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The association between nutrition risk status assessment and hospital mortality in Chinese older inpatients: a retrospective study

Journal of Health, Population and Nutrition volume 43, Article number: 229 (2024) Cite this article

Abstract

Purpose

The association between nutritional risk status assessment and hospital mortality in older patients remains controversial. The aim of this study was to assess the relationship between nutritional risk on admission and in-hospital mortality, and explore the best Nutritional Risk Status Screening 2002 (NRS2002) threshold for predicting in-hospital mortality of older inpatients in China.

Method

The elderly inpatients were recruited from a hospital in Hunan Province, China. Nutritional risk was screened and assessed using the NRS2002. Logistic regression was used to analyze whether NRS2002 scores were independently associated with hospital mortality, and the results were expressed as odds ratios (OR) and 95% confidence intervals (CIs). Receiver operating characteristic curve (ROC) was used to determine the best NRS2002 threshold for predicting in-hospital mortality in elderly inpatients. And 500 bootstrap re-samplings were performed for ROC analysis.

Result

In total, 464 elderly inpatients completed the survey (15 of whom died, 205 males and 259 females, mean age = 72.284 ± 5.626 years). Multifactorial analysis revealed that age, the NRS2002 score, and length of hospital stay significantly influenced in-hospital mortality among older inpatients (P < 0.05). The results also showed that higher NRS2002 scores were associated with an increased risk of in-hospital mortality in both the unadjusted (OR = 1.731,95%CI = 1.362–2.20, P < 0.0001), adjusted model I (OR = 1.736, 95% CI = 1.354–2.206, P < 0.0001) and model II (OR = 1.602, 95% CI = 1.734–2.488, P = 0.0005). The optimal NRS2002 threshold for predicting in-hospital mortality in older inpatients was 3.5, with the largest ROC area of 0.84.

Conclusion

Our findings indicated that nutritional risk was an independent predictor of in-hospital mortality, with a cut-off value of 3.50 for the NRS2002 nutritional risk assessment being more appropriate than a cut-off value of 3.0.

Introduction

Food intake decreases with age due to physiological decline and frailty. Hence, malnutrition is a major risk factor for chronic diseases and health-related deterioration in older adults [1,2,3]. Malnutrition risk during hospitalization may result from disease-related loss of appetite, drug-related side effects, fasting for diagnostic procedures, conditions that impair the digestive system, and poor management of patient nutrition [4]. Reviews published in the last 15 years have reported that in different countries and regions, the prevalence of malnutrition risk varies from 20–50% [5]. A recent study from the Middle East reported the prevalence of malnutrition risk among hospitalized patients to be 31.2% (measured using the NRS-2002). The same study indicated that malnutrition in hospitalized patients was directly associated with worsening clinical outcomes and a longer hospital stay [6]. A Chinese study of 487 elderly surgical inpatients found that 146 were at nutritional risk, with a malnutrition prevalence of nearly 30% [7]. Hence, nutritional risk status is a crucial determinant of inpatient prognosis [8,9,10,11,12,13,14]. Malnutrition is common in patients with acute coronary syndrome, and malnutrition is strongly associated with increased mortality and cardiovascular events [15]. Another study of 984 elderly inpatients demonstrated that the severe nutritional risk group experienced a higher risk of overall mortality, institutionalization, and rehospitalization [16]. Considerable evidence confirms that malnutrition is associated with worsening clinical outcomes among inpatients, including hospital readmission, nosocomial infections, and increased morbidity [6]. Many studies have also shown that malnutrition is associated with numerous adverse clinical outcomes, such as increased morbidity and mortality, lower muscle strength, reduced muscle mass, greater healthcare costs, and lower quality of life. This highlights the importance of screening for malnutrition risks in older patients to ensure timely and effective management [17].

Malnutrition risk is substantial among hospitalized older adults. To ensure targeted nutrition and appropriate interventions, standardized malnutrition screening at the time of hospital admission is warranted [18]. Despite the relevance and prevalence of malnutrition risk, malnutrition remains underdiagnosed and undertreated [19]. An accurate nutritional assessment is necessary to diagnose malnutrition. However, such assessment during a hospital stay is challenging to perform [20]. Screening patients for malnutrition risk at hospital admission, followed by nutritional assessment and individualized nutritional interventions as indicated should be a part of routine clinical care and multimodal treatment in hospitals worldwide [21]. However, the association between nutritional risk and hospital mortality in older inpatients remains controversial. A meta-analysis of malnourished hospitalized patients found that nutritional support increased patient weight and reduced readmission rates [21].Several nutrition guidelines recommend routine screening of older adults in all health care settings [22,23,24,25]. One study showed that detection of malnutrition or risk of developing malnutrition in the hospital using the NRS2002 screening tool at the time of hospital admission could identify patients who would benefit from nutritional support [26]. The double burden of malnutrition, and the emergence of diseases, such as type 2 diabetes, can further burden healthcare systems, especially public systems. This is a problem not only for the government, but also for affected individuals and their families [27]. This study aimed to assess the association between nutritional risk and hospital mortality, and explore the best Nutritional Risk Status Screening 2002 (NRS2002) threshold for predicting in-hospital mortality of older inpatients in China.

