Background: Patients with septic shock frequently require tracheal intubation in the emergency department (ED). However, the criteria for tracheal intubation are subjective, based on physician experience, or require serial evaluations over relatively long intervals to make accurate predictions, which might not be feasible in the ED. We used supervised learning approaches and features routinely available during the initial stages of evaluation and resuscitation to stratify the risks of tracheal intubation within a 24-hour time window. Methods: We retrospectively analyzed the data of patients diagnosed with septic shock based on the SEPSIS-3 criteria across 21 university hospital EDs in the Republic of Korea. A principal component analysis revealed a complex, non-linear decision boundary with respect to the application of tracheal intubation within a 24-hour time window. Stratified five-fold cross validation and a grid search were used with extreme gradient boost. Shapley values were calculated to explain feature importance and preferences. Results: In total, data for 4,762 patients were analyzed; within that population, 1,486 (31%) were intubated within a 24-hour window, and 3,276 (69%) were not. The area under the receiver operating characteristic curve and F1 scores for intubation within a 24-hour window were 0.829 (95% CI, 0.801–0.878) and 0.654 (95% CI, 0.627–0.681), respectively. The Shapley values identified lactate level after initial fluids, suspected lung infection, initial pH, Sequential Organ Failure Assessment score at enrollment, and respiratory rate at enrollment as important features for prediction. Conclusions An extreme gradient boosting machine can moderately discriminate whether intubation is warranted within 24 hours of the recognition of septic shock in the ED.

Objective: During the coronavirus disease 2019 (COVID-19) pandemic, the use of mechanical chest compression (meCC) devices for cardiopulmonary resuscitation (CPR) in emergency departments might have increased. However, there are few reports yet of such an increase in use. This study aimed to assess the current status of meCC device utilization in emergency medical institutions nationwide through a survey. Methods: This cross-sectional study conducted a survey from August 20, 2022 to September 29, 2022, using emails and text messages to target 287 out of a total of 409 emergency medical institutions nationwide for which contact information was obtained. Results: Of the 287 emergency medical institutions, 127 responded (44.2% response rate). Of these, 74 (58.3%) reported using meCC devices. The utilization rates were highest in the regional emergency medical center, followed by local emergency medical centers and local emergency medical agencies (93.3% vs. 67.3% vs. 27.1%, respectively; P<0.001). The most common reason for device purchases was to reduce rescuer fatigue and ensure high-quality CPR. The second reason was personnel shortages, while the regional emergency medical center gave a higher priority to the protection of medical staff from COVID-19. The meCC device group reported significantly higher cases of CPR (100 or more cases per year) compared to the non-meCC device group (64.9% vs. 24.6%; P<0.001) although no difference was shown in the total number of medical staff participated in CPR between the groups. Also, 90.5% of the meCC group expressed satisfaction with using the device. Conclusion More than 50% of emergency medical institutions used meCC devices in CPR, citing reasons such as reducing rescuer fatigue and ensuring high-quality CPR.

Type 2 diabetes mellitus (T2DM) is characterized by insulin resistance and β-cell dysfunction. Among available oral antidiabetic agents, only the thiazolidinediones (TZDs) primarily target insulin resistance. TZDs improve insulin sensitivity by activating peroxisome proliferator-activated receptor γ. Rosiglitazone and pioglitazone have been used widely for T2DM treatment due to their potent glycemic efficacy and low risk of hypoglycemia. However, their use has decreased because of side effects and safety issues, such as cardiovascular concerns and bladder cancer. Lobeglitazone (Chong Kun Dang Pharmaceutical Corporation), a novel TZD, was developed to meet the demands for an effective and safe TZD. Lobeglitazone shows similar glycemic efficacy to pioglitazone, with a lower effective dose, and favorable safety results. It also showed pleiotropic effects in preclinical and clinical studies. In this article, we summarize the pharmacologic, pharmacokinetic, and clinical characteristics of lobeglitazone.

