Anemia is a common and significant complication of chronic kidney disease (CKD). However, its prevalence and current management status has not been studied thoroughly in Korea. We examined the prevalence of anemia, its association with clinical and laboratory factors, and utilization of iron agents and erythropoiesis stimulating agents using the baseline data from the large-scale CKD cohort in Korea. We defined anemia when hemoglobin level was lower than 13.0 g/dL in males and 12.0 g/dL in females, or received by erythropoiesis stimulating agents. Overall prevalence of anemia was 45.0% among 2,198 non-dialysis CKD patients from stage 1 to 5. Diabetic nephropathy (DN) as a cause, CKD stages, body mass index (BMI), smoking, leukocyte count, serum albumin, iron markers, calcium, and phosphorus concentration were identified as independent risk factors for anemia. Considering the current coverage of Korean National Health Insurance System, only 7.9% among applicable patients were managed by intravenous iron agents, and 42.7% were managed by erythropoiesis stimulating agents.
Anemia* ; Body Mass Index ; Calcium ; Cohort Studies* ; Diabetic Nephropathies ; Female ; Hematinics ; Humans ; Iron ; Korea ; Leukocyte Count ; Male ; National Health Programs ; Phosphorus ; Prevalence* ; Renal Insufficiency, Chronic* ; Risk Factors ; Serum Albumin ; Smoke ; Smoking

Pulmonary arteriovenous malformations are rare vascular anomalies of the lung, only a few cases of which have been diagnosed prenatally. The diagnostic clue for prenatal diagnosis was cardiomegaly with a particularly enlarged left atrium. All previous cases of pulmonary arteriovenous malformations diagnosed prenatally have been reported as an isolated anomaly or in association with simple heart defects. We here describe the first case of a pulmonary arteriovenous malformation with a complex heart defect that was diagnosed prenatally at 21.0 weeks of gestation and confirmed by postmortem autopsy.
Arteriovenous Malformations* ; Autopsy ; Cardiomegaly ; Heart Atria ; Heart Defects, Congenital ; Heart* ; Lung ; Pregnancy ; Prenatal Diagnosis

Cardiovascular disease (CVD) is the most common cause of death in patients with chronic kidney disease (CKD). We report the baseline cardiovascular characteristics of 2,238 participants by using the data of the KoreaN Cohort Study for Outcomes in Patients With Chronic Kidney Disease (KNOW-CKD) study. The cohort comprises 5 subcohorts according to the cause of CKD: glomerulonephritis (GN), diabetic nephropathy (DN), hypertensive nephropathy (HTN), polycystic kidney disease (PKD), and unclassified. The average estimated glomerular filtration rate (eGFR) was 50.5 ± 30.3 mL/min⁻¹/1.73 m⁻² and lowest in the DN subcohort. The overall prevalence of previous CVD was 14.4% in all patients, and was highest in the DN followed by that in the HTN subcohort. The DN subcohort had more adverse cardiovascular risk profiles (higher systolic blood pressure [SBP], and higher levels of cardiac troponin T, left ventricular mass index [LVMI], coronary calcium score, and brachial-ankle pulse wave velocity [baPWV]) than the other subcohorts. The HTN subcohort exhibited less severe cardiovascular risk profiles than the DN subcohort, but had more severe cardiovascular risk features than the GN and PKD subcohorts. All these cardiovascular risk profiles were inversely correlated with eGFR. In conclusion, this study shows that the KNOW-CKD cohort exhibits high cardiovascular burden, as other CKD cohorts in previous studies. Among the subcohorts, the DN subcohort had the highest risk for CVD. The ongoing long-term follow-up study up to 10 years will further delineate cardiovascular characteristics and outcomes of each subcohort exposed to different risk profiles.
Adult* ; Blood Pressure ; Calcium ; Cardiovascular Diseases ; Cause of Death ; Cohort Studies* ; Diabetic Nephropathies ; Epidemiology ; Follow-Up Studies ; Glomerular Filtration Rate ; Glomerulonephritis ; Humans ; Hypertension ; Polycystic Kidney Diseases ; Prevalence ; Pulse Wave Analysis ; Renal Insufficiency, Chronic* ; Troponin T

Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality. Materials and Methods The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)–free survival, cancer-specific survival (CSS), and overall survival (OS). Results Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation. Conclusion The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions.