Materials and methods

Design, setting, and participants

This study surveyed elderly inpatients from a hospital in Hunan Province, China. A self-assessment questionnaire was completed from June 20, 2023, to September 02, 2023. The questionnaire was created using Wenjuanxing (www.wjx.cn)—an online crowd-sourcing platform founded in mainland China. The questionnaire was emailed to inpatients, aged ≥ 65 years, and anonymity was maintained. The elderly patients included in the study completed a nutritional risk assessment and questionnaire within 24 h of hospital admission. The informed consent page was viewed by potential participants first before completing the questionnaire. The informed consent page presented two options (agree or disagree). Only those who selected “agree” progressed to the questionnaire page. The investigators provided one-on-one guidance to assist the participants in completing the questionnaire. Participants could withdraw from the study at any time.

This study used convenience sampling via WeChat to recruit older adult inpatients. The sample size was calculated using the single population proportion formula: n = (Zα/2)² P(1 − P) / d², where Zα/2 is the confidence level (Zα/2 = 1.96 for α = 0.05), P is the estimated hospital mortality rate in older inpatients (20.27% [28]), and d is the desired margin of error. In our study, d = 0.05, and thus d² = 0.0025. The calculated minimum sample size was 248 (n= [1.96]2 × 0.2027× [1-0.2027] / [0.05]2). Considering a 20% contingency for non-response, the estimated sample size was 298 participants.

A total of 501 people participated in the survey, and 464 completed it, yielding a response rate of 92.6%. The inclusion criteria were as follows: (a) all adult inpatients, both males and females, aged 65 years or older, (b) admitted to different wards of the hospital during the period of data collection were recruited within 48 h of admission; and (c) inpatients who exhibited normal language skills and clear consciousness, and who used a smartphone. The exclusion criteria were as follows: older inpatients with serious vision, hearing, or reading impairments; patients in gynecology, intensive care, and psychiatry; and those with a hospital stay of less than 48 h.

Questionnaire

The questionnaire comprised three sections. The basic questionnaire included sociodemographic characteristics, laboratory data, nutritional risk screening (NRS2002), and the Self-Perceived Burden Scale (SPBS).

The basic and clinical questionnaires

The basic questionnaire covered demographics, including sex, age, education, marital status, occupation, income, medical payment, height, weight, body mass index (BMI, calculated as weight in kilograms divided by height in meters squared). It also included clinical data such as albumin, hemoglobin, random blood sugar, hospital department classification, length of stay in hospital, hospitalization expenses, NRS2002 scores, and total SPBS scores. Some elderly inpatients were uncertain about their clinical data (for example, albumin, hemoglobin, random blood sugar); so, these were obtained from hospital medical records by the investigators.

Nutritional Risk Status Screening 2002 (NRS2002)

Nutritional Risk Status Screening 2002 (NRS2002) was used to assess malnutrition and nutritional risks. The European Society for Clinical Nutrition and Metabolism recommended NRS2002 for hospitalized patients within 48 h of admission [29]. The Chinese version of the NRS2002 considers the severity of disease, impaired nutritional status, and an adjustment for patients aged ≥ 70 years. The categories were scored on a scale from 0 to 3 points [30]. Disease severity was analyzed as an indicator of metabolic stress, increased nutritional requirements, and impaired nutritional status including unintentional weight loss, reduced food intake, and BMI [31]. The final NRS2002 scores ranged from 0 to 7. An NRS2002 score of ≥ 3 indicates a potential nutritional risk [31].

The Self-Perceived Burden Scale (SPBS)

In 2003, Cousineau et al. [32] designed and developed the SPBS. The Chinese version of the SPBS consists of 10 items, which are grouped into three dimensions: body, emotional, and economic burden. Each dimension is rated on a five-point Likert-type scale (1 to 5: none of the time, occasionally, sometimes, often, and all the time). The total score ranges from 10 to 50. The Cronbach’s α for the Chinese version was 0.91 [33]. Scores in the range of 10–19, 20–30, 30–40, and ≥ 40 indicate normal, mild, moderate, and severe self-perceived burden, respectively [34]. The Cronbach’s α of this research was 0.83.

Statistical analysis

All statistical analyses were performed using the statistical packages R (http://www.R-project.org) and Empower Stats Version 4.1 (www.empowerstats.com, X&Y Solutions, Inc., Boston, MA, USA). The basic characteristics of all participants were stratified according to hospital mortality (Table 1). Continuous variables were tested for normality, with data that conformed to a normal distribution expressed with mean ± standard deviation (SD), and data that did not conform to a normal distribution expressed as inter quartile range. The t-test (normal distribution) or Mann-Whitney U test was used to detect differences among the different basic characteristics. Categorical variables are described as frequency and percentages (%). Pearson’s chi-square test was used for testing categorical variables and Fisher’s exact test was used when the expected frequencies were < 5%. Univariate and multivariate analyses were used to explore the factors influencing in-hospital mortality in older inpatients (Table 2). Logistic regression was used to analyze whether NRS2002 risk was independently associated with hospital mortality, and the results were expressed as odds ratios (OR) and 95% confidence intervals (CIs). Receiver operating characteristic curve (ROC) was used to determine the best NRS2002 threshold for predicting in-hospital mortality in elderly inpatients (Table 3). Furthermore, 500 bootstrap re-samplings were performed for the ROC analysis. Multiple logistic regression was used to determine whether the relationship between nutritional risk and hospital mortality in older inpatients using 3 models (Table 4). The first was a non-adjusted model in which no covariates were adjusted. In another model (Model I), only age and sex were adjusted. In Model II, age, sex, occupation, education, income, marital status, medical department, BMI, albumin, hemoglobin, random blood sugar, length of stay in hospital, and hospitalization expenses were adjusted. P < 0.05 (2-sided) was considered statistically significant.

