OBJECTIVES: Electrocardiogram (ECG) data are important for the study of cardiovascular disease and adverse drug reactions. Although the development of analytical techniques such as machine learning has improved our ability to extract useful information from ECGs, there is a lack of easily available ECG data for research purposes. We previously published an article on a database of ECG parameters and related clinical data (ECG-ViEW), which we have now updated with additional 12-lead waveform information. METHODS: All ECGs stored in portable document format (PDF) were collected from a tertiary teaching hospital in Korea over a 23-year study period. We developed software which can extract all ECG parameters and waveform information from the ECG reports in PDF format and stored it in a database (meta data) and a text file (raw waveform). RESULTS: Our database includes all parameters (ventricular rate, PR interval, QRS duration, QT/QTc interval, P-R-T axes, and interpretations) and 12-lead waveforms (for leads I, II, III, aVR, aVL, aVF, V1, V2, V3, V4, V5, and V6) from 1,039,550 ECGs (from 447,445 patients). Demographics, drug exposure data, diagnosis history, and laboratory test results (serum calcium, magnesium, and potassium levels) were also extracted from electronic medical records and linked to the ECG information. CONCLUSIONS: Electrocardiogram information that includes 12 lead waveforms was extracted and transformed into a form that can be analyzed. The description and programming codes in this case report could be a reference for other researchers to build ECG databases using their own local ECG repository.
Calcium ; Cardiovascular Diseases ; Demography ; Diagnosis ; Drug-Related Side Effects and Adverse Reactions ; Electrocardiography* ; Electronic Health Records ; Hospitals, Teaching ; Korea ; Machine Learning ; Magnesium ; Potassium

Insulin-like growth factor-I (IGF-I) plays a pivotal role in the diagnosis and treatment of growth hormone (GH) excess or deficiency. The GH study group of the Korean Endocrine Society aims to establish the Korean reference ranges of serum IGF-I and insulin-like growth factor binding protein-3 (IGFBP-3) and assess the relationship between IGF-I and IGFBP-3 and clinical parameters. Fasting serum was collected from healthy Korean adults at health promotion centers of five hospitals nationwide. Serum IGF-I and IGFBP-3 were measured via an immunoradiometric assay using a DSL kit (Diagnostic Systems Laboratories). Serum samples from 354 subjects (180 male, 174 female) were analyzed based on sex at 10-year intervals from 21 to 70 years. IGF-I levels were inversely correlated with age. After adjustment of age, the IGF-I/IGFBP-3 ratio was significantly negatively associated with blood pressure and free thyroxine and positively associated with weight, hemoglobin, creatinine, alanine transferase, fasting glucose, and thyroid stimulating hormone. Therefore, age- and sex-specific reference ranges of serum IGF-I and IGFBP-3 can be efficient in evaluating GH excess or deficiency in Korean population.

Purpose: Diagnostic biomarkers of pancreatic ductal adenocarcinoma (PDAC) have been used for early detection to reduce its dismal survival rate. However, clinically feasible biomarkers are still rare. Therefore, in this study, we developed an automated multi-marker enzyme-linked immunosorbent assay (ELISA) kit using 3 biomarkers (leucine-rich alpha-2-glycoprotein [LRG1], transthyretin [TTR], and CA 19-9) that were previously discovered and proposed a diagnostic model for PDAC based on this kit for clinical usage. Methods: Individual LRG1, TTR, and CA 19-9 panels were combined into a single automated ELISA panel and tested on 728 plasma samples, including PDAC (n = 381) and normal samples (n = 347). The consistency between individual panels of 3 biomarkers and the automated multi-panel ELISA kit were accessed by correlation. The diagnostic model was developed using logistic regression according to the automated ELISA kit to predict the risk of pancreatic cancer (high-, intermediate-, and low-risk groups). Results: The Pearson correlation coefficient of predicted values between the triple-marker automated ELISA panel and the former individual ELISA was 0.865. The proposed model provided reliable prediction results with a positive predictive value of 92.05%, negative predictive value of 90.69%, specificity of 90.69%, and sensitivity of 92.05%, which all simultaneously exceed 90% cutoff value. Conclusion This diagnostic model based on the triple ELISA kit showed better diagnostic performance than previous markers for PDAC. In the future, it needs external validation to be used in the clinic.