Abstract
Objectives
High rates of malnutrition have been reported in the older hospitalized patient population. This is recognised to impact on patient outcomes and health costs. This study aimed to assess the impact of nutrition screening and intervention on these parameters.
Design
Randomised controlled prospective study.
Setting
The study was performed in the acute geriatric medicine wards of the Prince of Wales Hospital, Sydney Australia.
Participants
All patients admitted to these wards under a geriatrician with an expected length of stay of at least 72 hours were considered for the study.
Intervention
Patients were screened on admission for malnutrition using the Mini Nutritional Assessment (MNA) tool and randomly assigned to control or intervention groups. Intervention patients were immediately commenced on a malnutrition care plan (MCP). Control patients were only commenced on a MCP if referred by clinical staff.
Measurements
Length of stay (LOS), weight change and frequency of readmission to hospital were compared between the groups.
Results
143 patients were screened. 119 were identified as malnourished (MN) or at risk of malnutrition (AR). Overall LOS was not different between the two groups (control v. intervention: 13.4 ± 1.3 days v. 12.5 ± 1.2 days, p=0.64). However there was a significant decrease in LOS in the MN (control v. intervention: 19.5 ± 3days v. 10.6 ± 1.6 days, p=0.013) and a trend to reduced readmissions. There was no difference in weight change over admission between the groups. Without screening, clinical staff identified only a small proportion of malnourished patients (35% of MN and 20% of AR).
Conclusions
Malnutrition in the older hospital population is common. Malnutrition screening on hospital admission facilitated targeted nutrition intervention, however length of stay and representations were only reduced in older malnourished patients with an MNA score less than 17.
Key words: Malnutrition, randomised controlled trial, outcomes, nutrition screening, hospital
Non-standard abbreviations
- LOS
length of stay
- MNA
Mini Nutritional Assessment
- MN
malnourished
- WN
well nourished
- AR
at risk of malnutrition
- MCP
Malnutrition Care Plan
Introduction
The prevalence of malnutrition in the acute hospital setting has been widely reported with rates as high as 83% in older patients (1., 2., 3., 4., 5.). Malnutrition has been associated with a wide range of poorer outcomes including increased risk of falls (6), institutionalisation (7), hospital presentations and admissions (6), postoperative complications (8, 9) infections (10), pressure ulcers (11), impaired wound healing (12), reduced respiratory function (10), longer length of hospital stay (6, 12, 13) and greater mortality (2, 14, 15).
Older people are at higher risk of malnutrition (2, 16). This is of particular concern given the current Australian demographic trends which will see the proportion of the population aged over sixty five years rapidly increase in the next decade (17).
The guidelines of leading nutrition groups now recommend addressing malnutrition by routine screening and early intervention (18., 19., 20., 21.). However, evidence that these measures will in fact improve clinical outcomes is wanting, as the published research is of limited quality and has often focussed on select subgroups. This has prompted calls for more robust trials to evaluate the effectiveness of focused nutritional intervention in general groups of hospitalised patients and in particular the hospitalised older population (22, 23).
Therefore, we sought to examine the prevalence of malnutrition in acutely ill older patients and to assess the impact of malnutrition screening and early dietetic intervention on weight, length of hospital stay, hospital costs, subsequent emergency presentations and hospital readmissions in geriatric patients at risk of malnutrition using a randomised controlled trial.
Materials & methods
Patients admitted under a geriatrician into one of the two acute, geriatric medicine wards of the Prince of Wales Hospital, Sydney, Australia between April and September, 2006, were considered for participation in the study. These wards admit patients direct from the Emergency Department with geriatric syndromes such as falls and delirium, as well as multiple medical problems. There is no internal medicine department, so other emergency department patients are admitted to the relevant subspecialty. Usual nutrition care for this unit does not include malnutrition screening, instead the Clinical Dietitian sees patients as referred by the medical and other health professionals. All admitted patients were considered for the study except for a period of two weeks due to ward closure because of a Norovirus outbreak. Patients with an expected length of stay less than 72 hours, palliative (not for active treatment) or who were unable to be nutritionally assessed (non-English speaking, severe dementia/confusion, non- cooperative/refused) were excluded. Patients already seen by a dietitian during the admission (eg transferred from another ward) or enrolled in the study during a previous admission were also excluded.