Background: We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning methods. Methods: We performed a retrospective analysis of an OHCA survivor registry. Patients aged ≥ 18 years were included. Study participants who had registered between March 31, 2013 and December 31, 2018 were divided into a develop dataset (80% of total) and an internal validation dataset (20% of total), and those who had registered between January 1, 2019 and December 31, 2019 were assigned to an external validation dataset. Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. The prediction performance of the model in the internal validation and the external validation dataset was described with accuracy, area under the receiver-operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Futhermore, we established multivariable logistic regression models with the develop set and compared prediction performance with the ensemble models. The primary outcome was an unfavorable 1-year neurological outcome. Results: A total of 1,207 patients were included in the study. Among them, 631, 139, and 153were assigned to the develop, the internal validation and the external validation datasets, respectively. Prediction performance metrics for the ensemble prediction model in the internal validation dataset were as follows: accuracy, 0.9620 (95% confidence interval [CI],0.9352–0.9889); area under receiver-operator characteristics curve, 0.9800 (95% CI, 0.9612– 0.9988); area under precision-recall curve, 0.9950 (95% CI, 0.9860–1.0000); sensitivity, 0.9594 (95% CI, 0.9245–0.9943); specificity, 0.9714 (95% CI, 0.9162–1.0000); PPV, 0.9916 (95% CI, 0.9752–1.0000); NPV, 0.8718 (95% CI, 0.7669–0.9767). Prediction performance metrics for the model in the external validation dataset were as follows: accuracy, 0.8509 (95% CI, 0.7825–0.9192); area under receiver-operator characteristics curve, 0.9301 (95% CI, 0.8845–0.9756); area under precision-recall curve, 0.9476 (95% CI, 0.9087–0.9867); sensitivity, 0.9595 (95% CI, 0.9145–1.0000); specificity, 0.6500 (95% CI, 0.5022–0.7978); PPV, 0.8353 (95% CI, 0.7564–0.9142); NPV, 0.8966 (95% CI, 0.7857–1.0000). All the prediction metrics were higher in the ensemble models, except NPVs in both the internal and the external validation datasets. Conclusion We established an ensemble prediction model for prediction of unfavorable 1-year neurological outcomes in OHCA survivors using four machine learning methods. The prediction performance of the ensemble model was higher than the multivariable logistic regression model, while its performance was slightly decreased in the external validation dataset.

Type 2 diabetes mellitus (T2DM) is characterized by insulin resistance and β-cell dysfunction. Among available oral antidiabetic agents, only the thiazolidinediones (TZDs) primarily target insulin resistance. TZDs improve insulin sensitivity by activating peroxisome proliferator-activated receptor γ. Rosiglitazone and pioglitazone have been used widely for T2DM treatment due to their potent glycemic efficacy and low risk of hypoglycemia. However, their use has decreased because of side effects and safety issues, such as cardiovascular concerns and bladder cancer. Lobeglitazone (Chong Kun Dang Pharmaceutical Corporation), a novel TZD, was developed to meet the demands for an effective and safe TZD. Lobeglitazone shows similar glycemic efficacy to pioglitazone, with a lower effective dose, and favorable safety results. It also showed pleiotropic effects in preclinical and clinical studies. In this article, we summarize the pharmacologic, pharmacokinetic, and clinical characteristics of lobeglitazone.

