Abstract
Background
Among patients admitted to the gastroenterology ward, especially those with upper gastrointestinal (UGI) diseases, malnutrition is markedly prevalent. Nutritional Risk Screening 2002 (NRS2002) is widely used globally to assess the nutritional risk of hospitalized patients.
Methods
A retrospective, single-center study that included 896 patients with UGI diseases was performed in the Department of Esophagogastric, Senior Department of Gastroenterology, the First Medical Center of PLA General Hospital. Data from each admitted patient, including demographic information, lifestyle factors, primary diagnosis, comorbidities, laboratory test values at admission and length of hospital stay (LOHS), were extracted from the Electronic Medical Record (EMR) system. An LOHS longer than the 75th percentile was defined as a long LOHS. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for long LOHS. Subsequently, receiver operating characteristic (ROC) curve analysis was used to assess the discriminative ability of the independent factors calculated for predicting long LOHS.
Results
Among the 896 inpatients in our study, 586 were males and 310 were females, with a median age of 61.0 years. The patients had a median LOHS of 14.0 days (Q1–Q3, 10.3–19.0 days). The inpatients whose LOHS was more than 19 days were classified into the long LOHS group, while others were classified into the non-long LOHS group. On the basis of the univariate and multivariate logistic regression analysis results, both the NRS2002 score and serum albumin level were identified as independent risk factors for long LOHS. An NRS2002 score ≥2 points (optimal cutoff value determined by maximum Youden’s index) reflected good predictive value, with an area under the curve (AUC) of 0.721 according to the ROC analysis.
Conclusion
In conclusion, the results of this study demonstrate that the serum albumin (ALB) concentration and NRS2002 score are predictors of long LOHS in patients with UGI diseases. These findings emphasize the importance of assessing nutritional status in clinical management and may provide guidance for improving the treatment outcomes of patients with UGI diseases.
Keywords: length of hospital stay, malnutrition, nutritional risk, Nutritional Risk Screening 2002, upper gastrointestinal diseases
Introduction
The upper gastrointestinal tract (UGT) is usually defined as the portion of the digestive tract located above the suspensory ligament of Treitz that extends continuously from the oral cavity through the pharynx, esophagus, and stomach to the proximal segment of the duodenum (1). Upper gastrointestinal (UGI) diseases are traditionally grouped according to their anatomic origin: esophageal, gastric, or duodenal. Commonly encountered conditions include gastroesophageal reflux disease (GERD), Barrett’s esophagus, esophageal adenocarcinoma (EC), gastritis, gastric ulceration, gastric cancer (GC), duodenal ulceration and nonvariceal upper gastrointestinal bleeding (NVUGIB) (1–4). These conditions usually cause difficulties in swallowing, loss of appetite, and sustained catabolic stress, collectively disrupting nutrient intake and utilization and markedly increasing the incidence of malnutrition (5–7). In addition, during the treatment of these diseases, patients may be required to undergo mandatory fasting. For instance, patients with superficial GC who undergo endoscopic submucosal dissection (ESD) must adhere to the prescribed fasting period to ensure proper healing of the mucosa and to prevent iatrogenic bleeding (8).
In China, the average prevalence of malnutrition or nutritional risk among hospitalized patients ranges from 12.5 to 29.4%, depending on the screening tool used and the case combination (9, 10). The prevalence is even higher for cancer patients and elderly patients, reaching 41.3 and 44.1%, respectively (11, 12). Among the elderly patients admitted to the hospital, the proportion of those with nutritional risks in the gastroenterology department is obviously greater than that in any other department (11). Moreover, inadequate dietary intake remains a key contributor to malnutrition, and the Department of Gastroenterology has the highest prevalence of recent and current low food intake (LIRC) (13).
Multiple studies have shown that malnutrition is prevalent among hospitalized patients and that it has a negative effect on clinical outcomes, such as poor prognosis, increased infection incidence, prolonged hospital stay and increased mortality rate (9, 14–16). In addition, emerging evidence has demonstrated that providing nutritional support to patients who are at nutritional risk is associated with fewer infectious complications and a shorter length of hospital stay (LOHS), while the resulting incremental cost-effectiveness ratio shows no significant increase in overall treatment costs (17). These findings further highlight the need to conduct nutritional assessments and implement corresponding intervention measures for inpatients in the gastroenterology department.
Nutritional Risk Screening 2002 (NRS2002) is a validated, guideline-endorsed tool that was developed by the European Society for Parenteral and Enteral Nutrition (ESPEN) in 2002 (18). It is currently the most widely adopted nutritional risk screening tool worldwide and was originally designed for hospitalized patients to evaluate the probability that a hospitalized patient will benefit from targeted nutritional intervention (18–20). Although several studies have linked the NRS2002 score to adverse outcomes, such as a prolonged hospital stay and increased in-hospital mortality, its prognostic value, specifically in in-patients with UGI diseases, remains unclear (21–23). Therefore, we designed a retrospective study to explore the relationship between nutritional risk and long LOHS for patients with UGI diseases through the NRS2002.
