Hyperemesis gravidarum severity, enteral tube feeding and cardiometabolic markers in offspring cord blood

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Predicting resting energy expenditure in underweight, normal weight, overweight, and obese adult hospital patients

BACKGROUND: When indirect calorimetry is not available, predictive equations are used to estimate resing energy expenditure (REE). There is no consensus about which equation to use in hospitalized patients. The objective of this study is to examine the validity of REE predictive equations for underweight, normal weight, overweight, and obese inpatients and outpatients by comparison with indirect calorimetry. METHODS: Equations were included when based on weight, height, age, and/or gender. REE was measured with indirect calorimetry. A prediction between 90 and 110% of the measured REE was considered accurate. The bias and root-mean-square error (RMSE) were used to evaluate how well the equations fitted the REE measurement. Subgroup analysis was performed for BMI. A new equation was developed based on regression analysis and tested. RESULTS: 513 general hospital patients were included, (253 F, 260 M), 237 inpatients and 276 outpatients. Fifteen predictive equations were used. The most used fixed factors (25 kcal/kg/day, 30 kcal/kg/day and 2000 kcal for female and 2500 kcal for male) were added. The percentage of accurate predicted REE was low in all equations, ranging from 8 to 49%. Overall the new equation performed equal to the best performing Korth equation and slightly better than the well-known WHO equation based on weight and height (49% vs 45% accurate). Categorized by BMI subgroups, the new equation, Korth and the WHO equation based on weight and height performed best in all categories except from the obese subgroup. The original Harris and Benedict (HB) equation was best for obese patients. CONCLUSIONS: REE predictive equations are only accurate in about half the patients. The WHO equation is advised up to BMI 30, and HB equation is advised for obese (over BMI 30). Measuring REE with indirect calorimetry is preferred, and should be used when available and feasible in order to optimize nutritional support in hospital inpatients and outpatients with different degrees of malnutrition

Protein intake during hospital admission; Dutch national data on protein intake in 339,720 malnourished patients from 2009–2019

Introduction To stimulate early recognition and treatment of malnutrition, the Dutch Healthcare Inspectorate obliged all hospitals from 2008–2019 to report the number of malnourished patients with an adequate protein intake on the fourth day of hospital admission. In this article we present results over the past 11 years and discuss success factors and barriers for adequate treatment of malnourished patients in hospitals. Methods The annual reports of hospitals on the numbers of patients with a screening result ‘malnourished’ and an adequate protein intake on the fourth day of admission were analysed. Hospitals were categorized based on the percentage of malnourished patients with an adequate protein intake on the fourth day of admission as ‘poor’ (60% of patients in a hospital achieve an adequate protein intake). To identify success factors and barriers for adequate treatment and registration of malnourished patients in hospitals, three focus groups were held in June and July 2020. Participants were dietitians and quality employees or nurses who were involved in data collection for malnutrition indicators in their hospitals. Results Between 2008–2019, data were reported of 339,720 malnourished patients. The relative number of patients with adequate intake of protein on the fourth day in hospital ranges from 44%-53% between 2011 and 2019. Before 2013, the number of hospitals that reported data was too small to draw conclusions about results of treatment of malnutrition. Data from 2013 to 2019, show a decline in the number of hospitals with a ‘poor’ score. The number of hospitals with a moderate score increased between 2015 and 2019 and the number of hospitals with a good score remained more or less stable, except for 2018 where more hospitals reached a ‘good’ score. Sixteen professionals from ten different hospitals participated in the focus groups and revealed several determinants of adequate treatment of malnourished patients in hospitals such as awareness, feeling responsible and the need of clear instructions and good collaboration. Conclusion This inventory of the protein intake of 339,720 hospital malnourished patients over 11 years shows that in one out of five Dutch hospitals >60% of malnourished patients had an adequate protein intake on the fourth day of admission. This shows that meeting protein requirements remains a difficult challenge. Early recognition of malnutrition, optimal multidisciplinary treatment and continuous evaluation is necessary to provide optimal nutritional care in the hospital and beyond

