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PubMed2026

Enhanced risk stratification in hypertrophic cardiomyopathy through the integration of extracellular volume fraction on cardiovascular magnetic resonance.

AIMS: This study investigated the incremental prognostic value of cardiovascular magnetic resonance (CMR)-derived extracellular volume fraction (ECV), a marker of diffuse interstitial fibrosis, beyond late gadolinium enhancement (LGE) in hypertrophic cardiomyopathy (HCM). METHODS AND RESULTS: We analysed 990 consecutive HCM patients (median age 58 years, male 68.3%) who underwent CMR between 2012 and 2024. LGE and global ECV were quantified, and their associations with the primary endpoint of HCM-related events-a composite of sudden cardiac death (SCD) events, heart failure (HF) events, and HCM-related death-were assessed. During a median follow-up of 3.2 years, 64 (6.5%) patients experienced the primary endpoint. While LGE (median 7.1%, IQR 2.3-16.9%) and ECV (median 29.0%, IQR 26.6-32.0%) were moderately correlated (R = 0.604, P < 0.001), both were significantly associated with increased risk of the primary endpoint and individual outcomes of SCD and HF events, and optimal cutoffs were determined as LGE ≥ 27% and ECV ≥ 35%. Patients with ECV ≥ 35% had more symptoms, a more severe phenotype with greater systolic and diastolic dysfunction, and more pathogenic gene variants. Notably, ECV remained a significant predictor of the primary endpoint (adjusted HR 1.08, 95% CI 1.02-1.15, per 1%) after adjustment for key disease variables, including left ventricular ejection fraction and LGE. Elevated ECV effectively identified high-risk individuals even among lower-risk subgroups, including those with low LGE burden. CONCLUSION: Increased ECV is an independent predictor of HCM-related outcomes. ECV may serve as a novel imaging biomarker to refine risk stratification in HCM patients who do not meet traditional LGE-based high-risk criteria.

Humans↗

PubMed2026

Comprehensive assessment of vasospastic angina using coronary computed tomography angiography: synergistic value of the presence of myocardial bridge, perivascular inflammation, and myocardial extracellular volume fraction.

AIMS: Coronary computed tomography angiography (CCTA) has evolved beyond anatomical assessment to include sophisticated tissue characterization. While an elevated perivascular fat attenuation index around the right coronary artery (FAI-RCA) is known to reflect coronary inflammation in vasospastic angina (VSA), recurrent vasospasms may also induce chronic subclinical myocardial injury and subsequent remodelling, potentially associated with an increased myocardial extracellular volume fraction (ECV). However, the diagnostic integration of ECV and FAI-RCA for identifying VSA in patients with angina with non-obstructive coronary arteries (ANOCA) remains to be elucidated. METHODS AND RESULTS: This study included consecutive ANOCA patients who underwent CCTA with a dedicated ECV protocol, followed by an invasive spasm provocation test. Comprehensive CCTA analysis quantified both FAI-RCA and the transmural ECV gradient (the difference between endocardial and epicardial ECV: ECVEndo - ECVEpi). Of the 100 patients analysed (mean age: 65.3 ± 11.8 years; 55% male), 27 were diagnosed with VSA. Multivariable logistic regression analysis identified transmural ECV gradient [odds ratio (OR): 1.12, 95% confidence interval (CI): 1.01-1.25], presence of myocardial bridging (MB) (OR: 3.49, 95% CI: 1.25-9.74), and high FAI-RCA (> -70.95 Hounsfield units [HU]) (OR: 5.79, 95% CI: 2.06-16.30) as significant independent predictors of VSA (all P < 0.05). Notably, the integration of transmural ECV gradient provided incremental diagnostic value beyond FAI-RCA and MB, as assessed by the Net Reclassification Improvement and Integrated Discrimination Improvement. CONCLUSION: A multi-parametric CCTA approach potentially identifies patients at high risk for VSA. The significant association of the transmural ECV gradient with VSA suggests that myocardial remodelling imaging provides a novel diagnostic window into the cumulative myocardial impact of vasospasm, independent of active adipose tissue inflammation and the presence of MB.