Materials and methods
Study design
A retrospective, single-center cohort study using an electronic medical record (EMR) database was conducted at the First Medical Center of PLA General Hospital. Consecutive hospitalized patients admitted between January 2023 and December 2024 to the Department of Esophagogastric, Senior Department of Gastroenterology, of our hospital were screened for eligibility. The inclusion criteria for our study were as follows: (1) a main discharge diagnosis of UGI disease; (2) complete blood cell count and serum chemical tests being completed within 24 h of admission; and (3) NRS2002 assessment being completed within 24 h of admission. The exclusion criteria for our study were as follows: (1) age less than 18 years; (2) length of hospital stay less than 24 h; (3) death during hospitalization; and (4) unavailable clinical data. In accordance with the inclusion and exclusion criteria, among the 926 hospitalized patients initially screened, 30 were excluded; thus, the final analysis sample group consisted of 896 patients (Figure 1). This study was approved by the ethics committee of Chinese PLA General Hospital (Approval No. S2025-351-01).
Figure 1.
Flow diagram.
Data collection
Data from each admitted patient were extracted from the EMR system, including demographic information [sex, age, and body mass index (BMI)], lifestyle factors (smoking and drinking history), primary diagnosis, comorbidities (hypertension, coronary heart disease (CHD), chronic obstructive pulmonary disease (COPD), diabetes, kidney disease and rheumatoid disease), laboratory test values at admission [red blood cell (RBC) count, hemoglobin (HGB), hematocrit (HCT), white blood cell (WBC) count, neutrophils (NEUT), platelets (PLT), total protein (TP), albumin (ALB), prothrombin time (PT), activated partial thromboplastin time (APTT), and fibrinogen (FIB)] and length of hospital stay. An LOHS longer than the 75th percentile (P75 = 19 days) was defined as a long LOHS.
Nutritional risk assessment
Within 24 h of admission, the attending physician conducted a systematic assessment of the patient’s nutritional risk using the NRS2002. The NRS2002 systematically evaluates three discrete domains: (1) the severity of the disease, (2) the degree of nutritional impairment, and (3) age. The first two domains are scored using an ordinal scale ranging from 0 to 3, resulting in a cumulative score ranging from 0 to 6, which reflects overall nutritional risk. Patients who reach the age threshold of 70 years or above (≥70 years old) would receive an additional point, and the total score of the final risk assessment ranges from 0 to 7.
Statistical analysis
Continuous variables are first tested for normality using the Kolmogorov-Smirnov test and visual inspection of Quantile-Quantile Plot (Q–Q plots) (Supplementary Figures S1-S14, Supplementary Table S1). Variables that followed a normal distribution are expressed as the mean ± standard deviation (SD) and were compared with the independent t test. Nonnormally distributed data are expressed as median values (interquartile ranges, Q1–Q3) and were compared using the Mann–Whitney U test. Categorical variables are summarized as frequencies (percentages) and were compared using the χ2 test or Fisher’s exact test, as appropriate. To quantify the magnitude of between-group differences, effect sizes were planned and calculated according to variable type. For continuous variables, Cohen’s d was considered as a measure of the effect size for mean differences. For categorical variables, Cohen’s h was computed to quantify the effect size of differences in proportions between groups (Supplementary Table S2). Variables with p values less than 0.05 in the univariable analysis were entered into a multivariable binary logistic regression model to identify independent factors associated with long LOHS (long LOHS ≥ 75th percentile of the overall cohort). The results are reported as adjusted odds ratios (ORs) with 95% confidence intervals (CIs). A receiver operating characteristic (ROC) curve was constructed, and the area under the curve (AUC) with 95% CI was calculated to evaluate the discriminative ability of the independent factors in predicting long LOHS. The optimal cutoff value was determined by the maximum Youden index (sensitivity + specificity – 1). All the statistical analyses were conducted using IBS SPSS 26.0 and GraphPad Prism 8.0 software, and the statistical results were plotted using GraphPad Prism 8.0. A two-sided p value of less than 0.05 was considered to indicate statistical significance.
Results
Basic characteristics of 896 patients
A total of 896 patients who were hospitalized from January 2023 to December 2024 in the Department of Esophagogastric, Senior Department of Gastroenterology of the First Medical Center of The PLA General Hospital were included in our study. The basic characteristics of these inpatients are presented in Table 1. There were 586 males and 310 females, with a median age of 61.0 years (Q1–Q3, 52.0–70.0 years) and a median BMI of 23.7 kg/m2 (Q1–Q3, 20.9–25.7 kg/m2). The patients had a median LOHS of 14.0 days (Q1–Q3, 10.3–19.0 days). Therefore, we define a hospitalization period exceeding 19 days as a long LOHS. Inpatients whose LOHS was more than 19 days were classified into the long LOHS group, while others were classified into the non-long LOHS group.
Table 1.
