Background: The effectiveness of intravenous tissue plasminogen activator (IV tPA) in patients with large-vessel occlusion (LVO) receiving endovascular treatment (EVT) for acute ischemic stroke (AIS) has been questioned. We investigated IV tPA effectiveness in real-world AIS patients, including those with intracranial LVO receiving EVT. Methods: We identified patients with AIS who presented to hospital with National Institutes of Health Stroke Scale ≥4 within 8 hours of symptom onset from the institutional stroke registry. The association of IV tPA use with effectiveness and safety outcomes was analyzed in overall enrolled AIS patients; LVO patients; and patients treated with EVT. The effect of IV tPA was assessed using multiple logistic regression. Results: Among the 654 patients meeting study entry criteria, 238 (36.4%) received IV tPA and 416 (63.6%) did not. Multiple logistic regression analysis and shift analysis revealed IV tPA was associated with improved outcomes in overall enrolled AIS population, LVO, and EVT-treated subgroups. Among EVT-treated patients, IV tPA was associated with higher likelihood of ambulatory or better outcome (modified Rankin Scale 0–3) with odds ratio of 1.95 (P=0.03). Conclusions In this real-world study, IV tPA use was associated with improved outcomes for patients with AIS, including among LVO patients treated and not treated with EVT, in the contemporary mechanical thrombectomy era.

Diabetes mellitus is a heterogeneous disease that encompasses a wide range of conditions, from mild cases to severe conditions where survival depends on insulin therapy. The Korean Diabetes Association Task Force Team for Diabetes with β-Cell Failure has established the term to classify severe refractory disease with β-cell failure. Individuals with β-cell failure are at high risk of diabetes-related complications. We propose that diabetes with β-cell failure can be diagnosed when individuals treated with multiple daily insulin injections or insulin pumps meet at least one of the following criteria: fasting C-peptide ≤ 0.6 ng/mL, non-fasting C-peptide ≤ 1.8 ng/mL, 24-hour urine C-peptide < 30 μg/day, or spot urine C-peptide/creatinine ratio ≤ 0.6 nmol/mmol. Among cases of diabetes with β-cell failure, β-cell failure with absolute insulin deficiency can be diagnosed when at least one of the following criteria is met: fasting C-peptide < 0.24 ng/mL, non-fasting C-peptide < 0.6 ng/mL, or spot urine C-peptide/ creatinine ratio < 0.2 nmol/mmol. Multiple daily insulin injections with long-acting insulin analogs and rapid-acting insulin analogs or insulin pumps are required for treatment of diabetes with β-cell failure. Continuous glucose monitoring and an automated insulin delivery system, sensor-augmented pump, or smart insulin pen, along with structured education, are necessary. We call for improvements in the relevant systems to ensure that such treatments can be provided.

Diabetes mellitus is a heterogeneous disease that encompasses a wide range of conditions, from mild cases to severe conditions where survival depends on insulin therapy. The Korean Diabetes Association Task Force Team for Diabetes with β-Cell Failure has established the term to classify severe refractory disease with β-cell failure. Individuals with β-cell failure are at high risk of diabetes-related complications. We propose that diabetes with β-cell failure can be diagnosed when individuals treated with multiple daily insulin injections or insulin pumps meet at least one of the following criteria: fasting C-peptide ≤ 0.6 ng/mL, non-fasting C-peptide ≤ 1.8 ng/mL, 24-hour urine C-peptide < 30 μg/day, or spot urine C-peptide/creatinine ratio ≤ 0.6 nmol/mmol. Among cases of diabetes with β-cell failure, β-cell failure with absolute insulin deficiency can be diagnosed when at least one of the following criteria is met: fasting C-peptide < 0.24 ng/mL, non-fasting C-peptide < 0.6 ng/mL, or spot urine C-peptide/ creatinine ratio < 0.2 nmol/mmol. Multiple daily insulin injections with long-acting insulin analogs and rapid-acting insulin analogs or insulin pumps are required for treatment of diabetes with β-cell failure. Continuous glucose monitoring and an automated insulin delivery system, sensor-augmented pump, or smart insulin pen, along with structured education, are necessary. We call for improvements in the relevant systems to ensure that such treatments can be provided.

Diabetes mellitus is a heterogeneous disease that encompasses a wide range of conditions, from mild cases to severe conditions where survival depends on insulin therapy. The Korean Diabetes Association Task Force Team for Diabetes with β-Cell Failure has established the term to classify severe refractory disease with β-cell failure. Individuals with β-cell failure are at high risk of diabetes-related complications. We propose that diabetes with β-cell failure can be diagnosed when individuals treated with multiple daily insulin injections or insulin pumps meet at least one of the following criteria: fasting C-peptide ≤ 0.6 ng/mL, non-fasting C-peptide ≤ 1.8 ng/mL, 24-hour urine C-peptide < 30 μg/day, or spot urine C-peptide/creatinine ratio ≤ 0.6 nmol/mmol. Among cases of diabetes with β-cell failure, β-cell failure with absolute insulin deficiency can be diagnosed when at least one of the following criteria is met: fasting C-peptide < 0.24 ng/mL, non-fasting C-peptide < 0.6 ng/mL, or spot urine C-peptide/ creatinine ratio < 0.2 nmol/mmol. Multiple daily insulin injections with long-acting insulin analogs and rapid-acting insulin analogs or insulin pumps are required for treatment of diabetes with β-cell failure. Continuous glucose monitoring and an automated insulin delivery system, sensor-augmented pump, or smart insulin pen, along with structured education, are necessary. We call for improvements in the relevant systems to ensure that such treatments can be provided.

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.

Background: We aimed to analyze the effects of an antimicrobial stewardship program (ASP) on the proportion of antimicrobial-resistant pathogens in bacteremia, antimicrobial use, and mortality in pediatric patients. Methods: A retrospective single-center study was performed on pediatric inpatients under 19 years old who received systemic antimicrobial treatment from 2001 to 2019. A pediatric infectious disease attending physician started ASP in January 2008. The study period was divided into the pre-intervention (2001–2008) and the post-intervention (2009–2019) periods. The amount of antimicrobial use was defined as days of therapy per 1,000 patientdays, and the differences were compared using delta slope (= changes in slopes) between the two study periods by an interrupted time-series analysis. The proportion of resistant pathogens and the 30-day overall mortality rate were analyzed by the χ2 . Results: The proportion of extended-spectrum beta-lactamase (ESBL)-producing Escherichia coli and Klebsiella pneumoniae bacteremia increased from 17% (39 of 235) in the pre-intervention period to 35% (189 of 533) in the post-intervention period (P < 0.001). The total amount of antimicrobial use significantly decreased after the introduction of ASP (delta slope value = −16.5; 95% confidence interval [CI], −30.6 to −2.3; P = 0.049). The 30-day overall mortality rate in patients with bacteremia did not increase, being 10% (55 of 564) in the pre-intervention and 10% (94 of 941) in the post-intervention period (P = 0.881). Conclusion The introduction of ASP for pediatric patients reduced the delta slope of the total antimicrobial use without increasing the mortality rate despite an increased incidence of ESBL-producing gram-negative bacteremia.