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. 2025 Mar 3;34(11):4630–4641. doi: 10.1111/jocn.17710

Geriatric Nutritional Risk Index and In‐Hospital Mortality and Costs in Older Inpatients With and Without Cancer: A Retrospective Observational Study

Lujiao Huang 1,2, Xue Zhou 2, Yi Song 2, Xiong Xiao 3, Mingyu Cui 1, Zhu Zhu 4, Mengjiao Yang 1,5, Yu Pei 1, Tokie Anme 6,✉
PMCID: PMC12489446  PMID: 40033381

ABSTRACT

Aims

To explore the association of the Geriatric Nutritional Risk Index (GNRI) with in‐hospital mortality and costs in older inpatients and to compare these associations between cancer and non‐cancer patients to inform clinical practice.

Design

Retrospective observational study.

Methods

A hospital‐based study was conducted in Southwest China between January 2018 and December 2020. Demographic, clinical, laboratory and anthropometric data of inpatients aged 65 and over, along with hospitalisation deaths and costs, were collected through the Hospital Information System of a general hospital and its affiliates. GNRI was calculated at admission to assess nutritional risk. Marginal structural models and stratified analyses estimated hospitalisation outcomes for older inpatients with and without various types of cancer across different nutritional risk grades.

Results

Among 37,267 participants, in‐hospital mortality and costs increased with higher nutritional risk. Older inpatients with major nutritional risk had significantly higher mortality and costs than those with no nutritional risk. Older cancer inpatients with major nutritional risk had the highest mortality and costs, significantly exceeding those of non‐cancer inpatients. For each cancer type, increased nutritional risk was associated with higher in‐hospital mortality and costs. Respiratory cancer inpatients with major nutritional risk had the highest mortality, while digestive cancer inpatients had the highest costs.

Conclusion

Higher GNRI‐assessed nutritional risk was associated with increased in‐hospital mortality and costs in older inpatients, with stronger associations observed in cancer patients compared to non‐cancer patients. Integrating GNRI into routine nursing practice could have significant clinical and economic benefits by promoting early nutritional screening in patient care and targeted interventions to reduce mortality and healthcare costs in high‐risk populations.

Implications for the Profession and/or Patient Care

Integrating GNRI assessment into routine patient care can effectively identify patients at high risk for in‐hospital mortality and costs, allowing for timely nutritional support to enhance patient outcomes. GNRI, as a simple and globally applicable tool, can be integrated into diverse healthcare settings, providing an effective method for nutritional risk screening in older patients. When applying GNRI in clinical nursing and medical practice, special consideration should be given to the presence and type of cancer, as cancer patients with severe nutritional risk may benefit the most from targeted interventions.

Impact

What problem did the study address? This study investigated the association between GNRI‐assessed nutritional risk and in‐hospital mortality and costs in older inpatients. It further examined whether these associations differ between cancer and non‐cancer patients and among different cancer types to improve clinical application.

What were the main findings? The study found that higher nutritional risk assessed by GNRI was associated with increased in‐hospital mortality and costs in older inpatients. These associations were stronger in older cancer patients compared to non‐cancer patients, with respiratory cancers showing the highest mortality and digestive cancers incurring the highest costs. These findings emphasise the important role of nutritional screening using GNRI in patient care with varying clinical profiles and informing nursing and medical strategies globally, particularly in resource‐limited settings.

Where and on whom will the research have an impact? The findings are relevant to older inpatients in hospital settings worldwide, particularly those with cancer, as well as to nurses and healthcare professionals. GNRI provides a practical and easily implementable tool for them to assess nutritional risks upon admission and guide timely nutritional support strategies based on clinical profiles including cancer presence and type in older inpatients. Incorporating GNRI into routine nursing care, nurses and healthcare professionals will be better equipped to address nutritional risks, ultimately improving patient care and optimising clinical and economic outcomes for older patients.

Reporting Method

We have adhered to relevant EQUATOR guidelines, specifically following the STROBE (strengthening the reporting of observational studies in epidemiology) guidelines for reporting this observational study.

Patient or Public Contribution

No public contribution was required in the design or conduct of this research. Patients contributed through data collected from the Hospital Information System, which was used for analysis.

Keywords: aged, geriatric nursing, geriatric nutritional risk index, GNRI, hospital costs, hospital mortality, inpatients, neoplasms, nutrition assessment

Abbreviations

Alb

albumin

BMI

body mass index

ESPEN

European Society of Clinical Nutrition and Metabolism

GNRI

geriatric nutritional risk index

HIS

hospital information system

ICD‐10

International Classification of Diseases, 10th Revision

IPW

inverse probability weighting

Summary.

