Social Inequities in the Survival of Liver Cancer: A Nationwide Cohort Study in Korea, 2007-2017.

BACKGROUND: To analyze the effects of socioeconomic status (type of insurance and income level) and cancer stage on the survival of patients with liver cancer in Korea. METHODS: A retrospective cohort study was constructed using data from the Healthcare Big Data Platform project in Korea between January 1, 2007, and December 31, 2017. A total of 143,511 patients in Korea diagnosed with liver cancer (International Classification of Diseases, 10th Revision [ICD-10] codes C22, C220, and C221) were followed for an average of 11 years. Of these, 110,443 died. The patient's insurance type and income level were used as indicators of socioeconomic status. Unadjusted and adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated using a Cox proportional hazards regression model to analyze the relationship between the effects of sex, age, and cancer stage at first diagnosis (Surveillance, Epidemiology, and the End Results; SEER), type of insurance, and income level on the survival of patients with liver cancer. The interactive effects of the type of insurance, income level, and cancer stage on liver cancer death were also analyzed. RESULTS: The lowest income group (medical aid) showed a higher risk for mortality (HR (95% CI); 1.37 (1.27-1.47) for all patients, 1.44 (1.32-1.57) for men, and 1.16 (1.01-1.34) for women) compared to the highest income group (1-6) among liver cancer (ICD-10 code C22) patients. The risk of liver cancer death was also higher in the lowest income group with a distant cancer stage (SEER = 7) diagnosis than for any other group. CONCLUSION: Liver cancer patients with lower socioeconomic status and more severe cancer stages were at greater risk of death. Reducing social inequalities is needed to improve mortality rates among patients in lower social class groups who present with advanced cancer.

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study.

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 (PaO2/FIO2 [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.

Study Protocol for a Hospital-to-Home Transitional Care for Older Adults Hospitalized with Chronic Obstructive Pulmonary Disease in South Korea: A Randomized Controlled Trial.

Chronic obstructive pulmonary disease (COPD) is a progressive respiratory condition characterized by persistent inflammation in the airways, resulting in narrowing and obstruction of the air passages. The development of COPD is primarily attributed to long-term exposure to irritants, such as cigarette smoke and environmental pollutants. Among individuals hospitalized for exacerbations of COPD, approximately one in five is readmitted within 30 days of discharge or encounters immediate post-discharge complications, highlighting a lack of adequate preparedness for self-management. To address this inadequate preparedness, transitional care services (TCS) have emerged as a promising approach. Therefore, this study primarily aims to present a detailed protocol for a multi-site, single-blind, randomized, controlled trial (RCT) aimed at enhancing self-management competency and overall quality of life for patients with COPD through the provision of TCS, facilitated by a proficient Clinical Research Coordinator. The RCT intervention commenced in September 2022 and is set to conclude in December 2024, with a total of 362 COPD patients anticipated to be enrolled in the study. The intervention program encompasses various components, including an initial assessment during hospitalization, comprehensive self-management education, facilitation of social welfare connections, post-discharge home visits, and regular telephone monitoring. Furthermore, follow-up evaluations are conducted at both one month and three months after discharge to assess the effectiveness of the intervention in terms of preventing re-hospitalization, reducing acute exacerbations, and enhancing disease awareness among participants. The results of this study are expected to provide a basis for the development of TCS fee payment policies for future health insurance.

Effects of Marital Status and Income on Hypertension: The Korean Genome and Epidemiology Study (KoGES).

OBJECTIVES: This study aimed to analyze the associations of income, marital status, and health behaviors with hypertension in male and female over 40 years of age in the Korea. METHODS: The data were derived from the Korean Genome and Epidemiology Study (KoGES; 4851-302) which included 211 576 participants. To analyze the relationships of income, marital status, and health behaviors with hypertension in male and female over 40 years of age, multiple logistic regression was conducted with adjustments for these variables. RESULTS: The prevalence of hypertension increased linearly as income decreased. The odds ratio for developing hypertension in people with an income of <0.5 million Korean won (KRW) compared to ≥6.0 million KRW was 1.55 (95% confidence interval [CI], 1.25 to 1.93) in the total population, 1.58 (95% CI, 1.27 to 1.98) in male, and 1.07 (95% CI, 0.35 to 3.28) in female. The combined effect of income level and marital status on hypertension was significant. According to income level and marital status, in male, low income and divorce were most associated with hypertension (1.76 times; 95% CI, 1.01 to 3.08). However, in female, the low-income, married group was most associated with hypertension (1.83 times; 95% CI, 1.71 to 1.97). CONCLUSIONS: The results of this study show that it is necessary to approach male and female marital status separately according to income in health policies to address inequalities in the prevalence of hypertension.