The applicability of the Phoenix criteria and Phoenix Sepsis Score in higher-resource pediatric intensive care units (PICUs) outside the United States requires further validation. A retrospective cohort study analyzed electronic health records of 1,304 PICU admissions under 18 years old with suspected infection between February 2017 and December 2023. The score was calculated using two methods: 24-hour assessment, based on worst sub-scores within 24 hours of admission, and prompt assessment, using values closest to admission within 6 hours before or after. Based on the 24-hour assessment, in-hospital mortality was 8.3% for sepsis and 10.3% for septic shock. The score demonstrated an area under the precision-recall curve of 0.42 (95% confidence interval, 0.31–0.55) for in-hospital mortality. Results were consistent across both assessment methods. The Phoenix criteria and the Phoenix Sepsis Score are reliable predictors of mortality outcomes. Further investigation in diverse clinical settings is warranted.

The applicability of the Phoenix criteria and Phoenix Sepsis Score in higher-resource pediatric intensive care units (PICUs) outside the United States requires further validation. A retrospective cohort study analyzed electronic health records of 1,304 PICU admissions under 18 years old with suspected infection between February 2017 and December 2023. The score was calculated using two methods: 24-hour assessment, based on worst sub-scores within 24 hours of admission, and prompt assessment, using values closest to admission within 6 hours before or after. Based on the 24-hour assessment, in-hospital mortality was 8.3% for sepsis and 10.3% for septic shock. The score demonstrated an area under the precision-recall curve of 0.42 (95% confidence interval, 0.31–0.55) for in-hospital mortality. Results were consistent across both assessment methods. The Phoenix criteria and the Phoenix Sepsis Score are reliable predictors of mortality outcomes. Further investigation in diverse clinical settings is warranted.

The applicability of the Phoenix criteria and Phoenix Sepsis Score in higher-resource pediatric intensive care units (PICUs) outside the United States requires further validation. A retrospective cohort study analyzed electronic health records of 1,304 PICU admissions under 18 years old with suspected infection between February 2017 and December 2023. The score was calculated using two methods: 24-hour assessment, based on worst sub-scores within 24 hours of admission, and prompt assessment, using values closest to admission within 6 hours before or after. Based on the 24-hour assessment, in-hospital mortality was 8.3% for sepsis and 10.3% for septic shock. The score demonstrated an area under the precision-recall curve of 0.42 (95% confidence interval, 0.31–0.55) for in-hospital mortality. Results were consistent across both assessment methods. The Phoenix criteria and the Phoenix Sepsis Score are reliable predictors of mortality outcomes. Further investigation in diverse clinical settings is warranted.

The applicability of the Phoenix criteria and Phoenix Sepsis Score in higher-resource pediatric intensive care units (PICUs) outside the United States requires further validation. A retrospective cohort study analyzed electronic health records of 1,304 PICU admissions under 18 years old with suspected infection between February 2017 and December 2023. The score was calculated using two methods: 24-hour assessment, based on worst sub-scores within 24 hours of admission, and prompt assessment, using values closest to admission within 6 hours before or after. Based on the 24-hour assessment, in-hospital mortality was 8.3% for sepsis and 10.3% for septic shock. The score demonstrated an area under the precision-recall curve of 0.42 (95% confidence interval, 0.31–0.55) for in-hospital mortality. Results were consistent across both assessment methods. The Phoenix criteria and the Phoenix Sepsis Score are reliable predictors of mortality outcomes. Further investigation in diverse clinical settings is warranted.

OBJECTIVES: The escalating burden of cardiovascular disease (CVD) is a critical public health issue worldwide. CVD, especially acute myocardial infarction (AMI) and stroke, is the leading contributor to morbidity and mortality in Korea. We aimed to develop algorithms for identifying AMI and stroke events from the National Health Insurance Service (NHIS) database and validate these algorithms through medical record review. METHODS: We first established a concept and definition of “hospitalization episode,” taking into account the unique features of health claims-based NHIS database. We then developed first and recurrent event identification algorithms, separately for AMI and stroke, to determine whether each hospitalization episode represents a true incident case of AMI or stroke. Finally, we assessed our algorithms’ accuracy by calculating their positive predictive values (PPVs) based on medical records of algorithm- identified events. RESULTS: We developed identification algorithms for both AMI and stroke. To validate them, we conducted retrospective review of medical records for 3,140 algorithm-identified events (1,399 AMI and 1,741 stroke events) across 24 hospitals throughout Korea. The overall PPVs for the first and recurrent AMI events were around 92% and 78%, respectively, while those for the first and recurrent stroke events were around 88% and 81%, respectively. CONCLUSIONS We successfully developed algorithms for identifying AMI and stroke events. The algorithms demonstrated high accuracy, with PPVs of approximately 90% for first events and 80% for recurrent events. These findings indicate that our algorithms hold promise as an instrumental tool for the consistent and reliable production of national CVD statistics in Korea.