  • What does this paper contribute to the wider global clinical community?
    • ○
      Highlighting GNRI‐assessed nutritional risk as a reliable indicator of in‐hospital mortality and costs in a large sample of older inpatients, addressing a pressing global healthcare challenge.
    • ○
      Demonstrating significant differences in the associations of GNRI with in‐hospital mortality and costs between cancer and non‐cancer patients, as well as among cancer types, providing evidence for context‐specific nursing and medical strategies.
    • ○
      Supporting the integration of GNRI into routine care worldwide, particularly in settings with growing aging populations and increasing cancer prevalence.

1. Introduction

Malnutrition is a prevalent yet often overlooked issue among older inpatients (Bellanti et al. 2022), significantly associated with adverse outcomes such as increased mortality, complications and higher healthcare costs (Söderström et al. 2017; Norman et al. 2008; Lim et al. 2012). Older adults, particularly those hospitalised with cancer, face a heightened risk of malnutrition due to age‐related physiological changes, disease burden and treatment‐related side effects (D'Almeida et al. 2020; Zhang and Edwards 2019). The Geriatric Nutritional Risk Index (GNRI), a simple and objective tool for identifying nutritional risks in older adults using serum albumin and anthropometric parameters (Bouillanne et al. 2005; Cereda et al. 2011), has been validated as an effective predictor of adverse outcomes across various diseases and complications (Ruan et al. 2021; Jia et al. 2020; Zhao et al. 2020). However, evidence on the associations between GNRI‐assessed nutritional risk and in‐hospital mortality and costs is limited, especially in relation to cancer presence and across different cancer types (Liu et al. 2022). This study aims to explore these associations and compare cancer and non‐cancer patients to inform clinical practice, particularly nursing care, as nurses play a critical role in nutritional screening, intervention and patient education, all of which are essential for improving clinical outcomes and reducing economic burdens in older inpatients.

2. Background

Cancer has emerged as a significant health and economic challenge worldwide, with its disease burden rapidly increasing due to population growth and ageing (Sung et al. 2021). Among older adults, the proportion of cancer cases has risen dramatically, accounting for over 56% of global cases, and cancer‐related deaths in seniors have increased from 52% to nearly 62% in recent decades (Ju, Zheng, Wang, et al. 2023). In China, where the ageing population is expanding rapidly, approximately 68.2% of cancer deaths in 2022 were estimated to occur in individuals aged 60 and older (Ju, Zheng, Zhang, et al. 2023). This dual burden of ageing and cancer strains individuals and healthcare systems, particularly in resource‐constrained settings (Wild et al. 2020). It is urgent to identify modifiable risk factors associated with health outcomes in these vulnerable populations to develop targeted interventions.

Malnutrition as an intervenable factor affects approximately half of hospitalised older patients globally (Bellanti et al. 2022) and is even more prevalent in oncology departments (Isenring and Elia 2015), which leads to adverse clinical and economic outcomes (Nigatu et al. 2021; Ruiz et al. 2019; Shakersain et al. 2016). Increased in‐hospital mortality and expenditures directly signal the adverse prognosis of older inpatients and economic burdens on health systems. These institutional indices better capture the clinical and policy implications of inpatients' admission nutrition status (Isabel and Correia 2003). In addition, early nutritional parameters are as effective as conventional diagnoses in predicting hospital mortality and charges (Guerra et al. 2016). Acknowledging the potential of early nutritional intervention to enhance prognosis, timely identification of nutritional risks represents the vital initial step in improving older patients' hospitalisation outcomes (Dent et al. 2019). GNRI has gained attention for its simplicity and reliability in identifying at‐risk older patients (Yamada et al. 2008), making it a valuable tool for nurses and other medical workers conducting nutritional screening. Limited studies have shown that GNRI is associated with mortality and complications in older cancer patients, and differences among cancer types remain insufficiently explored (Lidoriki et al. 2021). Categorising patients based on cancer status is important due to the distinct disease mechanisms, nutritional challenges and nursing practices experienced by cancer and non‐cancer patients. Cancer patients are especially prone to malnutrition due to the disease's high metabolic demands and the side effects of treatments like radiotherapy or chemotherapy, which impair appetite and nutrient absorption (Arends et al. 2017). They also have distinct nutritional needs, including maintaining muscle mass and immune function to support treatment tolerance and recovery (Prado et al. 2020; Rui and Yuqian 2023), while non‐cancer patients typically face risks from chronic conditions or insufficient intake. From a nursing perspective, this classification enables tailored interventions, with cancer patients requiring focused nutritional support to manage treatment side effects and improve prognosis (Holder 2003), while non‐cancer patients benefit from general strategies to address malnutrition. It is necessary to investigate whether the presence of cancer modifies the association between GNRI and hospitalisation outcomes in older inpatients, thereby guiding targeted interventions in clinical and nursing care.

3. Aims

This study aimed to answer: (1) What is the association of GNRI‐assessed nutritional risk with in‐hospital mortality and costs in older inpatients? (2) Do these associations differ between cancer and non‐cancer patients, and among different cancer types?