Baseline characteristics of the included patients.
| Variables | Total (n = 896) | LOHS ≤ 19 (n = 727) | LOHS > 19 (n = 169) | p value |
|---|---|---|---|---|
| Sex, male/female (n) | 586/310 | 477/250 | 109/60 | 0.784 |
| Age, median (Q1–Q3) (year) | 61.0 (52.0–70.0) | 60.0 (51.0–69.0) | 65.0 (56.0–74.0) | 0.000 |
| BMI, median (Q1–Q3) (kg/m2) | 23.7 (20.9–25.7) | 23.8 (21.1–25.7) | 22.5 (19.0–25.8) | 0.003 |
| UGI diseases, [n (%)] | ||||
| Superficial gastric cancer | 227 (25.3) | 190 (26.1) | 37 (21.9) | 0.253 |
| Superficial esophageal cancer | 139 (15.5) | 112 (15.4) | 27 (16.0) | 0.854 |
| NVUGIB | 103 (11.5) | 79 (10.9) | 24 (14.2) | 0.221 |
| Gastric SMT | 90 (10.0) | 83 (11.4) | 7 (4.1) | 0.005 |
| Achalasia | 69 (7.7) | 60 (8.3) | 9 (5.3) | 0.198 |
| Advanced gastric or esophageal cancer | 56 (6.3) | 35 (4.8) | 21 (12.4) | 0.000 |
| Esophageal SMT | 44 (4.9) | 41 (5.6) | 3 (1.8) | 0.036 |
| Duodenal lesion | 41 (4.6) | 34 (4.7) | 7 (4.1) | 0.764 |
| UGT stenosis | 33 (3.7) | 19 (2.6) | 14 (8.3) | 0.000 |
| UGI variceal bleeding | 31 (3.5) | 21 (2.9) | 10 (6.0) | 0.052 |
| GERD | 21 (2.3) | 18 (2.5) | 3 (1.8) | 0.795 |
| Others | 42 (4.7) | 35 (4.8) | 7 (4.1) | 0.710 |
| Chronic diseases, [n (%)] | ||||
| Cardiovascular disease | 283 (31.6) | 216 (29.7) | 67 (39.6) | 0.012 |
| Respiratory disease | 81 (9.0) | 48 (6.6) | 33 (19.5) | 0.000 |
| Diabetes | 59 (6.6) | 45 (6.2) | 14 (8.3) | 0.323 |
| Kidney disease | 16 (1.8) | 7 (1.0) | 9 (5.3) | 0.000 |
| Rheumatoid diseases | 2 (0.2) | 2 (0.3) | 0 (0.0) | 1.000 |
| Smoking history, [n (%)] | 301 (33.6) | 240 (33.0) | 61 (36.1) | 0.445 |
| Drinking history, [n (%)] | 302 (33.7) | 242 (33.3) | 60 (35.5) | 0.583 |
| NRS2002, median (Q1–Q3) | 1.0 (0.0–2.0) | 1.0 (0.0–1.0) | 2.0 (1.0–4.0) | 0.000 |
As shown in Table 1, there were 727 patients (81.1%) and 169 patients (18.9%) in the non-long LOHS and LOHS groups, respectively. There was no significant difference between the two groups in terms of sex. However, the patients in the long-LOHS group were relatively older than the patients in the non-long-LOHS group were (p = 0.000). The BMI of the patients in the long LOHS group was relatively lower than that of the patients in the non-long LOHS group (p = 0.003). Furthermore, the prevalence of UGI diseases, including gastric submucosal tumor (SMT) (p = 0.005), advanced gastric or esophageal cancer (p = 0.000), esophageal SMT (p = 0.036) and upper gastrointestinal tract (UGT) stenosis (p = 0.000), differed between the two groups.
In terms of chronic diseases, we found that the proportions of patients with cardiovascular diseases (p = 0.012), respiratory system diseases (p = 0.000) and kidney diseases (p = 0.000) were greater in the long LOHS group than those in the non-long LOHS group. When the smoking history (p = 0.445) and drinking history (p = 0.583) were analyzed, no statistically significant differences were observed between the two groups. Moreover, a significant difference in the NRS2002 score was detected between the non-long LOHS group and the LOHS group, which was 1.0 (Q1–Q3, 0.0–1.0) and 2.0 (Q1–Q3, 1.0–4.0), respectively (p = 0.000).
Laboratory measurements for patients by length of hospital stay
With respect to laboratory parameters, we found that RBC (p = 0.000), HGB (p = 0.000), HCT (p = 0.000), TP (p = 0.000) and ALB (p = 0.000) were significantly lower in the long LOHS group than in the non-long LOHS group. In the long LOHS group, the median RBC was 4.0 × 109/L (Q1–Q3, 3.1–4.5 × 109/L), the median HGB was 117.0 g/L (Q1–Q3, 89.5–138.5 g/L), the median HCT was 0.35 (Q1–Q3, 0.26–0.41), the median TP was 63.2 g/L (Q1–Q3, 59.5–67.4 g/L) and the median serum ALB concentration was 37.5 g/L (Q1–Q3, 33.3–41.4 g/L). In the non-long LOHS group, the corresponding values were 4.3 × 109/L (Q1–Q3, 3.9–4.8 × 109/L), 132.0 (Q1–Q3, 118.0–145.0 g/L), 0.39 (Q1–Q3, 0.35–0.42), 65.5 g/L (Q1–Q3, 62.4–69.1 g/L) and 41.2 g/L (Q1–Q3, 38.7–43.4 g/L).
