Yonsei Medical Journal最新文献

Purpose: Point-of-care ultrasound (POCUS) is widely used for heart function evaluation in emergency departments (EDs), but requires specific equipment and skilled operators. This study evaluates the diagnostic accuracy of a mobile application for estimating left ventricular (LV) systolic dysfunction [left ventricular ejection fraction (LVEF) <40%] using electrocardiography (ECG) and tests its non-inferiority to POCUS.

Materials and methods: Patients (aged ≥20 years) were included if they had both a POCUS-based EF evaluation and an ECG within 24 hours of their ED visit between January and May 2022, along with formal echocardiography within 2 weeks before or after the visit. A mobile app (ECG Buddy, EB) estimated LVEF (EF from EB) and the risk of LV dysfunction (LV-Dysfunction score) from ECG waveforms, which were compared to NT-proBNP levels and POCUS-evaluated LVEF (EF from POCUS). A non-inferiority margin was set at an area under the curve (AUC) difference of 0.05.

Results: Of the 181 patients included, 37 (20.4%) exhibited LV dysfunction. The AUCs for screening LV dysfunction using POCUS and NT-proBNP were 0.885 and 0.822, respectively. EF from EB and LV-Dysfunction score outperformed NT-proBNP, with AUCs of 0.893 and 0.884, respectively (p=0.017 and p=0.030, respectively). EF from EB was non-inferior to EF from POCUS, while LV-Dysfunction score narrowly missed the mark. A subgroup analysis of sinus-origin rhythm ECGs supported the non-inferiority of both EF from EB and LV-Dysfunction score to EF from POCUS.

Conclusion: A smartphone application that analyzes ECG image can screen for LV dysfunction with a level of accuracy comparable to that of POCUS.

Purpose: Rare diseases occur in <50 per 100000 people and require lifelong management. However, essential epidemiological data on such diseases are lacking, and a consecutive monitoring system across time and regions remains to be established. Standardized digital phenotypes are required to leverage an international data network for research on rare endocrine diseases. We developed digital phenotypes for rare endocrine diseases using the observational medical outcome partnership common data model.

Materials and methods: Digital phenotypes of three rare endocrine diseases (medullary thyroid cancer, hypoparathyroidism, pheochromocytoma/paraganglioma) were validated across three databases that use different vocabularies: Severance Hospital's electronic health record from South Korea; IQVIA's United Kingdom (UK) database for general practitioners; and IQVIA's United States (US) hospital database for general hospitals. We estimated the performance of different digital phenotyping methods based on International Classification of Diseases (ICD)-10 in the UK and the US or systematized nomenclature of medicine clinical terms (SNOMED CT) in Korea.

Results: The positive predictive value of digital phenotyping was higher using SNOMED CT-based phenotyping than ICD-10-based phenotyping for all three diseases in Korea (e.g., pheochromocytoma/paraganglioma: ICD-10, 58%-62%; SNOMED CT, 89%). Estimated incidence rates by digital phenotyping were as follows: medullary thyroid cancer, 0.34-2.07 (Korea), 0.13-0.30 (US); hypoparathyroidism, 0.40-1.20 (Korea), 0.59-1.01 (US), 0.00-1.78 (UK); and pheochromocytoma/paraganglioma, 0.95-1.67 (Korea), 0.35-0.77 (US), 0.00-0.49 (UK).

Conclusion: Our findings demonstrate the feasibility of developing digital phenotyping of rare endocrine diseases and highlight the importance of implementing SNOMED CT in routine clinical practice to provide granularity for research.

Mitochondrial diseases (MDs) are genetic disorders with diverse phenotypes that affect high-energy-demand organs, notably the central nervous system and muscles. Epilepsy is a common comorbidity, affecting 40%-60% of patients with MDs and significantly reducing their quality of life. This review discusses the different treatment modalities for epilepsy in patients with MDs. Advances in genetic sequencing have identified specific mutations in mitochondrial and nuclear DNA, enabling more precise diagnoses and tailored therapeutic strategies. Anti-seizure medications and dietary interventions, such as ketogenic diets and their variants, have been effective in reducing seizures and improving mitochondrial function. Emerging treatments include gene therapy, mitochondrial transplantation, and antioxidants such as EPI-743, which protect mitochondrial integrity and improve neurological function. Additionally, therapies that promote mitochondrial biogenesis, such as bezafibrate and epicatechin, are being explored for their potential to enhance mitochondrial proliferation and energy production. Gene therapy aims to correct genetic defects underlying MDs. Techniques like mitochondrial gene replacement and using viral vectors to deliver functional genes have shown promise in preclinical studies. Mitochondrial transplantation, an emerging experimental technique, involves transferring healthy mitochondria into cells with dysfunctional mitochondria. This technique has been demonstrated to restore mitochondrial function and energy metabolism in preclinical models. Patient-derived induced pluripotent stem cells can model specific mitochondrial dysfunctions in vitro, allowing for the testing of various treatments tailored to individual genetic and biochemical profiles. The future of mitochondrial medicine is promising, with the development of more targeted and personalized therapeutic strategies offering hope for improved management and prognosis of mitochondrial epilepsy.

