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Impact of CT-Defined Sarcopenia on Clinical Outcomes in Elderly Trauma Patients: A Retrospective Korean Cohort Study
No full textBackground/Objectives: Sarcopenia, the age-related decline in skeletal muscle mass and function, is increasingly recognized as an important prognostic factor among elderly patients. This study aimed to evaluate whether computed tomography (CT)-defined sarcopenia independently predicts short-term mortality in elderly Korean trauma patients. Methods: We retrospectively analyzed 722 patients aged >/=65 years admitted to a Korean Level I trauma center between January 2020 and December 2021. Sarcopenia was defined as the lowest sex-specific quartile of skeletal muscle index (SMI) measured at the third lumbar vertebra (L3) within 7 days of admission. Demographics, injury severity, and outcome variables were compared between groups. Kaplan-Meier survival analysis with a 24 h landmark and multivariable Cox regression were applied to identify independent predictors of 30-day mortality. Results: Among 722 patients, 181 (25.1%) were sarcopenic. They were older and had lower body mass index and serum albumin yet showed lower Injury Severity Score (ISS) at presentation. Despite this, in-hospital mortality was higher in sarcopenic patients (15.5% vs. 9.8%, p = 0.036), while 24 h mortality did not differ (4.4% vs. 3.7%, p = 0.663). Landmark analysis starting at 24 h demonstrated significantly worse 30-day survival in the sarcopenia group (log-rank p = 0.028). Multivariable Cox regression confirmed sarcopenia as an independent predictor of 30-day mortality (HR, 2.36; 95% CI, 1.07-5.23; p = 0.034), along with higher ISS and lower Glasgow Coma Scale (GCS) scores. Conclusions: CT-defined sarcopenia at the L3 level independently predicts 30-day mortality in elderly trauma patients and may support early risk stratification
NeuroFANN: Identification of neuropathological subtypes in dementia with plasma proteins by using functionally annotated neural network
No full textDementia diagnosis relies on identifying neuropathological features, such as beta-amyloid (Abeta) deposition, medial temporal lobe atrophy (MTA), and white matter hyperintensity (WMH). Recently, plasma protein biomarkers have emerged as a cost-effective and less invasive tool for identifying neuropathological features, enhanced by machine learning (ML) for precise diagnosis. However, most ML studies fail to account for protein-protein interactions (PPIs) and synergetic effects between proteins, overlooking their collective contributions to disease mechanisms. Additionally, the lack of consideration for functional properties may result in the redundant and imbalanced representation of proteins and their functions, potentially limiting the effectiveness of dementia diagnosis. In this study, we propose NeuroFANN, a method designed to classify three neuropathological subtypes in dementia-positivity for Abeta, MTA, and WMH-using plasma protein biomarkers. A key feature of NeuroFANN is the combination of the PPI network-based synergetic effects with the functional annotation-based protein biomarker clustering. NeuroFANN extracts synergetic effects by propagating independent effects of proteins across the PPI network, which are then aggregated in functional protein clusters, thereby enabling global PPI awareness and capturing the biological properties of protein biomarkers. From a South Korean cohort, 54 proteins were identified as plasma protein biomarkers for dementia subtypes and grouped into 16 clusters. NeuroFANN outperformed comparison methods in classifying dementia subtypes, with its core components validated as key contributors to superior performance. Additionally, the risk scores predicted by NeuroFANN showed a strong association with longitudinal cognitive decline, demonstrating its potential as a valuable diagnostic tool in clinical settings
Comparative Effectiveness and Safety of Moderate-Intensity Pravastatin Versus Atorvastatin in Patients with Dyslipidemia: A Retrospective Cohort Study Using a Common Data Model of Multicenter Electronic Health Records in South Korea