Are we overfeeding hemodialysis patients with protein? Exploring an alternative method to estimate protein needs

Background & aims: Sufficient protein intake is of great importance in hemodialysis (HD) patients, especially for maintaining muscle mass. Daily protein needs are generally estimated using bodyweight (BW), in which individual differences in body composition are not accounted for. As body protein mass is best represented by fat free mass (FFM), there is a rationale to apply FFM instead of BW. The agreement between both estimations is unclear. Therefore, the aim of this study is to compare protein needs based on either FFM or BW in HD patients. Methods: Protein needs were estimated in 115 HD patients by three different equations; FFM, BW and BW adjusted for low or high BMI. FFM was measured by multi-frequency bioelectrical impedance spectroscopy and considered the reference method. Estimations of FFM x 1.5 g/kg and FFM x 1.9 g/kg were compared with (adjusted)BW x 1.2 and x 1.5, respectively. Differences were assessed with repeated measures ANOVA and Bland–Altman plots. Results: Mean protein needs estimated by (adjusted)BW were higher compared to those based on FFM, across all BMI categories (P 30, protein needs were 69 ± 17.4 g/day higher based on BW and 45 ± 9.3 g/day higher based on BMI adjusted BW, compared to FFM. In males with BMI >30, protein needs were 51 ± 20.4 g/day and 23 ± 20.9 g/day higher compared to FFM, respectively. Conclusions: Our data show large differences and possible overestimations of protein needs when comparing BW to FFM. We emphasize the importance of more research and discussion on this topic

Malnutrition Screening Tools Are Not Sensitive Enough to Identify Older Hospital Patients with Malnutrition

This study evaluates the concurrent validity of five malnutrition screening tools to identify older hospitalized patients against the Global Leadership Initiative on Malnutrition (GLIM) diagnostic criteria as limited evidence is available. The screening tools Short Nutritional Assessment Questionnaire (SNAQ), Malnutrition Universal Screening Tool (MUST), Malnutrition Screening Tool (MST), Mini Nutritional Assessment-Short Form (MNA-SF), and the Patient-Generated Subjective Global Assessment-Short Form (PG-SGA-SF) with cut-offs for both malnutrition (conservative) and moderate malnutrition or risk of malnutrition (liberal) were used. The concurrent validity was determined by the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and the level of agreement by Cohen's kappa. In total, 356 patients were included in the analyses (median age 70 y (IQR 63-77); 54% male). The prevalence of malnutrition according to the GLIM criteria without prior screening was 42%. The conservative cut-offs showed a low-to-moderate sensitivity (32-68%) and moderate-to-high specificity (61-98%). The PPV and NPV ranged from 59 to 94% and 67-86%, respectively. The Cohen's kappa showed poor agreement (k = 0.21-0.59). The liberal cut-offs displayed a moderate-to-high sensitivity (66-89%) and a low-to-high specificity (46-95%). The agreement was fair to good (k = 0.33-0.75). The currently used screening tools vary in their capacity to identify hospitalized older patients with malnutrition. The screening process in the GLIM framework requires further consideration

Dietary advice with or without oral nutritional supplements for disease-related malnutrition in adults

