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Background Diastolic dysfunction is common in patients with aortic stenosis and may influence outcomes following surgical aortic valve replacement. We aimed to examine the association of preoperative artificial intelligence (AI)‐generated diastolic function grades with early and late outcomes following aortic valve replacement and how postoperative progression influence prognosis. Methods We identified 5503 patients undergoing aortic valve replacement between 2000 and 2023. Diastolic function was assessed using a validated deep‐learning AI model applied to 12‐lead ECGs done preoperatively and on postoperative follow‐up. Diastolic grades were classified by AI into Grades 1 to 3. Longitudinal trend analyses and multivariable regression models were used to assess study end points. Results Among 5503 patients (mean age 72.4±10.8 years; 39% female), higher AI ECG diastolic grades were associated with greater comorbidity burden, including diabetes, renal disease, and heart failure. AI ECG diastolic Grade 3 was independently associated with higher in‐hospital mortality (odds ratio, 2.5; P =0.007) and other complications. At 5‐year follow‐up, patients with Grade 3 showed the least improvement in diastolic function by both ECG and echocardiography. Grades 2 and 3 diastolic function at baseline were independently associated with increased late mortality (hazard ratio, 1.3 and 2.45, respectively; both P <0.001). Additionally, lack of improvement in AI ECG diastolic grade by 1 year was also independently associated with late mortality. Conclusions AI ECG–derived diastolic function grades strongly correlates with early complications and long‐term mortality and diastolic progression after aortic valve replacement. AI ECG provides a powerful, noninvasive tool for risk stratification and longitudinal monitoring of patients with aortic stenosis.

Purpose To evaluate implantation rates, time to implantation, and predictors of implantation following matrix‐induced autologous chondrocyte implantation (MACI) biopsy for knee chondral defects. Methods Patients undergoing MACI biopsy or implantation for symptomatic knee chondral defects from 2017 to 2024 at a single institution were reviewed. At a minimum 1‐year follow‐up, the decision to proceed with implantation, recurrent symptoms, complications, and reoperations were assessed. Kaplan‐Meier analysis estimated time to implantation, and multivariable logistic regression identified predictors of MACI implantation. Results A total of 208 knees in 202 patients (mean age 28.0 ± 10.9 years, range 11‐59) underwent MACI biopsy for 243 chondral defects (patella [38%], femoral condyle [34%], and trochlea [26%]). At a mean follow‐up of 4.3 years (range 1.0‐8.3), 64 knees (31%) proceeded to second‐stage MACI implantation. Among 144 knees not implanted, 87 (60%) reported symptom improvement, and 26 (18%) had persistent symptoms. The remaining 22% did not undergo implantation due to another cartilage procedure, insurance denial, or pending scheduling. Patients who underwent biopsy at the home institution had a shorter time from biopsy to implantation compared with those biopsied at outside institutions (6.1 vs 13.1 months, P = .001). Median time to implantation was 9.4 months (95% confidence level: 7.2‐12.2) for home biopsies. Lower body mass index was associated with progression to implantation after adjusting for covariates (odds ratios 0.92, P = .019). Patients implanted within 4 months had larger chondral defects (4.0 vs 3.0 cm 2 , P = .045). At a mean 4.1‐year follow‐up, 6 patients (8%) experienced postoperative complications, including 3 (4%) who required revision chondroplasty. Conclusions MACI biopsy for knee chondral defects resulted in approximately one‐third of patients proceeding to MACI implantation, at a median of 9.4 months after biopsy. Lower body mass index was associated with higher likelihood of progression to implantation, larger chondral defects with earlier implantation, and among patients who did not proceed to implantation, approximately 60% reported symptom improvement following concomitant chondroplasty at the time of biopsy. Level of Evidence Level IV, retrospective case series.