Cardio-oncology is a critical field due to the escalating significance of cardiovascular toxicity as a side effect of anti‑ cancer treatments. Cancer therapy-related cardiac dysfunction (CTRCD) is a prevalent condition associated with car‑ diovascular toxicity, necessitating effective strategies for prediction, monitoring, management, and tracking. This comprehensive review examines the definition and risk stratification of CTRCD, explores monitoring approaches during anticancer therapy, and highlights specific cardiovascular toxicities linked to various cancer treatments. These include anthracyclines, HER2-targeted agents, vascular endothelial growth factor inhibitors, immune checkpoint inhibitors, chimeric antigen receptor T-cell therapies, and tumor-infiltrating lymphocytes therapies. Incorporating the Korean data, this review offers insights into the regional nuances in managing CTRCD. Using systematic follow-up incorporating cardiovascular imaging and biomarkers, a better understanding and management of CTRCD can be achieved, optimizing the cardiovascular health of both cancer patients and survivors.

Purpose: Digital Imaging and Communications in Medicine (DICOM), a standard file format for medical imaging data, contains metadata describing each file. However, metadata are often incomplete, and there is no standardized format for recording metadata, leading to inefficiency during the metadata-based data retrieval process. Here, we propose a novel standardization method for DICOM metadata termed the Radiology Common Data Model (R-CDM). Materials and Methods: R-CDM was designed to be compatible with Health Level Seven International (HL7)/Fast Healthcare Interoperability Resources (FHIR) and linked with the Observational Medical Outcomes Partnership (OMOP)-CDM to achieve a seamless link between clinical data and medical imaging data. The terminology system was standardized using the RadLex playbook, a comprehensive lexicon of radiology. As a proof of concept, the R-CDM conversion process was conducted with 41.7 TB of data from the Ajou University Hospital. The R-CDM database visualizer was developed to visualize the main characteristics of the R-CDM database. Results: Information from 2801360 cases and 87203226 DICOM files was organized into two tables constituting the R-CDM. Information on imaging device and image resolution was recorded with more than 99.9% accuracy. Furthermore, OMOP-CDM and RCDM were linked to efficiently extract specific types of images from specific patient cohorts. Conclusion R-CDM standardizes the structure and terminology for recording medical imaging data to eliminate incomplete and unstandardized information. Successful standardization was achieved by the extract, transform, and load process and image classifier. We hope that the R-CDM will contribute to deep learning research in the medical imaging field by enabling the securement of large-scale medical imaging data from multinational institutions.

A retrospective observational study is one of the most widely used research methods in medicine. However, evidence postulated from a single data source likely contains biases such as selection bias, information bias, and confounding bias. Acquiring enough data from multiple institutions is one of the most effective methods to overcome the limitations.However, acquiring data from multiple institutions from many countries requires enormous effort because of financial, technical, ethical, and legal issues as well as standardization of data structure and semantics. The Observational Health Data Sciences and Informatics (OHDSI) research network standardized 928 million unique records or 12% of the world’s population into a common structure and meaning and established a research network of 453 data partners from 41 countries around the world. OHDSI is a distributed research network wherein researchers do not own or directly share data but only analyzed results. However, sharing evidence without sharing data is difficult to understand. In this review, we will look at the basic principles of OHDSI, common data model, distributed research networks, and some representative studies in the cardiovascular field using the network. This paper also briefly introduces a Korean distributed research network named FeederNet.

Objectives: We incorporated the Korean Electronic Data Interchange (EDI) vocabulary into Observational Medical Outcomes Partnership (OMOP) vocabulary using a semi-automated process. The goal of this study was to improve the Korean EDI as a standard medical ontology in Korea. Methods: We incorporated the EDI vocabulary into OMOP vocabulary through four main steps. First, we improved the current classification of EDI domains and separated medical services into procedures and measurements. Second, each EDI concept was assigned a unique identifier and validity dates. Third, we built a vertical hierarchy between EDI concepts, fully describing child concepts through relationships and attributes and linking them to parent terms. Finally, we added an English definition for each EDI concept. We translated the Korean definitions of EDI concepts using Google.Cloud.Translation.V3, using a client library and manual translation. We evaluated the EDI using 11 auditing criteria for controlled vocabularies. Results: We incorporated 313,431 concepts from the EDI to the OMOP Standardized Vocabularies. For 10 of the 11 auditing criteria, EDI showed a better quality index within the OMOP vocabulary than in the original EDI vocabulary. Conclusions The incorporation of the EDI vocabulary into the OMOP Standardized Vocabularies allows better standardization to facilitate network research. Our research provides a promising model for mapping Korean medical information into a global standard terminology system, although a comprehensive mapping of official vocabulary remains to be done in the future.