Background: Disease-related malnutrition has been reported in 10% to 55% of people in hospital and the community and is associated with significant health and social-care costs. Dietary advice (DA) encouraging consumption of energy- and nutrient-rich foods rather than oral nutritional supplements (ONS) may be an initial treatment. Objectives: To examine evidence that DA with/without ONS in adults with disease-related malnutrition improves survival, weight, anthropometry and quality of life (QoL). Search methods: We identified relevant publications from comprehensive electronic database searches and handsearching. Last search: 01 March 2021. Selection criteria: Randomised controlled trials (RCTs) of DA with/without ONS in adults with disease-related malnutrition in any healthcare setting compared with no advice, ONS or DA alone. Data collection and analysis: Two authors independently assessed study eligibility, risk of bias, extracted data and graded evidence. Main results: We included 94, mostly parallel, RCTs (102 comparisons; 10,284 adults) across many conditions possibly explaining the high heterogeneity. Participants were mostly older people in hospital, residential care and the community, with limited reporting on their sex. Studies lasted from one month to 6.5 years. DA versus no advice - 24 RCTs (3523 participants). Most outcomes had low-certainty evidence. There may be little or no effect on mortality after three months, RR 0.87 (95% confidence interval (CI) 0.26 to 2.96), or at later time points. We had no three-month data, but advice may make little or no difference to hospitalisations, or days in hospital after four to six months and up to 12 months. A similar effect was seen for complications at up to three months, MD 0.00 (95% CI -0.32 to 0.32) and between four and six months. Advice may improve weight after three months, MD 0.97 kg (95% CI 0.06 to 1.87) continuing at four to six months and up to 12 months; and may result in a greater gain in fat-free mass (FFM) after 12 months, but not earlier. It may also improve global QoL at up to three months, MD 3.30 (95% CI 1.47 to 5.13), but not later. DA versus ONS - 12 RCTs (852 participants). All outcomes had low-certainty evidence. There may be little or no effect on mortality after three months, RR 0.66 (95% CI 0.34 to 1.26), or at later time points. Either intervention may make little or no difference to hospitalisations at three months, RR 0.36 (95% CI 0.04 to 3.24), but ONS may reduce hospitalisations up to six months. There was little or no difference between groups in weight change at three months, MD -0.14 kg (95% CI -2.01 to 1.74), or between four to six months. Advice (one study) may lead to better global QoL scores but only after 12 months. No study reported days in hospital, complications or FFM. DA versus DA plus ONS - 22 RCTs (1286 participants). Most outcomes had low-certainty evidence. There may be little or no effect on mortality after three months, RR 0.92 (95% CI 0.47 to 1.80) or at later time points. At three months advice may lead to fewer hospitalisations, RR 1.70 (95% CI 1.04 to 2.77), but not at up to six months. There may be little or no effect on length of hospital stay at up to three months, MD -1.07 (95% CI -4.10 to 1.97). At three months DA plus ONS may lead to fewer complications, RR 0.75 (95% CI o.56 to 0.99); greater weight gain, MD 1.15 kg (95% CI 0.42 to 1.87); and better global QoL scores, MD 0.33 (95% CI 0.09 to 0.57), but this was not seen at other time points. There was no effect on FFM at three months. DA plus ONS if required versus no advice or ONS - 31 RCTs (3308 participants). Evidence was moderate- to low-certainty. There may be little or no effect on mortality at three months, RR 0.82 (95% CI 0.58 to 1.16) or at later time points. Similarly, little or no effect on hospitalisations at three months, RR 0.83 (95% CI 0.59 to 1.15), at four to six months and up to 12 months; on days in hospital at three months, MD -0.12 (95% CI -2.48 to 2.25) or for complications at any time point. At three months, advice plus ONS probably improve weight, MD 1.25 kg (95% CI 0.73 to 1.76) and may improve FFM, 0.82 (95% CI 0.35 to 1.29), but these effects were not seen later. There may be little or no effect of either intervention on global QoL scores at three months, but advice plus ONS may improve scores at up to 12 months. DA plus ONS versus no advice or ONS - 13 RCTs (1315 participants). Evidence was low- to very low-certainty. There may be little or no effect on mortality after three months, RR 0.91 (95% CI 0.55 to 1.52) or at later time points. No study reported hospitalisations and there may be little or no effect on days in hospital after three months, MD -1.81 (95% CI -3.65 to 0.04) or six months. Advice plus ONS may lead to fewer complications up to three months, MD 0.42 (95% CI 0.20 to 0.89) (one study). Interventions may make little or no difference to weight at three months, MD 1.08 kg (95% CI -0.17 to 2.33); however, advice plus ONS may improve weight at four to six months and up to 12 months. Interventions may make little or no difference in FFM or global QoL scores at any time point. Authors' conclusions: We found no evidence of an effect of any intervention on mortality. There may be weight gain with DA and with DA plus ONS in the short term, but the benefits of DA when compared with ONS are uncertain. The size and direction of effect and the length of intervention and follow-up required for benefits to emerge were inconsistent for all other outcomes. There were too few data for many outcomes to allow meaningful conclusions. Studies focusing on both patient-centred and healthcare outcomes are needed to address the questions in this review