We hypothesised that a lower GNRI score (indicating higher nutritional risk) is associated with increased in‐hospital mortality and costs, with more pronounced associations in cancer patients compared to non‐cancer patients, and with variations among cancer types.

4. Methods

4.1. Study Design and Setting

A retrospective observational study was designed to examine the association of GNRI with in‐hospital mortality and costs in older inpatients using medical data from the Hospital Information System (HIS) of a large general hospital (A comprehensive teaching hospital with 4300 beds and the Geriatrics Center of Sichuan Province) and its affiliated hospitals between January 2018 and December 2020.

4.2. Data Extraction and Preprocessing

All inpatients aged 65 and over were selected from the HIS. The extracted data fields, including admission and discharge details, demographics, diagnosis information, surgical information, hospital expenditure, laboratory results and anthropometric measurements, were sourced from clinical, laboratory and anthropometry datasets. The hospital information department mapped this data to its corresponding locations within the HIS database.

Information from the three datasets was then integrated into corresponding records for each patient using the unique inpatient number. For patients with multiple hospitalisations, the most recent record based on the ‘record date’ was selected. If the required data were unavailable for the most recent admission, the second most recent record was used. For multiple measurements of height, weight and albumin during a single admission, the earliest measurements based on the ‘measurement date’ were chosen. Finally, each patient had one hospitalisation record included in the preliminary screening dataset.

4.3. Sample Selection

The initial geriatric inpatient dataset comprised 39,653 patients (age ≥ 65 years). Inpatients missing height, weight or albumin information, essential for calculating GNRI and central to this study, were excluded (n = 14). Additionally, 2370 inpatients with hospital stays of less than 48 h were excluded, as they are typically day surgery patients (Wu et al. 2023), whose treatment protocols and outcomes differ significantly from those of typical inpatients. Inpatients with implausible body mass index (BMI) values (≥ 50 or ≤ 10) were also excluded (n = 2), as such extreme values are likely to be erroneous and do not accurately reflect the patients' nutritional status. Consequently, 37,267 inpatients were selected for the study after excluding patients with missing values for key variables. For other covariates with missing values (proportion of missing values < 1%), an analysis‐by‐analysis deletion approach was applied. These eligible inpatients were subsequently divided into cancer and non‐cancer groups for analysis (Figure 1).

FIGURE 1.

FIGURE 1

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Flow chart illustrating the sample selection process of the study.

4.4. Data Collection

4.4.1. Nutritional Risk Assessment

The GNRI was calculated to assess the nutritional risk of the older inpatients using height, body weight and albumin upon admission. Height (cm) was measured with a stadiometer (accuracy of 0.1 cm) or estimated from knee height for non‐ambulatory inpatients using validated equations (Chumlea et al. 1985). Body weight (kg) was recorded using a calibrated electronic scale (accuracy of 0.1 kg). If direct measurement was not possible, self‐reported or family‐reported values were used (Payette et al. 2000). All measurements were conducted by trained nursing staff. Serum albumin (g/L) was measured using chemical methods by the hospital laboratory department.

The formula for calculating GNRI is as follows: GNRI = 1.489 × serum albumin + 41.7 × (actual body weight/ideal body weight) (Bouillanne et al. 2005). Ideal body weight was calculated using the Lorentz formula (Nahler 2009). If the actual weight exceeded the ideal weight, the ratio of actual weight to ideal weight was set to 1. BMI was also calculated using the formula: BMI = weight (kg)/[height (m)]2.

Nutritional risk was classified into four grades based on GNRI values (Bouillanne et al. 2005): no nutritional risk (GNRI > 98); low nutritional risk (92 ≤ GNRI ≤ 98); moderate nutritional risk (82 ≤ GNRI < 92); major nutritional risk (GNRI < 82).

4.4.2. Hospitalisation Outcomes

4.4.2.1. In‐Hospital Mortality

This was defined as any death occurring during the hospitalisation period, from admission until discharge or death. The in‐hospital mortality rate was calculated as follows: (number of in‐hospital deaths/number of admissions) × 100%.

4.4.2.2. Hospital Costs

Total hospital costs were calculated by summing all charges incurred during the older patient's hospitalisation, including accommodation, medication, laboratory tests, surgeries and other healthcare services. This calculation captured all costs from admission until discharge or death.

4.4.3. Covariates

Covariates were selected based on existing literature and clinical relevance. These include demographic factors (age, sex, ethnicity, marital status) and clinical variables (admission diagnosis, surgical status) (Castel et al. 2006; Kwaśny et al. 2023; Sheean et al. 2019; Wanphen 2019; Leandro‐Merhi and de Aquino 2014), which are well‐established determinants of nutritional status, in‐hospital mortality and costs.

