Are nutritional sufficiency of ≥75% energy and protein requirements relevant targets in patients at nutritional risk? - A one month follow-up study

The accuracy of estimating equations for total resting energy expenditure in hospitalized patients

BACKGROUND & AIMS

Methods for estimation of nutritional expenditures for hospitalized patients may not be sufficiently specific. This study aimed to investigate the accuracy of predictive equations compared to indirect calorimetry (IC) and the effect of certain patient characteristics which might correlate with total daily energy expenditure on a heterogeneous population of hospitalized medical patients.

METHODS

A cross sectional study including demographic information, measures of bioelectric impedance analysis (BIA) including height and bodyweight (BW), IC, heart rate and from patient records, information was collected regarding nutritional risk by Nutrition Risk Screening 2002, biomarkers of C-reactive protein (CRP), albumin and leukocytes. The Harris-Benedict (HB), Mifflin St. Jeor (MSJ), and Schofield equations were calculated. Data were analyzed using T-test, linear and logistic regression analysis.

RESULTS

Overall, 197 patients, mean age 63.6 ± 16.0 years were measured with IC and had equations performed. BIA was performed in 187 and 46 withdrew, as they were too ill to measure, has oxygen or forgot fasting. All estimation methods underestimate energy expenditures for patients at nutritional risk (p < 0.001), and HB and MSJ underestimate for those with body mass index (BMI) < 18.5 (p = 0.029 and p < 0.001), while for BMI≥30 all overestimate but only HB significantly (p = 0.025). Elevated CRP and leukocytes, lower heart rate, lower and higher BMI, older patients and patients at nutritional risk can affect estimated total daily energy expenditure by equations compared measured by IC (p < 0.05).

CONCLUSION

HB, MSJ, and Schofield equations all underestimate energy expenditures with higher variations in patients at nutritional risk. In patients with BMI≥30, energy expenditures are overestimated. Considerations are to measure energy expenditures for patients at nutritional risk with continued weight loss and need for artificial nutrition, and for those with BMI≥30.

Energy and protein intake threshold modelling using nutrition dashboard technology and sensitivity of hospital malnutrition identification

AIMS

Hospital food provision/intake dashboards may improve malnutrition screening. The aim of this study was to use Nutrition Dashboard data to determine the optimal threshold for screening for malnutrition risk, and compare the accuracy of this method with estimated dietary requirements.

METHODS

Observational data were extracted from medical files and food service records of 267 patients for a 4-month period in a 99-bed hospital. Energy (2500-8000 kJ) and protein (30-90 g) thresholds were applied for Nutrition Dashboard categorisation by supply and intake of food. Deficits in estimated requirements (105 kJ/kg/day and 0.75 g/kg/day) were also applied as a comparative method. The association between Nutrition Dashboard categories and the Malnutrition Screening Tool was explored using generalised estimating equations.

RESULTS

A total of 267 patients and 1908 days of data were analysed. The use of estimated requirements for Nutrition Dashboard categorisation was not a statistically significant predictor of malnutrition risk. Application of energy (≤6000 kJ) and protein (≤65 g) thresholds for categorisation was significant (χ2 = 9.50, df = 3, p = 0.023). When 5000 kJ and 55 g of protein were used for categorisation, patients were more likely to be at malnutrition risk when within low supply (odds ratio = 2.11, p = 0.002) and low intake (odds ratio 2.23, p < 0.001) categories.

CONCLUSIONS

Nutrition Dashboard categories are associated with an increased risk of malnutrition when categorised using thresholds of up to 6000 kJ and 65 g protein. Technologies like the Nutrition Dashboard present innovative opportunities for dietitians to utilise nutrition informatics to enhance and optimise nutrition care.

Implementation of nutritional care in hospitals: A qualitative study of barriers and facilitators using implementation theory

BACKGROUND

Disease-related malnutrition is prevalent among hospitalised patients, but not all patients achieve the needed nutritional care. At a Danish University Hospital, focus has been on implementing nutritional practices based on clinical guidelines, but there is continuously variation between the wards regarding the quality of nutritional care.

AIM

The aim of this study was to identify the potential barriers and facilitators for implementation of the clinical guidelines for nutritional practices and to recommend suggestions for development of nutritional practices, using a theoretical implementation strategy.

METHOD

The design was a qualitative interview study of employees at a Danish University Hospital, using a semi-structured interview guide. The participants were nurses, nurse's assistant, nurse nutrition expert, head nurse and dieticians. We recruited 11 employees, representing eight different wards.

