Abstract
Objectives
The aims of this study were to assess malnutrition risk in Chinese geriatric inpatients using Nutritional Risk Screening 2002 (NRS2002) and Mini-Nutritional Assessment (MNA), and to identify the most appropriate nutritional screening tool for these patients.
Design
Cross-sectional study.
Setting
Eight medical centres in Hubei Province, China.
Participants
A total of 425 inpatients aged ≥70 years were consecutively recruited between December 2014 and May 2016.
Primary and secondary outcome measures
Nutritional risk was assessed using NRS2002, MNA, anthropometric measurements and biochemical parameters within 24 hours of admission. Comorbidities and length of hospitalisation were recorded. Nutritional parameters, body mass index (BMI) and length of hospital stay (LOS) were employed to compare MNA and NRS2002. Kappa analysis was used to evaluate the consistency of the two tools.
Results
The average age was 81.2±5.9 years (range, 70–98). The prevalence of undernutrition classified by NRS2002 and MNA was 40.9% and 58.6%, respectively. Patients undergoing malnutrition had lower BMI, haemoglobin, albumin and prealbumin (p<0.05), and longer LOS (p<0.05). The NRS2002 showed moderate agreement (κ=0.521, p<0.001) with MNA. Both tools presented significant correlation with age, BMI and laboratory parameters (p<0.001). In addition, a significant association between both tools and LOS was found (p<0.05). In addition, the NRS2002 was not different from MNA in predicting nutritional risk in terms of the area under the receiver operating characteristic curve (p>0.05).
Conclusions
The results show a relatively high prevalence of malnutrition risk in our sample cohort. We found that NRS2002 and MNA were both suitable in screening malnutrition risk among Chinese geriatric inpatients.
Keywords: malnutrition risk, Nrs2002, mna, older patients
Strengths and limitations of this study.
This study assessed the risk of malnutrition among Chinese elderly inpatients using two different tools (Nutritional Risk Screening 2002 and Mini-Nutritional Assessment [MNA]).
The study provides useful information to assess the risk of malnutrition among Chinese elderly inpatients.
The consensus statement with diagnostic criteria for malnutrition was proposed in 2015 after initiation of our study and thus was not used in our study.
With regard to the cross-classification of MNA, more patients will be categorised as at risk since the two groups of malnourished and at-risk patients are combined.
In addition to nutritional status, length of stay could also be influenced by age, financial situation, comorbidities and so on, which may explain the low R2 during the simple linear regression analysis.
Introduction
Malnutrition is a common condition affecting almost 13%–78% of the elderly population.1 It is highly associated with numerous adverse outcomes, including frailty,2 muscle wasting,3 weakened immune system,4 longer hospitalisation,5 and increased morbidity6 and mortality.7 8 These outcomes contribute to large increases in medical expenditures.9 Early identification and treatment of malnutrition can lead to better quality of life10 and improved outcomes11 in older inpatients. Therefore, it is essential that appropriate tools are applied when assessing the risk of malnutrition in geriatric patients.
Nutritional risk screening and nutritional assessment tools are readily available nowadays.12 These tools are used to identify patients with nutritional risk or nutritional deficiencies and to determine if intervention is needed. There are a variety of nutritional risk screening methods,13 mainly involving anthropometric and biochemical parameters. However, to date, no single tool has been considered as the universal gold standard for the assessment of nutritional risk among older inpatients.
The Nutritional Risk Screening 2002 (NRS2002) is recommended for hospitalised patients by the European Society of Parenteral and Enteral Nutrition (ESPEN).14 It can quickly determine whether a patient needs nutritional support, especially those with acute complications.15 16 However, the use of NRS2002 excludes patients who cannot be weighed or have problems with communication, and the tool is not specifically developed for older patients.
The Mini-Nutritional Assessment (MNA) is established for nutritional screening and assessment in the geriatrics settings.1 17 The ESPEN recommends using this tool for elderly populations.18 The MNA was validated against a clinical evaluation of two geriatrics, including biochemical parameters (nutritional parameters, C reactive protein, cholesterol, vitamins), anthropometry (body mass index [BMI], brachial circumference, calf circumference, skinfold width of the triceps and subscapular muscles), dietary components and functional assessment outcomes.19 It has been shown to predict outcomes, functional status, mortality, number of hospital visits and the related healthcare costs.18 20–23 In comparison with the NRS2002, the MNA is more time-consuming.24 Hence, two short forms of the MNA have been developed and validated, the most recent being the revised Mini-Nutritional Assessment-Short Form by Kaiser et al,25 which is currently the recommended version of the MNA for clinical use. This instrument only incorporates 6 of the original 18 items and takes approximately 5 min to perform.
Although a number of studies suggested that serum proteins are associated with malnutrition, the nature of this association is controversial.26–28 In addition, haemoglobin and total lymphocyte count were also proposed as useful indicators of nutritional status.27 29 The aim of our study was to assess the risk of malnutrition among Chinese older participants using two different tools (NRS2002 and MNA), to compare them in terms of nutritional biochemical parameters and length of hospital stay (LOS), and to determine the most appropriate tool for these patients. We hypothesised that both tools were suitable for the study population.