4.4.4. Classification of Diseases

Diseases of the inpatients were categorised according to the International Classification of Diseases 10th Revision (ICD‐10). All inpatients were divided into cancer and non‐cancer groups based on admission diagnosis (C00–C97). Cancer patients were further categorised into four sub‐groups: digestive system (C15–C26), respiratory and intrathoracic system (C30–C39), lymphoid and haematopoietic system (C81–C96) and others.

4.5. Statistical Analysis

Inpatient characteristics were described as mean (SD) for continuous variables and numbers (%) for categorical variables, based on cancer status and nutritional risk assessed by GNRI. T‐tests, Chi‐square tests and analysis of variance were used to compare differences among groups.

To estimate in‐hospital mortality and costs among older inpatients with varying nutritional risks, a marginal structural model based on inverse probability weighting (IPW) was implemented in a two‐step procedure. First, a logistic model using potential confounders was fitted to predict the propensity score for each individual inpatient; then, IPW with the propensity score was applied to balance confounders between different GNRI groups and ensure comparability. Second, logistic and log‐linear models were fitted as the marginal structural model, with in‐hospital death and costs as the dependent variables and GNRI‐based nutritional risk as the independent variable. This method was chosen because it better balances confounders via IPW and allows for direct estimation of outcomes for each nutritional risk subgroup without requiring a reference group (Hernan and Robins 2020). Bootstrapping (1000 samples) was applied to address the sampling errors in the two‐stage modelling process of the IPW method, providing robust confidence interval estimates by accounting for compounded errors from propensity score estimation and weighting. Based on the preceding analysis, further stratified analyses were conducted according to the presence or absence of cancer in older inpatients and by cancer types, given the significant differences in treatment approaches and nutritional challenges among cancer types. A chi‐square test was performed to assess heterogeneity between cancer and non‐cancer groups. A linear trend in outcomes was tested by coding nutritional risk grades (1–4) as a continuous variable and using a likelihood ratio test. All statistical analyses were conducted using R Project for Statistical Computing, version 4.0.2 (Vienna, Austria).

4.6. Ethical Considerations

This study was approved by the Medical Ethics Committee of the University of Tsukuba [Approval Number: No. 1829], as well as by the Medical Ethics Committee of Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, with exemption from informed consent granted [Approval Number: Lunshen (Yan), 2021, No. 258].

5. Results

5.1. Comparison of Patient Characteristics

A total of 37,267 older inpatients were enrolled in the study, including 2013 cancer patients (5.4%) and 35,254 non‐cancer patients (94.6%). There were 20,993 men (56.3%) and 16,274 women (43.7%), with an average age of 74.6 (7.4) years. More than half (56.3%) had varying nutritional risks: 9008 with low nutritional risk (24.2%), 8229 with moderate nutritional risk (22.1%) and 3716 with major nutritional risk (10.0%). The older inpatients' in‐hospital mortality was 1.7%, and the total hospital cost was 31,819 (38,635) CNY. Comparisons of characteristics by cancer status and nutritional risk grades are shown in Table 1.

TABLE 1.

Characteristics of the study population according to cancer status and nutritional risk grades a .