Results

Participant characteristics stratified by hospital mortality

This study included 464 older Chinese inpatients (259 women and 205 men, mean age = 72.284 ± 5.626 years). The older inpatients were stratified by hospital mortality (449 survived and 15 died). In this study, 247 (53.233%) patients were in the medical ward and 217 (46.767%) were in the surgical ward. Inpatients who died tended to be older and had higher random blood sugar levels, higher NRS2002 scores, longer hospital stays, and greater hospitalization expenses (p < 0.001). Statistically significant differences were observed between the health insurance payment and marital status models (p < 0.05). Table 1 shows the demographic and clinical characteristics, NRS2002 scores, SPBS scores, and patient outcomes.

Table 1 The basic characteristics of older inpatients stratified by hospital mortality (N = 464)
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Univariate and multivariate analyses of hospital mortality

Results from the univariate analysis showed that age (P = 0.011), college degree (P = 0.021), marital status (P = 0.013), models of health insurance payment (P < 0.05), random blood sugar (P = 0.004), NRS2002 scores, length of hospital stay, and hospitalization expenses (P < 0.001) were correlated with hospital mortality.

Results from the multivariate analysis showed that age (P = 0.004), NRS2002 score (P = 0.012), and length of hospital stay (P = 0.007) were significantly associated with hospital mortality. As shown in Table 2, the other variables were not statistically significant.

Table 2 The univariate and multivariate analysis for hospital mortality in old inpatients (N = 464)
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ROC analysis of the NRS2002 score for predicting hospital mortality

The prediction efficiency of the ROC analysis, using 500 bootstrap resampling, is shown in Table 3. When the cutoff value was higher than or equal to 3.50, sensitivity was 0.8 and specificity was 0.826. The best threshold was 3.5, and the largest area of ROC was 0.84. (Fig. 1).

Table 3 The ROC analysis of NRS2002 for predicating older inpatient’s hospital mortality (N = 464)
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Fig. 1
figure 1

Receiver operating characteristic curves (ROC) of NRS2002 for predicating older inpatient’s hospital mortality

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The relationship between NRS2002 and hospital mortality

Logistic regression models were used to evaluate the association between the NRS2002 scores and in-hospital mortality. Table 4 shows the results of the non-adjusted and adjusted models. When the NRS2002 score was considered as a continuous variable, higher NRS2002 scores were associated with an increased risk of in-hospital mortality in the non-adjusted model (OR = 1.731,95%CI = 1.362–2.20, P < 0.0001). We adjusted for age and sex in Model I (OR = 1.736, 95% CI = 1.354–2.206, P < 0.0001), and adjusted for age, sex, occupation, education, income, marital status, medical department classification, BMI, albumin, hemoglobin, random blood sugar, length of stay in hospital, and hospitalization expenses in Model II (OR = 1.602, 95% CI = 1.734–2.488, P = 0.0005).

When the NRS2002 score was treated as a categorical variable and analyzed in the non-adjusted and adjusted models, patients with a lower nutritional risk (NRS2002 score < 3) were included in the reference group. In Model I, a higher nutritional risk group (NRS2002 score ≥ 3) was associated with an increased risk of in-hospital mortality (OR 12.779, 95% CI 2.824–57.814). In Model II, the higher nutritional risk group remained positively associated with hospital mortality compared to the lower nutritional risk group (OR 10.135, 95% CI 3.554–67. 211; p = 0.0159). When using the best threshold of 3.5 to classify the NRS2002 scores, the results showed that in Model I, the higher nutritional risk group (NRS2002 ≥ 3.50) was associated with an increased risk of in-hospital mortality (OR 19.131, 95% CI 5.141–71.193). In Model II, the higher nutritional risk group remained positively associated with in-hospital mortality compared to the lower nutritional risk group (OR 13.159, 95% CI, 1.670–63. 477; p = 0.0394) (Table 4).

Table 4 The relationship between NRS2002(threshold = 3.0) and hospital mortality in older inpatients (N = 464)
Full size table

Discussion

In this study, hospital mortality was found to be associated with a higher nutritional risk status among older inpatients in a dose-dependent manner. The nutritional risk status was present after adjusting for demographic characteristics, lifestyle factors, and other variables. Moreover, we observed an effect modification of the relationship between hospital mortality and nutritional status.

