Artificial intelligence‐based analysis of body composition in Marfan: skeletal muscle density and psoas muscle index predict aortic enlargement

Background: Patients with Marfan syndrome are at risk for aortic enlargement and are routinely monitored by computed tomography (CT) imaging. The purpose of this study is to analyse body composition using artificial intelligence (AI)-based tissue segmentation in patients with Marfan syndrome in order to identify possible predictors of progressive aortic enlargement. Methods: In this study, the body composition of 25 patients aged <= 50 years with Marfan syndrome and no prior aortic repair was analysed at the third lumbar vertebra (L3) level from a retrospective dataset using an AI-based software tool (Visage Imaging). All patients underwent electrocardiography-triggered CT of the aorta twice within 2 years for suspected progression of aortic disease, suspected dissection, and/or pre-operative evaluation. Progression of aortic enlargement was defined as an increase in diameter at the aortic sinus or the ascending aorta of at least 2 mm. Patients meeting this definition were assigned to the 'progressive aortic enlargement' group (proAE group) and patients with stable diameters to the 'stable aortic enlargement' group (staAE group). Statistical analysis was performed using the Mann-Whitney U test. Two possible body composition predictors of aortic enlargement-skeletal muscle density (SMD) and psoas muscle index (PMI)-were analysed further using multivariant logistic regression analysis. Aortic enlargement was defined as the dependent variant, whereas PMI, SMD, age, sex, body mass index (BMI), beta blocker medication, and time interval between CT scans were defined as independent variants. Results: There were 13 patients in the proAE group and 12 patients in the staAE group. AI-based automated analysis of body composition at L3 revealed a significantly increased SMD measured in Hounsfield units (HUs) in patients with aortic enlargement (proAE group: 50.0 +/- 8.6 HU vs. staAE group: 39.0 +/- 15.0 HU; P = 0.03). PMI also trended towards higher values in the proAE group (proAE group: 6.8 +/- 2.3 vs. staAE group: 5.6 +/- 1.3; P = 0.19). Multivariate logistic regression revealed significant prediction of aortic enlargement for SMD (P = 0.05) and PMI (P = 0.04). Conclusions: Artificial intelligence-based analysis of body composition at L3 in Marfan patients is feasible and easily available from CT angiography. Analysis of body composition at L3 revealed significantly higher SMD in patients with progressive aortic enlargement. PMI and SMD significantly predicted aortic enlargement in these patients. Using body composition as a predictor of progressive aortic enlargement may contribute information for risk stratification regarding follow-up intervals and the need for aortic repair

Cardiovalve in mitral valve position—Additional solution for valve replacement

We report on a 72 years old male patient with recurrent heart failure hospitalizations caused by severe mitral regurgitation due to severe restriction of the posterior mitral leaflet treated with the transfemoral mitral valve replacement (TMVR) system Cardiovalve. Immediate interventional success was obtained resulting in a quick mobilization and discharge

Effects of Artificial Intelligence-Derived Body Composition on Kidney Graft and Patient Survival in the Eurotransplant Senior Program

The Eurotransplant Senior Program allocates kidneys to elderly transplant patients. The aim of this retrospective study is to investigate the use of computed tomography (CT) body composition using artificial intelligence (AI)-based tissue segmentation to predict patient and kidney transplant survival. Body composition at the third lumbar vertebra level was analyzed in 42 kidney transplant recipients. Cox regression analysis of 1-year, 3-year and 5-year patient survival, 1-year, 3-year and 5-year censored kidney transplant survival, and 1-year, 3-year and 5-year uncensored kidney transplant survival was performed. First, the body mass index (BMI), psoas muscle index (PMI), skeletal muscle index (SMI), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) served as independent variates. Second, the cut-off values for sarcopenia and obesity served as independent variates. The 1-year uncensored and censored kidney transplant survival was influenced by reduced PMI (p = 0.02 and p = 0.03, respectively) and reduced SMI (p = 0.01 and p = 0.03, respectively); 3-year uncensored kidney transplant survival was influenced by increased VAT (p = 0.04); and 3-year censored kidney transplant survival was influenced by reduced SMI (p = 0.05). Additionally, sarcopenia influenced 1-year uncensored kidney transplant survival (p = 0.05), whereas obesity influenced 3-year and 5-year uncensored kidney transplant survival. In summary, AI-based body composition analysis may aid in predicting short- and long-term kidney transplant survival

First PACS‐integrated artificial intelligence‐based software tool for rapid and fully automatic analysis of body composition from CT in clinical routine