NEUT (p = 0.021), PT (p = 0.000), APTT (p = 0.004) and FIB (p = 0.001) were significantly greater in the long LOHS group than in the non-long LOHS group (Table 2). In the long LOHS group, the median NEUT was 3.3 × 109/L (Q1–Q3, 2.2–5.4 × 109/L), the median PT was 13.3 s (Q1–Q3, 12.1–16.1 s), the median APPT was 28.6 s (Q1–Q3, 26.6–35.0 s), and the median FIB was 2.9 g/L (Q1–Q3, 2.4–3.6 g/L). In the non-long LOHS group, the corresponding values were 3.0 × 109/L (Q1–Q3, 2.4–3.9 × 109/L), 12.3 s (Q1–Q3, 11.4–14.3 s), 27.6 s (Q1–Q3, 26.2–31.5 s) and 2.6 g/L (Q1–Q3, 2.3–3.1 g/L).
Table 2.
Comparison of the laboratory parameters between the two groups.
| Variables | Total (n = 896) | LOHS ≤ 19 (n = 727) | LOHS > 19 (n = 169) | p value |
|---|---|---|---|---|
| RBC (×109/L), median (Q1–Q3) | 4.3 (3.8–4.8) | 4.3 (3.9–4.8) | 4.0 (3.1–4.5) | 0.000 |
| HGB (g/L), median (Q1–Q3) | 130.0 (115.0–144.0) | 132.0 (118.0–145.0) | 117.0 (89.5–138.5) | 0.000 |
| HCT, median (Q1–Q3) | 0.38 (0.34–0.42) | 0.39 (0.35–0.42) | 0.35 (0.26–0.41) | 0.000 |
| WBC (×109/L), median (Q1–Q3) | 5.5 (4.5–6.7) | 5.5 (4.5–6.6) | 5.6 (4.1–7.5) | 0.687 |
| NEUT (×109/L), median (Q1–Q3) | 3.0 (2.4–4.2) | 3.0 (2.4–3.9) | 3.3 (2.2–5.4) | 0.021 |
| PLT (×109/L), median (Q1–Q3) | 199.0 (160.0–241.0) | 199.0 (161.8–240.0) | 203.5 (152.5–243.8) | 0.955 |
| TP (g/L), median (Q1–Q3) | 65.2 (61.6–68.8) | 65.5 (62.4–69.1) | 63.2 (59.5–67.4) | 0.000 |
| ALB (g/L), median (Q1–Q3) | 40.9 (37.9–43.0) | 41.2 (38.7–43.4) | 37.5 (33.3–41.4) | 0.000 |
| PT (s), median (Q1–Q3) | 12.5 (11.5–14.8) | 12.3 (11.4–14.3) | 13.3 (12.1–16.1) | 0.000 |
| APTT (s), median (Q1–Q3) | 27.9 (26.3–32.2) | 27.6 (26.2–31.5) | 28.6 (26.6–35.0) | 0.004 |
| FIB (g/L), median (Q1–Q3) | 2.7 (2.3–3.2) | 2.6 (2.3–3.1) | 2.9 (2.4–3.6) | 0.001 |
Multivariate logistic regression analysis of long length of hospital stay
On the basis of the above findings, we conducted univariate logistic regression analysis by selecting relevant variables (Table 3). These variables included age, BMI, gastric SMT, advanced gastric or esophageal cancer, esophageal SMT, UGT stenosis, cardiovascular disease, respiratory disease, kidney disease, RBC, HGB, HCT, NEUT, TP, ALB, PT, APTT, FIB and NRS2002 score. All the significant factors within the univariate logistic regression analysis were included in the multivariable logistic regression analysis (Table 3). Among the various components of the multivariable logistic regression model, only the serum ALB concentration and NRS2002 score were independent factors influencing long LOHS for the inpatients. For every 1-point increase in the NRS2002 score, the probability of long LOHS for inpatients increased by 77.7% (OR = 1.777; CI = 1.485–2.126; p = 0.000). Additionally, the serum ALB concentration was negatively correlated with long LOHS for the inpatients (OR = 0.927; CI = 0.867–0.990; p = 0.024).
Table 3.
Analysis of univariate and multivariate logistic regression models to evaluate the relationship of NRS 2002 scores on long LOHS in patients with UGI diseases.