Methods
Study subjects
This study consecutively recruited patients older than 70 years of age who attended the internal medicine of the geriatrics department of eight large-sized, tertiary comprehensive hospitals in Hubei Province from December 2014 to May 2016. The eight hospitals are as follows: (1) Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology; (2) The Central Hospital of Wuhan University; (3) Zhongnan Hospital of Wuhan University; (4) Wuhan No 6 Hospital, Affiliated Hospital of Jianghan University; (5) Hubei General Hospital; (6) General Hospital of The Yangtze River Shipping; (7) Wuhan No 1 Hospital; and (8) The First College of Clinical Medical Science, Three Gorges University and Yichang Central People’s Hospital.
The following were the inclusion criteria: (1) age 70 years or older, (2) the patients themselves or their proxies could clearly answer the study questions, (3) hospital stay was longer than 24 hours, and (4) patients were willing to cooperate with the study. The exclusion criteria included (1) younger than 70 years old, (2) refusal to participate in the study and (3) unconscious or unable to answer the study questions (figure 1).
Figure 1.
Flow diagram of participant inclusion and exclusion.
Nutritional Risk Screening 2002
The NRS2002 consists of the following parameters: severity of acute illness, BMI, patient appetite, accidental weight loss and patient age.15 The total score ranges from 0 to 7.16 For investigational purposes, patients were categorised into two groups: a total score ≥3 indicates undernourished (at nutritional risk/malnourished) and 0–2 indicates normal nutritional status.14 30 A nutritional care plan was initiated for patients with a score ≥3.31
Mini-Nutritional Assessment
The MNA consisted of four parts: anthropometric assessment (0–8 points), general assessment (0–9 points), dietetic habits evaluation (0–9 points), and self-assessment of the nutrition and health status (0–4 points).32 A score below 17 indicates malnutrition, 17–23.5 indicates malnutrition risk and 24–30 indicates well nourished.33 In addition, a nutritional care plan was initiated for patients with a score <17.31
Data collection
Each hospital had two researchers trained before the start of the study. The main content was on how to interpret the questions in the instruments (MNA and NRS2002) and how to perform the measurements. Patients were evaluated using both MNA and NRS2002 by two trained researchers on the first day of admission or early morning of the next day.
Comorbidities and length of hospitalisation were obtained from medical records. Patients were diagnosed by their bedside clinician and superior clinicians. Two trained researchers of each hospital verified the diagnoses and acquired the data. Both body height and weight were measured at 06:00–08:00 within 24 hours after admission. Patients were asked to fast, to only wear ward clothes and to take off shoes before measurement. Height was measured with a calibrated scale (corrected to ±0.5 cm). The actual body mass was measured with a calibrated scale (corrected to ±0.2 kg). BMI was calculated as weight/height squared (kg/m2).
Blood samples were also obtained within 24 hours after admission. Nutritional biochemical parameters including serum haemoglobin (Hb), total lymphocyte count (TLC), albumin (ALB) and prealbumin (PAB) were examined in the hospital’s clinical chemistry laboratory. Malnutrition was determined when the value of Hb was <120 g/L for men and <110 g/L for women. The cut-off value for ALB was 35 g/L (normal range 35–55 g/L), for PAB was 200 mg/L (normal range 200–400 mg/L) and for TLC was 1.1×109/L (normal range 1.1–3.2×109/L) for both genders for malnutrition.34 Moreover, based on previous research,29 the cut-off value for BMI was set at 20.5 kg/m2 for malnutrition.
Patient and public involvement
Patients did not participate in the design and conception of the proposed study. No patients were asked to advise on the interpretation or writing up of the results. The results of the measurements would be disseminated to the participants immediately after the investigation.
Statistical analysis
Statistical analyses were conducted using SPSS V.19.0. Data were presented as mean±SD with ranges in bracket. The χ2 test or Student’s t-test was used according to data type. One-way analysis of variance was applied for multigroup comparisons, and correlation analysis was qualified using the Pearson correlation test. The simple linear regression analysis was used to estimate the relationship between both instruments (MNA and NRS2002) and LOS. Agreement between the two screening tools was determined using the kappa (κ) statistic. The results were interpreted as follows: <0, no agreement; 0–0.19, poor concordance; 0.20–0.39, fair agreement; 0.40–0.59, moderate agreement; 0.60–0.79, substantial agreement; and 0.80–1.00, almost perfect agreement.29 In addition, the receiver operating characteristic (ROC) curve was applied to compare MNA and NRS2002 separately with nutritional parameters using MedCalc V.18.6. The level of significance was set at p<0.05.
Results
Over the study period, a total of 425 individuals from the department of geriatrics of eight hospitals in Hubei Province met the eligibility criteria and completed a nutritional assessment within 24 hours of admission (figure 1). The average age was 81.2±5.9 years (range, 70–98) and 31.1% were female. The average BMI was 23.2±3.7 kg/m2 (range, 11.1–34.1). The average length of hospitalisation was 21.9±13.8 days (range, 4–133). The most frequent cause of hospitalisation was cardiac disease, followed by pulmonary infection, hypertension, cerebrovascular diseases, digestive disease and malignancies. However, the most frequent comorbidities were hypertension, diabetes mellitus, cardiac disease and cerebrovascular disease. The characteristics of study participants are shown in table 1.
Table 1.