Decision-making for treatment of newly diagnosed prostate cancer (PCa) is complex due to the multiple initial treatment modalities available. We aimed to externally validate the SCaP (Severance Study Group of Prostate Cancer) Survival Calculator that incorporates a long short-term memory artificial neural network (ANN) model to estimate survival outcomes of PCa according to initial treatment modality. Materials and Methods The validation cohort consisted of clinicopathological data of 4,415 patients diagnosed with biopsy-proven PCa between April 2005 and November 2018 at three institutions. Area under the curves (AUCs) and time-to-event calibration plots were utilized to determine the predictive accuracies of the SCaP Survival Calculator in terms of progression to castration-resistant PCa (CRPC)–free survival, cancer-specific survival (CSS), and overall survival (OS). Results Excellent discrimination was observed for CRPC-free survival, CSS, and OS outcomes, with AUCs of 0.962, 0.944, and 0.884 for 5-year outcomes and 0.959, 0.928, and 0.854 for 10-year outcomes, respectively. The AUC values were higher for all survival endpoints compared to those of the development cohort. Calibration plots showed that predicted probabilities of 5-year survival endpoints had concordance comparable to those of the observed frequencies. However, calibration performances declined for 10-year predictions with an overall underestimation. Conclusion The SCaP Survival Calculator is a reliable and useful tool for determining the optimal initial treatment modality and for guiding survival predictions for patients with newly diagnosed PCa. Further modifications in the ANN model incorporating cases with more extended follow-up periods are warranted to improve the ANN model for long-term predictions.

OBJECTIVE: To evaluate the frequency and adequacy of statistical analyses in a general radiology journal when reporting a reliability analysis for a diagnostic test. MATERIALS AND METHODS: Sixty-three studies of diagnostic test accuracy (DTA) and 36 studies reporting reliability analyses published in the Korean Journal of Radiology between 2012 and 2016 were analyzed. Studies were judged using the methodological guidelines of the Radiological Society of North America-Quantitative Imaging Biomarkers Alliance (RSNA-QIBA), and COnsensus-based Standards for the selection of health Measurement INstruments (COSMIN) initiative. DTA studies were evaluated by nine editorial board members of the journal. Reliability studies were evaluated by study reviewers experienced with reliability analysis. RESULTS: Thirty-one (49.2%) of the 63 DTA studies did not include a reliability analysis when deemed necessary. Among the 36 reliability studies, proper statistical methods were used in all (5/5) studies dealing with dichotomous/nominal data, 46.7% (7/15) of studies dealing with ordinal data, and 95.2% (20/21) of studies dealing with continuous data. Statistical methods were described in sufficient detail regarding weighted kappa in 28.6% (2/7) of studies and regarding the model and assumptions of intraclass correlation coefficient in 35.3% (6/17) and 29.4% (5/17) of studies, respectively. Reliability parameters were used as if they were agreement parameters in 23.1% (3/13) of studies. Reproducibility and repeatability were used incorrectly in 20% (3/15) of studies. CONCLUSION: Greater attention to the importance of reporting reliability, thorough description of the related statistical methods, efforts not to neglect agreement parameters, and better use of relevant terminology is necessary.
Biomarkers ; Diagnostic Tests, Routine* ; Methods*

Coronary artery calcification (CAC) is highly prevalent in patients with chronic kidney disease (CKD) and is associated with major adverse cardiovascular events and metabolic disturbances. The triglyceride-glucose index (TyGI), a novel surrogate marker of metabolic syndrome and insulin resistance, is associated with CAC in the general population and in patients with diabetes. This study investigated the association between the TyGI and CAC progression in patients with CKD, which is unknown. Methods: A total of 1,154 patients with CKD (grades 1–5; age, 52.8 ± 11.9 years; male, 688 [59.6%]) were enrolled from the KNOWCKD (KoreaN Cohort Study for Outcomes in Patients With Chronic Kidney Disease). The TyGI was calculated as follows: ln (fasting triglycerides × fasting glucose/2). Patients were classified into tertiles (low, intermediate, high) based on the TyGI. The primary outcome was annualized percentage change in CAC score [(percent change in CAC score + 1)12/follow-up months – 1] of ≥15%, defined as CAC progression. Results: During the 4-year follow-up, the percentage of patients with CAC progression increased across TyGI groups (28.6%, 37.5%, and 46.2% in low, intermediate, and high groups, respectively; p < 0.001). A high TyGI was associated with an increased risk of CAC progression (odds ratio [OR], 2.11; 95% confidence interval [CI], 1.14–3.88; p = 0.02) compared to the low group. Moreover, a 1-point increase in the TyGI was related to increased risk of CAC progression (OR, 1.55; 95% CI, 1.06–1.76; p = 0.02) after adjustment. Conclusion: A high TyGI may be a useful predictor of CAC progression in CKD.