Study design
All enrolled patients had their nutritional status assessed by the research dietitian using the Mini Nutritional Assessment tool (MNA) (24, 25) within 3 days of admission (to allow for stabilisation of acutely unwell patients and weekend days) and were then randomly allocated (by computerised random number generator) to intervention or control groups. Intervention patients assessed as either ‘at risk of malnutrition’ (AR) or ‘malnourished’ (MN) using the MNA were immediately referred to the clinical dietitian for nutrition intervention following an established ‘malnutrition care plan’ (MCP). This MCP was to be initiated within one business day of notification from the research dietitian. Patients in the control group were provided with usual nutrition care. That is, the clinical dietitian on the ward was not informed of the outcome of screening of the control patients and they were only seen by the clinical dietitian if and when referred by medical or other health professionals. If referred, the same MCP was implemented as for the intervention patients.
Baseline assessment was performed blind to group allocation as it occurred prior to randomisation. It was not possible to blind the clinical dietitian to group allocation given the referral of intervention patients was made by the research dietitian, however, this assignment was not revealed to any other members of the clinical team. As the outcomes are primarily objective measures they are mostly not open to the influence of bias.
Nutritional assessment
Nutritional status was assessed using the MNA, a tool validated for use in the older population (24, 25). This assessment includes questions related to recent food intake, weight loss, mobility, psychological stress or acute disease, neuropsychological problems, living situation (independently or not), quantity of medications being taken, pressure sores or skin ulcers, fluid consumption, mode of feeding, self view of nutritional and overall health status, and measurement of body mass index (BMI), mid-arm and calf circumference. Responses were scored and patients were then categorised as MN, AR or ‘well nourished’ (WN) based on their score.
Intervention
The MCP involved the modification of hospital meals (texture modification and fortification), prescription of nutrition supplements i.e. nutrient dense drinks and snacks including commercial supplements, flagging for assistance with meals by ward based staff, education of patients and their carers regarding optimisation of nutrition intake and referral to other health professionals for discharge planning. The MCP was tailored to individual patient requirements as per the clinical dietitian’s assessment and prescription.
Baseline Data collection
Age and sex were determined from the patients’ medical record.
The BMI (kg/m2) was calculated based on weight measured with chair scales and their standing height was measured via stadiometer. When they were not able to be mobilised to the chair scale weight was estimated subjectively by the research dietitian. When they were not able to stand their height was estimated from knee height (26).
The number and type of co-morbidities were obtained from the medical admission notes and were scored according to the Charlson Comorbidity Index (27).
Outcome Measures
The number of patients seen by a clinical dietitian, number of consults per patient and total consultation time per patient was captured from the hospital’s computerised dietitians’ statistics system (28). This system records information regarding dates, and duration of dietetic consultation for individual patients.
Timeliness of intervention was counted as days between date of admission to the ward and the date seen by the clinical dietitian.
Weight change over the course of admission was calculated from the weight on admission and the weight at discharge. Weight at discharge was collected as close to discharge date as practical.
Deaths during admission, number of presentations to emergency and number of hospital readmissions (1 and 6 months post discharge) were captured via the hospital’s computerised patient admission system.
Length of stay (LOS) was calculated as days from admission to the ward to discharge from acute services (for example to home or rehabilitation).
The cost of hospital admission for each of the malnourished patient’s was determined by identifying the diagnosis related grouping assigned to the admission and the cost of the diagnosis related grouping as per the NSW Hospital Cost Data Collection 2006-2007 Report (29).
The additional costs of a screening and nutritional intervention program were calculated using NSW Health State Dietitians’ Award salary and the averaged cost of frequently used commercial nutrition supplements.
Statistical Analysis
Pre-study power analysis based on the average LOS of the study population (11days) with 0.80 power using a test with significance of 0.05, would require at least 50 subjects in each group to detect a reduction in LOS of 20%.
Data analysis was on an intention to treat basis. Data from subjects who died during admission was included in analysis unless otherwise stated. Data summaries are given as mean ± standard error unless otherwise stated. Differences between groups were assessed using standard methods by the chi- squared test (for categorical data) or the Student’s t-test (for continuous data), or non-parametric alternatives where necessary. Fisher’s exact test was used for analysis of tables where appropriate. For LOS both Student’s t-test and a standard Kaplan-Meier survival analysis (Log Rank) were used. T-test enables comparison with other published results. Survival analysis incorporates data for all subjects, allowing for the possibility of subjects being lost to the analysis due to death. SPSS version 16 was used for all the statistical analyses, and a significance level of 0.05 was used for all tests.