In this study, the in-hospital mortality rate for elderly patients was 3.2%, which is lower than previous studies [35,36,37]. A study from MIMIC-III showed that the in-hospital mortality rate for elderly patients suffering from acute respiratory failure was 27.36% [35]. Another study from MIMIC-IV showed an all-cause in-hospital mortality rate of 22.3% in elderly patients in geriatric intensive care units [36]. A prospective cohort study from Egypt exploring nutritional risk indices and adverse medical outcomes in the elderly [37] found that the in-hospital mortality rate for elderly patients was 9%. This finding suggests that elderly hospitalized patients in mainland China may have lower mortality rates than patients in other countries; however, meaningful explanations for this difference are lacking. Possible influencing factors include the fact that subjects in this study were from general medical and surgical wards, while the other studies focused on intensive care units. The in-hospital mortality rate of elderly patients in the Egyptian study was 9%, which, although higher than in our study, is not a large difference. Therefore, the relatively low mortality rate in our study is understandable. Additionally, since this study only investigated mortality between June 20, 2023, and September 2, 2023, there may be a degree of bias. This is one of the limitations of the study. In the future, we will consider collecting data throughout the year and from multiple centers to validate our results.

The NRS-2002 is widely used in clinical practice. Studies have reported that the prevalence of malnutrition among European inpatients ranges from 18–45% [38, 39]. In our study, the prevalence of high nutritional risk among our Chinese elderly inpatients was 33.4%.

Evidence supports that age is an important risk factor for malnutrition. The prevalence of malnutrition tends to be higher in older patients than in younger patients with similar characteristics [40,41,42]. Similarly, malnutrition is more common in the older elderly group than in the younger elderly group (65–69 years) [43]. With this mind, the age of ≥ 70 years was considered in an additional score to the NRS-2002 instrument.

The NRS-2002 predicts short- and long-term prognoses in hospitalized patients [43,44,45]. Secondary analyses of the EFFORT study [44] indicated that malnutrition can influence 30- and 180-day mortality in elderly hospitalized patients. Therefore, we analyzed factors influencing mortality in hospitalized elderly patients. The results showed that age, NRS2002, and length of hospital stay were independent risk factors for mortality in elderly patients. Elderly patients at high risk of malnutrition are less likely to meet their nutritional needs through dietary intake during hospitalization. Therefore, medical staff must be aware of the patient’s nutritional status and provide timely supplementation.

Higher NRS scores were associated with increased mortality, with an OR of 1.883 [43]. Another retrospective observational study [46] reported a doubling of mortality in patients with NRS ≥ 3 defined upon admission. Similarly, malnutrition was strongly associated with in-hospital mortality in elderly inpatients. In this study, when the NRS2002 score was treated as a continuous variable, each 1-unit increase in the NRS2002 score was associated with a 1.736-fold increase in mortality among elderly inpatients, and this was observed even after adjusting for variables. Additionally, we analyzed the mortality rate in patients with an NRS ≥ 3, showing that the risk of death in elderly inpatients with an NRS ≥ 3 was 14.053 times higher compared to those with an NRS < 3. This association remained significant even after adjusting for multiple variables in Model II. However, some studies have shown that the NRS 2002 has low validity in predicting malnutrition in elderly patients [47]. To further investigate the relationship, this study used ROC curves to determine the optimal threshold for diagnosing of malnutrition in elderly inpatients. Our results showed that the best ROC curve fit of validity was achieved when the NRS 2002 score exceeded 3.5 points. Therefore, we reanalyzed the prediction of mortality in elderly inpatients using NRS2002 at a cut-off of 3.5 points, and the results showed that the risk of death in elderly inpatients with NRS ≥ 3.5 is increased by a factor of 19.026, compared to NRS < 3.5 points.

Previous investigations have shown that malnutrition and inflammation are intricately linked [48, 49]. In particular, inflammatory responses may inhibit albumin synthesis, a key protein involved in the maintenance of optimal nutritional status, and further exacerbate malnutrition, creating a self-perpetuating cycle of deleterious consequences [50]. Moreover, emerging evidence suggests that malnutrition can induce the onset of various pathological processes, such as free radical damage, impaired insulin secretion, lipolysis, and lipid oxidation. These adverse events may trigger tissue damage, diabetes mellitus, and fatty liver disease, and thus perpetuate a vicious cycle of malnutrition [51, 52], which is further exacerbated in older patients with more comorbidities. Therefore, the early recognition of malnutrition in elderly patients is particularly important. Previous studies have compared various nutritional screening tools but currently, there is no uniform screening tool for elderly patients [47]. At our hospital, we used the NRS2002 screening tool with fair predictive modeling. Our results suggest that for elderly inpatients at our hospital, when the NRS2002 score is higher than 3.5, caregivers should pay attention to potential malnutrition and provide treatment as necessary.

Limitations

This study has several limitations. First, while this study found an association between the NRS2002 score and in-hospital mortality in elderly patients, its cross-sectional design prevents the establishment of a causal relationship. Prospective studies are necessary to elucidate this relationship in the future. Second, this was a single-center study. The observed low mortality rate among elderly patients may be influenced by the selection of participants and the limited time frame of data collection. To address these issues, future studies will aim to collect data throughout the year and from multiple centers to improve the generalizability of our findings. Third, the nutritional status assessed in this study was at the time of admission and not assessed dynamically.