Background: The optimal self-expandable metallic stent (SEMS) choice for endoscopic drainage of unresectable extrahepatic malignant biliary obstruction (MBO) with endoscopic retrograde cholangiopancreatography (ERCP) remains unclear due to limited randomized controlled trial (RCT) evidence. Prior meta-analyses have relied on observational data, introducing heterogeneity and bias. To overcome these limitations, an RCT-only network meta-analysis (NMA) was performed to evaluate the efficacy and safety of SEMS types. Methods: A literature search was conducted to identify RCTs assessing fully covered SEMS (FCSEMS), partially covered SEMS (PCSEMS), and/or uncovered SEMS (UCSEMS) for ERCP drainage of unresectable extrahepatic MBO. Study outcomes included recurrent biliary obstruction (RBO), specific causes of RBO, overall survival (OS), and adverse events (AEs). A frequentist NMA was performed to estimate hazard ratios (HRs) and incidence rate ratios (IRRs) with their 95% confidence intervals and overall P-scores. Results: Ten RCTs (1,659 patients) were included. There were no significant differences in time to RBO, incidence of any RBO, or OS between stent types. UCSEMS had the lowest rates of stent migration, tumor overgrowth, and sludge occlusion, whereas FCSEMS most effectively prevented tumor ingrowth. AE rates were similar across groups; however, only FCSEMS, but not PCSEMS, had significantly higher rates of acute cholecystitis than UCSEMS in patients with an intact gallbladder. Conclusion: There were no differences in RBO, OS, or AE excluding acute cholecystitis between each SEMS type, although each type has its advantages and disadvantages. FCSEMS was associated with a higher risk of acute cholecystitis, emphasizing careful patient selection, particularly regarding gallbladder status.

ABSTRACT Introduction Patients often receive propofol for sedation and clevidipine for hypertension in the intensive care unit. Both agents are formulated as a lipid emulsion, which may increase the risk of hypertriglyceridemia and acute pancreatitis with their coadministration. We aimed to determine the incidence of hypertriglyceridemia and acute pancreatitis with concurrent administration of propofol and clevidipine and identify patient risk factors for hypertriglyceridemia. Methods This was a multicenter retrospective study of adult patients (≥ 18 years old) receiving concurrent propofol and clevidipine infusions for ≥ 6 h. The primary outcome was the frequency of triglyceride levels greater than 400 mg/dL and acute pancreatitis. The secondary outcome was risk factors associated with hypertriglyceridemia during concurrent propofol and clevidipine infusions until both infusions had been discontinued. Univariable and multivariable logistic regression analyses were used to assess risk factors of hypertriglyceridemia in patients receiving concurrent propofol and clevidipine infusions. Results Across 1,226 patients, the median duration of infusion overlap was 12.4 [interquartile range (IQR) 8.3, 22.7] hours. The mean age of the cohort was 59.6 [standard deviation (SD) 14.9] years, and 59.4% were male. Assuming those without a triglyceride level drawn did not have hypertriglyceridemia, hypertriglyceridemia after initiation of propofol and clevidipine infusion was found in 7.7% [95% confidence interval (CI) 6.3% to 9.3%] of the entire cohort. Twenty‐six patients (2.1%) had a diagnosis of pancreatitis after initiation of lipid infusions, of which 12 cases were possibly infusion‐related, and one case was likely infusion‐related. After calculating the Naranjo Scale, all 13 cases of pancreatitis were possibly related to propofol or clevidipine infusions. Based on a multivariable logistic regression of those with a triglyceride level available, risk factors for hypertriglyceridemia included pancreatitis within the past 5 years [adjusted odds ratio (aOR) 2.77, 95% CI, 1.23 to 6.23] and statin use (aOR 1.94, 95% CI, 1.13 to 3.34). Conclusions In our cohort of patients receiving concurrent propofol and clevidipine, the frequency of hypertriglyceridemia and acute pancreatitis was low. Further comparative studies are warranted to assess the risk of pancreatitis with the addition of clevidipine to propofol.

