Aim To provide insight into the basic characteristics of decision making in the treatment of symptomatic severe aortic stenosis (SSAS) in Dutch heart centres with specific emphasis on the evaluation of frailty, cognition, nutritional status and physical functioning/functionality in (instrumental) activities of daily living [(I)ADL]. Methods A questionnaire was used that is based on the European and American guidelines for SSAS treatment. The survey was administered to physicians and non-physicians in Dutch heart centres involved in the decision-making pathway for SSAS treatment. Results All 16 Dutch heart centres participated. Before a patient case is discussed by the heart team, heart centres rarely request data from the referring hospital regarding patients’ functionality (n = 5), frailty scores (n = 0) and geriatric consultation (n = 1) as a standard procedure. Most heart centres ‘often to always’ do their own screening for frailty (n = 10), cognition/mood (n = 9), nutritional status (n = 10) and physical functioning/functionality in (I)ADL (n = 10). During heart team meetings data are ‘sometimes to regularly’ available regarding frailty (n = 5), cognition/mood (n = 11), nutritional status (n = 8) and physical functioning/functionality in (I)ADL (n = 10). After assessment in the outpatient clinic patient cases are re-discussed ‘sometimes to regularly’ in heart team meetings (n = 10). Conclusions Dutch heart centres make an effort to evaluate frailty, cognition, nutritional status and physical functioning/functionality in (I)ADL for decision making regarding SSAS treatment. However, these patient data are not routinely requested from the referring hospital and are not always available for heart team meetings. Incorporation of these important data in a structured manner early in the decision-making process may provide additional useful information for decision making in the heart team meeting.
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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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BACKGROUND & AIMS: Diagnosed prevalence of malnutrition and dietary intake are currently unknown in patients with severe aortic stenosis planned to undergo Transcatheter Aortic Valve Implantation (TAVI). This study describes the preprocedural nutritional status, protein intake and diet quality.METHODS: Consecutive preprocedural TAVI patients were asked to participate in this explorative study. Nutritional status was diagnosed with the global leadership initiative on malnutrition (GLIM) criteria. Preprocedural protein intake and diet quality were assessed with a three-day dietary record. To increase the record's validity, a researcher visited the participants at their homes to confirm the record. Protein intake was reported as an average intake of three days and diet quality was assessed using the Dutch dietary guidelines (score range 0-14, 1 point for adherence to each guideline).RESULTS: Of the included patients (n = 50, median age 80 ± 5, 56% male) 32% (n = 16) were diagnosed with malnutrition. Patients diagnosed with malnutrition had a lower protein intake (1.02 ± 0.28 g/kg/day vs 0.87 ± 0.21 g/kg/day, p = 0.04). The difference in protein intake mainly took place during lunch (20 ± 13 g/kg vs 13 ± 7 g/kg, p = 0.03). Patients adhered to 6.4 ± 2.2 out of 14 dietary guidelines. Adherence to the guideline of whole grains and ratio of whole grains was lower in the group of patients with malnutrition than in patients with normal nutritional status (both 62% vs 19%, p = 0.01). In a multivariate analysis diabetes mellitus was found as an independent predictor of malnutrition.CONCLUSION: Prevalence of malnutrition among TAVI patients is very high up to 32%. Patients with malnutrition had lower protein and whole grain intake than patients with normal nutritional status. Furthermore, we found diabetes mellitus as independent predictor of malnutrition. Nutrition interventions in this older patient group are highly warranted.
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