Conclusion

Malnutrition is closely associated with increased mortality in elderly patients. Therefore, it is critically important to monitor, diagnose, and manage malnutrition during hospitalization. Medical professionals should pay particular attention to elderly hospitalized patients with NRS2002 scores exceeding 3.5.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Kaur D, Rasane P, Singh J, Kaur S, Kumar V, Mahato DK, et al. Nutritional interventions for Elderly and considerations for the development of Geriatric Foods. Curr Aging Sci. 2019;12(1):15–27. https://doiorg.publicaciones.saludcastillayleon.es/10.2174/1874609812666190521110548.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Tamura Y, Omura T, Araki KTA. Nutrition Management in older adults with diabetes: a review on the importance of shifting Prevention strategies from metabolic syndrome to Frailty. Nutrients. 2020;12(11). https://doiorg.publicaciones.saludcastillayleon.es/10.3390/nu12113367.

  3. Piccoli GB, Cederholm T, Avesani CM, Bakker SJL, Bellizzi V, Cuerda C, et al. Nutritional status and the risk of malnutrition in older adults with chronic kidney disease - implications for low protein intake and nutritional care: a critical review endorsed by ERN-ERA and ESPEN. Clin Nutr. 2023;42(4):443–57. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clnu.2023.01.018.

    Article  CAS  PubMed  Google Scholar 

  4. Schuetz P, Seres D, Lobo DN, Gomes F, Kaegi-BraunZ N. Stanga, Management of disease-related malnutrition for patients being treated in hospital. Lancet. 2021;398(10314):1927–38. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0140-6736(21)01451-3.

    Article  PubMed  Google Scholar 

  5. Norman K, Pichard C, Pirlich HLM. Prognostic impact of disease-related malnutrition. Clin Nutr. 2008;27(1):5–15. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clnu.2007.10.007.

    Article  PubMed  Google Scholar 

  6. Ouaijan K, Hwalla N, Mpinga NBKEK. Prevalence of Malnutrition in Hospitalized patients in Lebanon using Nutrition Risk Screening (NRS-2002) and global Leadership Initiative on Malnutrition (GLIM) Criteria and its association with length of Stay. Healthc (Basel). 2023;11(5). https://doiorg.publicaciones.saludcastillayleon.es/10.3390/healthcare11050730.

  7. Lili D, Lijuan W, Liru C, Bo C, ChengyuZ L, Mingwei. The association of frailty status with nutritional risk and the effect on outcomes in elderly surgical inpatients. Chin J Clin Nutr. 2023;31(02):82–6. https://doiorg.publicaciones.saludcastillayleon.es/10.3760/cma.j.cn115822-20230302-00030.

    Article  Google Scholar 

  8. Qiu J, Yang J, Yu Y, Wang Z, Lin H, Ke D, et al. Prognostic value of pre-therapeutic nutritional risk factors in elderly patients with locally advanced esophageal squamous cell carcinoma receiving definitive chemoradiotherapy or radiotherapy. BMC Cancer. 2023;23(1):597. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12885-023-11044-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Schoeman J. Nutritional assessment and intervention in a pediatric oncology unit. Indian J Cancer. 2015;52(2):186–90. https://doiorg.publicaciones.saludcastillayleon.es/10.4103/0019-509X.175832.

    Article  CAS  PubMed  Google Scholar 

  10. Tripodi SI, Bergami E, Panigari A, Caissutti V, Brovia C, De Cicco M, et al. The role of nutrition in children with cancer. Tumori. 2023;109(1):19–27. https://doiorg.publicaciones.saludcastillayleon.es/10.1177/03008916221084740.

    Article  CAS  PubMed  Google Scholar 

  11. Pedretti L, Massa S, Leardini D, Muratore E, Rahman S, Pession A, et al. Role of Nutrition in Pediatric patients with Cancer. Nutrients. 2023;15(3). https://doiorg.publicaciones.saludcastillayleon.es/10.3390/nu15030710.

  12. Hamaker ME, Oosterlaan F, van Huis LH, Thielen N, van den Bos AVF. Nutritional status and interventions for patients with cancer - A systematic review. J Geriatr Oncol. 2021;12(1):6–21. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jgo.2020.06.020.

    Article  CAS  PubMed  Google Scholar 

  13. Yu M, Li X, Chen M, Liu L, Yao T, Li J, et al. Prognostic potential of nutritional risk screening and assessment tools in predicting survival of patients with pancreatic neoplasms: a systematic review. Nutr J. 2024;23(1):17. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12937-024-00920-w.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Tsukagoshi M, Araki K, Igarashi T, Ishii N, Kawai S, Hagiwara K, et al. Lower Geriatric Nutritional Risk Index and Prognostic Nutritional Index Predict Postoperative Prognosis in patients with Hepatocellular Carcinoma. Nutrients. 2024;16(7). https://doiorg.publicaciones.saludcastillayleon.es/10.3390/nu16070940.

  15. Raposeiras Roubin S, Abu Assi E, Cespon Fernandez M, Barreiro Pardal C, Lizancos Castro A, Parada JA, et al. Prevalence and prognostic significance of malnutrition in patients with Acute Coronary Syndrome. J Am Coll Cardiol. 2020;76(7):828–40. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jacc.2020.06.058.