This research was approved by the Prince of Wales Hospital Human Research Ethics Committee.
Results
A total of 143 patients were able to be screened and randomised to the control (n=72) or intervention groups (n=71) during the study period.
There were no statistically significant differences at baseline between the control and intervention groups for age, gender, morbidity, weight on admission, BMI or MNA score both overall and within each nutritional status group (Table 1). The average age of participants was high, but reflective of the population of geriatric medicine wards. The Charlson Comorbidity Index reflected the high level of morbidity in this population and was also equal between the two groups.
Table 1.
Baseline characteristics
| Control | Intervention | P value | |
|---|---|---|---|
| All | |||
| N | 72 | 71 | |
| Age (yrs) | 83.4 ± 0.9 | 83.7 ± 0.8 | 0.84 |
| Sex (M/F) | 33/39 | 28/43 | |
| Weight (kg) | 62.8 ± 2.0 | 63.4 ± 2.3 | 0.85 |
| Body Mass Index (kg/m2) | 23.3 ± 0.7 | 23.8 ± 0.7 | 0.62 |
| Charlson Comorbidity | 5.4 ± 0.2 | 5.8 ± 0.2 | 0.11 |
| MNA Score | 18.8 ± 0.5 | 19.6 ± 0.5 | 0.28 |
| Well Nourished | |||
| N | 12 (17%) | 12 (17%) | |
| Age (yrs) | 82.1 ± 2.2 | 82.3 ± 1.9 | 0.89 |
| Sex (M/F) | 7/5 | 4/8 | |
| Weight (kg) | 72.3 ± 3.8 | 79.4 ± 6.8 | 0.37 |
| Body Mass Index (kg/m2) | 26.3 ± 1.2 | 29.3 ± 2.3 | 0.27 |
| MNA Score | 24.9 ± 0.4 | 25.0 ± 0.4 | 0.89 |
| At-Risk | |||
| N | 40 (56%) | 47 (66%) | |
| Age (yrs) | 83.1 ± 1.3 | 83.2 ± 0.9 | 0.98 |
| Sex (M/F) | 17/23 | 15/32 | |
| Weight (kg) | 65.0 ± 2.6 | 60.2 ± 2.4 | 0.18 |
| Body Mass Index (kg/m2) | 24.5 ± 0.9 | 23.0 ± 0.8 | 0.23 |
| Charlson Comorbidity | 5.4 ± 0.2 | 5.8 ± 0.2 | 0.26 |
| MNA Score | 19.8 ± 0.3 | 20.0 ± 0.3 | 0.64 |
| Malnourished | |||
| N | 20 (28%) | 12 (17%) | |
| Age (yrs) | 84.4 ± 1.1 | 86.9 ± 2.3 | 0.27 |
| Sex (M/F) | 9/11 | 9/3 | |
| Weight (kg) | 52.6 ± 3.9 | 59.7 ± 5.4 | 0.29 |
| Body Mass Index (kg/m2) | 19.0 ± 1.2 | 21.2 ± 1.5 | 0.28 |
| Charlson Co-morbidity | 5.4 ± 0.2 | 5.7 ± 0.4 | 0.50 |
| MNA Score | 13.2 ± 0.6 | 12.7 ± 0.7 | 0.59 |
Abbreviations: MNA, Mini Nutritional Assessment. Data reported as means ± SE.
Of the 143 randomised, eligible patients only 17% were WN. Overall 61% (n=87) of subjects were assessed as AR and 22% (n=32) as MN (Table 1).
The frequency and timing of dietetic intervention for all patients is presented in Table 2. All of the MN (n=12) and 85% of the AR (n=40) patients in the intervention group received dietetic intervention compared with only 35% MN (n=7) and 20% AR (n=8) in the control group (p<0.001, p<0.001 respectively). Of the seven patients in the intervention AR group who did not receive dietetic intervention, one patient was discharged on the 3rd day of admission, one patient became palliative and five were missed.
Table 2.
