Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea. Materials and Methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values. Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863. Conclusion The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant (B.1.1.529) is dominating coronavirus disease 2019 (COVID-19) worldwide. The waning protective effect of available vaccines against the Omicron variant is a critical public health issue. This study aimed to assess the impact of the third COVID-19 vaccination on immunity against the SARS-CoV-2 Omicron BA.1 strain in older individuals. Materials and Methods: Adults aged ≥60 years who had completed two doses of the homologous COVID-19 vaccine with either BNT162b2 (Pfizer/BioNTech, New York, NY, USA, BNT) or ChAdOx1 nCoV (SK bioscience, Andong-si, Gyeongsangbuk-do, Korea, ChAd) were registered to receive the third vaccination. Participants chose either BNT or mRNA-1273 (Moderna, Norwood, MA, USA, m1273) mRNA vaccine for the third dose and were categorized into four groups: ChAd/ChAd/BNT, ChAd/ChAd/m1273, BNT/BNT/BNT, and BNT/BNT/m1273. Four serum specimens were obtained from each participant at 0, 4, 12, and 24 weeks after the third dose (V1, V2, V3, and V4, respectively).Serum-neutralizing antibody (NAb) activity against BetaCoV/Korea/KCDC03/2020 (NCCP43326, ancestral strain) and B.1.1.529 (NCCP43411, Omicron BA.1 variant) was measured using plaque reduction neutralization tests. A 50% neutralizing dilution (ND 50 ) >10 was considered indicative of protective NAb titers. Results: In total, 186 participants were enrolled between November 24, 2021, and June 30, 2022. The respective groups received the third dose at a median (interquartile range [IQR]) of 132 (125 - 191), 123 (122 - 126), 186 (166 -193), and 182 (175 - 198) days after the second dose. Overall, ND 50 was lower at V1 against Omicron BA.1 than against the ancestral strain. NAb titers against the ancestral strain and Omicron BA.1 variant at V2 were increased at least 30-fold (median [IQR], 1235.35 [1021.45 - 2374.65)] and 129.8 [65.3 - 250.7], respectively). ND 50 titers against the ancestral strain and Omicron variant did not differ significantly among the four groups (P= 0.57). NAb titers were significantly lower against the Omicron variant than against the ancestral strain at V3 (median [IQR], 36.4 (17.55 - 75.09) vs. 325.9 [276.07 - 686.97]; P = 0.012). NAb titers against Omicron at V4 were 16 times lower than that at V3. Most sera exhibited a protective level (ND 50 >10) at V4 (75.0% [24/32], 73.0% [27/37], 73.3% [22/30], and 70.6% [12/17] in the ChAd/ChAd/BNT, ChAd/ChAd/m1273, BNT/BNT/BNT, and BNT/BNT/m1273 groups, respectively), with no significant differences among groups (P = 0.99). Conclusion A third COVID-19 mRNA vaccine dose restored waning NAb titers against Omicron BA.1. Our findings support a third-dose vaccination program to prevent the waning of humoral immunity to SARS-CoV-2.

Background: Neutrophilic inflammation is a characteristic feature of idiopathic pulmonary fibrosis (IPF). S100 calcium-binding protein A9 (S100A9) is a neutrophil-derived protein involved in the development of neutrophil-related chronic inflammatory disorders. However, the role of S100A9 in IPF remains unclear. Methods: We used enzyme-linked immunosorbent assays to measure S100A9 levels in bronchoalveolar lavage fluid (BALF) and serum obtained from healthy controls (HCs) and patients with IPF, non-specific interstitial pneumonia, hypersensitivity pneumonitis, and sarcoidosis. Results: Compared with HCs, BALF S100A9 levels were significantly higher in IPF patients (P < 0.001), patients with hypersensitivity pneumonitis (P = 0.043), and patients with nonspecific interstitial pneumonia (P < 0.001). The S100A9 level in BALF of 0.093 ng/mL could distinguish IPF patients from HCs, with a specificity of 78.8% and a sensitivity of 81.6%. Similarly, the S100A9 level in BALF of 0.239 ng/mL had a specificity of 64.7% and a sensitivity of 66.7% for distinguishing IPF patients from patients with other interstitial lung diseases. Additionally, BALF S100A9 levels were significantly correlated with neutrophil counts (r = 0.356, P < 0.001) in BALF. IPF patients with S100A9 levels in BALF > 0.533 ng/ mL had lower survival rates, compared with patients who had levels ≤ 0.553 ng/mL (n = 49; hazard ratio [HR], 3.62; P = 0.021). Combination analysis revealed that IPF patients with S100A9 levels in BALF> 0.553 ng/mL or neutrophil percentages > 49.1% (n = 43) had significantly lower survival rates than patients with S100A9 levels in BALF ≤ 0.553 ng/mL and neutrophil percentages ≤ 49.1% (n = 41) (HR, 3.91; P = 0.014). Additionally, patients with serum S100A9 levels > 0.077 ng/mL (n = 29) had significantly lower survival rates than patients with levels ≤ 0.077 ng/mL (n = 53, HR, 2.52; P = 0.013). S100A9 was expressed on neutrophils and macrophages in BALF from IPF patients as well as α-smooth muscle actin positive cells in the lung tissues. Conclusion S100A9 is involved in the development and progression of IPF. Moreover, S100A9 levels in BALF and serum may be surrogate markers for IPF diagnosis and survival prediction, particularly when analyzed in combination with neutrophil percentages.