| Variables | Univariate (unadjusted) | Multivariate (adjusted) | ||
|---|---|---|---|---|
| OR (95% CI) | p value | OR (95% CI) | p value | |
| Age | 1.026 (1.013–1.040) | 0.000 | 0.989 (0.973–1.005) | 0.173 |
| BMI | 0.915 (0.875–0.957) | 0.000 | 1.047 (0.988–1.111) | 0.123 |
| Gastric SMT | 0.335 (0.152–0.739) | 0.007 | 0.677 (0.294–1.563) | 0.361 |
| Advanced gastric or esophageal cancer | 2.805 (1.588–4.958) | 0.000 | 0.812 (0.369–1.789) | 0.606 |
| Esophageal SMT | 0.302 (0.093–0.988) | 0.048 | 0.340 (0.079–1.470) | 0.149 |
| UGT stenosis | 3.366 (1.652–6.859) | 0.001 | 1.655 (0.692–3.955) | 0.257 |
| Cardiovascular disease | 1.554 (1.099–2.198) | 0.013 | 1.516 (0.959–2.396) | 0.075 |
| Respiratory disease | 3.432 (2.124–5.547) | 0.000 | 1.191 (0.641–2.214) | 0.580 |
| Kidney disease | 5.786 (2.123–15.766) | 0.001 | 1.317 (0.361–4.812) | 0.677 |
| RBC | 0.583 (0.477–0.712) | 0.000 | 1.789 (0.850–3.762) | 0.125 |
| HGB | 0.979 (0.973–0.985) | 0.000 | 0.993 (0.971–1.016) | 0.570 |
| HCT | 0.001 (0.000–0.009) | 0.000 | 0.043 (0.000–6,376.724) | 0.605 |
| NEUT | 1.130 (1.053–1.213) | 0.001 | 1.052 (0.956–1.156) | 0.299 |
| TP | 0.955 (0.929–0.981) | 0.001 | 1.006 (0.965–1.049) | 0.777 |
| ALB | 0.864 (0.833–0.895) | 0.000 | 0.927 (0.867–0.990) | 0.024 |
| PT | 1.097 (1.038–1.159) | 0.001 | 1.056 (0.984–1.132) | 0.129 |
| APTT | 1.048 (1.020–1.077) | 0.001 | 1.004 (0.969–1.040) | 0.837 |
| FIB | 1.433 (1.194–1.720) | 0.000 | 1.232 (0.974–1.559) | 0.082 |
| NRS2002 | 1.875 (1.660–2.118) | 0.000 | 1.777 (1.485–2.126) | 0.000 |
Receiver operating characteristic curves for albumin and Nutritional Risk Screening 2002
The performance of the ALB and NRS2002 scores in predicting long LOHS for inpatients with UGI disease was compared by ROC analysis. As shown in Figure 2, both the serum ALB concentration and the NRS2002 score could reasonably predict long LOHS for inpatients with UGI disease (the ROC curve of the serum ALB concentration and the long LOHS shown in Figure 2 is actually based on the reciprocal of the actual value of the serum ALB concentration). The NRS2002 score was more accurate, with an AUC of 0.721 (95% CI = 0.675–0.768) versus 0.691 (95% CI = 0.644–0.738) for the serum ALB concentration (Figure 2). When the serum ALB concentration was 37.6, the specificity was 0.831, and the sensitivity was 0.509, which were both optimal, as indicated by the highest Youden index. This finding suggests that for inpatients with UGI diseases, using an ALB concentration ≥ 37.6 g/L as a predictor of long LOHS offers an optimal balance of specificity and sensitivity. When the NRS2002 score was 1.5, the specificity was 0.792, and the sensitivity was 0.568, which were both optimal, as indicated by the highest Youden index. This finding suggests that for inpatients with UGI diseases, using an NRS2002 score of ≥ 2 as a predictor of long LOHS offers an optimal balance of specificity and sensitivity.
Figure 2.
The ROC curves to predict the long LOHS in UGI patients.
Discussion
The LOHS serves as a key barometer of hospital operational efficiency and medical quality (24). Prolonged stays not only occupy hospital beds, increasing the consumption of medical resources (such as medical staff and medical equipment) but also affect the relevant performance evaluation indicators of hospitals and operational efficiency (25). Moreover, such prolonged occupancies can lead to a shortage of medical resources, preventing patients who truly need medical resources from receiving effective treatment (26, 27). A more serious consideration is that a long LOHS may increase the risk of complications and infections for inpatients, and at the same time, lead to a decline in patient satisfaction (27).
Many factors influence inpatient LOHS. The type, size and regional differences of the hospital where the patient is hospitalized may affect the length of hospital stay (28). In addition, patients’ educational level, occupation, and type of medical insurance may influence their medical-seeking behavior and hospitalization decisions, thereby affecting the length of their hospital stay (28, 29). In terms of lifestyle factors, smoking, alcohol consumption and drug use are associated with longer hospital stays for upper gastrointestinal bleeding (UGIB) patients (30). With respect to the nutritional status of inpatients, poor nutritional status is associated with prolonged hospital stays (31, 32).
Addressing malnutrition has become a key priority in global health efforts. NRS2002 is a rapid, low-burden tool for quantifying nutritional risk (33). By conducting routine screenings for all newly admitted patients, medical staff can promptly identify those who are malnourished or at risk of developing such conditions. They can then formulate targeted dietary adjustment plans or provide nutritional supplements for these patients, thereby reducing postoperative complications, shortening LOHS, accelerating the recovery process, and improving patient quality of life and long-term prognosis. A recent study indicated that the incidence of malnutrition risk according to the NRS2002 is approximately 31.2% (34). Multiple studies have shown that early nutritional risk screening for specific nutritional risk groups, such as patients with advanced tumors, elderly patients and critically ill patients, and timely intervention measures can improve prognosis and reduce LOHS (35–38). Inpatients with UGI diseases typically present with a constellation of alarming symptoms, such as anorexia, dysphagia, epigastric pain, bloating, diarrhea, hematemesis, or hematochezia, which may simultaneously suppress appetite, obstruct ingestion, impair intraluminal digestion, and accelerate catabolic loss.