Dietetic intervention
| Control | Intervention | P value | |
|---|---|---|---|
| All | |||
| Number seen by dietitian/n | 16/72 | 54/71 | <0.001 |
| Days to dietitian consult (true days) | 4.2 ± 1.0 | 3.3 ± 0.3 | 0.39 |
| Number of dietitian consultations per patient | 0.6 ± 0.2 | 1.5 ± 0.2 | <0.001 |
| overall Number of dietitian consultations per patient seen | 2.6 ± 0.4 | 2.0 ± 0.2 | 0.088 |
| Consultation time overall (minutes) | 16.5 ± 4.7 | 43.0 ± 4.5 | <0.001 |
| Consultation time per patient seen (minutes) Well Nourished | 74 ± 13 | 56 ± 4 | 0.23 |
| Number seen by dietitian/n | 1/12 | 2/12 | 1.0 |
| Days to dietitian consult (true days) | 0.00 ± 0.0 | 2.5 ± 0.5 | 0.21 |
| Number of dietitian consultations per patient | 0.3 ± 0.3 | 0.4 ± 0.3 | 0.67 |
| overall Number of dietitian consultations per patient | 3 | 3, 2 | − |
| seen (actual values) Consultation time overall (minutes) | 11.3 ± 11.3 | 8.3 ± 6.4 | 0.82 |
| Consultation time per patient seen (minutes) | 135 | 75, 25 | − |
| (actual values) | |||
| At-Risk | |||
| Number seen by dietitian/n | 8/40 | 40/47 | <0.001 |
| Days to dietitian consult (true days) | 5.4 ± 1.7 | 3.4 ± 0.3 | 0.28 |
| Number of dietitian consultations per patient | 0.5 ± 0.2 | 1.6 ± 0.2 | <0.001 |
| overall Number of dietitian consultations per patient seen | 2.3 ± 0.7 | 1.9 ± 0.2 | 0.41 |
| Consultation time overall (minutes) | 9.0 ± 4.6 | 46.6 ± 5.3 | <0.001 |
| Consultation time per patient seen (minutes) Malnourished | 45 ± 19 | 55 ± 5 | 0.50 |
| Number seen by dietitian/n | 7/20 | 12/12 | <0.001 |
| Days to dietitian consult (true days) | 3.4 ± 1.0 | 3.2 ± 0.6 | 0.82 |
| Number of dietitian consultations per patient | 1.1 ± 0.4 | 2.5 ± 0.4 | 0.012 |
| overall Number of dietitian consultations per patient seen | 3.0 ± 0.4 | 2.5 ± 0.4 | 0.40 |
| Consultation time overall (minutes) | 34.5 ± 11.7 | 63.3 ± 10.2 | 0.073 |
| Consultation time per patient seen (minutes) | 99 ± 14 | 63 ± 10 | 0.053 |
Data reported as means ± SE, except where n<2 as indicated.
In the control group, those MN patients who were referred for dietetic intervention had a significantly lower BMI compared to those who were not referred (15.2 ± 0.8 vs 21.1 ± 1.5 kg/m2, respectively, p=0.013). This was not the case for those in the AR group (25.3 ± 3.3 vs 24.3 ± 0.8 kg/m2, p=0.77).
Intervention patients received a significantly greater number of Dietitian consultations per patient overall (1.54 intervention vs 0.58 controls, p<0.0005). This difference was due to the greater number of consultations in the AR (1.6 intervention vs 0.5 controls, p<0.0005) and MN (2.5 intervention vs 1.1 controls, p=0.012) intervention groups. However, considering only those who were referred for dietetic consultation, there was no significant difference in the number of consultations per referred patient between control and intervention arms (MN p=0.40, AR, p=0.41).
Intervention patients received a significantly greater amount of consultation time per patient overall (43.0 minutes intervention vs 16.5 minutes controls, p<0.0005). This difference was due to the greater consultation time in the AR (46.6 minutes intervention vs 9.0 minutes controls, p<0.0005) and MN (63.3 minutes intervention vs 34.5 minutes controls, p=0.073) intervention groups. However, considering only those who were referred for dietetic consultation, there was no significant difference in consultation time per referred patient between control and intervention arms (MN p=0.053, AR p=0.50). Intervention patients in the MN and AR groups had less time to dietetic consultation ie received earlier dietetic intervention, however this did not reach statistical significance.
