OBJECTIVES: Humidifier disinfectants (HDs) were sold in Korea from 1994 until their recall in 2011. We examined the incidence patterns of 8 respiratory diseases before and after the HD recall and estimated the attributable risk in the Korean population. METHODS: Using National Health Insurance data from 2002 to 2019, we performed age–cohort–period and differences-in-diffference analyses (comparing periods before vs. after the recall) to estimate the population-attributable fraction and the excess number of episodes. The database comprised 51 million individuals (99% of the Korean population). The incidence of 8 diseases—acute upper respiratory infection (AURI), acute lower respiratory infection (ALRI), asthma, pneumonia, chronic sinusitis (CS), interstitial lung disease (ILD), bronchiectasis, and chronic obstructive pulmonary disease (COPD)—was defined by constructing episodes of care based on patterns of medical care and the clinical characteristics of each disease. RESULTS: The relative risks (RRs) for AURI, ALRI, asthma, pneumonia, CS, and ILD were elevated among younger individuals (with an RR as high as 82.18 for AURI in males), whereas chronic conditions such as bronchiectasis, COPD, and ILD showed higher RRs in older individuals. During the HD exposure period, the population-attributable risk percentage ranged from 4.6% for bronchiectasis to 25.1% for pneumonia, with the excess number of episodes ranging from 6,218 for ILD to 3,058,861 for CS. Notably, females of reproductive age (19-44 years) experienced 1.1-9.2 times more excess episodes than males. CONCLUSIONS This study provides epidemiological evidence that inhalation exposure to HDs affects the entire respiratory tract and identifies vulnerable groups.

OBJECTIVES: Humidifier disinfectants (HDs) were sold in Korea from 1994 until their recall in 2011. We examined the incidence patterns of 8 respiratory diseases before and after the HD recall and estimated the attributable risk in the Korean population. METHODS: Using National Health Insurance data from 2002 to 2019, we performed age–cohort–period and differences-in-diffference analyses (comparing periods before vs. after the recall) to estimate the population-attributable fraction and the excess number of episodes. The database comprised 51 million individuals (99% of the Korean population). The incidence of 8 diseases—acute upper respiratory infection (AURI), acute lower respiratory infection (ALRI), asthma, pneumonia, chronic sinusitis (CS), interstitial lung disease (ILD), bronchiectasis, and chronic obstructive pulmonary disease (COPD)—was defined by constructing episodes of care based on patterns of medical care and the clinical characteristics of each disease. RESULTS: The relative risks (RRs) for AURI, ALRI, asthma, pneumonia, CS, and ILD were elevated among younger individuals (with an RR as high as 82.18 for AURI in males), whereas chronic conditions such as bronchiectasis, COPD, and ILD showed higher RRs in older individuals. During the HD exposure period, the population-attributable risk percentage ranged from 4.6% for bronchiectasis to 25.1% for pneumonia, with the excess number of episodes ranging from 6,218 for ILD to 3,058,861 for CS. Notably, females of reproductive age (19-44 years) experienced 1.1-9.2 times more excess episodes than males. CONCLUSIONS This study provides epidemiological evidence that inhalation exposure to HDs affects the entire respiratory tract and identifies vulnerable groups.

OBJECTIVES: Humidifier disinfectants (HDs) were sold in Korea from 1994 until their recall in 2011. We examined the incidence patterns of 8 respiratory diseases before and after the HD recall and estimated the attributable risk in the Korean population. METHODS: Using National Health Insurance data from 2002 to 2019, we performed age–cohort–period and differences-in-diffference analyses (comparing periods before vs. after the recall) to estimate the population-attributable fraction and the excess number of episodes. The database comprised 51 million individuals (99% of the Korean population). The incidence of 8 diseases—acute upper respiratory infection (AURI), acute lower respiratory infection (ALRI), asthma, pneumonia, chronic sinusitis (CS), interstitial lung disease (ILD), bronchiectasis, and chronic obstructive pulmonary disease (COPD)—was defined by constructing episodes of care based on patterns of medical care and the clinical characteristics of each disease. RESULTS: The relative risks (RRs) for AURI, ALRI, asthma, pneumonia, CS, and ILD were elevated among younger individuals (with an RR as high as 82.18 for AURI in males), whereas chronic conditions such as bronchiectasis, COPD, and ILD showed higher RRs in older individuals. During the HD exposure period, the population-attributable risk percentage ranged from 4.6% for bronchiectasis to 25.1% for pneumonia, with the excess number of episodes ranging from 6,218 for ILD to 3,058,861 for CS. Notably, females of reproductive age (19-44 years) experienced 1.1-9.2 times more excess episodes than males. CONCLUSIONS This study provides epidemiological evidence that inhalation exposure to HDs affects the entire respiratory tract and identifies vulnerable groups.

OBJECTIVES: Humidifier disinfectants (HDs) were sold in Korea from 1994 until their recall in 2011. We examined the incidence patterns of 8 respiratory diseases before and after the HD recall and estimated the attributable risk in the Korean population. METHODS: Using National Health Insurance data from 2002 to 2019, we performed age–cohort–period and differences-in-diffference analyses (comparing periods before vs. after the recall) to estimate the population-attributable fraction and the excess number of episodes. The database comprised 51 million individuals (99% of the Korean population). The incidence of 8 diseases—acute upper respiratory infection (AURI), acute lower respiratory infection (ALRI), asthma, pneumonia, chronic sinusitis (CS), interstitial lung disease (ILD), bronchiectasis, and chronic obstructive pulmonary disease (COPD)—was defined by constructing episodes of care based on patterns of medical care and the clinical characteristics of each disease. RESULTS: The relative risks (RRs) for AURI, ALRI, asthma, pneumonia, CS, and ILD were elevated among younger individuals (with an RR as high as 82.18 for AURI in males), whereas chronic conditions such as bronchiectasis, COPD, and ILD showed higher RRs in older individuals. During the HD exposure period, the population-attributable risk percentage ranged from 4.6% for bronchiectasis to 25.1% for pneumonia, with the excess number of episodes ranging from 6,218 for ILD to 3,058,861 for CS. Notably, females of reproductive age (19-44 years) experienced 1.1-9.2 times more excess episodes than males. CONCLUSIONS This study provides epidemiological evidence that inhalation exposure to HDs affects the entire respiratory tract and identifies vulnerable groups.

This manuscript represents the official position of the Korean Society of Echocardiography on valvular heart diseases.This position paper focuses on the clinical management of valvular heart diseases with reference to the guidelines recently published by the American College of Cardiology/American Heart Association and the European Society of Cardiology. The committee tried to reflect the recently published results on the topic of valvular heart diseases and Korean data by a systematic literature search based on validity and relevance. In part I of this article, we will review and discuss the current position of aortic valve disease in Korea.

This manuscript represents the official position of the Korean Society of Echocardiography on valvular heart diseases.This position paper focuses on the diagnosis and management of valvular heart diseases with referring to the guide‑ lines recently published by the American College of Cardiology/American Heart Association and the European Society of Cardiology. The committee sought to reflect national data on the topic of valvular heart diseases published to date through a systematic literature search based on validity and relevance. In the part II of this article, we intend to pre‑ sent recommendations for diagnosis and treatment of mitral valve disease and tricuspid valve disease.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.