OBJECTIVE: Type D personality can be regarded as a promising cardiovascular risk marker that has been repeatedly linked to relevant indicators of mental health, quality of life, morbidity, and mortality in cardiac patients. Heart rate variability (HRV) is a non-invasive technology that can provide information regarding a patient's sympathetic/parasympathetic balance and the control mechanisms of the autonomic systems in the cardiovascular system. As both type D personality and HRV are parameters related to the cardiovascular system, we assumed a relationship between type D personality and HRV. This study set out to identify the relationship between type D and HRV and the differences in HRV variables between type D and non-type D personalities. METHODS: Patients who visited Guro Community Mental Health Center from January 2011 to December 2012 were surveyed. They were evaluated using both the Korean version of the Type D Personality-14 for type D personality and HRV. During the survey, those who reported major cardiovascular disease that can affect heart rate variability were excluded from the study. RESULTS: Our analysis included 559 participants, 249 of whom were classified as type D personality. No significant differences were found in the HRV variables between the type D group and the non-type D group. There were also no clinically meaningful correlations between HRV variables and type D total/subscale scores when controlled for patient age. CONCLUSION: A relationship between HRV and type D personality was not identified using short-term HRV measurements in non-clinical patients with no definitive cardiovascular disease. Further studies using long-term HRV measurements in patients with cardiovascular disease are necessary to conclude an association between HRV and type D personality.
Cardiovascular Diseases ; Cardiovascular System ; Heart Rate* ; Humans ; Mental Health* ; Mortality ; Quality of Life ; Type D Personality*

Idiopathic hypereosinophilic syndrome (iHES) is a rare systemic disease that is characterized by persistent peripheral eosinophilia (absolute eosinophil count ≥1500/uL) for more than 6 months, with end-organ damage and absence of a primary cause for eosinophilia. Coronary artery aneurysm (CAA) is a rare but life-threatening complication. Here, we report a case of CAA with thrombosis in a patient with iHES in whom the disease activity was well-controlled (eosinophil count <500/uL) for several years. Despite modest control of the disease activity, giant CAA can be associated with iHES; and therefore, close surveillance and monitoring for the development of complications is warranted.

Purpose: Contrast media is one of the most common cause of adverse drug reaction (ADR) in adult. However, there was little data reported about differences in ADR ratio and severity among iodinated contrast media (ICM). Methods: Medical records of 627,049 patients who performed computed tomography scan using low-osmolar nonionic iodinated contrast media from January 2015 to December 2018 were retrospectively reviewed. A total of 6 ICMs including iomeprol, iohexol, iopromide, iobitridol, ioversol, and iopamidol were used in this period. The incidence of ADR was compared to their total usage and dosage between each contrast media. Results: The incidence of ADR of iopromide (1.36%) and iomeprol (1.27%) was the highest when compared with the average incidence of 1% of 6 ICMs. Ioversol (0.67%), iohexol, and iobitridol (0.74%) had the lower incidence of ADR. The order of results adjusted by actual administered dosage, the use of premedication, and the prior exposure history of ICMs was similar. The fraction of moderate and severe ADR in overall ADR was slightly different, but not proportional to the incidence of ADR. Conclusion The incidence of ADR among 6 low-osmolar nonionic ICMs was significantly different when compared by the total number of usage and the total volume of dose. The incidence of ADR varied by nearly 2-fold depending on ICMs. Further study might need to explore the reason for the difference of incidence.

In recent years, artificial intelligence (AI) technologies have greatly advanced and become a reality in many areas of our daily lives. In the health care field, numerous efforts are being made to implement the AI technology for practical medical treatments. With the rapid developments in machine learning algorithms and improvements in hardware performances, the AI technology is expected to play an important role in effectively analyzing and utilizing extensive amounts of health and medical data. However, the AI technology has various unique characteristics that are different from the existing health care technologies. Subsequently, there are a number of areas that need to be supplemented within the current health care system for the AI to be utilized more effectively and frequently in health care. In addition, the number of medical practitioners and public that accept AI in the health care is still low;moreover, there are various concerns regarding the safety and reliability of AI technologyimplementations. Therefore, this paper aims to introduce the current research and application status of AI technology in health care and discuss the issues that need to be resolved.