Nutritional problems of patients with COVID-19 receiving dietetic treatment in primary care

Background: The nutritional problems of patients who are hospitalised for COVID-19 are becoming increasingly clear. However, a large group of patients have never been hospitalised and also appear to experience persistent nutritional problems. The present study describes the nutritional status, risk of sarcopaenia and nutrition-related complaints of patients recovering from COVID-19 receiving dietetic treatment in primary care. Methods: In this retrospective observational study, data were collected during dietetic treatment by a primary care dietitian between April and December 2020. Both patients who had and had not been admitted to the hospital were included at their first visit to a primary care dietitian. Data on nutritional status, risk of sarcopaenia and nutrition-related complaints were collected longitudinally. Results: Data from 246 patients with COVID-19 were collected. Mean ± SD age was 57 ± 16 years and 61% of the patient population was female. At first consultation, two thirds of patients were classified as overweight or obese (body mass index >25 kg m–2). The majority had experienced unintentional weight loss because of COVID-19. Additionally, 55% of hospitalised and 34% of non-hospitalised patients had a high risk of sarcopaenia. Most commonly reported nutrition-related complaints were decreased appetite, shortness of breath, changed or loss of taste and feeling of being full. Nutrition-related complaints decreased after the first consultation, but remained present over time. Conclusions: In conclusion, weight changes, risk of sarcopaenia and nutrition-related complaints were prevalent in patients with COVID-19, treated by a primary care dietitian. Nutrition-related complaints improved over time, but remained prevalent until several months after infection

Calculation of protein requirements; a comparison of calculations based on bodyweight and fat free mass

Background & aims: In dietary practice, it is common to estimate protein requirements on actual bodyweight, but corrected bodyweight (in cases with BMI <20 kg/m 2 and BMI ≥30 kg/m 2) and fat free mass (FFM) are also used. Large differences on individual level are noticed in protein requirements using these different approaches. To continue this discussion, the answer is sought in a large population to the following question: Will choosing actual bodyweight, corrected bodyweight or FFM to calculate protein requirements result in clinically relevant differences? Methods: This retrospective database study, used data from healthy persons ≥55 years of age and in- and outpatients ≥18 years of age. FFM was measured by air displacement plethysmography technology or bioelectrical impedance analysis. Protein requirements were calculated as 1) 1.2 g (g) per kilogram (kg) actual bodyweight or 2) corrected bodyweight or 3) 1.5 g per kg FFM. To compare these three approaches, the approach in which protein requirement is based on FFM, was used as reference method. Bland–Altman plots with limits of agreement were used to determine differences, analyses were performed for both populations separately and stratified by BMI category and gender. Results: In total 2291 subjects were included. In the population with relatively healthy persons (n = 506, ≥55 years of age) mean weight is 86.5 ± 18.2 kg, FFM is 51 ± 12 kg and in the population with adult in- and outpatients (n = 1785, ≥18 years of age) mean weight is 72.5 ± 18.4 kg, FFM is 51 ± 11 kg. Clinically relevant differences were found in protein requirement between actual bodyweight and FFM in most of the participants with overweight, obesity or severe obesity (78–100%). Using corrected bodyweight, an overestimation in 48–92% of the participants with underweight, healthy weight and overweight is found. Only in the Amsterdam UMC population, protein requirement is underestimated when using the approach of corrected bodyweight in participants with severe obesity. Conclusion: The three approaches in estimation of protein requirement show large differences. In the majority of the population protein requirement based on FFM is lower compared to actual or corrected bodyweight. Correction of bodyweight reduces the differences, but remain unacceptably large. It is yet unknown which method is the best for estimation of protein requirement. Since differences vary by gender due to differences in body composition, it seems more accurate to estimate protein requirement based on FFM. Therefore, we would like to advocate for more frequent measurement of FFM to determine protein requirements, especially when a deviating body composition is to be expected, for instance in elderly and persons with overweight, obesity or severe obesity