Our study collected data on 896 UGI diseases and analyzed the predictive value of nutritional risk screening for long LOHS in a wide population to test its discriminative power across an undifferentiated, universally admitted population. Furthermore, we defined a long LOHS as hospitalization for patients with UGI diseases lasting longer than the 75th percentile (P75 = 19 days) and compared the data between the two groups: those with a LOHS of more than 19 days and those with a LOHS of 19 days or less. There were significant differences in age, BMI, gastric SMT, advanced gastric or esophageal cancer, esophageal SMT, UGT stenosis, cardiovascular disease, respiratory disease, kidney disease, RBC, HGB, HCT, NEUT, TP, ALB, PT, APTT, FIB and NRS2002 score between the long LOHS group and the non-long LOHS group. After multivariable logistic regression analysis was performed, we found that the serum ALB concentration and NRS2002 score were independent factors influencing long LOHS in patients with UGI diseases. On the basis of the ROC curves of the serum ALB concentration and the NRS2002 score, we found that the NRS2002 had good predictive value for long LOHS, with an AUC of 0.721. That is, an NRS2002 score of UGI diseases in patients ≥ 2 indicates a high risk of long LOHS.
As mentioned above, malnutrition can lead to prolonged LOHS. Multiple studies have shown that the NRS2002 has good predictive value in terms of LOHS and mortality, and the threshold value is set as an NRS2002 score ≥ 3 (22, 23, 39). Given that disease severity and degree of nutritional impairment are the two main components of the NRS2002 assessment, an increase in either of these two indicators is associated with a longer LOS (40). However, the impact of different disease types (such as respiratory diseases or cardiovascular diseases) on LOHS varies significantly. For instance, owing to their high prevalence of comorbidities, severe inflammation, and metabolic disorders, patients with tumors, patients in the intensive care unit (ICU), and elderly patients require a higher NRS2002 threshold to accurately identify high-risk individuals and determine long LOHS among these patients. For patients with UGI diseases, owing to a significant reduction in oral intake and a slow recovery of gastrointestinal function, even if their scores are less than 3 points, the risk of malnutrition significantly increases and significantly affects the LOHS. For hospitalized patients with upper gastrointestinal diseases, an NRS2002 score ≥ 2 may serve as a valuable early warning threshold to identify those at increased risk of prolonged hospitalization. Close nutritional monitoring and further comprehensive nutritional assessment are encouraged to guide decisions regarding early nutritional intervention.
Although this study revealed that the serum ALB concentration and NRS2002 score significantly predict long LOHS for inpatients with UGI diseases, several limitations still exist. First, as our study was retrospective and was conducted at a single center, multicenter prospective studies are necessary to validate our findings. Second, whether nutritional intervention can shorten the LOHS of inpatients with UGI diseases cannot be analyzed in retrospective studies. Third, in our study, the NRS2002 was conducted within 24 h after admission; therefore, continuous and dynamic observational data throughout the entire hospitalization period were lacking. Fourth, the predictive model only exhibits moderate discriminative efficiency based on its AUC value. Additional prospective studies with expanded sample sizes are necessary to further verify the clinical applicability and generalizability of NRS2002 in predicting prolonged LOHS among patients with UGI diseases.
Conclusion
This study demonstrated that both the serum ALB concentration and the NRS2002 score are potential predictors of long LOHS for patients admitted with UGI diseases.
Funding Statement
The author(s) declared that financial support was received for this work and/or its publication. This study was supported by the National Key Research and Development Program of China (2022YFC2503604).
Footnotes
Edited by: Ze Xiang, Zhejiang University, China
Reviewed by: Cristiano Capurso, University of Foggia, Italy
Yang Fu, Shanghai JiaoTong University School of Medicine, China
Data availability statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.
Ethics statement
The studies involving humans were approved by the Ethics Committee of Chinese PLA General Hospital (Approval No. S2025-351-01). The studies were conducted in accordance with the local legislation and institutional requirements. Ethics committee approved the exemption of informed consent for this study.
Author contributions
RZ: Writing – original draft, Writing – review & editing. XL: Writing – original draft, Writing – review & editing. XF: Writing – original draft, Writing – review & editing. HL: Writing – original draft, Writing – review & editing. FD: Writing – original draft, Writing – review & editing.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnut.2026.1743320/full#supplementary-material
References
- 1.Lanas A, Dumonceau J-M, Hunt RH, Fujishiro M, Scheiman JM, Gralnek IM, et al. Non-variceal upper gastrointestinal bleeding. Nat Rev Dis Primers. (2018) 4:18020. doi: 10.1038/nrdp.2018.20, [DOI] [PubMed] [Google Scholar]