Background: We performed this study to establish a prediction model for 1-year neurological outcomes in out-of-hospital cardiac arrest (OHCA) patients who achieved return of spontaneous circulation (ROSC) immediately after ROSC using machine learning methods. Methods: We performed a retrospective analysis of an OHCA survivor registry. Patients aged ≥ 18 years were included. Study participants who had registered between March 31, 2013 and December 31, 2018 were divided into a develop dataset (80% of total) and an internal validation dataset (20% of total), and those who had registered between January 1, 2019 and December 31, 2019 were assigned to an external validation dataset. Four machine learning methods, including random forest, support vector machine, ElasticNet and extreme gradient boost, were implemented to establish prediction models with the develop dataset, and the ensemble technique was used to build the final prediction model. The prediction performance of the model in the internal validation and the external validation dataset was described with accuracy, area under the receiver-operating characteristic curve, area under the precision-recall curve, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Futhermore, we established multivariable logistic regression models with the develop set and compared prediction performance with the ensemble models. The primary outcome was an unfavorable 1-year neurological outcome. Results: A total of 1,207 patients were included in the study. Among them, 631, 139, and 153were assigned to the develop, the internal validation and the external validation datasets, respectively. Prediction performance metrics for the ensemble prediction model in the internal validation dataset were as follows: accuracy, 0.9620 (95% confidence interval [CI],0.9352–0.9889); area under receiver-operator characteristics curve, 0.9800 (95% CI, 0.9612– 0.9988); area under precision-recall curve, 0.9950 (95% CI, 0.9860–1.0000); sensitivity, 0.9594 (95% CI, 0.9245–0.9943); specificity, 0.9714 (95% CI, 0.9162–1.0000); PPV, 0.9916 (95% CI, 0.9752–1.0000); NPV, 0.8718 (95% CI, 0.7669–0.9767). Prediction performance metrics for the model in the external validation dataset were as follows: accuracy, 0.8509 (95% CI, 0.7825–0.9192); area under receiver-operator characteristics curve, 0.9301 (95% CI, 0.8845–0.9756); area under precision-recall curve, 0.9476 (95% CI, 0.9087–0.9867); sensitivity, 0.9595 (95% CI, 0.9145–1.0000); specificity, 0.6500 (95% CI, 0.5022–0.7978); PPV, 0.8353 (95% CI, 0.7564–0.9142); NPV, 0.8966 (95% CI, 0.7857–1.0000). All the prediction metrics were higher in the ensemble models, except NPVs in both the internal and the external validation datasets. Conclusion We established an ensemble prediction model for prediction of unfavorable 1-year neurological outcomes in OHCA survivors using four machine learning methods. The prediction performance of the ensemble model was higher than the multivariable logistic regression model, while its performance was slightly decreased in the external validation dataset.

BACKGROUND: Non-invasive prenatal testing (NIPT) using cell-free fetal DNA from maternal plasma for fetal aneuploidy identification is expanding worldwide. The objective of this study was to evaluate the clinical utility of NIPT for the detection of trisomies 21, 18, and 13 of high-risk fetus in a large Korean population. METHODS: This study was performed retrospectively, using stored maternal plasma from 1,055 pregnant women with singleton pregnancies who underwent invasive prenatal diagnosis because of a high-risk indication for chromosomal abnormalities. The NIPT results were confirmed by karyotype analysis. RESULTS: Among 1,055 cases, 108 cases of fetal aneuploidy, including trisomy 21 (n = 57), trisomy 18 (n = 42), and trisomy 13 (n = 9), were identified by NIPT. In this study, NIPT showed 100% sensitivity and 99.9% specificity for trisomy 21, and 92.9% sensitivity and 100% specificity for trisomy 18, and 100% sensitivity and 99.9% specificity for trisomy 13. The overall positive predictive value (PPV) was 98.1%. PPVs for trisomies 21, 18, and 13 ranged from 90.0% to 100%. CONCLUSION: This study demonstrates that our NIPT technology is reliable and accurate when applied to maternal DNA samples collected from pregnant women. Further large prospective studies are needed to adequately assess the performance of NIPT.
Aneuploidy ; Chromosome Aberrations ; DNA ; Down Syndrome ; Female ; Fetus ; High-Throughput Nucleotide Sequencing ; Humans ; Karyotype ; Plasma ; Pregnancy ; Pregnant Women ; Prenatal Diagnosis ; Prospective Studies ; Retrospective Studies ; Sensitivity and Specificity ; Trisomy