The outcome results for all patients are presented in Table 3. There was one death during the course of admission in the control group and four deaths in the intervention group (p=0.21). Excluding deaths, there was no significant difference in LOS between the control (13.4 ±1.3 days) and intervention (12.5 ± 1.2 days) groups overall (p=0.64). However, a significant reduction in LOS was found for the MN intervention group (19.5 ± 3.0 days vs 10.6 ±1.6 days, p=0.013; where n=1 death in control and n=2 deaths in the intervention group). Using the Kaplan-Meier survival analysis, the reduction in LOS for the MN intervention group was marginally significant (19.8 ± 3.0 days vs 11.2 ± 1.5 days, p=0.054).
Table 3.
Outcomes post-intervention
| Control | Intervention | P value | |
|---|---|---|---|
| All | |||
| Deaths during admission | 1/72 | 4/71 | 0.21 |
| Length of stay - excluding deaths (days) | 13.4 ± 1.3 | 12.5 ± 1.2 | 0.64 |
| Length of stay - from survival analysis, i.e. | 13.5 ± 1.3 | 13.7 ± 1.4 | 0.85 |
| adjusted for deaths (days) | |||
| Weight change over admission (kg) 1 | −0.9 ± 0.4 | −0.9 ± 0.6 | 0.98 |
| 1 month emergency presentation frequency2 | 0.14 ± 0.05 | 0.15 ± 0.4 | 0.90 |
| 6 month emergency presentation frequency2 | 0.73 ± 0.11 | 0.63 ± 0.11 | 0.51 |
| 1 month readmission frequency2 | 0.11 ± 0.04 | 0.09 ± 0.04 | 0.66 |
| 6 month readmission frequency2 | 0.53 ± 0.08 | 0.45 ± 0.09 | 0.51 |
| Well Nourished | |||
| Deaths during admission | 0/12 | 1/12 | |
| Length of stay - excluding deaths (days) | 11.7 | 9.0 ± 1.3 | 0.48 |
| Length of stay - from survival analysis, i.e. | 11.7 ± 3.4 | 10.2 ± 1.6 | 0.94 |
| adjusted for deaths (days) | |||
| Weight change over admission (kg) 1 | −3.1 ± 1.8 | −1.0 ± 0.6 | 0.20 |
| 1 month emergency presentation frequency2 | 0.08 ± 0.08 | 0.18 ± 0.122 | 0.50 |
| 6 month emergency presentation frequency2 | 0.25 ± 0.18 | 1.27 ± 0.449 | 0.054 |
| 1 month readmission frequency2 | 0.08 ± 0.08 | 0.09 ± 0.091 | 0.95 |
| 6 month readmission frequency2 | 0.08 ± 0.08 | 0.73 ± 0.304 | 0.065 |
| At-Risk | |||
| Deaths during admission | 0/40 | 1/47 | |
| Length of stay - excluding deaths (days) | 11.0 ± 1.4 | 13.8 ± 1.6 | 0.20 |
| Length of stay - from survival analysis, i.e. | 11.0 ± 1.4 | 14.5 ± 1.8 | 0.097 |
| adjusted for deaths (days) | |||
| Weight change over admission (kg) 1 | −1.1 ± 0.5 | −1.2 ± 0.8 | 0.08 |
| 1 month emergency presentation frequency2 | 0.13 ± 0.06 | 0.17 ± 0.06 | 0.57 |
| 6 month emergency presentation frequency2 | 0.88 ± 0.17 | 0.50 ± 0.10 | 0.057 |
| 1 month readmission frequency2 | 0.10 ± 0.05 | 0.11 ± 0.05 | 0.90 |
| 6 month readmission frequency2 | 0.62 ± 0.12 | 0.37 ± 0.09 | 0.11 |
| Malnourished | |||
| Deaths during admission | 1/20 | 2/12 | |
| Length of stay - excluding deaths (days) | 19.5 ± 3.0 | 10.6 ± 1.6 | 0.013 |
| Length of stay - from survival analysis, i.e. | 19.8 ± 3.0 | 11.2 ± 1.5 | 0.054 |
| adjusted for deaths (days) | |||
| Weight change over admission (kg)1 | −0.1 ± 0.6 | 2.1 ± 3.4 | 0.59 |
| 1 month emergency presentation frequency2 | 0.21 ± 0.10 | 0.00 ± 0.00 | 0.042 |
| 6 month emergency presentation frequency2 | 0.74 ± 0.19 | 0.50 ± 0.31 | 0.49 |
| 1 month readmission frequency2 | 0.16 ± 0.09 | 0.00 ± 0.00 | 0.083 |
| 6 month readmission frequency2 | 0.63 ± 0.14 | 0.50 ± 0.31 | 0.31 |
Data reported as means ± SE. 1. Weight change based on All - 32 controls, 37 intervention; WN - 3 controls, 6 intervention; AR - 15 controls, 28 intervention; MN - 14 controls, 3 intervention. 2. Analysis excluding deaths during admission.