No full textAIM: To compare the effectiveness and safety of moderate-intensity pravastatin 40 mg/day and atorvastatin 10 mg/day in patients with dyslipidemia. METHODS: We conducted a retrospective cohort study using electronic health records of 19 million patients across 14 secondary/tertiary hospitals, standardized to a Common Data Model. New users of pravastatin (40 mg/day) and atorvastatin (10 mg/day) were identified. Six distinct cohorts were used to assess the comparative effectiveness in preventing major adverse cardiovascular events (MACE) and the risks of new-onset diabetes mellitus (NODM), myalgia or rhabdomyolysis, and hepatotoxicity (measured by aspartate aminotransferase [AST]/alanine aminotransferase [ALT]). Propensity score matching (PSM) was applied to each cohort for effectiveness and safety analyses, followed by a meta-analysis of hospital-specific results. RESULTS: After PSM, patients were equally assigned to the pravastatin and atorvastatin groups for primary (n = 2,688/group) and secondary MACE prevention (n = 1,258/group) and to assess the risk of NODM (n = 2,391/group), new-onset myalgia or rhabdomyolysis (n = 11,799/group), and hepatotoxicity (AST, n = 4,034/group; ALT, n = 3,655/group). No significant differences were observed in the hazard ratios (HRs) for primary (HR = 0.84; 95% CI, 0.59-1.20) and secondary MACE prevention (HR = 0.89; 95% CI, 0.68-1.16). Similarly, no significant difference was observed in the risk of NODM (HR, 0.99; 95% CI, 0.79-1.23). The risk of new-onset myalgia/rhabdomyolysis (HR = 0.82, 95% CI, 0.69-0.96) and the incidence of abnormal elevations in AST levels (2.35% vs. 3.37%, p＜0.05) were significantly lower in the pravastatin group. CONCLUSION: Moderate-intensity pravastatin (40 mg/day) showed comparable effectiveness to moderate-intensity atorvastatin (10 mg/day) in preventing MACE with a more favorable safety profile
Using a Deep Learning-Based Decision Support System to Predict Emergent Large Vessel Occlusion Using Non-Contrast Computed Tomography
No full textBackground: This retrospective, multi-reader, blinded, pivotal trial assessed the performance of artificial intelligence (AI)-based clinical decision support system used to improve the clinician detection of emergent large vessel occlusion (ELVO) using brain non-contrast computed tomography (NCCT) images. Methods: We enrolled 477 patients, of which 112 had anterior circulation ELVO, and 365 served as controls. First, patients were evaluated by the consensus of four clinicians without AI assistance through the identification of ELVO using NCCT images. After a 2-week washout period, the same investigators performed an AI-assisted evaluation. The primary and secondary endpoints in ELVO prediction between unassisted and assisted readings were sensitivity and specificity and AUROC and individual-level sensitivity and specificity, respectively. The standalone predictive ability of the AI system was also analyzed. Results: The assisted evaluations resulted in higher sensitivity and specificity than the unassisted evaluations at 75.9% vs. 92.0% (p < 0.01) and 83.0% vs. 92.6% (p < 0.01) while also resulting in higher accuracy and AUROC at 81.3% vs. 92.5%, (p < 0.01) and 0.87 [95% CI: 0.84-0.90] vs. 0.95 [95% CI: 0.93-0.97] (p < 0.01). Furthermore, the AI system improved sensitivity and specificity for three and four readers, respectively, and had a standalone sensitivity of 88.4% (95% CI: 81.0-93.7) and a specificity of 91.2% (95% CI: 87.9-93.9). Conclusions: This study shows that an AI-based clinical decision support system can improve the clinical detection of ELVO using NCCT. Moreover, the AI system may facilitate acute stroke reperfusion therapy by assisting physicians in the initial triaging of patients, particularly in thrombectomy-incapable centers
REV-ERBα regulates brain NAD+ levels and tauopathy via an NFIL3–CD38 axis
No full textNicotinamide adenine dinucleotide (NAD(+)) is a critical metabolic co-enzyme implicated in brain aging, and augmenting NAD(+) levels in the aging brain is an attractive therapeutic strategy for neurodegeneration. However, the molecular mechanisms of brain NAD(+) regulation are incompletely understood. In cardiac tissue, the circadian nuclear receptor REV-ERBalpha has been shown to regulate NAD(+) via control of the NAD(+)-producing enzyme NAMPT. Here we show that REV-ERBalpha controls brain NAD(+) levels through a distinct pathway involving NFIL3-dependent suppression of the NAD(+)-consuming enzyme CD38, particularly in astrocytes. REV-ERBalpha deletion does not affect NAMPT expression in the brain and has an opposite effect on NAD(+) levels as in the heart. Astrocytic REV-ERBalpha deletion augments brain NAD(+) and prevents tauopathy in P301S mice. Our data reveal that REV-ERBalpha regulates NAD(+) in a tissue-specific manner via opposing regulation of NAMPT versus CD38 and define an astrocyte REV-ERBalpha-NFIL3-CD38 pathway controlling brain NAD(+) metabolism and neurodegeneration
Changes in antiviral treatment rate for hepatitis B virus before hepatocellular carcinoma diagnosis: a nationwide Korean study