Calculation of protein requirements: a comparison of calculations based on bodyweight and fat free mass

Background & aims: In dietary practice, it is common to estimate protein requirements on actual bodyweight, but corrected bodyweight (in cases with BMI <20 kg/m2 and BMI ≥30 kg/m2) and fat free mass (FFM) are also used. Large differences on individual level are noticed in protein requirements using these different approaches. To continue this discussion, the answer is sought in a large population to the following question: Will choosing actual bodyweight, corrected bodyweight or FFM to calculate protein requirements result in clinically relevant differences? Methods: This retrospective database study, used data from healthy persons ≥55 years of age and in- and outpatients ≥18 years of age. FFM was measured by air displacement plethysmography technology or bioelectrical impedance analysis. Protein requirements were calculated as 1) 1.2 g (g) per kilogram (kg) actual bodyweight or 2) corrected bodyweight or 3) 1.5 g per kg FFM. To compare these three approaches, the approach in which protein requirement is based on FFM, was used as reference method. Bland–Altman plots with limits of agreement were used to determine differences, analyses were performed for both populations separately and stratified by BMI category and gender. Results: In total 2291 subjects were included. In the population with relatively healthy persons (n = 506, ≥55 years of age) mean weight is 86.5 ± 18.2 kg, FFM is 51 ± 12 kg and in the population with adult in- and outpatients (n = 1785, ≥18 years of age) mean weight is 72.5 ± 18.4 kg, FFM is 51 ± 11 kg. Clinically relevant differences were found in protein requirement between actual bodyweight and FFM in most of the participants with overweight, obesity or severe obesity (78–100%). Using corrected bodyweight, an overestimation in 48–92% of the participants with underweight, healthy weight and overweight is found. Only in the Amsterdam UMC population, protein requirement is underestimated when using the approach of corrected bodyweight in participants with severe obesity. Conclusion: The three approaches in estimation of protein requirement show large differences. In the majority of the population protein requirement based on FFM is lower compared to actual or corrected bodyweight. Correction of bodyweight reduces the differences, but remain unacceptably large. It is yet unknown which method is the best for estimation of protein requirement. Since differences vary by gender due to differences in body composition, it seems more accurate to estimate protein requirement based on FFM. Therefore, we would like to advocate for more frequent measurement of FFM to determine protein requirements, especially when a deviating body composition is to be expected, for instance in elderly and persons with overweight, obesity or severe obesity

Validity of the "Rate-a-Plate" Method to Estimate Energy and Protein Intake in Acutely Ill, Hospitalized Patients

Background: Prevalence of malnutrition in hospitals has been reported around 20% and increases during hospitalization. The "Rate-a-Plate" method has been developed to monitor dietary intake and identify patients whose nutrition status deteriorates during hospitalization, but has not yet been validated. The objective was to study the validity and reliability of the method (phase 1) and redesign and revalidate a revised version (phase 2).Methods: Detailed food records provided a reference method. A priori difference of >20% in energy or protein between the reference and the "Rate-a-Plate" method was determined as clinically relevant. Intraclass correlation coefficients were used to determine the reliability.Results: In phase 1, 24 patients were included with a total 67 test days. In phase 2, 14 patients were included, 28 test days. In phase 1, the "Rate-a-Plate" method underestimated intake by 422 kcal (29%, ICC 0.349, 95% CI 304-541) and 5.7 g protein (10%, ICC 0.511, 95% CI 0.0-11.5). Underestimation was found in 65% and 23% for energy and protein intake, respectively. Underestimation was higher when patients had higher intake. In phase 2, underestimation was 109 kcal (7%, ICC 0.788, 95% CI -273 to 56) and 3.7 g protein (6%, ICC 0.905, 95% CI -8.4 to 1.0). In 32% and 21% of the cases, energy and protein intake were underestimated.Conclusion: The revised version of the "Rate-a-Plate" method is a valid method to monitor energy and protein intake of hospitalized patients and can be filled out by nutrition assistants. A larger validation study is required