    Article  PubMed  Google Scholar 

  16. Pilotto A, Veronese N, Daragjati J, Cruz-Jentoft AJ, Polidori MC, Mattace-Raso F, et al. Using the Multidimensional Prognostic Index to predict clinical outcomes of hospitalized older persons: a prospective, Multicenter, International Study. J Gerontol Biol Sci Med Sci. 2019;74(10):1643–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/gerona/gly239.

    Article  Google Scholar 

  17. van Zwienen-Pot JI, Reinders I, de Groot L, Beck AM, Feldblum I, Jobse I, et al. Effects of Nutritional interventions in older adults with malnutrition or at risk of Malnutrition on muscle strength and mortality: results of pooled analyses of individual participant data from nine RCTs. Nutrients. 2023;15(9). https://doiorg.publicaciones.saludcastillayleon.es/10.3390/nu15092025.

  18. Ligthart-Melis GC, Luiking YC, Kakourou A, Cederholm T, MaierM AB, Frequently M. (Co-)occur in hospitalized older adults: a systematic review and Meta-analysis. J Am Med Dir Assoc. 2020;21(9):1216–28. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.jamda.2020.03.006.

    Article  PubMed  Google Scholar 

  19. Dent E, Wright ORL, Hoogendijk JWEO. Malnutrition in older adults. Lancet. 2023;401(10380):951–66. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/S0140-6736(22)02612-5.

    Article  PubMed  Google Scholar 

  20. Bellanti F, Lo Buglio A, QuieteG S, Vendemiale. Malnutrition in Hospitalized Old patients: screening and diagnosis, clinical outcomes, and management. Nutrients. 2022;14(4). https://doiorg.publicaciones.saludcastillayleon.es/10.3390/nu14040910.

  21. Bally MR, Blaser Yildirim PZ, Bounoure L, Gloy VL, Mueller B, Briel M, et al. Nutritional support and outcomes in Malnourished Medical inpatients: a systematic review and Meta-analysis. JAMA Intern Med. 2016;176(1):43–53. https://doiorg.publicaciones.saludcastillayleon.es/10.1001/jamainternmed.2015.6587.

    Article  PubMed  Google Scholar 

  22. Volkert D, Beck AM, Cederholm T, Cruz-Jentoft A, Goisser S, Hooper L, et al. ESPEN guideline on clinical nutrition and hydration in geriatrics. Clin Nutr. 2019;38(1):10–47. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clnu.2018.05.024.

    Article  PubMed  Google Scholar 

  23. Volkert D, Beck AM, Cederholm T, Cruz-Jentoft A, Hooper L, Kiesswetter E, et al. ESPEN practical guideline: clinical nutrition and hydration in geriatrics. Clin Nutr. 2022;41(4):958–89. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clnu.2022.01.024.

    Article  CAS  PubMed  Google Scholar 

  24. Cederholm T, Jensen GL, Correia M, Gonzalez MC, Fukushima R, Higashiguchi T, et al. GLIM criteria for the diagnosis of malnutrition - A consensus report from the global clinical nutrition community. Clin Nutr. 2019;38(1):1–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clnu.2018.08.002.

    Article  CAS  PubMed  Google Scholar 

  25. Weimann A, Braga M, Carli F, Higashiguchi T, Hubner M, Klek S, et al. ESPEN practical guideline: clinical nutrition in surgery. Clin Nutr. 2021;40(7):4745–61. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clnu.2021.03.031.

    Article  PubMed  Google Scholar 

  26. Kondrup J, Rasmussen HH, Hamberg O, Ad ZSEWG, Hoc. Nutritional risk screening (NRS 2002): a new method based on an analysis of controlled clinical trials. Clin Nutr. 2003;22(3):321–36. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/s0261-5614(02)00214-5.

    Article  PubMed  Google Scholar 

  27. Batal M, SteinhouseH L, Delisle. The nutrition transition and the double burden of malnutrition. Med Sante Trop. 2018;28(4):345–50. https://doiorg.publicaciones.saludcastillayleon.es/10.1684/mst.2018.0831.

    Article  CAS  PubMed  Google Scholar 

  28. Mao Z, Wen T, Liu X, Chen J, Hu P, Liu C, et al. Geriatric nutritional risk index is Associated with Hospital Death in Elderly patients with multiple organ dysfunction syndrome: a retrospective study based on the MIMIC-III database. Front Nutr. 2022;9:834256. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fnut.2022.834256.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Cederholm T, Bosaeus I, Barazzoni R, Bauer J, Van Gossum A, Klek S, et al. Diagnostic criteria for malnutrition - an ESPEN Consensus Statement. Clin Nutr. 2015;34(3):335–40. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clnu.2015.03.001.

    Article  CAS  PubMed  Google Scholar 

  30. Zhang Z, Wan Z, Zhu Y, Zhang L, ZhangH L, Wan. Prevalence of malnutrition comparing NRS2002, MUST, and PG-SGA with the GLIM criteria in adults with cancer: a multi-center study. Nutrition. 2021;83:111072. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.nut.2020.111072.