No full textBACKGROUND AND AIMS: Antiviral treatment (AVT) reduces hepatitis B virus (HBV) reactivation and hepatocsellular carcinoma (HCC) development; however, the impact of AVT timing - before versus after HCC diagnosis - on prognosis remains unclear. This study aimed to evaluate the current status, changes, and clinical outcomes of AVT before HCC diagnosis in Korea. METHODS: Data were extracted from the Korean National Health Insurance Service for patients newly diagnosed with HBV-related HCC from 2008 to 2018. Patients were categorized into an early cohort (2008-2013) and a late cohort (2014-2018). AVT trends were analyzed using Joinpoint regression, and clinical outcomes were compared between groups. RESULTS: Among 82 609 patients (early cohort: n = 45 804; late cohort: n = 36 805), the proportion receiving AVT before HCC diagnosis increased from 22.4% in 2008 to 46.8% in 2018. AVT after diagnosis also rose from 16.3 to 21.3%. Overall survival rates in the late cohort were significantly improved compared with the early cohort (P < 0.001). More than half of the patients with HCC who received transplantation or local ablation treatment had received AVT before HCC diagnosis. AVT before HCC diagnosis was associated with reduced mortality rate (adjusted hazard ratio = 0.592; 95% confidence interval: 0.580-0.604; P < 0.001). Elderly patients (>/=80 years) consistently had a lower AVT rate before HCC diagnosis compared with other age groups (P < 0.05). CONCLUSION: The AVT rate before HCC diagnosis significantly increased over the past 10 years in Korea. Further efforts are needed to improve the AVT rate in elderly patients with HBV-related HCC
Characteristics and management of mechanically ventilated patients in South Korea compared with other high-income Asian countries and regions
No full textBACKGROUND: This study investigated the characteristics of mechanically ventilated patients in South Korean intensive care units (ICUs). METHODS: We conducted a subgroup analysis of a multinational observational study. Data from 271 mechanically ventilated patients in South Korean ICUs were analyzed for demographics, ventilation practices, and mortality, and were compared with those of 327 patients from other high-income Asian countries. RESULTS: South Korean patients were older (mean age: 67 vs. 62 years, P<0.001) and had lower ratio of the partial pressure of arterial oxygen to the fraction of inspired oxygen (255.5 vs. 306.2, P<0.001). South Korean ICUs exhibited higher patient-to-nurse ratios (2.6 vs. 1.9, P<0.001) and more beds per unit (20.5 vs. 16.0, P=0.017). The use of sufficient positive end-expiratory pressure for patients (PEEP) for acute respiratory distress syndrome (ARDS) was less frequent in South Korea (62.2% vs. 91.2%, P=0.005). Mortality rates were similar between South Korean patients and those in other high-income Asian countries (38.0% vs. 34.2%, P=0.401). Significant mortality predictors in South Korea included age >/=65 years (odds ratio [OR], 4.03; P=0.039) and a Sequential Organ Failure Assessment score >/=8 (OR, 2.36; P=0.031). The presence of respiratory therapists was associated with reduced mortality (OR, 0.52; P=0.034). CONCLUSIONS: Despite higher age and patient-to-nurse ratios in South Korean ICUs, outcomes were comparable to those in other high-income Asian countries. The suboptimal use of sufficient PEEP with ARDS indicates potential areas for improvement. Additionally, the beneficial impact of respiratory therapists on mortality rates warrants further investigation
Impact of ERAP1 downregulation on the pathogenesis of DSS-induced colitis and therapeutic response to sulfasalazine
No full textINTRODUCTION: Ulcerative colitis (UC) is a life-threatening heterogeneous condition characterized by inflammation of the colon. Endoplasmic reticulum aminopeptidase 1 (ERAP1) is essential for antigen processing and immune regulation, however, its specific role in UC pathogenesis and therapeutic response remains unclear. This study aimed to investigate the role of ERAP1 in the response to sulfasalazine, a standard treatment for UC, using an ERAP1-heterozygous (ERAP1(+/-)) mouse model susceptible to colitis. METHODS: Wild-type (WT) and ERAP1(+/-) mice were treated with 2.5% dextran sulfate sodium to induce colitis, followed by sulfasalazine administration. Colitis severity was assessed through histopathology. Immune cell populations, including neutrophils, dendritic cells, T cells, and NK1.1+ cells, were analyzed using flow cytometry. RNA sequencing of colonic tissues was performed to assess gene expression changes associated with reduced ERAP1 expression. RESULTS: ERAP1(+/-) mice exhibited mildly increased susceptibility to DSS-induced colitis, with greater weight loss and distinct alterations in immune cell infiltration compared to WT mice. These differences were further pronounced after sulfasalazine treatment. RNA sequencing identified 428 differentially expressed genes between ERAP1(+)/(-) and WT mice. Among these, 28 genes were previously associated with colitis or colorectal cancer, of which 11 were upregulated and 17 downregulated in ERAP1(+/-) mice. RT-qPCR confirmed significantly elevated expression of Anxa9, Atp2a1, and Hepacam2 in ERAP1(+/-) mice after sulfasalazine treatment, indicating a differential therapeutic response. CONCLUSION: Collectively, our findings show that partial ERAP1 deficiency promotes immune dysregulation, alters the expression of inflammation-associated genes, and impairs sulfasalazine efficacy. Therefore, ERAP1 may serve as a key regulator in the pathogenesis of UC and a potential target for therapy
