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: Postpartum depression (PPD) can negatively affect infant well-being and child development. Although the frequency and risk factors of PPD symptoms might vary depending on the country and culture, there is limited research on these risk factors among Korean women. This study aimed to elucidate the potential risk factors of PPD throughout pregnancy to help improve PPD screening and prevention in Korean women. Methods: The pregnant women at 12 gestational weeks (GW) were enrolled from two obstetric specialized hospitals from March 2013 to November 2017. A questionnaire survey was administered at 12 GW, 24 GW, 36 GW, and 4 weeks postpartum. Depressive symptoms were assessed using the Edinburgh Postnatal Depression Scale, and PPD was defined as a score of ≥ 10. Results: PPD was prevalent in 16.3% (410/2,512) of the participants. Depressive feeling at 12 GW and postpartum factors of stress, relationship with children, depressive feeling, fear, sadness, and neonatal intensive care unit admission of baby were significantly associated with a higher risk of PPD. Meanwhile, high postpartum quality of life and marital satisfaction at postpartum period were significantly associated with a lower risk of PPD. We developed a model for predicting PPD using factors as mentioned above and it had an area under the curve of 0.871. Conclusion Depressive feeling at 12 GW and postpartum stress, fear, sadness, relationship with children, low quality of life, and low marital satisfaction increased the risk of PPD. A risk model that comprises significant factors can effectively predict PPD and can be helpful for its prevention and appropriate treatment.

This study aimed to evaluate the differences in the baseline characteristics and patterns of antibiotic usage among hospitals based on their participation in the Korea National Antimicrobial Use Analysis System (KONAS). We obtained claims data from the National Health Insurance for inpatients admitted to all secondary- and tertiary-care hospitals between January 2020 and December 2021 in Korea. 15.9% (58/395) of hospitals were KONAS participants, among which the proportion of hospitals with > 900 beds (31.0% vs.2.6%, P < 0.001) and tertiary care (50.0% vs. 5.2%, P < 0.001) was higher than that among non-participants. The consumption of antibiotics targeting antimicrobial-resistant gram positive bacteria (33.7 vs. 27.1 days of therapy [DOT]/1,000 patient-days, P = 0.019) and antibiotics predominantly used for resistant gram-negative bacteria (4.8 vs. 3.7 DOT/1,000 patient-days, P = 0.034) was higher in KONAS-participating versus -non-participating hospitals. The current KONAS data do not fully represent all secondary- and tertiary-care hospitals in Korea; thus, the KONAS results should be interpreted with caution.

Background: The optimal level of glycosylated hemoglobin (HbA1c) to prevent adverse clinical outcomes is unknown in patients with chronic kidney disease (CKD) and type 2 diabetes mellitus (T2DM). Methods: We analyzed 707 patients with CKD G1-G5 without kidney replacement therapy and T2DM from the KoreaN Cohort Study for Outcome in Patients With Chronic Kidney Disease (KNOW-CKD), a nationwide prospective cohort study. The main predictor was time-varying HbA1c level at each visit. The primary outcome was a composite of development of major adverse cardiovascular events (MACEs) or all-cause mortality. Secondary outcomes included the individual endpoint of MACEs, all-cause mortality, and CKD progression. CKD progression was defined as a ≥50% decline in the estimated glomerular filtration rate from baseline or the onset of end-stage kidney disease. Results: During a median follow-up of 4.8 years, the primary outcome occurred in 129 (18.2%) patients. In time-varying Cox model, the adjusted hazard ratios (aHRs) for the primary outcome were 1.59 (95% confidence interval [CI], 1.01 to 2.49) and 1.99 (95% CI, 1.24 to 3.19) for HbA1c levels of 7.0%–7.9% and ≥8.0%, respectively, compared with <7.0%. Additional analysis of baseline HbA1c levels yielded a similar graded association. In secondary outcome analyses, the aHRs for the corresponding HbA1c categories were 2.17 (95% CI, 1.20 to 3.95) and 2.26 (95% CI, 1.17 to 4.37) for MACE, and 1.36 (95% CI, 0.68 to 2.72) and 2.08 (95% CI, 1.06 to 4.05) for all-cause mortality. However, the risk of CKD progression did not differ between the three groups. Conclusion This study showed that higher HbA1c levels were associated with an increased risk of MACE and mortality in patients with CKD and T2DM.

Sudden death during or after percutaneous coronary intervention (PCI) could be led to potential medicolegal disputes. This study aimed to investigate the clinical and postmortem findings in PCI-related deaths-focusing on the current statusto inform preventive strategies against these fatalities. Forty-three cases were retrieved from the National Forensic Service's postmortem records between 2015 and 2021, and the corresponding postmortem findings and clinical information were analyzed. The analyses revealed a relatively consistent annual incidence of PCI-related deaths. Immediate deaths during or shortly after PCI occurred in 17 cases (39.5%), and delayed PCI-related deaths after discharge from the hospital occurred in 26 cases (60.5%). The causes of PCI-related deaths in the postmortem cases were categorized into four groups: PCI complications (11 cases, 26%), acute myocardial infarction (23 cases, 53%), ischemic heart disease (8 cases, 19%), and others (1 case, 2%). Postmortem examinations played a critical role in determining the cause of death and obtaining medical evidence, including pathological findings of the heart as well as those of coronary artery and stent insertion. Our findings suggest that a detailed examination of the heart, coronary arteries, stent status, and atherosclerosis in PCI-related deaths could help provide more accurate information as medical evidence and prevent/resolve potential medicolegal issues. Further, this could advance our understanding of PCI-related deaths and inform future preventive strategies.

We experienced a case of disseminated Staphylococcus aureus infection with bacterial pericarditis that progressed to septic shock and multiorgan failure despite pericardiocentesis and surgical removal of the original abscess with intensive antibiotic therapy. We report this case because of the patient’s very rare and remarkable echocardiographic findings and highly turbid pericardial effusion.

Neutralizing antibodies targeted at the receptor-binding domain of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein have been developed and now under evaluation in clinical trials. The US Food and Drug Administration currently issued emergency use authorizations for neutralizing monoclonal antibodies in non-hospitalized patients with mild to moderate coronavirus disease 2019 (COVID-19) who are at high risk for progressing to severe disease and/or hospitalization. In terms of this situation, there is an urgent need to investigate the clinical aspects and to develop strategies to deploy them effectively in clinical practice. Here we provide guidance for the use of anti-SARS-CoV-2 monoclonal antibodies for the treatment of COVID-19 based on the latest evidence.

Despite the global effort to mitigate the spread, coronavirus disease 2019 (COVID-19) has become a pandemic that took more than 2 million lives. There are numerous ongoing clinical studies aiming to find treatment options and many are being published daily. Some effective treatment options, albeit of variable efficacy, have been discovered. Therefore, it is necessary to develop an evidence-based methodology, to continuously check for new evidence, and to update recommendations accordingly. Here we provide guidelines on pharmaceutical treatment for COVID-19 based on the latest evidence.