Background: To externally evaluate the first picture archiving communications system (PACS)-integrated artificial intelligence (AI)-based workflow, trained to automatically detect a predefined computed tomography (CT) slice at the third lumbar vertebra (L3) and automatically perform complete image segmentation for analysis of CT body composition and to compare its performance with that of an established semi-automatic segmentation tool regarding speed and accuracy of tissue area calculation. Methods: For fully automatic analysis of body composition with L3 recognition, U-Nets were trained (Visage) and compared with a conventional image segmentation software (TomoVision). Tissue was differentiated into psoas muscle, skeletal muscle, visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT). Mid-L3 level images from randomly selected DICOM slice files of 20 CT scans acquired with various imaging protocols were segmented with both methods. Results: Success rate of AI-based L3 recognition was 100%. Compared with semi-automatic, fully automatic AI-based image segmentation yielded relative differences of 0.22% and 0.16% for skeletal muscle, 0.47% and 0.49% for psoas muscle, 0.42% and 0.42% for VAT and 0.18% and 0.18% for SAT. AI-based fully automatic segmentation was significantly faster than semi-automatic segmentation (3 ± 0 s vs. 170 ± 40 s, P < 0.001, for User 1 and 152 ± 40 s, P < 0.001, for User 2). Conclusion: Rapid fully automatic AI-based, PACS-integrated assessment of body composition yields identical results without transfer of critical patient data. Additional metabolic information can be inserted into the patient’s image report and offered to the referring clinicians

Lipid metabolite biomarkers in cardiovascular disease: Discovery and biomechanism translation from human studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phos-pholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites

Lipid metabolite biomarkers in cardiovascular disease: Discovery and biomechanism translation from human studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phos-pholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites

Biomarker-based phenotyping of myocardial fibrosis identifies patients with heart failure with preserved ejection fraction resistant to the beneficial effects of spironolactone: results from the Aldo-DHF trial

Background: Myocardial fibrosis is characterized by excessive cross‐linking and deposition of collagen type I and is involved in left ventricular stiffening and left ventricular diastolic dysfunction (LVDD). We investigated whether the effect of spironolactone on LVDD in patients with heart failure with preserved ejection fraction (HFpEF) depends on its effects on collagen cross‐linking and/or deposition. Methods and results: We investigated 381 HFpEF patients from the multicentre, randomized, placebo‐controlled Aldo‐DHF trial with measures of the E:e' ratio. The ratio of serum carboxy‐terminal telopeptide of collagen type I to serum matrix metalloproteinase‐1 (CITP:MMP‐1, an inverse index of myocardial collagen cross‐linking) and serum carboxy‐terminal propeptide of procollagen type I (PICP, a direct index of myocardial collagen deposition) were determined at baseline and after 1‐year treatment with spironolactone 25 mg once daily or placebo. Patients were classified by CITP:MMP‐1 and PICP tertiles at baseline. While CITP:MMP‐1 tertiles at baseline interacted (P &lt; 0.05) with spironolactone effect on E:e', PICP tertiles did not. In fact, while spironolactone treatment did not modify E:e' in patients with lower CITP:MMP‐1 levels, this ratio was significantly reduced in the remaining spironolactone‐treated patients. In addition, PICP was unchanged in patients with lower CITP:MMP‐1 levels but was reduced in the remaining spironolactone‐treated patients. Conclusions: A biochemical phenotype of high collagen cross‐linking identifies HFpEF patients resistant to the beneficial effects of spironolactone on LVDD. It is suggested that excessive collagen cross‐linking, which stabilizes collagen type I fibres, diminishes the ability of spironolactone to reduce collagen deposition in these patients

Plasma Biomarker Profiling in Heart Failure Patients with Preserved Ejection Fraction before and after Spironolactone Treatment: Results from the Aldo-DHF Trial

The pathophysiology of heart failure with preserved ejection fraction (HFpEF) is poorly understood and therapeutic strategies are lacking. This study aimed to identify plasma proteins with pathophysiological relevance in HFpEF and with respect to spironolactone-induced effects. We assessed 92 biomarkers in plasma samples from 386 HFpEF patients—belonging to the Aldo-DHF trial—before (baseline, BL) and after one-year treatment (follow up, FU) with spironolactone (verum) or a placebo. At BL, various biomarkers showed significant associations with the two Aldo-DHF primary end point parameters: 33 with E/e’ and 20 with peak VO2. Ten proteins including adrenomedullin, FGF23 and inflammatory peptides (e.g., TNFRSF11A, TRAILR2) were significantly associated with both parameters, suggesting a role in the clinical HFpEF presentation. For 13 proteins, expression changes from BL to FU were significantly different between verum and placebo. Among them were renin, growth hormone, adrenomedullin and inflammatory proteins (e.g., TNFRSF11A, IL18 and IL4RA), indicating distinct spironolactone-mediated effects. BL levels of five proteins, e.g., inflammatory markers such as CCL17, IL4RA and IL1ra, showed significantly different effects on the instantaneous risk for hospitalization between verum and placebo. This study identified plasma proteins with different implications in HFpEF and following spironolactone treatment. Future studies need to define their precise mechanistic involvement