Advanced insights into the pathophysiology and imaging features of COVID-19 along with vaccine benefits have improved patient care, but emerging knowledge of post–COVID-19 condition (long COVID) al... COVID-19 has emerged as a pandemic leading to a global public health crisis of unprecedented morbidity. A comprehensive insight into the imaging of COVID-19 has enabled early diagnosis, stratificat...

Kim SH, et al. J Korean Med Sci. 2021 Apr;36(14):e107. https://doi.org/10.3346/jkms.2021.36.e107

Background: The epidemiology of bloodstream infection (BSI) is well-established; however, little is known about the contribution of different pathogens to mortality. To understand true burden of BSI, pathogens contributing to mortality were investigated and compared according to where the BSI was acquired.Methods: Data from deceased patients in two teaching hospitals in the Republic of Korea were collected. BSI contributing mortality was defined as BSI within 2-weeks before death. Cases were grouped by acquisition sites: community-acquired (CA)-, healthcare-associated (HCA)-, and hospital-acquired (HA)-BSI. Drug resistance, BSI focus, and appropriateness of empirical antimicrobial therapy were also compared.Results: Among 1849 deceased patients in the hospitals, 280 (15.1%) patients experienced BSI within 2-weeks before death. In all, 71, 53, and 156 patients in the CA-, HCA-, and HA-BSI groups, respectively, with 316 total isolated pathogens were analyzed. The three most common pathogens wereKlebsiella pneumoniae(17.1%), Escherichia coli(16.4%), andStaphylococcus aureus(11.4%). WhileK. pneumoniaeandE. coliwere the most common pathogens in CA- and HCA-BSI,Acinetobacter baumanniiandCandidaspecies were in HA-BSI. 26.3% (41/156) of patients experienced breakthrough HCA-BSI during administration of carbapenem and/or vancomycin. The proportion of central venous catheter-related infection (0%, 3.4% and 28.3%), carbapenem resistant-Gram negative bacilli (0%, 6.9% and 21.9%), and inappropriate empirical antimicrobial therapy (21.1%, 37.7% and 51.9%; all P < 0.001) were more frequently observed in HA-BSI.Conclusion: The epidemiology of BSI related to mortality had unique characteristics according to the acquisition site. Given the epidemiology of HA-BSI, infection control and antibiotics stewardship programs should be emphasized.

Kim SH, et al. J Korean Med Sci. 2022 Apr;37(18):e134. https://doi.org/10.3346/jkms.2022.37.e134

Background: Appropriate empirical treatment for candidemia is associated with reduced mortality; however, the timely diagnosis of candidemia in patients with sepsis remains poor. Objective: We aimed to use machine learning algorithms to develop and validate a candidemia prediction model for patients with cancer. Methods: We conducted a single-center retrospective study using the cancer registry of a tertiary academic hospital. Adult patients diagnosed with malignancies between January 2010 and December 2018 were included. Our study outcome was the prediction of candidemia events. A stratified undersampling method was used to extract control data for algorithm learning. Multiple models were developed—a combination of 4 variable groups and 5 algorithms (auto-machine learning, deep neural network, gradient boosting, logistic regression, and random forest). The model with the largest area under the receiver operating characteristic curve (AUROC) was selected as the Candida detection (CanDETEC) model after comparing its performance indexes with those of the Candida Score Model. Results: From a total of 273,380 blood cultures from 186,404 registered patients with cancer, we extracted 501 records of candidemia events and 2000 records as control data. Performance among the different models varied (AUROC 0.771- 0.889), with all models demonstrating superior performance to that of the Candida Score (AUROC 0.677). The random forest model performed the best (AUROC 0.889, 95% CI 0.888-0.889); therefore, it was selected as the CanDETEC model. Conclusions: The CanDETEC model predicted candidemia in patients with cancer with high discriminative power. This algorithm could be used for the timely diagnosis and appropriate empirical treatment of candidemia.