- 2.Fass R, Boeckxstaens GE, El-Serag H, Rosen R, Sifrim D, Vaezi MF. Gastro-oesophageal reflux disease. Nat Rev Dis Primers. (2021) 7:55. doi: 10.1038/s41572-021-00287-w [DOI] [PubMed] [Google Scholar]
- 3.Gotink AW, et al. Nonthermal resection device for ablation of Barrett's esophagus: a feasibility and safety study. Endoscopy. (2022) 54:545–52. [DOI] [PubMed] [Google Scholar]
- 4.Katona BW, Powers J, McKenna DB, Long JM, le AN, Hausler R, et al. Upper gastrointestinal cancer risk and surveillance outcomes in Li-Fraumeni syndrome. Am J Gastroenterol. (2020) 115:2095–7. doi: 10.14309/ajg.0000000000000935, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Sharma P, Yadlapati R. Pathophysiology and treatment options for gastroesophageal reflux disease: looking beyond acid. Ann N Y Acad Sci. (2021) 1486:3–14. doi: 10.1111/nyas.14501, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Cotton CC, Eluri S, Shaheen NJ. Management of dysplastic Barrett's esophagus and early esophageal adenocarcinoma. Gastroenterol Clin N Am. (2022) 51:485–500. doi: 10.1016/j.gtc.2022.06.004, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Smyth EC, Nilsson M, Grabsch HI, van Grieken NCT, Lordick F. Gastric cancer. Lancet. (2020) 396:635–48. doi: 10.1016/S0140-6736(20)31288-5, [DOI] [PubMed] [Google Scholar]
- 8.Xu S, Chai N, Tang X, Linghu E, Lu Z, Wang S, et al. Outcomes of simultaneous endoscopic submucosal dissection for synchronous multiple gastric neoplastic lesions: a retrospective comparative study. Surg Endosc. (2022) 36:4014–24. doi: 10.1007/s00464-021-08722-5, [DOI] [PubMed] [Google Scholar]
- 9.Liu Y, Song C, Wang X, Ma X, Zhang P, Chen G, et al. Prevalence of malnutrition among adult inpatients in China: a nationwide cross-sectional study. Sci China Life Sci. (2025) 68:1487–97. doi: 10.1007/s11427-023-2619-7 [DOI] [PubMed] [Google Scholar]
- 10.Song D, et al. Risk factors for inpatient malnutrition and length of stay assessed by 'NutritionDay' in China. Asia Pac J Clin Nutr. (2022) 31:561–9. [DOI] [PubMed] [Google Scholar]
- 11.Lin YM, Wang M, Sun N-X, Liu Y-Y, Yin T-F, Chen C. Screening and application of nutritional support in elderly hospitalized patients of a tertiary care hospital in China. PLoS One. (2019) 14:e0213076. doi: 10.1371/journal.pone.0213076, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Li Z, et al. Nutrition support in hospitalized cancer patients with malnutrition in China. Asia Pac J Clin Nutr. (2018) 27:1216–24. [DOI] [PubMed] [Google Scholar]
- 13.Böhne SEJ, Hiesmayr M, Sulz I, Tarantino S, Wirth R, Volkert D. Recent and current low food intake - prevalence and associated factors in hospital patients from different medical specialities. Eur J Clin Nutr. (2022) 76:1440–8. doi: 10.1038/s41430-022-01129-y, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Burgos R, et al. Prevalence of malnutrition and its etiological factors in hospitals. Nutr Hosp. (2012) 27:469–76. [DOI] [PubMed] [Google Scholar]
- 15.Muscaritoli M, Amabile MI, Gallicchio C, Imbimbo G, Conte C, Ceriani E, et al. Prevalence of malnutrition and its impact on clinical outcomes in internal medicine wards: the SIMI-NUTRO study. Eur J Intern Med. (2025) 138:83–90. doi: 10.1016/j.ejim.2025.05.025, [DOI] [PubMed] [Google Scholar]
- 16.Liu J, Liu J, Wang J, Yan Z, Liang Q, Wang X, et al. Prevalence and impact of malnutrition on readmission among hospitalized patients with heart failure in China. ESC Heart Fail. (2022) 9:4271–9. doi: 10.1002/ehf2.14152 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Zhang H, Wang Y, Jiang ZM, Kondrup J, Fang H, Andrews M, et al. Impact of nutrition support on clinical outcome and cost-effectiveness analysis in patients at nutritional risk: a prospective cohort study with propensity score matching. Nutrition. (2017) 37:53–9. doi: 10.1016/j.nut.2016.12.004 [DOI] [PubMed] [Google Scholar]
- 18.Kondrup J, Allison SP, Elia M, Vellas B, Plauth M, Educational and Clinical Practice Committee, European Society of Parenteral and Enteral Nutrition (ESPEN) . ESPEN guidelines for nutrition screening 2002. Clin Nutr. (2003) 22:415–21. doi: 10.1016/S0261-5614(03)00098-0, [DOI] [PubMed] [Google Scholar]
- 19.Tevik K, Thürmer H, Husby MI, de Soysa AK, Helvik AS. Nutritional risk screening in hospitalized patients with heart failure. Clin Nutr. (2015) 34:257–64. doi: 10.1016/j.clnu.2014.03.014 [DOI] [PubMed] [Google Scholar]
- 20.Liu P, Zhang ZF, Cai JJ, Wang BS, Yan X. NRS2002 assesses nutritional status of leukemia patients undergoing hematopoietic stem cell transplantation. Chin J Cancer Res. (2012) 24:299–303. doi: 10.1007/s11670-012-0267-8, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Müller M, Dahdal S, Saffarini M, Uehlinger D, Arampatzis S. Evaluation of nutrition risk screening score 2002 (NRS) assessment in hospitalized chronic kidney disease patient. PLoS One. (2019) 14:e0211200. doi: 10.1371/journal.pone.0211200, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Fatemeh G, Fotsing G, Marques-Vidal P, Kopp P, Barigou M. Predictive value of multiple variable models including nutritional risk score (NRS 2002) on mortality and length of stay of patients with covid-19 infections. The INCOVO study. Clin Nutr ESPEN. (2023) 55:357–63. doi: 10.1016/j.clnesp.2023.04.001, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Chen