Characteristics Total Cancer status p b Nutritional risk (Assessed by GNRI) p b
Cancer Non‐cancer No risk (GNRI > 98) Low risk (92 ≤ GNRI ≤ 98) Moderate risk (82 ≤ GNRI < 92) Major risk (GNRI < 82)
Number of the study population 37,267 2013 35,254 — 16,314 (43.7%) 9008 (24.2%) 8229 (22.1%) 3716 (10.0%) —
Age (years) 74.6 (7.4) 73.1 (6.6) 74.7 (7.4) < 0.001 73.2 (6.56) 75.1 (7.38) 76.3 (7.98) 76.2 (7.99) < 0.001
Sex
Male 20,993 (56.3%) 1349 (67.0%) 19,644 (55.7%) < 0.001 8393 (51.4%) 5116 (56.8%) 5114 (62.1%) 2370 (63.8%) < 0.001
Female 16,274 (43.7%) 664 (33.0%) 15,610 (44.3%) 7921 (48.6%) 3892 (43.2%) 3115 (37.9%) 1346 (36.2%)
BMI 22.97 (3.6) 22.3 (3.3) 23.0 (3.6) < 0.001 24.3 (3.00) 23.2 (3.37) 21.5 (3.56) 19.8 (3.83) < 0.001
Thinness (BMI < 18.5) 3947 (10.6%) 266 (13.2%) 3681 (10.4%) < 0.001 205 (1.26%) 539 (5.98%) 1648 (20.0%) 1555 (41.8%) < 0.001
Normal (18.5 ≤ BMI ≤ 24) 19,187 (51.5%) 1126 (55.9%) 18,061 (51.2%) 7849 (48.1%) 5081 (56.4%) 4632 (56.3%) 1625 (43.7%)
Overweight (24 ≤ BMI < 28) 11,135 (29.9%) 532 (26.4%) 10,603 (30.1%) 6514 (39.9%) 2632 (29.2%) 1572 (19.1%) 417 (11.2%)
Obesity (BMI ≥ 28) 2998 (8.0%) 89 (4.4%) 2909 (8.2%) 1746 (10.7%) 756 (8.39%) 377 (4.58%) 119 (3.20%)
Ethnicity
Ethnic minority 1495 (4.0%) 76 (3.8%) 1419 (4.0%) 0.619 479 (2.94%) 421 (4.67%) 396 (4.81%) 199 (5.36%) < 0.001
Non‐ethnic minority 35,772 (96.0%) 1937 (96.2%) 33,835 (96.0%) 15,835 (97.1%) 8587 (95.3%) 7833 (95.2%) 3517 (94.6%)
Marital status
Married 35,058 (94.2%) 1934 (96.2%) 33,124 (94.1%) < 0.001 15,488 (94.9%) 8483 (94.2%) 7653 (93.0%) 3434 (92.4%) < 0.001
Unmarried 205 (0.6%) 12 (0.6%) 193 (0.6%) 81 (0.50%) 40 (0.44%) 58 (0.70%) 26 (0.70%)
Others 1955 (5.3%) 65 (3.2%) 1890 (5.4%) 745 (4.57%) 485 (5.38%) 518 (6.29%) 256 (6.89%)
Disease type c
Malignant neoplasms 2013 (5.4%) 2013 (100%) — — 539 (3.30%) 495 (5.50%) 583 (7.08%) 396 (10.7%) < 0.001
Diseases of the circulatory system 10,676 (28.6%) — 10,676 (30.3%) 5937 (36.4%) 2562 (28.4%) 1751 (21.3%) 426 (11.5%)
Diseases of the respiratory system 4230 (11.4%) — 4230 (12.0%) 1062 (6.51%) 1078 (12.0%) 1367 (16.6%) 723 (19.5%)
Diseases of the digestive system 3725 (10.0%) — 3725 (10.6%) 1406 (8.62%) 767 (8.51%) 959 (11.7%) 593 (16.0%)
Diseases of the genitourinary system 2024 (5.4%) — 2024 (5.8%) 632 (3.87%) 505 (5.61%) 542 (6.59%) 345 (9.28%)
Diseases of the nervous system 1344 (3.6%) — 1344 (3.8%) 758 (4.65%) 318 (3.53%) 222 (2.70%) 46 (1.24%)
Diseases of the blood 281 (0.8%) — 281 (0.8%) 58 (0.36%) 68 (0.75%) 96 (1.17%) 59 (1.59%)
Endocrine, nutritional, metabolic diseases 1356 (3.6%) — 1356 (3.9%) 723 (4.43%) 324 (3.60%) 237 (2.88%) 72 (1.94%)
Injury, poisoning 701 (1.9%) — 701 (2.0%) 181 (1.11%) 183 (2.03%) 226 (2.75%) 111 (2.99%)
Others 10,917 (29.3%) — 10,917 (31.0%) 5018 (30.8%) 2708 (30.1%) 2246 (27.3%) 945 (25.4%)
Undergoing surgery
Yes 23,177 (37.8%) 1589 (78.9%) 21,588 (61.2%) < 0.001 10,121 (62.0%) 5336 (59.2%) 5093 (61.9%) 2627 (70.7%) < 0.001
No 14,090 (62.2%) 424 (21.1%) 13,666 (38.8%) 6193 (38.0%) 3672 (40.8%) 3136 (38.1%) 1089 (29.3%)
Laboratory indicator
Serum Albumin (Alb) (g/L) 37.3 (5.6) 34.8 (6.05) 37.4 (5.5) < 0.001 41.8 (2.65) 37.1 (1.99) 33.2 (2.91) 26.9 (4.27) < 0.001
GNRI 95.2 (9.6) 91.0 (10.2) 95.4 (9.4) < 0.001 103 (3.86) 95.2 (1.75) 87.7 (2.82) 75.7 (5.30) < 0.001
No nutritional risk (GNRI > 98) 16,314 (43.7%) 539 (26.8%) 15,775 (44.7%) < 0.001 16,314 (100%) — — — —
Low nutritional risk (92 ≤ GNRI ≤ 98) 9008 (24.2%) 495 (24.6%) 8513 (24.1%) — 9008 (100%) — — —
Moderate nutritional risk (82 ≤ GNRI < 92) 8229 (22.1%) 583 (29.0%) 7646 (21.7%) — — 8229 (100%) — —
Major nutritional risk (GNRI < 82) 3716 (10.0%) 396 (19.7%) 3320 (9.42%) — — — 3716 (100%) —
In‐hospital mortality 632 (1.7%) 85 (4.22%) 547 (1.55%) < 0.001 84 (0.51%) 89 (0.99%) 220 (2.67%) 239 (6.43%) < 0.001
Hospital costs (CNY) 31,819 (38,635) 51,846 (49,139) 30,676 (37,628) < 0.001 27,134 (32,686) 30,713 (37,367) 35,810 (42,960) 46,232 (49,665) < 0.001
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a