Purpose: Acute myocardial infarction (AMI) and stroke are leading global causes of death and can be used to assess acute care quality. We examined the 30-day mortality trends after emergency department admission for AMI and stroke in Korea from 2008 to 2019, focusing on regional and income disparities. Materials and Methods: The AMI and stroke patients admitted to hospitals in Korea were collected from the claims data. We analyzed age and sex-standardized 30-day mortality for AMI, as well as hemorrhagic and ischemic strokes. Disparities in mortality were analyzed using absolute differences and relative ratios between the Organization for Economic Cooperation Development (OECD) and Korea, and among income levels and regions in Korea. A 12-year joinpoint regression was used to determine the annual percent change and the average annual percent change. Results: The trends in the 30-day AMI mortality of Korea were not significantly changed from 2008 to 2019; the gap remained at 1.2 between the OECD and Korea. Korea maintained lower mortality rates for hemorrhagic and ischemic stroke than the mean of OECD. In Korea, the 30-day hemorrhagic stroke mortality showed a constant decreasing trend for the higher-income group living in urban areas; it led to a widened gap based on income levels in urban areas. The 30-day mortality for ischemic stroke tended to decrease in the higher-income group and urban areas. Conclusion National-level intervention is needed to manage regional and income-based disparities in AMI and stroke 30-day mortality. It is important to understand the variance in mortality rate by different geographical regions and income levels to establish an appropriate public health strategy.

Currently, antimicrobial resistance (AMR) is a major threat to global public health. The antimicrobial stewardship program (ASP) has been proposed as an important approach to overcome this crisis. ASP supports the optimal use of antimicrobials, including appropriate dosing decisions, administration duration, and administration routes. In Korea, efforts are being made to overcome AMR using ASPs as a national policy. The current study aimed to develop core elements of ASP that could be introduced in domestic medical facilities. A Delphi survey was conducted twice to select the core elements through expert consensus.The core elements for implementing the ASP included (1) leadership commitment, (2) operating system, (3) action, (4) tracking, (5) reporting, and (6) education. To ensure these core elements are present at medical facilities, multiple departments must collaborate as teams for ASP operations. Establishing a reimbursement system and a workforce for ASPs are prerequisites for implementing ASPs. To ensure that ASP core elements are actively implemented in medical facilities, it is necessary to provide financial support for ASPs in medical facilities, nurture the healthcare workforce in performing ASPs, apply the core elements to healthcare accreditation, and provide incentives to medical facilities by quality evaluation criteria.

Background and Objectives: 18F-fluorodeoxyglucose PET/CT scans can be a useful method to detect recurrence. However, its role in locally advanced head and neck squamous cell carcinomas (HNSCCs) patients after definitive chemoradiotherapy (CRT) has not yet been determined. The current study was performed to identify the role of therapeutic response evaluation using PET/CT after definitive CRT.Subjects and Method We conducted a retrospective review of patients with locally advanced HNSCCs, and who have undergone definitive CRT from 2009 to 2017 at a single institution. The patients were divided into two groups according to their responses to the treatment (metabolic complete remission [mCR] group or non-mCR group), assessed by PET/CT scans after definitive CRT. Results: Twenty-eight patients were consecutively enrolled. The most common primary site of cancer was the oropharynx, followed by the oral cavity, hypopharynx, and nasal cavity. The therapeutic response assessed by PET/CT scans was mCR in 14 patients. The median progression- free survival (PFS) was not reached in the mCR group but was 13.3 months for the non-mCR group (p=0.001). The median overall survival was significantly longer for the mCR group (52.5 months) than for the non-mCR group (15.2 months, p=0.002). A multivariate analysis showed PET/CT response and high-sensitivity C-reactive protein (hsCRP) as independent prognostic factors for PFS (mCR: p=0.027; hsCRP: p=0.042) and for the overall survival (mCR: p=0.006; hsCRP: p=0.020). Conclusion PET/CT scans after definitive CRT predicted the prognosis in patients with locally advanced HNSCCs. CRP was a prognostic factor affecting the outcomes of treatments.