    Article  PubMed  Google Scholar 

  31. Kasapoglu US, Gok A, DelenA LA, Ozer B. Comparison of nutritional risk status assessment tools in predicting 30-day survival in critically ill COVID-19 pneumonia patients. Ann Saudi Med. 2022;42(4):236–45. https://doiorg.publicaciones.saludcastillayleon.es/10.5144/0256-4947.2022.236.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Cousineau N, McDowell I, Hebert SHP. Measuring chronic patients’ feelings of being a burden to their caregivers: development and preliminary validation of a scale. Med Care. 2003;41(1):110–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1097/00005650-200301000-00013.

    Article  PubMed  Google Scholar 

  33. Tang B, Fu Y, Yi BLQ. Self-perceived burden and associated factors in Chinese adult epilepsy patients: a cross-sectional study. Front Neurol. 2022;13:994664. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fneur.2022.994664.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Chen Y, Wei Y, Lang H, Xiao T, Hua Y, Li L, et al. Effects of a goal-oriented intervention on Self-Management behaviors and Self-Perceived Burden after Acute Stroke: a Randomized Controlled Trial. Front Neurol. 2021;12:650138. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fneur.2021.650138.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Shi X, Shen Y, Yang J, Yang WDJ. The relationship of the geriatric nutritional risk index to mortality and length of stay in elderly patients with acute respiratory failure: a retrospective cohort study. Heart Lung. 2021;50(6):898–905. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.hrtlng.2021.07.012.

    Article  PubMed  Google Scholar 

  36. Peng JC, Zhu YW, Xing SP, Li W, Gong YGWW. Association of geriatric nutritional risk index with all-cause hospital mortality among elderly patients in intensive care unit. Front Nutr. 2023;10:1117054. https://doiorg.publicaciones.saludcastillayleon.es/10.3389/fnut.2023.1117054.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Mohammed HO, Hassan AM, Mostafa A, Khater MS, Abd AAKM, Elaziz. Geriatric nutritional risk index and adverse medical outcomes among Egyptian patients admitted to a geriatric hospital: a prospective cohort study. BMC Geriatr. 2024;24(1):62. https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12877-024-04671-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Khalatbari-Soltani SP, Marques-Vidal. The economic cost of hospital malnutrition in Europe; a narrative review. Clin Nutr ESPEN. 2015;10(3):e89–94. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clnesp.2015.04.003.

    Article  PubMed  Google Scholar 

  39. Imoberdorf R, Meier R, Krebs P, Hangartner PJ, Hess B, Staubli M, et al. Prevalence of undernutrition on admission to Swiss hospitals. Clin Nutr. 2010;29(1):38–41. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clnu.2009.06.005.

    Article  PubMed  Google Scholar 

  40. Dent E, Hoogendijk EO, Wright RVORL. Malnutrition Screening and Assessment in Hospitalised Older people: a review. J Nutr Health Aging. 2019;23(5):431–41. https://doiorg.publicaciones.saludcastillayleon.es/10.1007/s12603-019-1176-z.

    Article  CAS  PubMed  Google Scholar 

  41. Koren-Hakim T, Weiss A, Hershkovitz A, Otzrateni I, Anbar R, Gross Nevo RF, et al. Comparing the adequacy of the MNA-SF, NRS-2002 and MUST nutritional tools in assessing malnutrition in hip fracture operated elderly patients. Clin Nutr. 2016;35(5):1053–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clnu.2015.07.014.

    Article  PubMed  Google Scholar 

  42. Drescher T, Singler K, Ulrich A, Koller M, Keller U, Christ-Crain M, 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(8):887–93. https://doiorg.publicaciones.saludcastillayleon.es/10.1038/ejcn.2010.64.

    Article  CAS  PubMed  Google Scholar 

  43. Bakkaloglu OK, Bektas M, Ince B, Amikishiyev S, Tor YB, Altinkaynak M, et al. Malnutrition risk in hospitalized patients measured with Nutrition Risk Screening 2002 tool and its association with in-hospital mortality. Eur Rev Med Pharmacol Sci. 2023;27(12):5812–21. https://doiorg.publicaciones.saludcastillayleon.es/10.26355/eurrev_202306_32820.

    Article  CAS  PubMed  Google Scholar 

  44. Hersberger L, Bargetzi L, Bargetzi A, Tribolet P, Fehr R, Baechli V, et al. Nutritional risk screening (NRS 2002) is a strong and modifiable predictor risk score for short-term and long-term clinical outcomes: secondary analysis of a prospective randomised trial. Clin Nutr. 2020;39(9):2720–9. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clnu.2019.11.041.

    Article  PubMed  Google Scholar 

  45. Muller M, Dahdal S, Saffarini M, Arampatzis DUS. Evaluation of Nutrition Risk Screening score 2002 (NRS) assessment in hospitalized chronic kidney disease patient. PLoS ONE. 2019;14(1):e0211200. https://doiorg.publicaciones.saludcastillayleon.es/10.1371/journal.pone.0211200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Sanson G, Sadiraj M, Barbin I, Confezione C, De Matteis D, Boscutti G, et al. Prediction of early- and long-term mortality in adult patients acutely admitted to internal medicine: NRS-2002 and beyond. Clin Nutr. 2020;39(4):1092–100. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clnu.2019.04.011.