Statistical descriptions for continuous variables were presented as mean and standard deviation (SD), and categorical variables were presented as numbers and percentages (%).

b

Chi‐square test, t‐test, or ANOVA was used to compare groups according to variable attributes.

c

The disease types were classified according to the ICD‐10 code of admission diagnosis of patients. The admission diagnosis of patients collected in this study was the only major diagnosis, so the disease classifications of cancer patients and non‐cancer patients were consistent with that of all patients.

Older cancer inpatients had lower Alb levels 34.8 (6.05) g/L and higher rates of thinness (13.2%), surgery (78.9%) and low, moderate and major nutritional risks assessed by GNRI (24.6%, 29.0% 19.7%), in‐hospital mortality (4.22%), and greater hospital costs 51,846 (49,139) CNY than non‐cancer inpatients. Among different nutritional risk grades, older inpatients with lower GNRI had higher rates of thinness (41.8%), surgery (70.7%), in‐hospital mortality (6.43%), elevated hospital costs 46,232 (49,665) CNY and lower albumin levels 26.9 (4.27) g/L compared to those with higher GNRI. All differences were statistically significant (p < 0.001).

5.2. The Associations of GNRI With Hospitalisation Outcomes

The estimated in‐hospital mortality and hospital costs of older inpatients associated with GNRI‐assessed nutritional risks were calculated using a marginal structural model. After adjusting for potential confounders, a consistent increase in the values of the hospitalisation outcomes from no nutritional risk to major nutritional risk was observed, as shown in Table 2.

TABLE 2.

Marginal structural model estimates of in‐hospital mortality and costs across nutritional risk grades assessed by GNRI in cancer and non‐cancer patients a .

Nutritional risk assessed by GNRI In‐hospital mortality (%)
χ2
b 		p Hospital costs (CNY)
χ2
b 		p
Total Cancer Non‐cancer Total Cancer Non‐cancer
No nutritional risk

0.6

(0.5, 0.7)

1.4

(1.0, 2.1)

0.5

(0.5, 0.6)

 25.4 < 0.001

26,606

(25,791, 27,446)

43,633

(39,573, 48,109)

25,621

(24,800, 26,469)

102.7

< 0.001
Low nutritional risk

1.0

(0.8, 1.2)

1.4

(0.8, 2.3)

1.0

(0.8, 1.1)

1.2

 0.279

31,511

(30,253, 32,821)

48,456

(42,474, 55,279)

30,537

(29,265, 31,864)

27.5

< 0.001

Moderate nutritional risk

2.5

(2.2, 2.9)

4.0

(2.7, 6)

2.4

(2.1, 2.8)

3.0

 0.084

36,775

(35,389, 38,216)

52,612

(46,327, 59,750)

35,865

(34,456, 37,331)

19.9

< 0.001
Major nutritional risk

5.5

(4.9, 6.3)

12.3

(8.7, 17)

5.1

(4.5, 5.9)

 10.6 0.001

42,739

(41,197, 44,340)

65,670

(58,400, 73,845)

41,379

(39,818, 43,002)

 31.7 < 0.001
t c 42.4 15.0 39.67 — — 27.6 7.3 26.8 — —
p < 0.001 < 0.001 < 0.001 — — < 0.001 < 0.001 < 0.001 — —
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a

In‐hospital mortality and Hospital costs were presented by estimated values and 95% Confidence Intervals.

b

The χ2 statistic was used to test if the estimated outcomes between cancer and non‐cancer groups show significant heterogeneity.

c

The t statistic was used to test if there is a linear trend in the estimated outcomes with the increase of nutritional risk.

The in‐hospital mortality and costs were significantly affected by the nutritional risk assessed by GNRI. Compared to patients with no nutritional risk, those with major nutritional risk had a higher in‐hospital mortality rate of 5.5%, representing a 4.9% increase. Similarly, there was a noticeable increase in hospital costs among patients with major nutritional risk, amounting to 42,739 CNY compared to 26,606 CNY for those with no nutritional risk. Significant differences were observed across nutritional risk groups, with a linear trend in estimated outcomes with increasing nutritional risk (p < 0.001).

