ABSTRACT
Background@#There are several differences in the clinical course of hypertension due to the biological and social differences between men and women. Resistant hypertension is an advanced disease state, and significant gender difference could be expected, but much has not been revealed yet. The purpose of this study was to compare gender differences on the current status of blood pressure (BP) control and clinical prognosis in patients with resistant hypertension. @*Methods@#This is a multicenter, retrospective cohort study using common data model databases of 3 tertiary hospitals in Korea. Total 4,926 patients with resistant hypertension were selected from January 2017 to December 2018. Occurrence of dialysis, heart failure (HF) hospitalization, myocardial infarction, stroke, dementia or all-cause mortality was followed up for 3 years. @*Results@#Male patients with resistant hypertension were younger but had a higher cardiovascular risk than female patients. Prevalence of left ventricular hypertrophy and proteinuria was higher in men than in women. On-treatment diastolic BP was lower in women than in men and target BP achievement rate was higher in women than in men.During 3 years, the incidence of dialysis and myocardial infarction was higher in men, and the incidence of stroke and dementia was higher in women. After adjustment, male sex was an independent risk factor for HF hospitalization, myocardial infarction, and all-cause death. @*Conclusion@#In resistant hypertension, men were younger than women, but end-organ damage was more common and the risk of cardiovascular event was higher. More intensive cardiovascular prevention strategies may be required in male patients with resistant hypertension.
ABSTRACT
Objectives@#Electrocardiography (ECG)-based diagnosis by experts cannot maintain uniform quality because individual differences may occur. Previous public databases can be used for clinical studies, but there is no common standard that would allow databases to be combined. For this reason, it is difficult to conduct research that derives results by combining databases. Recent commercial ECG machines offer diagnoses similar to those of a physician. Therefore, the purpose of this study was to construct a standardized ECG database using computerized diagnoses. @*Methods@#The constructed database was standardized using Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) and Observational Medical Outcomes Partnership–common data model (OMOP-CDM), and data were then categorized into 10 groups based on the Minnesota classification. In addition, to extract high-quality waveforms, poor-quality ECGs were removed, and database bias was minimized by extracting at least 2,000 cases for each group. To check database quality, the difference in baseline displacement according to whether poor ECGs were removed was analyzed, and the usefulness of the database was verified with seven classification models using waveforms. @*Results@#The standardized KURIAS-ECG database consists of high-quality ECGs from 13,862 patients, with about 20,000 data points, making it possible to obtain more than 2,000 for each Minnesota classification. An artificial intelligence classification model using the data extracted through SNOMED-CT showed an average accuracy of 88.03%. @*Conclusions@#The KURIAS-ECG database contains standardized ECG data extracted from various machines. The proposed protocol should promote cardiovascular disease research using big data and artificial intelligence.