ABSTRACT Background Family history of pancreatic cancer (PC), pathogenic germline variants (PGVs), and increased intrapancreatic fat (IPF) are individually associated with increased PC risk. Whether IPF is elevated in high‐risk individuals (HRIs) with PC family history or PGVs is unknown. Although MRI is the preferred modality for IPF measurement, region‐of‐interest–based approaches are prone to sampling bias because pancreatic fat is heterogeneously distributed. We aimed to compare IPF among HRIs and matched non‐HRI controls using an Artificial intelligence (AI)‐based tool for whole pancreatic IPF measurement applied to magnetic resonance imaging (MRI). Methods We developed and validated a deep‐learning pipeline for IPF measurement that incorporates automated pancreas segmentation on T1‐weighted MRI images registered to the corresponding iterative decomposition of water and fat with echo asymmetry and least squares estimation images to compute IPF using proton density fat fraction (PDFF). This pipeline was used to quantify IPF and assess IPF distribution patterns in 85 HRIs and 170 non‐HRIs matched 1:2 for age, sex, BMI, and diabetes status. A surface erosion approach was developed to minimize the impact of peripancreatic fat on IPF measurement. Linear models adjusting for matching variables were used to assess associations. Results Model‐expert agreement for whole‐pancreas PDFF was excellent (intraclass correlation coefficient 0.99; 95% CI, 0.98–0.99 mean absolute error 1.4%). Mean PDFF did not differ between HRIs and controls (25.8% [11.5] vs. 26.9% [11.4]; p = 0.32). PDFF increased with age ( p = 0.015), male sex ( p < 0.001), presence of diabetes ( p = 0.009), BMI ( p < 0.001). IPF distribution patterns did not differ by risk group. Conclusion An AI‐based approach provides accurate, scalable, and objective assessment of IPF quantity and distribution. IPF quantity and spatial distribution do not appear to differ based on germline or familial predisposition to PC.

Objective Antinuclear antibody (ANA) indirect immunofluorescence (IIF) is the reference standard but depends on subjective pattern interpretation. We tested whether artificial intelligence (AI) applied to ANA IIF images could identify specific autoantibodies. Methods We assembled a cohort of ANA‐positive patients (human epithelial type 2 IIF titer ≥1:80) with anti–double‐stranded DNA (dsDNA), anti‐Smith (Sm) antibody, ribonucleoprotein (RNP), anti‐Sjögren's disease‐related antigen A (SSA), anti‐Sjögren's disease‐related antigen B (SSB), anti‐topoisomerase I (Scl‐70), or centromere antibody (CMA) testing within 90 days. The development cohort (December 12, 2016–December 31, 2022) included the first ANA test per patient. Two AI architectures were trained for each autoantibody. Performance was assessed using area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy; comparisons with IIF patterns were summarized as pattern‐antibody agreement. A prospective cohort (January 1, 2023–August 20, 2024), including a dsDNA assay transition, provided temporal validation. Results Among 512,959 patients, 47,890 met inclusion criteria (mean age 55 [SD 17.6], 78% female). Models for CMA, dsDNA, Sm, SSA, and SSB achieved AUROC ≥0.80 (CMA highest: AUROC 0.95, accuracy 94.2%, specificity 95.3%). Compared with associated IIF patterns, dsDNA and Sm models more often classified antibody‐negative cases as negative (dsDNA 88.2% vs 54.7%; Sm 82.6% vs 60.3%) while similarly classifying antibody‐positive cases. Performance for RNP (AUROC 0.68) and Scl‐70 (AUROC 0.59) was lower. In the prospective cohort (n = 21,427), performance was similar; dsDNA discrimination remained stable across assays (AUROC ~0.85). Conclusion AI applied to ANA IIF images identified specific autoantibodies and often improved classification of antibody‐negative cases versus simplified pattern‐based comparators. These findings support its potential to standardize ANA interpretation and triage or reduce confirmatory testing. image

Cardiovascular-kidney-metabolic (CKM) syndrome is a complex disease that affects up to 90% of US adults. This work characterized the renal transcriptomic landscape of a novel model of CKM in aging swine. The cardiovascular, renal, and metabolic parameters in pigs are very similar to those in humans, and our work may guide future studies aimed at modulating specific age-related renal abnormalities in CKM to slow disease progression and reduce mortality.