Characteristics of study participants
| Variable | All subjects (N=425) |
| Age (year) | 81.2±5.9 (70–98) |
| Gender | |
| Male | 293 (68.9) |
| Female | 132 (31.1) |
| Height (m) | 1.6±0.1 (1.4–1.8) |
| Weight (kg) | 62.9±11.5 (28.4–92.0) |
| Body mass index (kg/m2) | 23.2±3.7 (11.1–34.1) |
| Length of hospital stay (days) | 21.9±13.8 (4–133) |
| Primary cause of admission to hospital | |
| Cardiac disease | 160 (37.6) |
| Pulmonary infection | 84 (19.8) |
| Hypertension | 76 (17.9) |
| Cerebrovascular disease | 45 (10.6) |
| Digestive disease | 25 (5.9) |
| Malignancy | 19 (4.5) |
| Others | 16 (3.8) |
| Comorbidities | |
| Hypertension | 210 (49.4) |
| Diabetes mellitus | 101 (23.8) |
| Cardiac disease (including atrial fibrillation) | 94 (22.1) |
| Cerebrovascular disease | 30 (7.1) |
Values are mean±SD (with ranges in brackets) or n (%), respectively.
According to the NRS2002, approximately 174 patients (40.9%) were undernourished and 251 patients (59.1%) had normal nutritional status. The MNA demonstrated that 99 patients (23.3%) were malnourished, 150 patients (35.3%) were at risk of malnutrition and 176 patients (41.4%) had normal nutritional status.
Table 2 shows the risk of undernutrition, varying from 23.7% to 58.6%, according to the different methods employed in the current work. The risk of undernourished participants classified by NRS2002, MNA, BMI, ALB, PAB and TLC was 40.9%, 58.6%, 23.7%, 28.7%, 47.3% and 35.9%, respectively. The risk of malnutrition was higher in men than that in women according to Hb (41.7% vs 25.6%, respectively).
Table 2.
Nutritional status of 425 patients classified by NRS2002, MNA, BMI and serum parameters
| Undernourished, % (n) | Well nourished, % (n) | |
| NRS2002 | 40.9 (174) | 59.1 (251) |
| MNA | 58.6 (249) | 41.4 (176) |
| BMI | 23.7 (93) | 76.3 (300) |
| ALB | 28.7 (122) | 71.3 (303) |
| PAB | 47.3 (70) | 52.7 (78) |
| Hb (male) | 41.7 (121) | 58.3 (169) |
| Hb (female) | 25.6 (33) | 74.4 (96) |
| TLC | 35.9 (149) | 64.1 (266) |
Undernourished: malnourished + at risk of malnutrition.
ALB, albumin; BMI, body mass index; Hb, haemoglobin; MNA, Mini-Nutritional Assessment; NRS2002, Nutritional Risk Screening 2002; PAB, prealbumin; TLC, total lymphocyte count.
Although both instruments were closely related to each other (p<0.001), they showed substantial differences. Table 3 shows the cross-classification of MNA and NRS2002 with regard to two nutritional categories. The MNA classified more patients as undernourished than the NRS2002 (249 vs 174, respectively). Among the 249 patients classified as undernourished by the MNA, the NRS2002 coincidently categorised 159 as malnourished and 90 as well nourished. The NRS2002, on the other hand, classified 174 patients as undernourished. Within this group of participants, the MNA classified 159 patients as undernourished and 15 as well nourished. Moreover, a participant considered by the MNA to be well nourished can be classified as undernourished using the NRS2002. The individual categorisation of nutritional status presented moderate agreement between MNA and NRS2002 (κ=0.521, p<0.001).
Table 3.
Cross-classification
| MNA | NRS2002 | Total | |
| Undernourished | Well nourished | ||
| Undernourished | 159 | 90 | 249 |
| Well nourished | 15 | 161 | 176 |
| Total | 174 | 251 | 425 |
| Kappa | 0.521 | ||
| P value | <0.001 | ||
Number of patients classified into two categories according to MNA and NRS2002, respectively.
Undernourished: malnutrition + at risk of malnutrition.
MNA, Mini-Nutritional Assessment; NRS2002, Nutritional Risk Screening 2002.
Data on Hb, TLC, ALB and PAB concentrations are listed in table 2 and online supplementary table 1. There existed a significant decrease in ALB, PAB, Hb and TLC in the three groups of MNA scores (p<0.001 and p=0.023; figure 2A). Specifically, the group with the lowest MNA score (<17) (n=99), indicating poor nutritional state, had an average serum ALB of 33.3±4.8 g/L (range, 22.8–43.2), an average PAB of 140.8±81.7 mg/L (range, 12–339) and an average Hb of 109.0±20.8 g/L (range, 57–150.2), as well as an average lymphocyte count of 1.5±1.2×109/L (range, 0.3–6.6). With regard to the risk of the malnutrition group with an MNA score of 17–23.5 (n=150), the average serum ALB was 37.9±5.6 g/L (range, 21.6–70), the average PAB was 192.3±61.8 mg/L (range, 8–380), the average Hb was 120.2±22.0 g/L (range, 55–181) and the average lymphocyte count was 1.3±0.56×109/L (range, 0.2–2.9). The group with MNA score ≥24 (n=180) had an average serum ALB concentration of 39.8±3.8 g/L (range, 29.3–54), an average PAB of 216.7±54.4 mg/L (range, 25–337), an average Hb of 127.6±15.3 g/L (range, 81–173) and an average lymphocyte count of 1.5±0.7×109/L (range, 0.3–4.3) (figure 2A and online supplementary table 1).