Humans↗

PubMed2026

Detectable C-Peptide and Diabetic Ketoacidosis Risk in Type 1 Diabetes.

OBJECTIVE: To investigate whether detectable C-peptide levels in type 1 diabetes is associated with a lower risk of diabetic ketoacidosis (DKA). RESEARCH DESIGN AND METHODS: We analyzed the Diabetes Control and Complications Trial publicly available data repository for the association between detectable stimulated C-peptide (>0.2 nmol/mL, measured annually) and DKA incidence over an average 6.5-year follow-up. We used crude and adjusted Andersen-Gill models for recurrent DKA events with time-dependent covariates. RESULTS: Of the 1,441 participants (53% male, median age 27 years), 129 (9%) experienced 180 DKA events. Of these events, 179 (99.44%) occurred after C-peptide was ≤0.2 nmol/L in the prior year and only 1 event occurred with C-peptide >0.2 nmol/L. C-peptide >0.2 nmol/L was associated with a DKA hazard ratio of 0.07 (95% CI 0.01-0.48; P = 0.007), consistent across adjusted models. CONCLUSIONS: Endogenous insulin production was significantly associated with lower DKA risk, suggesting that treatments preserving insulin production could decrease long-term DKA risk.

C-Peptide↗

PubMed2026

Spinal meningiomas: histopathological grading using a benchmark radiomics model with notes on disease control.

OBJECTIVE: Spinal meningiomas (SMs) are common primary spinal tumors for which surgery is considered the first-line treatment when safe and feasible. The ability to extrapolate the tumor grade from preoperative imaging may significantly inform early patient expectation-setting regarding recurrence. Building on radiomics studies in cranial meningiomas, the authors aimed to construct a benchmark radiomics model to preoperatively identify the histological grade of SMs. METHODS: Institutional surgical records from May 2012 to November 2025 were queried for pathology-confirmed meningiomas below the foramen magnum, with preoperative contrast-enhanced imaging available for segmentation. SMs were classified as low-grade (WHO grade 1) and high-grade (WHO grade 2 tumors and grade 1 tumors with atypia). Tumors were manually segmented, and features were extracted using the PyRadiomics software package. An ensemble model of k-nearest neighbors, random forest, and support vector machine classifiers was trained using nested cross-validation on a subset of 10 features to differentiate tumor grades. Clinical data for the cohort were also extracted, and disease control in an adjunctive clinical series was assessed. RESULTS: Seventy-four patients were included in radiomics analysis, with an area under the receiver operating characteristic curve of 0.879 and a mean F1 score of 0.748. The model's top 5 features were all texture features that differed significantly (p < 0.05) across low- and high-grade SMs. These included measures of tumor textural and contrast-enhancement heterogeneity, with overlap with features reported in radiomics models for histological grading of intracranial meningiomas. Fifty-five patients with a median radiographic follow-up of 22.2 (range 1.9-86.4) months remained for clinical analysis after exclusion of patients with less than 1 month of follow-up and syndromic meningiomas. Four recurrences occurred at a median of 20.8 (range 1.8-41.8) months. High-grade tumor pathology did not significantly impact progression-free survival (p = 0.682, log-rank test; Cox regression high vs low grade hazard ratio [HR] 0.62, 95% CI 0.06-6.11, p = 0.685). Subtotal resection was associated with poorer progression-free survival than gross-total resection (p = 0.004, log-rank test; Cox regression subtotal vs gross-total resection HR 10.62, 95% CI 1.46-77.05, p = 0.019). These findings remain contextualized within a relatively limited follow-up window and small recurrence event count, suggesting a need to characterize the interplay between tumor grade and extent of resection as drivers of local disease control in SMs. CONCLUSIONS: A preoperative radiomics model can stratify high-grade SMs using open-source tools applied to single-institution data.

Humans↗