    Article  PubMed  Google Scholar 

  47. van Bokhorst-de MA, Guaitoli PR, JansmaH EP, de Vet C. Nutrition screening tools: does one size fit all? A systematic review of screening tools for the hospital setting. Clin Nutr. 2014;33(1):39–58. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.clnu.2013.04.008.

    Article  Google Scholar 

  48. Nakagomi A, Kohashi K, Morisawa T, Kosugi M, Endoh I, Kusama Y, et al. Nutritional status is Associated with inflammation and predicts a poor outcome in patients with chronic heart failure. J Atheroscler Thromb. 2016;23(6):713–27. https://doiorg.publicaciones.saludcastillayleon.es/10.5551/jat.31526.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hasper D, Hummel M, Kleber FX, Volk IRHD. Systemic inflammation in patients with heart failure. Eur Heart J. 1998;19(5):761–5. https://doiorg.publicaciones.saludcastillayleon.es/10.1053/euhj.1997.0858.

    Article  CAS  PubMed  Google Scholar 

  50. Purser JL, Kuchibhatla MN, Fillenbaum GG, Harding T, PetersonK ED, Alexander P. Identifying frailty in hospitalized older adults with significant coronary artery disease. J Am Geriatr Soc. 2006;54(11):1674–81. https://doiorg.publicaciones.saludcastillayleon.es/10.1111/j.1532-5415.2006.00914.x.

    Article  PubMed  Google Scholar 

  51. Spoelstra MN, Mari A, Mendel M, Senga E, van Rheenen P, van Dijk TH, et al. Kwashiorkor and marasmus are both associated with impaired glucose clearance related to pancreatic beta-cell dysfunction. Metabolism. 2012;61(9):1224–30. https://doiorg.publicaciones.saludcastillayleon.es/10.1016/j.metabol.2012.01.019.

    Article  CAS  PubMed  Google Scholar 

  52. Badaloo AV, Forrester T, Jahoor MRF. Lipid kinetic differences between children with kwashiorkor and those with marasmus. Am J Clin Nutr. 2006;83(6):1283–8. https://doiorg.publicaciones.saludcastillayleon.es/10.1093/ajcn/83.6.1283.

    Article  CAS  PubMed  Google Scholar 

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Funding

Funding: This study was supported by Health Research Project of Hunan Provincial Health Commission (grant number: 202203072801).

Author information

Authors and Affiliations

  1. Department of Orthopedics, Changde Hospital, Xiangya School of Medicine, Central South University（The first people’s hospital of Changde city), Changde, Hunan Province, China

    Jie-Qiong Liu

  2. Department of Nursing, Changde Hospital, Xiangya School of Medicine, Central South University（The first people’s hospital of Changde city), Changde, Hunan Province, China

    Meng-Jun He, Xue-Qing Zhang & Jin-Hua Shen

  3. Department of Surgery, Changde Hospital, Xiangya School of Medicine, Central South University（The first people’s hospital of Changde city), Changde, Hunan Province, China

    Feng-Hua Zeng & Jin-Hua Shen

  4. Department of Nutrition, Changde Hospital, Xiangya School of Medicine, Central South University（The first people’s hospital of Changde city), Changde, Hunan Province, China

    Hui Mo

  5. Department of Surgery, Changde Hospital, Xiangya School of Medicine, Central South University（The first people’s hospital of Changde city), 818 Ren min Road, Changde, Hunan Province, China

    Feng-Hua Zeng

  6. Department of Nutrition, Changde Hospital, Xiangya School of Medicine, Central South University（The first people’s hospital of Changde city), 818 Ren min Road, Changde, Hunan Province, China

    Hui Mo

  7. Department of Nursing, Changde Hospital, Xiangya School of Medicine, Central South University（The first people’s hospital of Changde city), 818 Ren min Road, Changde, Hunan Province, China

    Jin-Hua Shen

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  1. Jie-Qiong Liu
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  2. Meng-Jun He
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  3. Xue-Qing Zhang
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  4. Feng-Hua Zeng
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  5. Hui Mo
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Contributions

Collected data: Jie-qiong Liu and Feng-hua Zeng; Formal analysis: Xue-qing Zhang and Meng-jun He; Funding Acquisition: Hui MoInvestigation: Xue-qing ZhangProject administration: Jin-hua Shen and Hui MoSupervision: Jin-hua Shen and Hui MoWriting – original draft preparation: Jin-hua Shen, Jie-qiong LiuWriting – review & editing: Feng-hua Zeng, Hui Mo, Xue-qing Zhang.

Corresponding authors

Correspondence to Feng-Hua Zeng, Hui Mo or Jin-Hua Shen.

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Ethics approval and informed consent

Ethical approval was obtained from the Ethics Committee of the First People’s Hospital of Changde City (YX-2023-119-01), and all participants signed an informed consent form before the study was initiated. The study was conducted according to the Declaration of Helsinki.

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Liu, JQ., He, MJ., Zhang, XQ. et al. The association between nutrition risk status assessment and hospital mortality in Chinese older inpatients: a retrospective study. J Health Popul Nutr 43, 229 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41043-024-00726-w

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Keywords

Journal of Health, Population and Nutrition

ISSN: 2072-1315

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