5.3. Stratified Analysis

The estimated values for in‐hospital mortality and hospital costs varied substantially between cancer and non‐cancer inpatients. Older cancer patients had even worse hospitalisation outcomes, with the highest mortality (12.3%) in those with major nutritional risk, while non‐cancer patients with major nutritional risk had a 5.1% mortality rate. Non‐cancer patients with no nutritional risk had the lowest mortality, indicating the benefits of maintaining proper nutrition. Regarding the financial implications of GNRI, older cancer patients with major nutritional risk incurred costs (65,670 CNY) nearly 2.6 times higher than non‐cancer patients with no nutritional risk (25,621 CNY). Furthermore, our study revealed potential cost savings of up to 22,037 and 15,758 CNY for cancer and non‐cancer patients with no nutritional risk, respectively. Significant differences were observed across cancer status and nutritional risk groups, with a linear trend in estimated outcomes with increasing nutritional risk (p < 0.001).

In the sub‐analysis of cancer patients, as nutritional risk increased, both in‐hospital mortality and costs rose for each type of cancer, and the differences were statistically significant (Figure 2). The highest mortality rate (38.4%) was in respiratory system cancer patients with major nutritional risk, followed by lymphoid and haematopoietic system cancer patients (10.7%). The highest costs (75,785 CNY) were among digestive system cancer patients with major nutritional risk. Significant differences were found across cancer types and nutritional risk groups (p < 0.001).

FIGURE 2.

FIGURE 2

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Estimated in‐hospital mortality and costs across nutritional risk grades assessed by GNRI in various types of cancer. *The value could not be estimated due to a relatively small sample size, likely caused by unstable weights in the IPW method. Sample sizes for each cancer type and nutritional risk grade are provided in Table S1. [Colour figure can be viewed at wileyonlinelibrary.com]

6. Discussion

The present study revealed that a higher nutritional risk assessed by GNRI was significantly associated with increased in‐hospital mortality and costs in both cancer and non‐cancer inpatients, with the association being stronger in cancer patients, and respiratory and digestive cancers being the most affected. These results support our hypothesis that GNRI‐assessed nutritional risk is a key factor associated with hospitalisation outcomes, particularly in older cancer patients who face unique nutritional challenges due to the disease and its treatments.

Our study found that over half (56.3%) of older inpatients had varying degrees of nutritional risk, similar to reports from Europe (58.0%) and America (66.7%) (Cereda et al. 2015; Jia et al. 2020). Importantly, 73.3% of older cancer patients were at nutritional risk, with 19.7% having major nutritional risk, higher than the previously reported 58.0% in China (Yu et al. 2013). Despite the high prevalence of nutritional risk, nutritional screening rates in China remain low compared to European countries (Schindler et al. 2010), leaving many at‐risk patients unrecognised and untreated (Reber et al. 2021). These findings highlight the urgent need to integrate GNRI into routine nursing practice to early identify high‐risk older patients, especially those with cancer, as recommended by the ESPEN guidelines, which advocate using a validated tool to detect potential nutritional risks (Volkert et al. 2019; Arends et al. 2017). In resource‐limited regions, such as Southwest China, GNRI offers a potentially cost‐effective solution to improve patient outcomes and reduce medical costs.

Unadjusted descriptive analysis in Table 1 showed higher in‐hospital mortality and costs in patients with lower GNRI. After adjusting for covariates, the marginal structural model revealed that GNRI remained an independent predictor of hospitalisation outcomes, consistent with previous findings (Cereda et al. 2006; Dou et al. 2017). GNRI‐assessed Nutritional risk indicates undernutrition or risk of nutrition‐related complications (Bouillanne et al. 2005), such as protein breakdown, weight loss, muscle decline, impaired immune function and frailty in older patients, all of which contribute to increased morbidity and mortality (Norman et al. 2008, 2021). These factors are reflected in the two indicators, BMI and albumin, used in GNRI to assess nutritional status and disease severity. Advanced age further exacerbates these risks, impairing recovery and clinical outcomes (Niccoli and Partridge 2012). Our study found that older inpatients with major nutritional risk had the highest mortality and hospital costs. Identifying high‐risk patients and implementing nutritional interventions within nursing and treatment processes can benefit both clinical and economic outcomes for older inpatients.

It is worth noting that our results showed differences in the associations between GNRI and hospitalisation outcomes for cancer and non‐cancer patients. Cancer patients with major nutritional risk had the highest mortality and costs, while non‐cancer patients with no nutritional risk had the lowest. For cancer patients, a lower GNRI not only indicated high‐risk malnutrition but also reflected overall deterioration, including immune suppression, reduced treatment compliance and cachexia, leading to poor prognosis (Ruan et al. 2021). In contrast, for non‐cancer patients, a lower GNRI was mainly related to insufficient nutritional intake or other underlying diseases. This specific interpretation helps the GNRI play a more valuable role in clinical practice, guiding and adjusting targeted nursing and medical interventions. Among cancer patients with lower GNRI, those with respiratory cancers had the highest mortality due to malnutrition‐related impairments in respiratory muscle strength and lung function, increasing the risk of respiratory failure and death (Ferrari‐Baliviera et al. 1989). Patients with digestive cancers incurred the highest hospital costs due to major surgeries, complications and the need for additional nutritional support, including enteral or parenteral feeding due to severely impaired oral intake and nutrient absorption induced by their diseases and treatments (Pimiento et al. 2021). These suggest that GNRI could help identify increased risks of mortality and costs in patients with specific cancer types, making it essential to consider the cancer characteristics.