There was a trend to reduction in emergency presentation and readmission rates for MN patients at 1,3 and 6 months post discharge; and this reached statistical significance for 1 month emergency presentations (0.21 ± 0.1 presentations in control versus 0.0 ± 0.0 presentations in intervention, p=0.042). This difference represents presentations by four separate control patients versus none in the intervention group. There were no significant differences in weight change over admission, however discharge weights were only obtained for 69 (48.3%) patients.
The average cost per hospital bed day for MN patients was $AUD720. Dietitians’ salary was $AUD 26.70/hr plus on costs of 20%. The average cost of a commercial nutrition supplement was $AUD 1.05.
Discussion
The high prevalence of malnutrition found in our study signifies the extent of the problem, with 83% of geriatric admissions to the acute care setting found to be malnourished or at risk of malnutrition. Our findings are consistent with results of other studies, reporting rates of 48-83% in acutely unwell, hospitalised older patients (2, 3, 15). This is not surprising given that malnutrition rates are known to increase with age, physical dependence and frailty (4).
The association between malnutrition and poor clinical outcome is widely acknowledged (30). There is evidence that treating malnutrition with nutrition support can improve patient outcomes (31., 32., 33.). The key finding of the present study is that screening and early intervention with a malnutrition care plan is associated with a decreased LOS for malnourished geriatric patients. This finding is critical as whilst numerous organisations now recommend malnutrition screening for hospitalised patients, (18., 19., 20., 21.) evidence that implementing malnutrition screening can positively impact on patient and health care outcomes is wanting.
Brugler and co-workers in 1999 demonstrated reduced complications, LOS and readmission rates when auditing pre (n=247) and post (n=388) implementation of a range of quality improvement strategies that included malnutrition screening in a community hospital (34). However, as screening was only one component of this intervention and as the audits were separated by two years, the improvements cannot be attributed to screening alone. Kruizenga and co-workers’ 2005 investigation of the effectiveness of early screening in acute hospital wards was unable to demonstrate a reduction in LOS except in the subgroup of malnourished patients with low hand grip strength (35). As their investigation also assessed outcomes pre and post implementation of a screening protocol, its results cannot necessarily be ascribed to nutrition screening alone. Rypkema et al compared outcomes in 298 patients admitted to two different hospitals (36). They demonstrated a reduced rate of nosocomial infections, and non-significant reductions in LOS and pressure sore incidence in the centre where nutrition screening was implemented compared to that with usual care. However, the study design may have introduced other factors that could account for the findings.
The present study is the first that we are aware of to demonstrate a reduction in length of stay for malnourished patients per se as a result of malnutrition screening using a randomised controlled trial. Johansen and co-workers in 2004 used a randomised controlled trial of malnutrition screening and intervention but were only able to demonstrate a benefit on LOS for the subgroup of malnourished patients who developed complications during their hospital stay (that is, 31% of the malnourished intervention group) (37). They hypothesised that the screening tool used (the NRS-2002) was not specific enough to differentiate those patients who would benefit from nutrition intervention. The present study focused on an older, acutely unwell population with a high level of morbidity and was able to demonstrate that screening with the MNA tool differentiated those patients for which nutrition intervention would have a positive outcome.
We recognise that the time frame of nutrition intervention in this study is short, whilst the repletion of protein stores occurs slowly (38). We postulate that identifying vulnerable patients at risk of poor outcomes and instituting a range of measures to optimise nutritional intake may have prevented the further decline in nutritional status that is usually seen during hospitalisation.