Figure 2.
(A) Mini-Nutritional Assessment (MNA) and (B) Nutritional Risk Screening 2002 (NRS2002) referring to serum albumin, prealbumin, haemoglobin and blood lymphocytes count, respectively.
bmjopen-2018-022993supp001.pdf (183.3KB, pdf)
Similarly, ALB, PAB and Hb gradually declined with increasing risk of malnutrition according to the NRS2002 results (p<0.001; figure 2B). In undernourished patients (NRS2002: 3–7, n=174), the average serum ALB was 35.1±5.6 g/L (range, 21.6–50.2), the average PAB was 148.5±79.3 mg/L (range, 17–339) and the average haemoglobin was 113.3±23.6 g/L (range, 55–181). In the normal nutritional state group (NRS2002: 0–2, n=251), the results were as follows: ALB 39.4±4.5 g/L (range, 26.4–70), PAB 210.2±56.1 mg/L (range, 8–380) and Hb 125.8±16.2 g/L (range, 72–173). By contrast, no difference in lymphocyte count was found in both groups (p=0.089; figure 2B and online supplementary table 1).
Table 4 shows the Pearson correlation coefficients of MNA and NRS2002 scores with BMI, serum parameters and LOS. BMI, Hb, ALB and PAB correlated negatively with malnutrition scores of NRS2002, while they showed positive correlation with the MNA scores (p<0.001). There existed an inverse correlation between age and the two tools (p<0.001). In addition, a significant association between both tools and LOS was demonstrated (p<0.05). No correlation was found between TLC and the two tools.
Table 4.
Pearson correlation coefficients of MNA and NRS2002 scores with BMI, serum parameters and LOS
| MNA | NRS2002 | |||
| r | P value | r | P value | |
| Age | −0.239 | <0.001 | 0.238 | <0.001 |
| BMI | 0.578 | <0.001 | −0.347 | <0.001 |
| Hb | 0.387 | <0.001 | −0.321 | <0.001 |
| TLC | −0.002 | 0.966 | 0.011 | 0.819 |
| ALB | 0.501 | <0.001 | −0.383 | <0.001 |
| PAB | 0.481 | <0.001 | −0.332 | <0.001 |
| LOS | −0.109 | 0.048 | 0.178 | 0.001 |
| NRS2002 | −0.640 | <0.001 | – | – |
ALB, albumin; BMI, body mass index; Hb, haemoglobin; LOS, length of hospital stay; MNA, Mini-Nutritional Assessment; NRS2002, Nutritional Risk Screening 2002; PAB, prealbumin; TLC, total lymphocyte count.
Table 5 shows the simple linear regression of LOS. There was a linear correlation between the MNA scores and LOS (p<0.05) and so with the NRS2002 scores (p<0.01).
Table 5.
Simple linear regression analysis of LOS
| B | SE | β | t | P value | 95% CI | R2 | F | |
| MNA | −0.261 | 0.131 | −0.109 | −1.987 | <0.05 | −0.519 to 0.003 | 0.012 | 3.949 |
| NRS2002 | 1.891 | 0.575 | 0.178 | 3.291 | <0.01 | 0.761 to 3.022 | 0.032 | 10.834 |
LOS, length of hospital stay; MNA, Mini-Nutritional Assessment; NRS2002, Nutritional Risk Screening 2002.
Figure 3 shows the ROC curve analysis of the sensitivities and specificities of MNA and NRS2002 in predicting nutritional risk. The comparison between MNA and NRS2002 in those patients revealed the area under the ROC curve (AUC) values for MNA (0.794, ALB; 0.704, PAB; 0.702, Hb; 0.581, TLC) and NRS2002 (0.761, ALB; 0.616, PAB; 0.677, Hb; 0.586, TLC). In addition, the comparison between the AUC showed that NRS2002 was not different from MNA in predicting nutritional risk (p=0.191, ALB; p=0.063, PAB; p=0.299, Hb; p=0.866, TLC).
Figure 3.
Receiver operating characteristics of sensitivity and specificity of predicted probabilities for nutritional risk incorporating the Mini-Nutritional Assessment (MNA) score or the Nutritional Risk Screening 2002 (NRS2002) score.
Discussion
Based on the current study, the nutritional health of older inpatients was assessed using NRS2002, MNA, BMI, Hb, TLC, ALB and PAB. The results showed that the overall prevalence of malnutrition risk for the enrolled older patients ranged from 23.7% to 58.6%. The highest prevalence of malnutrition risk was detected by MNA and the lowest by BMI. These results illustrated the differences in nutritional risk detected by different screening tools.
Biochemical markers possess many advantages in the assessment of nutritional status, such as fast application and low cost. In addition, they can be incorporated into the routine clinical application. As the most abundant plasmatic protein, ALB is commonly used in the assessment of malnutrition.35 In several studies, low serum ALB correlated with longer hospitalisation, medical complications and increased mortality.35–37 Nevertheless, its value is still limited by its long half-life (14–20 days), associated inflammation and impairment of hepatic or renal function, and possibly ageing itself.27 Due to the reasons above, the prevalence of malnutrition detected by ALB is lower than MNA and NRS2002 in our study. Comparatively, a study by Covinsky et al 38 showed ALB is highly sensitive yet low specific in the diagnosis of malnutrition in hospitalised older adults.