Z, Jiang H, He W, Li D, Lin M, Wang M, et al. The association of nutritional risk screening 2002 with 1-year re-hospitalization and the length of initial hospital stay in patients with heart failure. Front Nutr. (2022) 9:849034. doi: 10.3389/fnut.2022.849034 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Martini WA, Hodgson NR. Retrospective cohort analysis of the relationship between emergency department length of stay and timing of first laboratory orders. Cureus. (2024) 16:e68966. doi: 10.7759/cureus.68966, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Hurisa Dadi H, Habte N, Mulu Y. Length of hospital stay and associated factors among adult surgical patients admitted to surgical wards in Amhara regional state comprehensive specialized hospitals, Ethiopia. PLoS One. (2024) 19:e0296143. doi: 10.1371/journal.pone.0296143 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Lee SY, Lee SH, Tan JHH, Foo HSL, Phan PH, Kow AWC, et al. Factors associated with prolonged length of stay for elective hepatobiliary and neurosurgery patients: a retrospective medical record review. BMC Health Serv Res. (2018) 18:5. doi: 10.1186/s12913-017-2817-8, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Al Harbi S, et al. Streamlining patient flow and enhancing operational efficiency through case management implementation. BMJ Open Qual. (2024) 13. doi: 10.1136/bmjoq-2023-002484 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Liu W, Shi J, He S, Luo X, Zhong W, Yang F. Understanding variations and influencing factors on length of stay for T2DM patients based on a multilevel model. PLoS One. (2021) 16:e0248157. doi: 10.1371/journal.pone.0248157, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Strik Lievers L, Curt F, Wallier J, Perdereau F, Rein Z, Jeammet P, et al. Predictive factors of length of inpatient treatment in anorexia nervosa. Eur Child Adolesc Psychiatry. (2009) 18:75–84. doi: 10.1007/s00787-008-0706-8 [DOI] [PubMed] [Google Scholar]
- 30.Belete MW, Kebede MA, Bedane MR, Berhe TT, Tekle AB, Shash EP, et al. Factors associated with severity and length of hospital stay in patients with acute upper gastrointestinal bleeding: insights from two Ethiopian hospitals. Int J Emerg Med. (2024) 17:185. doi: 10.1186/s12245-024-00768-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Wang X, Naito Y, Nakatani H, Ida M, Kawaguchi M. Prevalence of undernutrition in surgical patients and the effect on length of hospital stay. J Anesth. (2022) 36:89–95. doi: 10.1007/s00540-021-03013-8 [DOI] [PubMed] [Google Scholar]
- 32.Czapla M, Juárez-Vela R, Łokieć K, Wleklik M, Karniej P, Smereka J. The association between nutritional status and length of hospital stay among patients with hypertension. Int J Environ Res Public Health. (2022) 19. doi: 10.3390/ijerph19105827, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Crestani MS, Grassi T, Steemburgo T. Methods of nutritional assessment and functional capacity in the identification of unfavorable clinical outcomes in hospitalized patients with cancer: a systematic review. Nutr Rev. (2022) 80:786–811. doi: 10.1093/nutrit/nuab090, [DOI] [PubMed] [Google Scholar]
- 34.Ouaijan K, Hwalla N, Kandala NB, Mpinga EK. 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. Healthcare (Basel). (2023) 11:11(5). doi: 10.3390/healthcare11050730 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Holyday M, Daniells S, Bare M, Caplan GA, Petocz P, Bolin T. Malnutrition screening and early nutrition intervention in hospitalised patients in acute aged care: a randomised controlled trial. J Nutr Health Aging. (2012) 16:562–8. doi: 10.1007/s12603-012-0022-3, [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Le B, et al. Malnutrition risk, outcomes, and costs among older adults undergoing elective surgical procedures: a retrospective cohort study. Nutr Clin Pract. (2023) 38:1045–62. doi: 10.1002/ncp.11043 [DOI] [PubMed] [Google Scholar]
- 37.Omidvari AH, et al. Nutritional screening for improving professional practice for patient outcomes in hospital and primary care settings. Cochrane Database Syst Rev. (2013) 2021:Cd005539. doi: 10.1002/14651858.CD005539.pub2 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Villar-Taibo R, Calleja-Fernández A, Vidal-Casariego A, Pintor-de-la-Maza B, Álvarez-del-Campo C, Arias-García R, et al. A short nutritional intervention in a cohort of hematological inpatients improves energy and protein intake and stabilizes nutritional status. Nutr Hosp. (2016) 33:1347–53. doi: 10.20960/nh.794 [DOI] [PubMed] [Google Scholar]
- 39.Del Giorno R, et al. Nutritional risk screening and body composition in COVID-19 patients hospitalized in an internal medicine ward. Int J Gen Med. (2020) 13:1643–51. doi: 10.2147/IJGM.S286484 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Sahli L, Hagenbuch N, Ballmer PE, Rühlin M, Imoberdorf R. NRS-2002 components, nutritional score and severity of disease score, and their association with hospital length of stay and mortality. Swiss Med Wkly. (2021) 151:w20517. doi: 10.4414/smw.2021.20517, [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The original contributions presented in the study are included in the article/Supplementary material, further inquiries can be directed to the corresponding authors.