinical practice guidelines provide overall guidance on the identification, evaluation, intervention, and monitoring of frailty, making them applicable in primary care settings. As aging and “healthy aging” become more and more important, these guidelines are also expected to increase in clinical usefulness.

PURPOSE: Sperm cryopreservation before cancer treatment is the most effective method to preserve the fertility of male patients. We present our 21 years experience with sperm cryopreservation for cancer patients, including an examination of semen quality, the current status of cryopreserved sperm, and the rate of sperm use for assisted reproductive technology (ART). MATERIALS AND METHODS: A total of 721 cancer patients at Fertility Center of CHA Gangnam Medical Center successfully performed sperm cryopreservation for fertility preservation from January 1996 to December 2016. Medical chart review was used to analyze patient age, marital status, cancer type, semen volume, sperm counts and motility, length of storage, and current banking status. RESULTS: The major cancers of the 721 patients were leukemia (28.4%), lymphoma (18.3%), testis cancer (10.0%). The mean age at cryopreservation was 27.0 years, and 111 patients (15.4%) performed sperm cryopreservation during or after cancer treatment. The mean sperm concentration was 66.7±66.3 ×106/mL and the mean sperm motility was 33.8%±16.3%. During median follow-up duration of 75 months (range, 1–226 months), 44 patients (6.1%) used their banked sperm at our fertility center for ART and 9 patients (1.2%) transferred their banked sperm to another center. The median duration from cryopreservation to use was 51 months (range, 1–158 months). CONCLUSIONS: Sperm cryopreservation before gonadotoxic treatment is the most reliable method to preserve the fertility of male cancer patients. Sperm cryopreservation should be offered as a standard of care for all men planning cancer therapy.
Cryopreservation ; Fertility Preservation ; Fertility ; Follow-Up Studies ; Humans ; Leukemia ; Lymphoma ; Male ; Marital Status ; Methods ; Reproductive Techniques, Assisted ; Semen ; Semen Analysis ; Semen Preservation ; Sperm Count ; Sperm Motility ; Spermatozoa ; Standard of Care ; Testicular Neoplasms

BACKGROUND: Yersinia pseudotuberculosis is known to cause fever, gastroenteritis, or acute kidney injury (AKI). There have been several Y. pseudotuberculosis infection outbreaks to date associated with ingestion of contaminated food or unsterile water. While this disease was considered to have practically been eradicated with the improvement in public health, we encountered several cases of AKI associated with Yersinia infection. METHODS: We retrospectively collected data from medical records of patients with suspected Y. pseudotuberculosis infection who visited Seoul National University Children’s Hospital in 2017. RESULTS: There were nine suspected cases of Yersinia infection (six males and three females; age range 2.99–12.18 years). Among them, five cases occurred in May, and seven patients were residing in the metropolitan Seoul area. Three patients had history of drinking mountain water. Every patient first presented with fever for a median of 13 days, followed by gastrointestinal symptoms and oliguria. Imaging studies revealed mesenteric lymphadenitis, terminal ileum wall thickening, and increased renal parenchymal echogenicity. Creatinine levels increased to 5.72 ± 2.18 mg/dL. Urinalysis revealed sterile pyuria, proteinuria, and glycosuria. Oliguria continued for 4 to 17 days, and two patients required dialysis; however, all of them recovered from AKI. Mucocutaneous manifestations developed later. In the diagnostic work-up, Yersinia was isolated from the stool culture in one patient. Anti-Yersinia immunoglobulin (Ig) A and IgG were positive in 6 patients. CONCLUSION: Y. pseudotuberculosis infection is an infrequent cause of interstitial nephritis presenting with AKI. When a patient presents with fever, gastroenteritis, and AKI not resolving despite hydration, the clinician should suspect Y. pseudotuberculosis infection.
Acute Kidney Injury ; Creatinine ; Dialysis ; Disease Outbreaks ; Drinking ; Eating ; Female ; Fever ; Gastroenteritis ; Glycosuria ; Humans ; Ileum ; Immunoglobulin G ; Immunoglobulins ; Male ; Medical Records ; Mesenteric Lymphadenitis ; Nephritis, Interstitial ; Oliguria ; Proteinuria ; Public Health ; Pyuria ; Retrospective Studies ; Seoul ; Urinalysis ; Water ; Yersinia Infections ; Yersinia pseudotuberculosis ; Yersinia