PAB has been regarded as a more sensitive marker than ALB for acute nutritional changes, as it has a shorter half-life (2–3 days). However, its functions in nutritional screening and mortality prediction remain controversial.39–42 In our study, PAB levels showed a great decline with deteriorating nutritional status assessed by both NRS2002 and MNA. The correlation between serum PAB and MNA is consistent with one study,43 yet in contrast with another report.41 This latter study had a small sample size (23 older patients), which may account for the discrepancies. Similar to serum ALB, the usage of PAB in predicting malnutrition is limited due to systemic inflammatory diseases.40 44
Hb has been demonstrated to decrease with progressive malnutrition.45 46 This relationship, consistent with a recent analysis performed in Northern China,29 is found in the proposed study. TLC was assumed to be suitable as a screening test to assess malnutrition and as an indicator of poor prognosis.27 In agreement with Lei et al,47 we also found a significant correlation between TLC and MNA. However, no correlation was found between NRS2002 and TLC, which was consistent with a previous study.29
The prevalence of malnutrition diagnosed by BMI was far below that detected by MNA. Consistent with findings from another study,48 this study suggested that malnutrition was also underdiagnosed when using BMI as the sole criterion. The reason is most possibly related to water-sodium retention in patients, leading to an overestimation of their true weight.49 In spite of this, low BMI was significantly associated with mortality.50
Our study revealed that NRS2002 and MNA, in moderate agreement with each other, were consistently associated with age, BMI, Hb, ALB and PAB. Nevertheless, the MNA identified more patients with or at risk of malnutrition than the NRS2002 did. The lower percentage of malnutrition classified by NRS2002 may be explained in several ways. First, the NRS2002 mainly considers the influence of acute diseases on nutritional status, while the MNA takes chronic long-term condition into consideration, such as psychological factors and BMI. Psychological factors may play a large role in the nutritional status of older inpatients. In addition, we categorised undernutrition as NRS2002 ≥3, while a score of 1–2 indicates low risk of malnutrition.51 This may have underestimated the percentage of malnutrition. Moreover, our study subjects were internal medicine patients, among which the proportion of overweight and obesity was high. The NRS2002 takes BMI <18.5 kg/m2 as one of the criteria, leading to a lower proportion of malnutrition with NRS2002 than that of MNA. Our results agree with the findings from Raslan et al,5 yet differ from the study conducted by Drescher et al.51 The latter study was conducted in older patients with acute disease, which may explain the difference.
Norman et al 52 suggested that there exists a close relationship between the degree of malnutrition and LOS. In the present study, we found a linear relation between both instruments (MNA and NRS2002) and LOS. Our findings corroborated those of the study conducted by Bauer et al,53 showing longer hospital stay was associated with malnutrition assessed by the MNA. Comparison of the MNA and NRS2002 and their ability to predict nutritional risk according to different standards showed that these tools were both suitable as a screening tool, in terms of the ROC curve area. Therefore, we conclude that in Chinese geriatric inpatients the NRS2002 and MNA might reflect malnutrition or the studied biochemical parameters, as well as predict the length of hospitalisation. However, the MNA was available for identifying most of the patients with or at risk of malnutrition.
Limitations
Our study still has some limitations. First, a consensus statement with diagnostic criteria for malnutrition was proposed in 201554 after our study was initiated and thus was not used in this study. Second, BMI and age were part of the screening tools, therefore already correlated with the MNA and NRS2002 assessment. In addition to nutritional status, LOS could also be influenced by age, financial situation, comorbidities and so on, which may explain the low R2 between both instruments and LOS. Finally, regarding the cross-classification of MNA, more patients will be categorised as at risk since the two groups of malnourished and at-risk patients are combined, which may contribute to the low kappa value between the two instruments.
Conclusion
To conclude, the results of the present study show a high prevalence of malnutrition risk in Chinese geriatric hospitalised patients. Although the nutritional risk varied depending on the applied method, both NRS2002 and MNA correlated with each other and with age, BMI and laboratory parameters. In addition, both tools proved to be a good predictor of the length of hospitalisation. Moreover, the NRS2002 was not different from MNA in predicting nutritional risk according to the AUC. Therefore, this study suggested NRS2002 and MNA were both suitable for screening malnutrition risk among Chinese geriatric inpatients. We recommend using one of them on admission.
Supplementary Material
Acknowledgments
We appreciate the participating volunteer patients, the local research ethics committee, and the individual researchers, clinicians and nurses for their participation in the data collection.
Footnotes
J-PM and X-QQ contributed equally.
Contributors: J-PM, X-QQ and H-LZ are the main contributors to the design of the present study. J-PM and X-QQ contributed to the study conception and design. H-LZ and C-TZ took part in the discussion and modification of the study design. HZ, MY, L-YS, Q-HG, G-YZ, Q-JM, Y-XW and S-GL contributed to the study conception. X-QQ, HZ, MY, L-YS, Q-HG, G-YZ, Q-JM, Y-XW and S-GL were involved in the acquisition of data. J-PM and X-QQ made contributions to the drafting of the manuscript. H-LZ and C-TZ were involved in the critical revision. All the authors were involved in the analysis and interpretation of the data and gave their final approval.