FINDINGS

The analysis identified six themes: (1) clear allocation of responsibilities and committed management enhances nutrition practices, (2) leadership support is essential, (3) physical settings and tools affect possibilities for action, (4) selection of equivalent staff is core, (5) teaching promotes the knowledge and skills and (6) a dietitian in the ward facilitates implementation of nutritional care. Barriers and facilitators among the themes were identified and has led to suggestions to strengthen nutritional care, based on implementation theory.

CONCLUSION

Various factors were identified as having impact on the implementation of nutrition practices and different suggestions have emerged to accommodate those factors, as well as to apply an implementation strategy to facilitate change in practice.

A Novel Facet of In-Hospital Food Consumption Associated with Hospital Mortality in Patients with Scheduled Admission-Addition of a Study Protocol to Test the Existence of Effects of COVID-19 in the Same Study in the Post-COVID-19 Period

BACKGROUND

Humankind has faced unexperienced pandemic events since 2020. Since the COVID-19 pandemic has calmed down, we felt the need to verify whether in-hospital mortality had worsened compared to pre-pandemic conditions due to the COVID-19 pandemic.

OBJECTIVE

To test the hypothesis that daily food consumption is associated with in-hospital mortality during hospitalization and to provide baseline data to examine whether the effects of COVID-19 exist or not in post-pandemic period.

METHODS

All hospitalized patients staying in a single institution on the third Thursday of May, August, November, and the following February were included. Compared data: (1) among four seasons, (2) between age < 75 vs. ≥75 years, (3) between <75% vs. ≥75% of in-hospital food, and (4) logistic regression analysis to identify factors associated with in-hospital mortality.

RESULTS

In 365 inpatients, the following results were obtained: (1) no seasonality or age effect in in-hospital mortality, (2) the novel cutoff value of 75% of the hospital food requirement was used to identify poor in-hospital survivors, (3) logistic regression analysis showed low food consumption, with <75% of the hospital food requirement as the predictor of high in-hospital mortality.

CONCLUSIONS

A small eater of in-hospital food < 75% during hospitalization was associated with significantly higher in-hospital mortality in patients with scheduled hospitalization in the pre-pandemic period. Then, a study protocol is proposed to test the existence of the effects of COVID-19 in the same study in the post-COVID-19 period. This study protocol is, to our knowledge, the first proposal to test the effects of food consumption in the post-COVID-19 period on in-hospital mortality in the clinical nutritional areas.

An educative nutritional intervention supporting older hospital patients to eat sufficiently using eHealth: a mixed methods feasibility and pilot study

BACKGROUND

Insufficient food intake is common in older hospital patients and increases the risk of readmission, mortality, and decline in functional status. To improve food intake in older patients, an eHealth solution (Food'n'Go) enabling them to participate in their own nutritional care was implemented in a hospital unit. We developed an educative nutritional intervention (ENI) to support hospitalized older adults (aged ≥ 65 years) to participate in their own nutritional care using Food'n'Go. In this study, we evaluate the feasibility of the ENI and its potential to improve nutritional intake.

METHODS

Feasibility was evaluated using process evaluation, and nutritional intake was examined by using a pre- and post-test design. Assessment of feasibility: Contextual factors (availability of Food'n'Go and prevalence of counseling by a dietitian); Intervention fidelity (whether patients were informed of nutrition and Food'n'Go, and whether their needs for support were assessed); and Mechanism of impact (patients' knowledge and skills related to nutrition and the use of Food'n'Go and their acceptance of the ENI). Assessment of nutritional intake: Patients' intake of protein and energy based on one-day observations before implementation of the ENI (pre-test; n = 65) and after a three-month intervention (post-test; n = 65).

RESULTS

Feasibility: Food'n'Go was available for more patients after the intervention (85 vs. 64%, p = .004). Most patients managed the use of Food'n'Go and were involved in ordering their food, but only a few monitored their food intake. Information on nutrition was not provided sufficiently to all patients. In general, the ENI had high acceptability among the patients. Nutritional intake: Compared to patients in the pre-test, patients in the post-test had a higher daily mean intake of energy (kJ) (6712 (SD: 2964) vs. 5660 (SD: 2432); difference 1052 (95% CI 111-1993)), and of protein (g) (60 (SD: 28) vs. 43 (SD: 19); difference 17 (95% CI 9-26)). Likewise, there was an increase in the mean attainment of protein requirements: 73% (SD: 34) vs. 59% (SD: 29) (p = .013).

CONCLUSION

The ENI is feasible for supporting hospitalized older adults to participate in their own nutrition using eHealth and preliminary results indicate that it may lead to an increasing energy and protein intake.