Many studies have reported the negative impact of hospitalisation on nutritional status. Dupertvis et al. reported that 43% of 1707 patients did not meet minimum energy requirements (39). Sullivan et al. demonstrated that 21% of older hospitalised patients consumed less than 50% of maintenance energy requirements, and this was associated with increased weight loss and mortality (16). Weinsier and coworkers reported a downward trend in nutritional parameters during hospitalisation, including loss of subcutaneous fat and muscle mass.40 McWhirter and Pennington found that 64% of all patients lose weight during hospitalisation, and in the already malnourished patients this figure is 80% (1).
Others have noted that the prevalence of malnutrition increases with longer LOS. Davalos and co-workers demonstrated an increased malnutrition rate from 16% on admission to 35% after 2 weeks admission post stroke (41). Larsson et al also demonstrated a decline in nutritional status in previously well-nourished geriatric patients during extended hospitalisation (42). Further, they demonstrated that nutritional supplementation was able to minimise this decline. We believe this may explain our findings, that is focussed nutrition intervention reduced LOS in the MN geriatric patient by minimising the decline in nutrition status.
While there was a trend to reduction in post-discharge returns to hospital for malnourished intervention patients, the small patient numbers and rates of presentation limited ability to reach statistical significance other than for rate of emergency presentations 1 month post-discharge, although our sample size was not calculated to detect this outcome. As the study design did not include post discharge treatment any benefit of inpatient intervention would likely be early as demonstrated by our data.
Our study did not show a decreased LOS in the AR group. The MNA does not diagnose malnutrition, rather it categorises patients based largely on known risk factors for malnutrition such as polypharmacy and immobility. Hence, the nutritional status of those in the AR group is undefined and heterogeneous, that is, some may be well nourished, some moderately malnourished and it has been recommended that they require further clinical evaluation to assess their nutrition status (24). Therefore, the impact of nutritional intervention in this group may be less consistent. An assessment tool that diagnoses and ranks patients’ nutritional status primarily using clinical markers such as Subjective Global Assessment (43, 44) would result in a category of ‘moderately malnourished’ patients for whom the intervention may have shown benefits.
In an environment without malnutrition screening the identification of malnutrition is poor (1, 35, 45., 46., 47.). In our study 65% of AR and 80% of MN control patients were not referred for nutritional intervention. Its unclear if the failure to refer arose from lack of identification of malnutrition or a failure to refer malnourished patients for nutrition intervention. It is possible that both of these factors are at play and therefore this is an area for further investigation. As malnutrition is so prolific in this population, in effect it may not be noticed or acted on. Singh and colleagues have suggested that poor identification may be due to a lack of medical officer training in nutritional assessment (46). Alternatively, health professionals may not see nutrition as a priority or may be pessimistic about the ability of nutritional intervention to improve patients’ outcomes in the acute setting. Jordan et al reported on the results of a trial implementing nursing initiated malnutrition screening and noted improved nursing documentation of patient nutrition status (47). However, this did not translate into improved referrals to dietitians. It is possible that our ability to demonstrate a reduction in LOS was enhanced by the use of dietitian lead malnutrition screening, that is, clinicians with a primary focus on nutrition.
Factors that prompted referral of control patients in the current study were not identified. However, it was noted that referred malnourished control patients had a significantly lower BMI than those not referred, suggesting an underweight physical appearance may have been the trigger for referral. However, as Baker and colleagues have noted, no single measurement is of consistent value in identifying malnutrition (44). Nutrition screening, however, ensured the provision of nutrition support to all malnourished and 85% of AR patients. That intervention did not occur in a small number of AR patients can be attributed to the constraints of the acute clinical setting.
The data from this study allows the financial benefits of implementing nutrition screening and early nutrition intervention in our study population to be estimated. The average cost per hospital bed day for patients in the malnourished group was $AUD720. Therefore in saving 8 bed days per admission for the MN intervention patients, the cost reduction by treating malnutrition in this group of patients totalled $AUD 63,360. Extrapolating this further, for a hospital with approximately 500 acute aged care admissions a year with a length of stay greater than 3 days, if 22% of the patients are malnourished, but only 35% are usually identified and referred for dietetic intervention, then there would be another 70 patients a year for whom early nutrition screening and intervention will potentially reduce LOS. Basing cost estimates on the reduction in LOS observed in this study indicates that a program of focussed nutrition intervention would deliver a gross saving of approximately $AUD400,000 annually. Assuming additional Dietitian staffing (20hrs/wk) and nutrition supplements, the costs to operate this program for this number of patients is estimated to be approximately $AUD 35,000 annually, therefore the nett savings are estimated to be $AUD 365,000 annually.
