Background: The preanalytical phase is more vulnerable to errors. This study aimed to establish preanalytical quality indicators (QIs) suitable for Korean clinical laboratories and investigate the current status of preanalytical phase performance monitoring in Korea using these QIs. Methods: We reviewed previous studies investigating preanalytical QIs including the International Federation of Clinical Chemistry (IFCC) model of QIs, to establish a set of QIs for Korean clinical laboratories. An e-mail survey consisting of this QI set was sent to 90 clinical laboratories. The collected data were analyzed, and performance measures were evaluated according to the quality specifications defined by the IFCC and the sigmascale method. Results: A model consisting of 23 preanalytical phase QIs was established.Approximately 47% (42/90) of clinical laboratories responded to the survey.The average result submission rate for each QI was 56% (standard deviation, 26%). The QIs with the highest and lowest result submission rates were “rejected samples due to hemolysis” (95%) and “recollected sample due to errors caused outside the laboratory” (17%). The QIs with the highest and lowest error rates were “hemolyzed sample detected by hemolytic index” (median, 0.546%; sigma performance level, “good”) and “samples not received” (median, 0.001%; sigma performance level, “very good”), respectively. Conclusions This survey findings on preanalytical phase QIs could serve as a foundation for developing an external quality assessment program for clinical laboratories in Korea.

In Harrison’s Principles of Internal Medicine, human understanding is emphasized as one of three necessary characteristics that a physician must have. Inflammation, which is caused by inflammatory inducers (inf-ids), is a fundamental feature of disease at the cellular and molecular levels. Inflammation protects the body, but excessive or prolonged inflammation can be damaging and can cause disease. Humans are repeatedly exposed to external and internal environmental factors that generate inf-ids throughout their lives. External environmental factors include microbial and non-microbial inf-ids, as well as stressors that inevitably arise during social interactions. Internal environmental factors include the adaptive physiological response that is present from birth. Inf-ids may also be produced by the four-step habit loop, which consists of a cue (e.g., stressor), emotions, routine act (adaptive response), and a reward. Immune cells in the circulatory system and in tissues may have positive and negative effects in inflammatory responses. However, low-grade inflammation may be difficult to detect. We propose a model of disease development that integrates external and internal environmental factors from the perspective of human understanding.

Background: The preanalytical phase is more vulnerable to errors. This study aimed to establish preanalytical quality indicators (QIs) suitable for Korean clinical laboratories and investigate the current status of preanalytical phase performance monitoring in Korea using these QIs. Methods: We reviewed previous studies investigating preanalytical QIs including the International Federation of Clinical Chemistry (IFCC) model of QIs, to establish a set of QIs for Korean clinical laboratories. An e-mail survey consisting of this QI set was sent to 90 clinical laboratories. The collected data were analyzed, and performance measures were evaluated according to the quality specifications defined by the IFCC and the sigmascale method. Results: A model consisting of 23 preanalytical phase QIs was established.Approximately 47% (42/90) of clinical laboratories responded to the survey.The average result submission rate for each QI was 56% (standard deviation, 26%). The QIs with the highest and lowest result submission rates were “rejected samples due to hemolysis” (95%) and “recollected sample due to errors caused outside the laboratory” (17%). The QIs with the highest and lowest error rates were “hemolyzed sample detected by hemolytic index” (median, 0.546%; sigma performance level, “good”) and “samples not received” (median, 0.001%; sigma performance level, “very good”), respectively. Conclusions This survey findings on preanalytical phase QIs could serve as a foundation for developing an external quality assessment program for clinical laboratories in Korea.

Background: The preanalytical phase is more vulnerable to errors. This study aimed to establish preanalytical quality indicators (QIs) suitable for Korean clinical laboratories and investigate the current status of preanalytical phase performance monitoring in Korea using these QIs. Methods: We reviewed previous studies investigating preanalytical QIs including the International Federation of Clinical Chemistry (IFCC) model of QIs, to establish a set of QIs for Korean clinical laboratories. An e-mail survey consisting of this QI set was sent to 90 clinical laboratories. The collected data were analyzed, and performance measures were evaluated according to the quality specifications defined by the IFCC and the sigmascale method. Results: A model consisting of 23 preanalytical phase QIs was established.Approximately 47% (42/90) of clinical laboratories responded to the survey.The average result submission rate for each QI was 56% (standard deviation, 26%). The QIs with the highest and lowest result submission rates were “rejected samples due to hemolysis” (95%) and “recollected sample due to errors caused outside the laboratory” (17%). The QIs with the highest and lowest error rates were “hemolyzed sample detected by hemolytic index” (median, 0.546%; sigma performance level, “good”) and “samples not received” (median, 0.001%; sigma performance level, “very good”), respectively. Conclusions This survey findings on preanalytical phase QIs could serve as a foundation for developing an external quality assessment program for clinical laboratories in Korea.

In Harrison’s Principles of Internal Medicine, human understanding is emphasized as one of three necessary characteristics that a physician must have. Inflammation, which is caused by inflammatory inducers (inf-ids), is a fundamental feature of disease at the cellular and molecular levels. Inflammation protects the body, but excessive or prolonged inflammation can be damaging and can cause disease. Humans are repeatedly exposed to external and internal environmental factors that generate inf-ids throughout their lives. External environmental factors include microbial and non-microbial inf-ids, as well as stressors that inevitably arise during social interactions. Internal environmental factors include the adaptive physiological response that is present from birth. Inf-ids may also be produced by the four-step habit loop, which consists of a cue (e.g., stressor), emotions, routine act (adaptive response), and a reward. Immune cells in the circulatory system and in tissues may have positive and negative effects in inflammatory responses. However, low-grade inflammation may be difficult to detect. We propose a model of disease development that integrates external and internal environmental factors from the perspective of human understanding.

Background and Objectives: The number of people with heart failure (HF) is increasing worldwide, and the social burden is increasing as HF has high mortality and morbidity. We aimed to provide updated trends on the epidemiology of HF in Korea to shape future social measures against HF. Methods: We used the National Health Information Database of the National Health Insurance Service to determine the prevalence, incidence, hospitalization rate, mortality rate, comorbidities, in-hospital mortality, and healthcare cost of patients with HF from 2002 to 2020 in Korea. Results: The prevalence of HF in the total Korean population rose from 0.77% in 2002 to 2.58% (1,326,886 people) in 2020. Although the age-standardized incidence of HF decreased over the past 18 years, the age-standardized prevalence increased. In 2020, the hospitalization rate for any cause in patients with HF was 1,166 per 100,000 persons, with a steady increase from 2002. In 2002, the HF mortality was 3.0 per 100,000 persons, which rose to 15.6 per 100,000 persons in 2020. While hospitalization rates and in-hospital mortality for patients with HF increased, the mortality rate for patients with HF did not (5.8% in 2020), and the one-year survival rate from the first diagnosis of HF improved. The total healthcare costs for patients with HF were approximately $2.4 billion in 2020, a 16-fold increase over the $0.15 billion in 2002. Conclusions The study’s results underscore the growing socioeconomic burden of HF in Korea, driven by an aging population and increasing HF prevalence.