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Association between epilepsy duration and glymphatic dysfunction assessed by DTI-ALPS: A systematic review and meta-analysis

Wed, 01 Jul 2026 00:00:00 GMT

Title: Association between epilepsy duration and glymphatic dysfunction assessed by DTI-ALPS: A systematic review and meta-analysis Authors: Lee, Su Ji; Cho, Soomi; Shin, Hui Jin; Lee, Hyunji; Cho, Minjae; 신희진 Abstract: Objective: To systematically evaluate whether epilepsy duration is associated with glymphatic dysfunction as measured by diffusion tensor image analysis along the perivascular space (DTI-ALPS). Methods: A systematic review and correlation-based meta-analysis were conducted in accordance with PRISMA guidelines. PubMed, Embase, Scopus, Web of Science, and Google Scholar were searched from inception through January 20, 2026, for observational studies reporting correlations between epilepsy duration and DTI-ALPS values. Correlation coefficients were pooled using random-effects models after Fisher's z transformation. Subgroup analyses and meta-regression were performed to explore heterogeneity. Results: Ten observational studies comprising 449 patients with epilepsy were included. Pooled analysis demonstrated a significant negative association between epilepsy duration and the DTI-ALPS index (r = -0.37, 95% confidence interval [CI]: -0.53 to -0.19), indicating lower glymphatic function with longer disease duration. A significant association persisted in temporal lobe epilepsy (r = -0.30, 95% CI: -0.54 to -0.02) and was stronger in late-onset epilepsy (r = -0.68, 95% CI: -0.79 to -0.54). Meta-regression identified age as a significant moderator of effect size, whereas mean disease duration did not significantly explain variability. Sensitivity analyses confirmed the robustness of findings, and no publication bias was detected. Conclusion: Longer epilepsy duration is associated with greater glymphatic dysfunction as measured by DTIALPS. Age significantly modulates this relationship, suggesting that seizure chronicity and aging-related vulnerability may synergistically influence perivascular clearance pathways. These findings support DTI-ALPS as a promising non-invasive marker of cumulative glymphatic burden in epilepsy and provide a quantitative framework for future longitudinal studies.

Role of Fat-Free Mass-Adjusted Cardiorespiratory Fitness in Predicting Hospitalization Risk in Patients with Heart Failure

Mon, 01 Jun 2026 00:00:00 GMT

Title: Role of Fat-Free Mass-Adjusted Cardiorespiratory Fitness in Predicting Hospitalization Risk in Patients with Heart Failure Authors: Kim, Young Seok; Lee, Wonhee; Yi, Tae Im; 김영석 Abstract: Purpose: Reduced cardiorespiratory fitness (CRF) is associated with hospitalization risk in heart failure (HF). Traditional peak oxygen uptake (VO2) scaling uses total body weight (TBW), potentially underestimating CRF due to adiposity. The prognostic value of fat-free mass (FFM)-adjusted peak VO2 remains unclear, particularly in Asian populations. Materials and Methods: A retrospective cohort study included HF patients who underwent cardiopulmonary exercise testing and bioelectrical impedance analysis. Two peak VO2 cutoffs-14 mL/TBW kg/min and 19 mL/FFM kg/min-were applied to predict all-cause and HF-specific 1-year hospitalization. The prognostic performance was assessed using Cox proportional hazards models adjusted for the Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) risk score. Likelihood ratio tests were conducted to evaluate the incremental value of adding the FFM-adjusted cutoff. Results: A total of 83 patients (mean age, 60.0 years; 14 female; 37 obese) were analyzed. Both TBW-and FFM-adjusted cutoffs were significantly associated with increased risk of all-cause hospitalization [hazard ratio (HR)=3.86, 95% confidence interval (CI), 1.40-10.63 vs. HR=5.02, 95% CI, 1.98-12.72] and HF-specific hospitalization (HR=4.00, 95% CI, 1.00-16.05 vs. HR=7.26, 95% CI, 1.92-27.46). Adding the FFM-adjusted cutoff significantly improved model fit when added to a model with the TBW-adjusted cutoff (p<0.05). The difference in c-indices between the two cutoffs after bootstrapping was not statistically significant. Conclusion: The FFM-adjusted cutoff can complement the traditional TBW-adjusted cutoff by correcting the confounding bias of excessive adiposity or low muscle mass, providing incremental prognostic value for risk stratification in Asian patients with HF.