We had hypothesised that screening would also result in earlier dietetic intervention for malnourished patients. However, there was no difference in time to dietetic intervention between the groups. One factor contributing to this is that surprisingly, the control patients who were identified as malnourished were identified early in admission. In addition and importantly, it is not possible to analyse time to intervention for patients that were never referred and as noted previously this was a high proportion of control patients.
On average it took approximately 3 days from admission for the clinical dietitian to consult on referred intervention patients. This was longer than anticipated and arose from resourcing constraints. Firstly, nutrition screening was conducted by the research dietitian who was not available to immediately screen patients on admission. Both research and clinical dietitians worked only weekdays, hence delays occurred for patients admitted on a weekend. Further, study funding provided only limited enhancement to clinical dietitian staffing, resulting in some delays. In retrospect, a standardised protocol to commence nutrition intervention immediately after screening and allowing for modification upon review by the Dietitian is likely to have enhanced the timeliness of intervention. Addressing these factors has the potential to further enhance the capacity of nutrition intervention to improve patient outcomes.
Weight change over admission was intended as an indicator of change in patient nutrition status. However, weight proved difficult to capture due to patient immobility and the acuity of their illness, a common issue when studying this population.48 Discharge weight proved particularly difficult to capture due to rapid changes in patient discharge plans. Recorded weight changes did not demonstrate any consistent pattern in regards to impact of nutritional intervention. In this acute setting, weight is likely to be influenced by changes in fluid balance such as dehydration, fluid overload, diuresis and intravenous fluids. We did not include another marker of nutritional status, as reliable and valid markers are not easily obtainable or sensitive within the time frame of our study. Our findings would have been supported by utilising a marker of functional status such as handgrip strength. Nonetheless LOS reflects functional status and was easily obtained retrospectively.
One of the limitations of this study is that the model of care at our hospital, with acutely unwell frail older patients being admitted directly under Geriatric Medicine, is not used at all hospitals, but as it is becoming increasingly popular, so the study will be increasingly relevant.
We note that by excluding non English speaking and cognitively impaired patients i.e. a high risk population, the prevalence of malnutrition may be underestimated in the study. In turn, this limits the generalisation of the results to these groups.
We recognise that ward staff awareness of the study and enhanced nutritional practices for the intervention arm had the potential to influence referral practices in the control group. However, an alternative study design to reduce this contamination (for example using two separate wards) was not consistent with a randomised controlled trial. As noted earlier, referral rates in the control arm were still low and consistent with rates reported in other studies.
The limitations of this trial reflect the difficulties of conducting research in a real-life clinical setting. However, this is the strength of our trial in that it is immediately applicable to the care of acutely unwell geriatric patients. Furthermore, the use of a randomised controlled trial design provides high quality evidence.
In conclusion, as widely reported we have also found that rates of malnutrition are high and that malnourished patients are not routinely referred for nutrition intervention. More importantly, we have demonstrated that early, focussed dietetic intervention reduced hospital length of stay in malnourished acutely unwell geriatric patients, and this has the potential to improve health outcomes and deliver substantial savings in health care costs.
Acknowledgements
This work was supported by The Gut Foundation (Randwick, Australia). We thank Pharmatel Fresenius Kabi Pty Ltd for the unrestricted research grant provided to support this study. Pharmatel Fresenius Kabi Pty Ltd had no other involvement in the work. The authors wish to acknowledge the input of the Dietitians of the Department of Nutrition and Dietetics, Prince of Wales Hospital for assistance in project implementation.
Conflict of interest statement
The authors have no conflicts of interest.
Statement of authorship
MH conceived of the study, and participated in its design and coordination, analysis and interpretation of data and writing of the manuscript. SD participated in study design and coordination, analysis and interpretation of data and writing of the manuscript. MB participated in study design, implementation, data collection, data analysis and helped draft the manuscript. GC & TB assisted in study conception, design, analysis and interpretation of data and writing of the manuscript. PP performed the statistical analysis and interpretation of data and contributed to manuscript preparation. All authors read and approved the final manuscript.
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