Purpose: To investigate the association between soluble suppressor of tumorigenicity 2 (sST2) levels and cardiovascular disease predictors in patients with gout.

Materials and methods: We retrospectively reviewed the medical records of patients with gout who were tested for sST2 but did not receive uric acid-lowering therapy. These patients were classified into elevated and normal sST2 groups using a cut-off of >49.6 ng/mL and >35.4 ng/mL in males and females, respectively. Correlations between clinical and laboratory variables, sST2 levels, and elevated sST2 level predictors were assessed using linear and logistic regression analyses.

Results: Notably, 27 (11.3%) and 211 (88.7%) of the 238 identified patients had elevated and normal sST2 levels, respectively. Linear regression analysis revealed that male sex (β=-0.190, p=0.002), body mass index (BMI) (β=-0.184, p=0.002), white blood cell count (β=0.231, p<0.001), C-reactive protein (β=0.135, p=0.031), and fasting blood glucose (β=0.210, p<0.001) were independently associated with sST2 levels. In multivariate logistic regression analysis, male sex [odds ratio (OR) 0.112, p=0.001], BMI (OR 0.836, p=0.008), creatinine (OR 5.730, p=0.024), and fasting blood glucose (OR 1.042, p=0.002) predicted elevated sST2 levels. Patients with increased sST2 levels had a significantly higher atherosclerotic cardiovascular disease risk score and a greater proportion of high-risk Framingham Risk Score compared to the normal sST2 group (p=0.002 and p<0.001).

Conclusion: Patients with gout and elevated sST2 levels have a higher risk of future cardiovascular disorders, which may provide insights into risk stratification and the implementation of intervention strategies.

Purpose: Adjuvant treatment for craniopharyngioma after surgery is controversial. Adjuvant external beam radiation therapy (EBRT) can increase the risk of long-term sequelae. Stereotactic radiosurgery (SRS) is used to reduce treatment-related toxicity. In this study, we compared the treatment outcomes and toxicities of adjuvant therapies for craniopharyngioma.

Materials and methods: We analyzed patients who underwent craniopharyngioma tumor removal between 2000 and 2017. Of the 153 patients, 27 and 20 received adjuvant fractionated EBRT and SRS, respectively. We compared the local control (LC), progression-free survival (PFS), and overall survival between groups that received adjuvant fractionated EBRT, SRS, and surveillance.

Results: The median follow-up period was 77.7 months. For SRS and surveillance, the 10-year LC was 57.2% and 57.4%, respectively. No local progression was observed after adjuvant fractionated EBRT. One patient in the adjuvant fractionated EBRT group died owing to glioma 94 months after receiving radiotherapy (10-year PFS: 80%). The 10-year PFS was 43.6% and 50.7% in the SRS and surveillance groups, respectively. The treatment outcomes significantly differed according to adjuvant treatment in non-gross total resection (GTR) patients. Additional treatment-related toxicity was comparable in the adjuvant fractionated EBRT and other groups.

Conclusion: Adjuvant fractionated EBRT could be effective in controlling local failure, especially in patients with non-GTR, while maintaining acceptable treatment-related toxicity.

This paper develops a new model for forecasting potential shortfalls in the healthcare sector, providing an economically grounded framework for projections. The model is applied to assess potential doctor shortages in South Korea over the next decade under reasonable economic scenarios. Our analysis indicates that demand for healthcare, driven by aging-related factors, is projected to grow at an annual rate of 1.3% to 1.9%. In contrast, the supply of healthcare-bolstered by technological advancements, improved medical equipment, and natural growth in the doctor workforce-is expected to increase by 3.2% annually. These findings suggest that South Korea's healthcare system is likely to meet future demand without necessitating an expansion of medical school admissions.

Purpose: This study assessed the validity of the hospital standardized mortality ratio (HSMR) risk-adjusted model by comparing models that include clinical information and the current model based on administrative information in South Korea.

