BACKGROUND: Prothrombin time (PT) measurement is an important test for screening blood coagulation disorders and monitoring anticoagulant therapy. In this study, we evaluated the analytical performance of HemosIL ReadiPlasTin (Instrumentation Laboratory, USA), a liquid reagent for PT measurement. METHODS: The precision of HemosIL ReadiPlasTin was evaluated according to the Clinical and Laboratory Standards Institute (CLSI) EP5-A3 guidelines. Further, comparison with HemosIL RecombiPlasTin 2G (Instrumentation Laboratory, USA) was made according to the CLSI EP9-A3 guidelines. The reference intervals were established according to the CLSI C28-A3 guidelines. RESULTS: The coefficient of variation values for repeatability and total imprecision at two levels of control materials were lower than 1.1% and 3.4%, respectively. The performance of HemosIL ReadiPlasTin was comparable to that of HemosIL RecombiPlasTin 2G, with a high correlation (r=0.996). The reference interval for normal subjects was 10.4–13.3 seconds. CONCLUSIONS: HemosIL ReadiPlasTin showed an acceptable degree of imprecision and its performance showed high correlation with that of a conventional reagent. Therefore, it is expected to be useful for PT measurement in clinical laboratories.
Blood Coagulation Disorders ; Blood Coagulation Tests ; Mass Screening ; Prothrombin Time ; Prothrombin ; Thromboplastin

The purpose of this study was to investigate the effect of blood contact and tooth mobility on volumetric changes of calcium silicate-based root-end filling materials using a micro-CT. Three calcium silicate-based materials (ProRoot MTA, Biodentine, and RetroMTA) were used in this study. Seventy-two extracted human single-rooted premolars were obturated with gutta percha. Root-end resection and root-end preparation were performed. After root-end filling with tested materials, the tooth specimens were immersed in saline or blood for 5 days in a 37 ℃ incubator (n=8). The tooth specimens were mounted in a chewing simulator to simulate tooth mobility with a force of 30 N and 20,000 cycles. Micro-CT scans were performed immediately after root-end filling and after exposure to storage media or simulation of tooth mobility. The volume loss (%) was obtained from difference in the percentage of defects of materials between first and second micro-CT scans. Apical volume loss (%; volume loss from resected surface to 1 mm from the surface) was calculated for tooth mobility simulating groups. Biodentine showed larger total volume loss than ProRoot MTA and RetroMTA in saline and blood. ProRoot MTA had smaller total volume loss in blood than in saline. Under the condition simulating tooth mobility, total volume loss was similar among materials, and apical volume loss of Biodentine was larger than that of RetroMTA. In conclusion, ProRoot MTA or Retro MTA is recommended in clinical situation of intentional replantation where tooth mobility or direct contact with blood may occur.

Background: Alpha-toxin (AT), a major virulence factor of Staphylococcus aureus, is an important immunotherapeutic target to prevent or treat invasive S. aureus infections. Previous studies have suggested that anti-AT antibodies (Abs) may have a protective role against S. aureus bacteremia (SAB), but their function remains unclear. Therefore, we aimed to investigate the association between serum anti-AT Ab levels and clinical outcomes of SAB. Methods: Patients from a prospective SAB cohort at a tertiary-care medical center (n = 51) were enrolled in the study from July 2016 to January 2019. Patients without symptoms or signs of infection were enrolled as controls (n = 100). Blood samples were collected before the onset of SAB and at 2- and 4-weeks post-bacteremia. Anti-AT immunoglobin G (IgG) levels were measured using an enzyme-linked immunosorbent assay. All clinical S. aureus isolates were tested for the presence of hla using polymerase chain reaction. Results: Anti-AT IgG levels in patients with SAB before the onset of bacteremia did not differ significantly from those in non-infectious controls. Pre-bacteremic anti-AT IgG levels tended to be lower in patients with worse clinical outcomes (7-day mortality, persistent bacteremia, metastatic infection, septic shock), although the differences were not statistically significant. Patients who needed intensive care unit care had significantly lower anti-AT IgG levels at 2 weeks post-bacteremia (P = 0.020). Conclusion The study findings suggest that lower anti-AT Ab responses before and during SAB, reflective of immune dysfunction, are associated with more severe clinical presentations of infection.

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.