Funding: This project was funded by the National Natural Science Foundation of China (Grant No 81400255).
Competing interests: None declared.
Patient consent: Obtained.
Ethics approval: This cross-sectional study received approval from the institutional ethics committee (Ethics Review Board of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology [TJ-C20141112]), and all subjects signed written informed consent before participation.
Provenance and peer review: Not commissioned; externally peer reviewed.
Data sharing statement: No additional data are available.
References
- 1. Guigoz Y. The Mini Nutritional Assessment (MNA) review of the literature–What does it tell us? J Nutr Health Aging 2006;10:466–85. Discussion 485-7. [PubMed] [Google Scholar]
- 2. Chang SF. Frailty is a major related factor for at risk of malnutrition in community-dwelling older adults. J Nurs Scholarsh 2017;49:63–72. 10.1111/jnu.12258 [DOI] [PubMed] [Google Scholar]
- 3. Lumalé S. [Nutritional management of elderly people, everyone’s concern]. Rev Infirm 2017;66:25–6. 10.1016/j.revinf.2017.10.005 [DOI] [PubMed] [Google Scholar]
- 4. Lesourd B. Nutritional factors and immunological ageing. Proc Nutr Soc 2006;65:319–25. 10.1079/PNS2006507 [DOI] [PubMed] [Google Scholar]
- 5. Raslan M, Gonzalez MC, Dias MC, et al. . Comparison of nutritional risk screening tools for predicting clinical outcomes in hospitalized patients. Nutrition 2010;26:721–6. 10.1016/j.nut.2009.07.010 [DOI] [PubMed] [Google Scholar]
- 6. Ehwerhemuepha L, Bendig D, Steele C, et al. . The effect of malnutrition on the risk of unplanned 7-day readmission in pediatrics. Hosp Pediatr 2018;8:207–13. 10.1542/hpeds.2017-0195 [DOI] [PubMed] [Google Scholar]
- 7. Ahmed N, Choe Y, Mustad VA, et al. . Impact of malnutrition on survival and healthcare utilization in Medicare beneficiaries with diabetes: a retrospective cohort analysis. BMJ Open Diabetes Res Care 2018;6:e000471 10.1136/bmjdrc-2017-000471 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Sharma Y, Miller M, Kaambwa B, et al. . Malnutrition and its association with readmission and death within 7 days and 8-180 days postdischarge in older patients: a prospective observational study. BMJ Open 2017;7:e018443 10.1136/bmjopen-2017-018443 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Khalatbari-Soltani S, Marques-Vidal P. The economic cost of hospital malnutrition in Europe; a narrative review. Clin Nutr ESPEN 2015;10:e89–e94. 10.1016/j.clnesp.2015.04.003 [DOI] [PubMed] [Google Scholar]
- 10. Bouillanne O, Curis E, Hamon-Vilcot B, et al. . Impact of protein pulse feeding on lean mass in malnourished and at-risk hospitalized elderly patients: a randomized controlled trial. Clin Nutr 2013;32:186–92. 10.1016/j.clnu.2012.08.015 [DOI] [PubMed] [Google Scholar]
- 11. Ballesteros-Pomar MD, Martínez Llinàs D, Goates S, et al. . Cost-effectiveness of a specialized oral nutritional supplementation for malnourished older adult patients in Spain. Nutrients 2018;10:246 10.3390/nu10020246 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12. Račić M, Ivković N, Kusmuk S. [Screening of nutritional status among elderly people at family medicine]. Acta Med Croatica 2015;69:347–56. [PubMed] [Google Scholar]
- 13. Abd Aziz NAS, Teng N, Abdul Hamid MR, et al. . Assessing the nutritional status of hospitalized elderly. Clin Interv Aging 2017;12:1615–25. 10.2147/CIA.S140859 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Elia M, Stratton R. On the ESPEN guidelines for nutritional screening 2002. Clin Nutr 2004;23:131–2. 10.1016/j.clnu.2003.09.016 [DOI] [PubMed] [Google Scholar]
- 15. Kondrup J, Rasmussen HH, Hamberg O, et al. . Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clin Nutr 2003;22:321–36. 10.1016/S0261-5614(02)00214-5 [DOI] [PubMed] [Google Scholar]
- 16. Kondrup J, Allison SP, Elia M, et al. . ESPEN guidelines for nutrition screening 2002. Clin Nutr 2003;22:415–21. 10.1016/S0261-5614(03)00098-0 [DOI] [PubMed] [Google Scholar]
- 17. Guigoz Y, Vellas B, Garry PJ. Assessing the nutritional status of the elderly: the mini nutritional assessment as part of the geriatric evaluation. Nutr Rev 1996;54:S59–65. 10.1111/j.1753-4887.1996.tb03793.x [DOI] [PubMed] [Google Scholar]
- 18. Bleda MJ, Bolibar I, Parés R, et al. . Reliability of the mini nutritional assessment (MNA) in institutionalized elderly people. J Nutr Health Aging 2002;6:134–7. [PubMed] [Google Scholar]
