Objective: Post-mortem computed tomography (PMCT) is an established method for disease, complications, and cause of death determination in both clinical and forensic cases. By adding intravascular infusion of contrast medium, computed tomography angiography (PMCTA) provides additional information on vascular structures and hemorrhages. When easily applicable and low in costs, this technique would be more frequently applied and of additional value to clinical and educational purposes, particularly in forensic scientific context. Materials and Methods: PMCTA was performed on 10 bodies of the anatomy department. First, a metal T-piece was inserted into the femoral artery as part of standard practice for conservation. Secondly, surplus contrast medium with sodium chloride was infused into the body through a catheter tube set attached to the metal T-piece, using a readily available enteroclysis pump from our radiology department. Results: With added costs of approximately € 266 (personnel and materials) and an additional procedure time of 15-20 minutes, successful infusion of contrast mixture was achieved with the enteroclysis pump. Partial or complete opacification was measured in 89% of arteries, with enhancement of soft tissue visualization. Conclusion: This study successfully evaluated an inexpensive and easy to use method to perform PMCTA for post-mortem investigations.
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Background & aims: Accurate diagnosis of sarcopenia requires evaluation of muscle quality, which refers to the amount of fat infiltration in muscle tissue. In this study, we aim to investigate whether we can independently predict mortality risk in transcatheter aortic valve implantation (TAVI) patients, using automatic deep learning algorithms to assess muscle quality on procedural computed tomography (CT) scans. Methods: This study included 1199 patients with severe aortic stenosis who underwent transcatheter aortic valve implantation (TAVI) between January 2010 and January 2020. A procedural CT scan was performed as part of the preprocedural-TAVI evaluation, and the scans were analyzed using deep-learning-based software to automatically determine skeletal muscle density (SMD) and intermuscular adipose tissue (IMAT). The association of SMD and IMAT with all-cause mortality was analyzed using a Cox regression model, adjusted for other known mortality predictors, including muscle mass. Results: The mean age of the participants was 80 ± 7 years, 53% were female. The median observation time was 1084 days, and the overall mortality rate was 39%. We found that the lowest tertile of muscle quality, as determined by SMD, was associated with an increased risk of mortality (HR 1.40 [95%CI: 1.15–1.70], p < 0.01). Similarly, low muscle quality as defined by high IMAT in the lowest tertile was also associated with increased mortality risk (HR 1.24 [95%CI: 1.01–1.52], p = 0.04). Conclusions: Our findings suggest that deep learning-assessed low muscle quality, as indicated by fat infiltration in muscle tissue, is a practical, useful and independent predictor of mortality after TAVI.
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Objectives: The aim of this study was to assess the predictive value of PMA measurement for mortality. Background: Current surgical risk stratification have limited predictive value in the transcatheter aortic valve implantation (TAVI) population. In TAVI workup, a CT scan is routinely performed but body composition is not analyzed. Psoas muscle area (PMA) reflects a patient's global muscle mass and accordingly PMA might serve as a quantifiable frailty measure. Methods: Multi-slice computed tomography scans (between 2010 and 2016) of 583 consecutive TAVI patients were reviewed. Patients were divided into equal sex-specific tertiles (low, mid, and high) according to an indexed PMA. Hazard ratios (HR) and their confidence intervals (CI) were determined for cardiac and all-cause mortality after TAVI. Results: Low iPMA was associated with cardiac and all-cause mortality in females. One-year adjusted cardiac mortality HR in females for mid-iPMA and high-iPMA were 0.14 [95%CI, 0.05–0.45] and 0.40 [95%CI, 0.15–0.97], respectively. Similar effects were observed for 30-day and 2-years cardiac and all-cause mortality. In females, adding iPMA to surgical risk scores improved the predictive value for 1-year mortality. C-statistics changed from 0.63 [CI = 0.54–0.73] to 0.67 [CI: 0.58–0.75] for EuroSCORE II and from 0.67 [CI: 0.59–0.77] to 0.72 [CI: 0.63–0.80] for STS-PROM. Conclusions: Particularly in females, low iPMA is independently associated with an higher all-cause and cardiac mortality. Prospective studies should confirm whether PMA or other body composition parameters should be extracted automatically from CT-scans to include in clinical decision making and outcome prediction for TAVI.
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