Materials and methods: The data of 53976 inpatients were analyzed. The current HSMR risk-adjusted model (Model 1) adjusts for sex, age, health coverage, emergency hospitalization status, main diagnosis, surgery status, and Charlson Comorbidity Index (CCI) using administrative data. As candidate variables, among clinical information, the American Society of Anesthesiologists score, Acute Physiology and Chronic Health Evaluation (APACHE) II, Simplified Acute Physiology Score (SAPS) 3, present on admission CCI, and cancer stage were collected. Surgery status, intensive care in the intensive care unit, and CCI were selected as proxy variables among administrative data. In-hospital death was defined as the dependent variable, and a logistic regression analysis was performed. The statistical performance of each model was compared using C-index values.

Results: There was a strong correlation between variables in the administrative data and those in the medical records. The C-index of the existing model (Model 1) was 0.785; Model 2, which included all clinical data, had a higher C-index of 0.857. In Model 4, in which APACHE II and SAPS 3 were replaced with variables recorded in the administrative data from Model 2, the C-index further increased to 0.863.

Conclusion: The HSMR assessment model improved when clinical data were adjusted. Simultaneously, the validity of the evaluation method could be secured even if some of the clinical information was replaced with the information in the administrative data.

Purpose: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries.

Materials and methods: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an open-source registry. PIH was defined as sustained mean arterial pressure (MAP) <65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers.

Results: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769; 95% confidence interval (CI), 0.767-0.771], followed by the gradient boosting regressor (0.768; 95% CI, 0.763-0.772), AdaBoost regressor (0.752; 95% CI, 0.743-0.761), and automatic relevance determination regression (0.685; 95% CI, 0.669-0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all p<0.001).

Conclusion: ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.

Purpose: Early identification of patients at risk for acute respiratory failure (ARF) could help clinicians devise preventive strategies. Analyzing biosignals with artificial intelligence (AI) can uncover hidden information and variability within time series. We aimed to develop and validate AI models to predict ARF within 72 h after emergency department admission, primarily using high-resolution biosignals collected within 4 h of arrival.

Materials and methods: Our AI model, built on convolutional recurrent neural networks, combines biosignal feature extraction and sequence modeling. The model was developed and internally validated with data from 5284 admissions [1085 (20.5%) positive for ARF], and externally validated using data from 144 admissions [7 (4.9%) positive for ARF] from another institution. We defined ARF as the application of advanced respiratory support devices.

Results: Our AI model performed well in predicting ARF, achieving area under the receiver operating characteristic curve (AUROC) of 0.840 and 0.743 in internal and external validations, respectively. It outperformed the Modified Early Warning Score (MEWS) and XGBoost models built only with clinical variables. High predictive ability for mortality was observed, with AUROC up to 0.809. A 10% increase in AI prediction scores was associated with 1.44-fold and 1.42-fold increases in ARF risk and mortality risk, respectively, even after adjusting for MEWS and demographic variables.

Conclusion: Our AI model demonstrates high predictive accuracy and significant associations with clinical outcomes. Our AI model has the potential to promptly aid in triage decisions. Our study shows that using AI to analyze biosignals advances disease detection and prediction.

Purpose: Obesity and metabolic syndrome are acknowledged as key factors contributing to the development of non-alcoholic fatty liver disease (NAFLD). Vitamin D (VitD) is a multifaceted secosteroid hormone known for its anti-fibrotic and anti-inflammatory properties, with its deficiency often linked to obesity. Our study aimed to investigate whether VitD supplementation could mitigate the liver pathology associated with NAFLD.

Materials and methods: The NAFLD model was developed by subjecting male C57BL/6 mice to a high-fat diet (HFD) for 14 weeks. These mice were supplemented with VitD through intraperitoneal injection at a dosage of 7 µg/kg, administered three times per week for 7 weeks.

Results: HFD resulted in VitD deficiency, insulin resistance, and increased liver weight. It elevated serum levels of liver aminotransferases and triglyceride, ultimately leading to steatohepatitis with fibrosis. This model exhibited increased levels of transforming growth factor (TGF)-β1, pro-inflammatory cytokines, HNF4α transcription factors, reactive oxygen species (ROS), renin-angiotensin system activity, and epithelial-mesenchymal transitions (EMT) within the liver. Supplementation with VitD resulted in the recovery of liver weight, improvement in histologic features associated with steatohepatitis, and reduction in alanine aminotransferases and triglyceride levels induced by the HFD. Additionally, it mitigated the HFD-induced over-expressions of TGF-β1 and fibrosis-related genes, along with pro-inflammatory cytokines and ROS. Notably, no adverse effect was found due to VitD supplementation in this model.

Conclusion: VitD ameliorates steatohepatitis within obesity-induced NAFLD through its multifaceted pathways. VitD supplementation emerges as a potentially safe, cost-effective, and direct treatment approach for NAFLD patients dealing with obesity or metabolic dysfunction.