Accuracy of artificial intelligence-assisted soft tissue landmark identification in serial lateral cephalograms of Class III two-jaw surgery patients
No full textOBJECTIVE: To evaluate the accuracy of artificial intelligence (AI)-assisted soft tissue landmark identification (STLI) on serial lateral cephalograms (Lat-Cephs) of Class III patients treated with two-jaw orthognathic surgery across four different time-points. METHODS: A convolutional neural network model was developed for STLI, trained and validated using 3,004 Lat-Cephs from 751 patients. The test set included 224 Lat-Cephs from 56 patients, divided into the genioplasty (n = 22) and non-genioplasty (n = 34) groups. The four time-points included initial (T0), pre-surgery (T1, brackets), post-surgery (T2, brackets, surgical plates, and screws [S-PS]), and debonding (T3, S-PS and fixed retainers). AI accuracy was compared with a human standard for 13 soft tissue landmarks. Mean radial errors (MREs), horizontal and vertical errors, and statistical differences were analyzed. RESULTS: The total MRE across all time-points was 1.50 +/- 0.48 mm, with 64.9% of values being less than 1.5 mm MRE. There were no significant differences in accuracy among the four time-points (T0, 1.41 mm; T1, 1.53 mm; T2, 1.58 mm; T3, 1.47 mm). The pronasale, stomion inferius (Stmi), stomion superius (Stms) showed an increase in MRE (P < 0.01, P < 0.05, and P < 0.05, respectively), whereas the Lower Lip showed a decrease in MRE (P < 0.01). There were no significant differences in errors across time-points for the soft-tissue B point, soft-tissue Pogonion, or soft-tissue Menton between the genioplasty and non-genioplasty groups. CONCLUSIONS: The AI algorithm in this study might be an effective tool for STLI in Lat-Cephs at T1, T2, and T3, despite the presence of brackets, S-PS, fixed retainers, genioplasty, and bone remodeling
Automated CT segmentation for lower extremity tissues in lymphedema evaluation using deep learning
No full textOBJECTIVES: Clinical assessment of lymphedema, particularly for lymphedema severity and fluid-fibrotic lesions, remains challenging with traditional methods. We aimed to develop and validate a deep learning segmentation tool for automated tissue component analysis in lower extremity CT scans. MATERIALS AND METHODS: For development datasets, lower extremity CT venography scans were collected in 118 patients with gynecologic cancers for algorithm training. Reference standards were created by segmentation of fat, muscle, and fluid-fibrotic tissue components using 3D slicer. A deep learning model based on the Unet++ architecture with an EfficientNet-B7 encoder was developed and trained. Segmentation accuracy of the deep learning model was validated in an internal validation set (n = 10) and an external validation set (n = 10) using Dice similarity coefficient (DSC) and volumetric similarity (VS). A graphical user interface (GUI) tool was developed for the visualization of the segmentation results. RESULTS: Our deep learning algorithm achieved high segmentation accuracy. Mean DSCs for each component and all components ranged from 0.945 to 0.999 in the internal validation set and 0.946 to 0.999 in the external validation set. Similar performance was observed in the VS, with mean VSs for all components ranging from 0.97 to 0.999. In volumetric analysis, mean volumes of the entire leg and each component did not differ significantly between reference standard and deep learning measurements (p > 0.05). Our GUI displays lymphedema mapping, highlighting segmented fat, muscle, and fluid-fibrotic components in the entire leg. CONCLUSION: Our deep learning algorithm provides an automated segmentation tool enabling accurate segmentation, volume measurement of tissue component, and lymphedema mapping. KEY POINTS: Question Clinical assessment of lymphedema remains challenging, particularly for tissue segmentation and quantitative severity evaluation. Findings A deep learning algorithm achieved DSCs > 0.95 and VS > 0.97 for fat, muscle, and fluid-fibrotic components in internal and external validation datasets. Clinical relevance The developed deep learning tool accurately segments and quantifies lower extremity tissue components on CT scans, enabling automated lymphedema evaluation and mapping with high segmentation accuracy