Incremental diagnostic value of multiregional single-slice CT muscle areas over L3 for sarcopenia: a deep learning-based segmentation study

Fri, 01 May 2026 00:00:00 GMT

Title: Incremental diagnostic value of multiregional single-slice CT muscle areas over L3 for sarcopenia: a deep learning-based segmentation study Authors: Lee, Hong-Seon; Kim, Doyoung; Choi, Chung Hwan; Kim, Sungjun; Park, Jung Hyun Abstract: Objective To compare the diagnostic performance of multiregional CT-based muscle assessment with conventional single-level (L3) evaluation. Materials and methods This retrospective study included 83 adults who underwent multiregional non-contrast CT and completed sarcopenia assessments based on the Asian Working Group for Sarcopenia (AWGS) 2019 criteria. Skeletal muscle areas at six anatomical levels (T4, L3, ASIS, femoral head, midthigh, and proximal calf) were automatically quantified using a deep learning-based segmentation software (DeepCatch). The diagnostic performance of single-slice muscle areas was evaluated using the area under the receiver operating characteristic curve (AUC). Pearson's or Spearman's correlation coefficients were analyzed to assess the relationship between CT-derived muscle metrics and functional status. Results Multiregional muscle assessment demonstrated the highest diagnostic performance. In a clinical prediction model, models incorporating three-site and six-site muscle areas improved discrimination compared with the clinical base model (Delta AUC 0.123 and 0.136, respectively), and these improvements remained significant after BH-FDR adjustment (both q = 0.034). In contrast, the addition of the midthigh muscle area showed a modest improvement (Delta AUC 0.089; p = 0.029), which did not remain significant after FDR adjustment (q = 0.064), and L3 muscle area provided limited incremental value. Conclusion Multiregional CT-based muscle assessment provides improved diagnostic performance for sarcopenia compared with single-level evaluation. Lower-extremity muscle measurements, particularly at the midthigh, contribute to this improvement, whereas reliance on L3 alone may be insufficient.

Machine Learning-Based Prediction of Transition to Functional Upper Limb Recovery After Intensive Inpatient Rehabilitation in Early Subacute Stroke

Fri, 01 May 2026 00:00:00 GMT

Title: Machine Learning-Based Prediction of Transition to Functional Upper Limb Recovery After Intensive Inpatient Rehabilitation in Early Subacute Stroke Authors: Park, Jong-Mi; Lee, Sang-Chul; Kim, Yong-Wook; Yoon, Seo-Yeon Abstract: Background/Objectives: Recovery of upper limb function after stroke is highly heterogeneous, and accurate prediction of clinically meaningful functional transition remains a major challenge in rehabilitation medicine. We developed and temporally validated machine learning (ML)-based prognostic models for predicting transition from non-functional movement to functionally usable upper limb capacity in patients undergoing intensive inpatient rehabilitation during the early subacute phase of stroke. Methods: This retrospective cohort study included 960 patients with ischemic or hemorrhagic stroke admitted to a tertiary rehabilitation center between 2010 and 2025. Three functional recovery outcomes were defined: motor impairment recovery, defined as Fugl-Meyer Assessment for Upper Extremity score >= 32; gross manual dexterity recovery, defined as Box and Block Test score >= 2 blocks/min; and functional pinch strength recovery, defined as pinch strength >= 1.1 kgf. Multidimensional predictors spanning demographic, clinical, neurophysiological, neuroimaging, and rehabilitation-related domains were integrated. Four ML algorithms were evaluated using stratified 5-fold cross-validation and temporal validation in a chronologically independent cohort (2024-2025). Models were developed under two tracks: Track A, incorporating only baseline variables available at admission (primary prognostic model), and Track B, additionally incorporating cumulative rehabilitation-related variables (exploratory). Results: Random Forest demonstrated the best overall performance. During temporal validation, models achieved AUROC of 0.800 for motor impairment recovery, 0.958 for gross manual dexterity recovery, and 0.888 for functional strength recovery. Baseline motor severity and corticospinal tract integrity were the dominant biological determinants of recovery. Earlier rehabilitation initiation and greater upper-limb robot-assisted therapy exposure were also associated with improved outcomes; however, these findings should be interpreted as observational associations subject to treatment-selection bias rather than evidence of causal effects. Conclusions: Probabilistic ML prediction integrating neural reserve and rehabilitation-related exposure variables can support individualized precision rehabilitation planning and improve functional outcome stratification in early subacute stroke.