Additionally, older cancer patients, who are highly susceptible to malnutrition due to both ageing and cancer‐related factors, often experience higher mortality rates (Zhang et al. 2021). The findings from our study, conducted in a comprehensive hospital with more critically ill patients, suggest that GNRI may be particularly useful in comprehensive hospitals rather than in community hospitals, where it can effectively distinguish nutritional risks in older, severely ill patients, such as those with cancer.

This study has some limitations that should be noted. This research was conducted in a single comprehensive hospital, potentially introducing selection bias and limiting the generalisability of findings to other regions or populations. The theoretical applicability of GNRI findings may also vary across different healthcare settings and patient populations, influenced by cultural and systemic factors. Additionally, as a retrospective observational study, causality cannot be established. This study also did not account for temporal changes in GNRI scores or other clinical variables during hospitalisation, which could provide evidence into the dynamic relationship between nutritional risk and outcomes. While well‐established covariates were included to adjust for confounding, residual confounding may still exist due to unmeasured factors such as comorbidities, variations in treatment protocols or differences in healthcare access.

7. Implications

Integrating GNRI into routine nursing care provides a practical tool for early identification of nutritional risk among older inpatients, particularly those with cancer. By incorporating GNRI screening at admission, healthcare providers can target high‐risk patients for timely nutritional interventions, potentially reducing in‐hospital mortality and healthcare costs. Special attention should be given to older cancer patients with major nutritional risks, as they are particularly vulnerable to poor outcomes.

Training programmes for healthcare professionals should focus on the importance of nutritional risk screening and the application of GNRI in diverse clinical settings. Nurses, as primary facilitators of nutritional care, should be equipped with skills to interpret GNRI scores and implement targeted interventions. Interdisciplinary training that includes dietitians and physicians can foster collaboration, ensuring a comprehensive approach to managing malnutrition.

Future research should validate these findings in multi‐centre studies across diverse healthcare settings and explore the cost‐effectiveness of GNRI‐based interventions to support their practical application in resource‐limited environments. Longitudinal studies are also needed to estimate the long‐term association of nutritional interventions guided by GNRI with post‐discharge outcomes. Furthermore, addressing unmeasured confounding factors, such as comorbidities and treatment protocols, could refine the clinical utility of GNRI and ensure its effective integration into routine care for older inpatients.

8. Conclusion

In conclusion, GNRI is an easy‐to‐assess nutritional screening tool that shows an association with in‐hospital mortality and costs in older inpatients with and without cancer. Higher nutritional risk assessed by GNRI is significantly associated with increased in‐hospital mortality and costs in older inpatients, with stronger associations observed in cancer patients compared to non‐cancer patients. These findings highlight the importance of nutritional risk assessment in identifying high‐risk populations upon admission. Integrating GNRI into routine nursing practice provides a practical approach to early nutritional screening and intervention. By enabling timely identification of high‐risk patients, particularly those with cancer, GNRI can help improve clinical outcomes and reduce healthcare costs.

Author Contributions

Lujiao Huang, Tokie Anme: Conceptualisation. Xue Zhou: Project administration. Lujiao Huang, Xue Zhou, Yi Song, Xiong Xiao: Data Curation. Lujiao Huang, Xiong Xiao: Formal analysis. Xiong Xiao: Software. Tokie Anme: Supervision. Lujiao Huang: Writing – original draft. Mingyu Cui, Zhu Zhu, Mengjiao Yang, Yu Pei: Writing – review and editing. All authors have read and approved the final version of the manuscript.

Disclosure

The authors confirm that the data utilised in this manuscript have been lawfully acquired and comply with The Nagoya Protocol on Access to Genetic Resources and the Fair and Equitable Sharing of Benefits Arising from Their Utilisation to the Convention on Biological Diversity.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Table S1

JOCN-34-4630-s002.docx (12.3KB, docx)

Data S1

JOCN-34-4630-s001.docx (34.2KB, docx)

Acknowledgements

The authors are deeply grateful to the patients whose data were used in this study; their participation is invaluable. We would also like to thank all the staff at the hospital information department for their assistance in providing the necessary data for this research.

Funding: The authors received no specific funding for this work.

Statistician: Xiong Xiao.

Data Availability Statement

The datasets analysed in this study are available from the corresponding author upon a reasonable request.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1

JOCN-34-4630-s002.docx (12.3KB, docx)

Data S1

JOCN-34-4630-s001.docx (34.2KB, docx)

Data Availability Statement

The datasets analysed in this study are available from the corresponding author upon a reasonable request.

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