OBJECTIVE: To investigate whether serum levels of high-density lipoprotein (HDL) and apolipoprotein A-1 (ApoA1), after the return of spontaneous circulation, can predict the neurologic outcome in patients with out-of-hospital cardiac arrest (OHCA). METHODS: This was a retrospective observational study conducted in a single tertiary hospital intensive care unit. All adult OHCA survivors with admission lipid profiles were enrolled from March 2013 to December 2015. Good neurologic outcome was defined as discharge cerebral performance categories 1 and 2. RESULTS: Among 59 patients enrolled, 13 (22.0%) had a good neurologic outcome. Serum levels of HDL (56.7 vs. 40 mg/dL) and ApoA1 (117 vs. 91.6 mg/dL) were significantly higher in patients with a good outcome. Areas under the HDL and ApoA1 receiver operating curves to predict good outcomes were 0.799 and 0.759, respectively. The proportion of good outcome was significantly higher in patients in higher tertiles of HDL and ApoA1 (test for trend, both P=0.003). HDL (P=0.018) was an independent predictor in the multivariate logistic regression model. CONCLUSION: Admission levels of HDL and ApoA1 are associated with neurologic outcome in patients with OHCA. Prognostic and potential therapeutic values of HDL and ApoA1 merit further evaluation in the post-cardiac arrest state, as in other systemic inflammatory conditions such as sepsis.
Adult ; Apolipoprotein A-I* ; Apolipoproteins* ; Cholesterol, HDL ; Heart Arrest ; Humans ; Intensive Care Units ; Lipoproteins* ; Logistic Models ; Observational Study ; Out-of-Hospital Cardiac Arrest* ; Prognosis ; Retrospective Studies ; Sepsis ; Survivors ; Tertiary Care Centers

Necrotizing sialometaplasia usually heals within 4 to 10 weeks with conservative treatment, and rarely recurs. When necrotizing sialometaplasia is present on the hard palate it may occur unilaterally or bilaterally. In this case, necrotizing ulceration occurred on the left hard palate of a 36-year-old woman after root canal treatment of the upper left first premolar under local anesthesia. After only saline irrigation the defect of the lesion completely healed and filled with soft tissue. After 5 months, however, a similar focal necrosis was found on the contralateral hard palate without any dental treatment having been performed on that side and progressed in similar fashion as the former lesion. We conducted an incisional biopsy and obtained a final pathological diagnosis for the palatal mass of necrotizing sialometaplasia. At the 3-year follow-up, the patient's oral mucosa of the hard palate was normal, without any signs and symptoms of the condition. We report a case of a second occurrence of necrotizing sialometaplasia on the contralateral side from the first, with a time lapse between the first and second occurrence.
Adult ; Anesthesia, Local ; Bicuspid ; Biopsy ; Dental Pulp Cavity ; Diagnosis ; Female ; Follow-Up Studies ; Humans ; Mouth Mucosa ; Necrosis ; Palate ; Palate, Hard* ; Recurrence* ; Sialometaplasia, Necrotizing* ; Ulcer

PURPOSE: We aimed to determine the prevalence of anti-cyclic citrullinated peptide (anti-CCP) antibodies in a large group of Korean patients with Behcet's disease (BD), with and without joint involvement, and to compare these findings with the prevalences of anti-CCP antibodies in patients with rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE). MATERIALS AND METHODS: We tested 189 patients with BD, 105 with RA, and 36 with SLE for anti-CCP antibodies and IgM rheumatoid factor in serum. We reviewed the medical records of patients with BD to investigate their personal and clinical characteristics as well as their laboratory test results. RESULTS: Anti-CCP antibodies were detected in seven of the 189 BD patients (3.7%), at a mean titer of 30.6+/-44.4 U/mL, in 86 of the 105 RA patients (81.9%) with a mean titer of 198.8+/-205.7 U/mL, and in nine of the 36 SLE patients (25%) with a mean titer of 180.4+/-113.9 U/mL. One of the seven anti-CCP-positive BD patients fulfilled the diagnostic criteria for both BD and RA. Five of the seven anti-CCP-positive BD patients (71.4%) had polyarticular joint involvement, and the other two patients (28.6%) had oligoarticular involvement. CONCLUSION: We determined the prevalence of anti-CCP antibodies in a large group of Korean BD patients with and without joint involvement. Negative anti-CCP test in patients with BD may help to differentiate BD from RA and SLE, all of which present with similar clinical features.
Adolescent ; Adult ; Aged ; Antibodies/*blood ; Arthritis, Rheumatoid/blood/immunology ; Behcet Syndrome/*blood/immunology ; Child ; Female ; Humans ; Lupus Erythematosus, Systemic/blood/immunology ; Male ; Middle Aged ; Peptides, Cyclic/*immunology ; Young Adult