- 19. Bauer JM, Kaiser MJ, Anthony P, et al. . The Mini Nutritional Assessment--its history, today’s practice, and future perspectives. Nutr Clin Pract 2008;23:388–96. 10.1177/0884533608321132 [DOI] [PubMed] [Google Scholar]
- 20. Söderström L, Rosenblad A, Adolfsson ET, et al. . Nutritional status predicts preterm death in older people: a prospective cohort study. Clin Nutr 2014;33:354–9. 10.1016/j.clnu.2013.06.004 [DOI] [PubMed] [Google Scholar]
- 21. Dent E, Visvanathan R, Piantadosi C, et al. . Use of the Mini Nutritional Assessment to detect frailty in hospitalised older people. J Nutr Health Aging 2012;16:764–7. 10.1007/s12603-012-0405-5 [DOI] [PubMed] [Google Scholar]
- 22. Cabré M, Ferreiro C, Arus M, et al. . Evaluation of CONUT for clinical malnutrition detection and short-term prognostic assessment in hospitalized elderly people. J Nutr Health Aging 2015;19:729–33. 10.1007/s12603-015-0536-6 [DOI] [PubMed] [Google Scholar]
- 23. Chavarro-Carvajal D, Reyes-Ortiz C, Samper-Ternent R, et al. . Nutritional assessment and factors associated to malnutrition in older adults: a cross-sectional study in Bogotá, Colombia. J Aging Health 2015;27:304–19. 10.1177/0898264314549661 [DOI] [PubMed] [Google Scholar]
- 24. Kyle UG, Kossovsky MP, Karsegard VL, et al. . Comparison of tools for nutritional assessment and screening at hospital admission: a population study. Clin Nutr 2006;25:409–17. 10.1016/j.clnu.2005.11.001 [DOI] [PubMed] [Google Scholar]
- 25. Kaiser MJ, Bauer JM, Ramsch C, et al. . Validation of the Mini Nutritional Assessment short-form (MNA-SF): a practical tool for identification of nutritional status. J Nutr Health Aging 2009;13:782–8. 10.1007/s12603-009-0214-7 [DOI] [PubMed] [Google Scholar]
- 26. Sergi G, Coin A, Enzi G, et al. . Role of visceral proteins in detecting malnutrition in the elderly. Eur J Clin Nutr 2006;60:203–9. 10.1038/sj.ejcn.1602289 [DOI] [PubMed] [Google Scholar]
- 27. Omran ML, Morley JE. Assessment of protein energy malnutrition in older persons, Part II: Laboratory evaluation. Nutrition 2000;16:131–40. 10.1016/S0899-9007(99)00251-8 [DOI] [PubMed] [Google Scholar]
- 28. Omran ML, Morley JE. Assessment of protein energy malnutrition in older persons, part I: History, examination, body composition, and screening tools. Nutrition 2000;16:50–63. 10.1016/S0899-9007(99)00224-5 [DOI] [PubMed] [Google Scholar]
- 29. Zhou J, Wang M, Wang H, et al. . Comparison of two nutrition assessment tools in surgical elderly inpatients in Northern China. Nutr J 2015;14:68 10.1186/s12937-015-0054-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30. Thoresen L, Frykholm G, Lydersen S, et al. . Nutritional status, cachexia and survival in patients with advanced colorectal carcinoma. Different assessment criteria for nutritional status provide unequal results. Clin Nutr 2013;32:65–72. 10.1016/j.clnu.2012.05.009 [DOI] [PubMed] [Google Scholar]
- 31. Wei J, Chen W, Zhu M, et al. . Guidelines for parenteral and enteral nutrition support in geriatric patients in China. Asia Pac J Clin Nutr 2015;24:336–46. 10.6133/apjcn.2015.24.2.11 [DOI] [PubMed] [Google Scholar]
- 32. Jacobsen EL, Brovold T, Bergland A, et al. . Prevalence of factors associated with malnutrition among acute geriatric patients in Norway: a cross-sectional study. BMJ Open 2016;6:e011512 10.1136/bmjopen-2016-011512 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33. Guigoz Y, Vellas B. The Mini Nutritional Assessment (MNA) for grading the nutritional state of elderly patients: presentation of the MNA, history and validation. Nestle Nutr Workshop Ser Clin Perform Programme 1999;1:3–11. [DOI] [PubMed] [Google Scholar]
- 34. Blackburn GL, Bistrian BR, Maini BS, et al. . Nutritional and metabolic assessment of the hospitalized patient. JPEN J Parenter Enteral Nutr 1977;1:11–21. 10.1177/014860717700100101 [DOI] [PubMed] [Google Scholar]
- 35. Cabrerizo S, Cuadras D, Gomez-Busto F, et al. . Serum albumin and health in older people: Review and meta analysis. Maturitas 2015;81:17–27. 10.1016/j.maturitas.2015.02.009 [DOI] [PubMed] [Google Scholar]
- 36. O’Daly BJ, Walsh JC, Quinlan JF, et al. . Serum albumin and total lymphocyte count as predictors of outcome in hip fractures. Clin Nutr 2010;29:89–93. 10.1016/j.clnu.2009.07.007 [DOI] [PubMed] [Google Scholar]
