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Happy feet, food & fat

Voor het eerst in de geschiedenis van de mensheid zijn er meer mensen met overgewicht dan met ondervoeding. Overgewicht is in snel tempo toegenomen en is een van de grootste maatschappelijke (gezondheids)problemen van deze tijd. Overgewicht heeft ondervoeding overschaduwd. In "The land of plenty"’ is geen gebrek meer, en dus geen ondervoeding zou je verwachten. Ondervoeding blijkt echter ook in deze tijd nog steeds een van de grootste maatschappelijke (gezondheids). Door inactiviteit of door ziekte kan spiermassa afnemen en wordt het steeds lastiger ondervoeding door de laag vet heen te ontdekken. Het aanpakken van overgewicht en ondervoeding door de professional vergt goed gewichtsmanagement. Voeding en beweging spelen hierbij een cruciale rol. Energie uit de voeding is gerelateerd aan vetmassa, en eiwit uit de voeding en beweging is gerelateerd aan spiermassa

Standard Operating Procedure - Single Frequency Bio-Impedantie Analyse

GLIM-based malnutrition, protein intake and diet quality in preprocedural Transcatheter Aortic Valve Implantation (TAVI) patients

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.

The performance of the Dutch Safety Management System frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients

BackgroundEarly identification of older cardiac patients at high risk of readmission or mortality facilitates targeted deployment of preventive interventions. In the Netherlands, the frailty tool of the Dutch Safety Management System (DSMS-tool) consists of (the risk of) delirium, falling, functional impairment, and malnutrition and is currently used in all older hospitalised patients. However, its predictive performance in older cardiac patients is unknown.AimTo estimate the performance of the DSMS-tool alone and combined with other predictors in predicting hospital readmission or mortality within 6 months in acutely hospitalised older cardiac patients.MethodsAn individual patient data meta-analysis was performed on 529 acutely hospitalised cardiac patients ≥70 years from four prospective cohorts. Missing values for predictor and outcome variables were multiply imputed. We explored discrimination and calibration of: (1) the DSMS-tool alone; (2) the four components of the DSMS-tool and adding easily obtainable clinical predictors; (3) the four components of the DSMS-tool and more difficult to obtain predictors. Predictors in model 2 and 3 were selected using backward selection using a threshold of p = 0.157. We used shrunk c-statistics, calibration plots, regression slopes and Hosmer-Lemeshow p-values (PHL) to describe predictive performance in terms of discrimination and calibration.ResultsThe population mean age was 82 years, 52% were males and 51% were admitted for heart failure. DSMS-tool was positive in 45% for delirium, 41% for falling, 37% for functional impairments and 29% for malnutrition. The incidence of hospital readmission or mortality gradually increased from 37 to 60% with increasing DSMS scores. Overall, the DSMS-tool discriminated limited (c-statistic 0.61, 95% 0.56–0.66). The final model included the DSMS-tool, diagnosis at admission and Charlson Comorbidity Index and had a c-statistic of 0.69 (95% 0.63–0.73; PHL was 0.658).DiscussionThe DSMS-tool alone has limited capacity to accurately estimate the risk of readmission or mortality in hospitalised older cardiac patients. Adding disease-specific risk factor information to the DSMS-tool resulted in a moderately performing model. To optimise the early identification of older hospitalised cardiac patients at high risk, the combination of geriatric and disease-specific predictors should be further explored.

The performance of the Dutch Safety Management System frailty tool to predict the risk of readmission or mortality in older hospitalised cardiac patients

Background: Early identification of older cardiac patients at high risk of readmission or mortality facilitates targeted deployment of preventive interventions. In the Netherlands, the frailty tool of the Dutch Safety Management System (DSMS-tool) consists of (the risk of) delirium, falling, functional impairment, and malnutrition and is currently used in all older hospitalised patients. However, its predictive performance in older cardiac patients is unknown. Aim: To estimate the performance of the DSMS-tool alone and combined with other predictors in predicting hospital readmission or mortality within 6 months in acutely hospitalised older cardiac patients. Methods: An individual patient data meta-analysis was performed on 529 acutely hospitalised cardiac patients ≥70 years from four prospective cohorts. Missing values for predictor and outcome variables were multiply imputed. We explored discrimination and calibration of: (1) the DSMS-tool alone; (2) the four components of the DSMS-tool and adding easily obtainable clinical predictors; (3) the four components of the DSMS-tool and more difficult to obtain predictors. Predictors in model 2 and 3 were selected using backward selection using a threshold of p = 0.157. We used shrunk c-statistics, calibration plots, regression slopes and Hosmer-Lemeshow p-values (PHL) to describe predictive performance in terms of discrimination and calibration. Results: The population mean age was 82 years, 52% were males and 51% were admitted for heart failure. DSMS-tool was positive in 45% for delirium, 41% for falling, 37% for functional impairments and 29% for malnutrition. The incidence of hospital readmission or mortality gradually increased from 37 to 60% with increasing DSMS scores. Overall, the DSMS-tool discriminated limited (c-statistic 0.61, 95% 0.56-0.66). The final model included the DSMS-tool, diagnosis at admission and Charlson Comorbidity Index and had a c-statistic of 0.69 (95% 0.63-0.73; PHL was 0.658). Discussion: The DSMS-tool alone has limited capacity to accurately estimate the risk of readmission or mortality in hospitalised older cardiac patients. Adding disease-specific risk factor information to the DSMS-tool resulted in a moderately performing model. To optimise the early identification of older hospitalised cardiac patients at high risk, the combination of geriatric and disease-specific predictors should be further explored.

Evaluating routine dietetic interventions in geriatric rehabilitation: implications to address malnutrition, sarcopenia and underweight

Background: Although diagnosing and treating malnutrition, sarcopenia and underweight are recommended to be embedded and sustained within nutritional care, it is unknown if that is facilitated in geriatric rehabilitation. This study determined the proportion of geriatric rehabilitation inpatients with malnutrition, sarcopenia or underweight receiving dietetic interventions as part of routine clinical care and if these patients have greater improvements in body weight and composition compared to patients not receiving dietetic interventions.Methods: Geriatric rehabilitation inpatients from the observational REStORing health of acutely unwell adulTs (RESORT) cohort were included (n=971, median age 83.2 [77.7-88.8] years, 58.5% (n=568) females). Malnutrition, sarcopenia and underweight were defined by the Global Leadership Initiative of Malnutrition, European Working Group on Sarcopenia in Older People 2 and age-specific body mass index cut-offs. Data on dietetic interventions initiated by dietitians as part of clinical care was extracted from the centralised hospital database. Changes in body weight (kg), skeletal muscle mass (kg, %), and fat mass (kg, %) from admission to discharge were determined using linear mixed models.Results: Dietetic interventions were received by 306 (62.0%), 138 (71.5%) and 153 (76.9%) of patients with malnutrition (n=493), sarcopenia (n=193) and underweight (n=199). Duration and frequency of dietetic interventions were higher in patients with malnutrition, sarcopenia or underweight compared to patients without those conditions. There were no differences in body weight/composition changes in patients with malnutrition, sarcopenia or underweight receiving dietetic interventions compared to those not receiving interventions.Conclusions: One-third of geriatric rehabilitation inpatients with malnutrition, sarcopenia or underweight are not receiving dietetic interventions and therefore the referral and diagnostic process require improvements. Patients with malnutrition, sarcopenia or underweight receiving dietetic interventions had no greater improvements in body weight/composition compared to those who did not receive interventions. Tailoring dietetic interventions for malnutrition, sarcopenia and underweight diagnosis may improve patient outcomes.