- 37. Chiu PF, Tsai CC, Wu CL, et al. . Trajectories of serum albumin predict survival of peritoneal dialysis patients: a 15-year follow-up study. Medicine 2016;95:e3202 10.1097/MD.0000000000003202 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38. Covinsky KE, Covinsky MH, Palmer RM, et al. . Serum albumin concentration and clinical assessments of nutritional status in hospitalized older people: different sides of different coins? J Am Geriatr Soc 2002;50:631–7. 10.1046/j.1532-5415.2002.50156.x [DOI] [PubMed] [Google Scholar]
- 39. Carriere I, Dupuy AM, Lacroux A, et al. . Biomarkers of inflammation and malnutrition associated with early death in healthy elderly people. J Am Geriatr Soc 2008;56:840–6. 10.1111/j.1532-5415.2008.01677.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40. Devoto G, Gallo F, Marchello C, et al. . Prealbumin serum concentrations as a useful tool in the assessment of malnutrition in hospitalized patients. Clin Chem 2006;52:2281–5. 10.1373/clinchem.2006.080366 [DOI] [PubMed] [Google Scholar]
- 41. Langkamp-Henken B, Hudgens J, Stechmiller JK, et al. . Mini nutritional assessment and screening scores are associated with nutritional indicators in elderly people with pressure ulcers. J Am Diet Assoc 2005;105:1590–6. 10.1016/j.jada.2005.07.005 [DOI] [PubMed] [Google Scholar]
- 42. Kanda M, Mizuno A, Tanaka C, et al. . Nutritional predictors for postoperative short-term and long-term outcomes of patients with gastric cancer. Medicine 2016;95:e3781 10.1097/MD.0000000000003781 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43. Vellas B, Guigoz Y, Baumgartner M, et al. . Relationships between nutritional markers and the mini-nutritional assessment in 155 older persons. J Am Geriatr Soc 2000;48:1300–9. 10.1111/j.1532-5415.2000.tb02605.x [DOI] [PubMed] [Google Scholar]
- 44. López-Hellin J, Baena-Fustegueras JA, Schwartz-Riera S, et al. . Usefulness of short-lived proteins as nutritional indicators surgical patients. Clin Nutr 2002;21:119–25. 10.1054/clnu.2001.0515 [DOI] [PubMed] [Google Scholar]
- 45. Wham CA, Teh RO, Robinson M, et al. . What is associated with nutrition risk in very old age? J Nutr Health Aging 2011;15:247–51. 10.1007/s12603-010-0304-6 [DOI] [PubMed] [Google Scholar]
- 46. Weng CH, Hsu CW, Hu CC, Cc H, et al. . Association Between Hemodiafiltration and Hypoalbuminemia in Middle-Age Hemodialysis Patients. Medicine 2016;95:e3334 10.1097/MD.0000000000003334 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Lei Z, Qingyi D, Feng G, et al. . Clinical study of mini-nutritional assessment for older Chinese inpatients. J Nutr Health Aging 2009;13:871–5. 10.1007/s12603-009-0244-1 [DOI] [PubMed] [Google Scholar]
- 48. Antaki F, French MM, Moonka DK, et al. . Bioelectrical impedance analysis for the evaluation of hepatic fibrosis in patients with chronic hepatitis C infection. Dig Dis Sci 2008;53:1957–60. 10.1007/s10620-007-0071-3 [DOI] [PubMed] [Google Scholar]
- 49. Roongpisuthipong C, Sobhonslidsuk A, Nantiruj K, et al. . Nutritional assessment in various stages of liver cirrhosis. Nutrition 2001;17:761–5. 10.1016/S0899-9007(01)00626-8 [DOI] [PubMed] [Google Scholar]
- 50. Akinyemiju T, Meng Q, Vin-Raviv N. Association between body mass index and in-hospital outcomes: Analysis of the nationwide inpatient database. Medicine 2016;95:e4189 10.1097/MD.0000000000004189 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51. Drescher T, Singler K, Ulrich A, et al. . Comparison of two malnutrition risk screening methods (MNA and NRS 2002) and their association with markers of protein malnutrition in geriatric hospitalized patients. Eur J Clin Nutr 2010;64:887–93. 10.1038/ejcn.2010.64 [DOI] [PubMed] [Google Scholar]
- 52. Norman K, Pichard C, Lochs H, et al. . Prognostic impact of disease-related malnutrition. Clin Nutr 2008;27:5–15. 10.1016/j.clnu.2007.10.007 [DOI] [PubMed] [Google Scholar]
- 53. Bauer JM, Vogl T, Wicklein S, et al. . Comparison of the Mini Nutritional Assessment, Subjective Global Assessment, and Nutritional Risk Screening (NRS 2002) for nutritional screening and assessment in geriatric hospital patients. Z Gerontol Geriatr 2005;38:322–7. 10.1007/s00391-005-0331-9 [DOI] [PubMed] [Google Scholar]
- 54. Cederholm T, Bosaeus I, Barazzoni R, et al. . Diagnostic criteria for malnutrition - An ESPEN Consensus Statement. Clin Nutr 2015;34:335–40. 10.1016/j.clnu.2015.03.001 [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
bmjopen-2018-022993supp001.pdf (183.3KB, pdf)