Abstract
Malnutrition is common in patients with acute stroke and may increase susceptibility to stroke-associated pneumonia (SAP). This study aimed to assess the predictive value of the Mini Nutritional Assessment Short Form (MNA-SF) for SAP and to compare its performance with laboratory and anthropometric nutritional indicators. In this prospective cohort study, 317 patients hospitalized with acute stroke were enrolled. Nutritional status was evaluated at admission using MNA-SF, routine laboratory markers, and anthropometric measurements. SAP occurring within 7 days of stroke onset was the primary outcome. Multivariable logistic regression models were applied to examine independent associations between nutritional indicators and SAP. Predictive performance was evaluated using receiver operating characteristic (ROC) curve analysis. SAP occurred in 86 patients (27.1%). Patients at risk of malnutrition (MNA-SF score 8–11) had a significantly higher risk of SAP compared with those with normal nutritional status (adjusted OR up to 3.94, 95% CI 1.26–12.32), independent of demographic, clinical, laboratory, and anthropometric factors. An MNA-SF cutoff score of 11 demonstrated good discriminative ability for SAP prediction (AUROC 0.84, 95% CI 0.79–0.88), outperforming blood-based nutritional biomarkers and anthropometric measures. The negative predictive value was 90.6%. Lower MNA-SF scores are strongly associated with the development of stroke-associated pneumonia. Compared with commonly used nutritional indicators, MNA-SF provides superior predictive performance and represents a simple, non-invasive tool for early identification of stroke patients at high risk for SAP.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-026-50651-7.
Keywords: Stroke-associated pneumonia, Mini Nutritional Assessment Short Form, Malnutrition, Logistic regression, ROC analysis
Subject terms: Biomarkers, Diseases, Health care, Medical research, Neurology, Neuroscience, Risk factors
Introduction
Stroke-associated pneumonia (SAP) is a frequent and serious complication during the acute phase of stroke and is associated with increased mortality, prolonged hospitalization, and poor functional outcomes1. Although several clinical risk factors for SAP—such as advanced age, stroke severity, and dysphagia—have been well established, many of these factors are not readily modifiable at hospital admission2,3. Identifying additional, modifiable risk factors is therefore important for improving early prevention strategies.
Malnutrition is common in patients with acute stroke and has been increasingly recognized as an important contributor to adverse clinical outcomes, including infection4. Reduced dietary intake, swallowing dysfunction, immobilization, and acute metabolic stress may rapidly impair nutritional status after stroke5,6. Impaired nutrition, in turn, can negatively affect immune function, respiratory muscle strength, and the ability to clear airway secretions, potentially increasing susceptibility to pneumonia. For these reasons, early nutritional assessment is recommended in stroke care; however, the most effective and practical nutritional screening approach for predicting SAP remains unclear.
Previous studies have explored the relationship between nutritional status and SAP using laboratory-based indicators such as serum albumin, albumin-to-globulin ratio7, micronutrient levels8–10, and composite indices like the Geriatric Nutritional Risk Index11. While these measures provide objective information, they primarily reflect biochemical or inflammatory status and require blood sampling. Moreover, they do not fully capture functional and dietary components of nutrition that are relevant in acute illness.
The Mini Nutritional Assessment Short Form (MNA-SF) is a validated, non-invasive nutritional screening tool widely used in clinical nutrition practice12. Recommended by the European Society for Clinical Nutrition and Metabolism, MNA-SF evaluates multiple dimensions of nutritional risk, including recent changes in food intake and body weight, mobility, psychological stress, and body mass index13,14. Because it integrates functional, dietary, and anthropometric elements, MNA-SF may better reflect overall nutritional vulnerability than isolated laboratory markers. However, evidence regarding its value in predicting stroke-associated pneumonia is limited.
Therefore, the aim of this study was to evaluate the association between MNA-SF scores and the occurrence of SAP in patients with acute stroke and to compare the predictive performance of MNA-SF with commonly used laboratory and anthropometric nutritional indicators. By focusing on a practical nutritional screening tool, this study seeks to support early identification of patients at high nutritional and infectious risk and to inform nutrition-focused preventive strategies in acute stroke care.
Materials and methods
This study was conducted and reported in accordance with the STROBE guidelines15 for observational cohort studies.
Study population
This prospective, single-center cohort study was conducted at the First Affiliated Hospital of Nanchang University (FAHNU) and approved by the institutional ethics committee (Approval No. 2021026). Between November 2021 and May 2022, a total of 317 consecutive patients with acute stroke were enrolled.
Inclusion criteria were: (a) age ≥ 18 years; (b) stroke confirmed by brain CT or MRI; and (c) hospital admission within 48 h of stroke onset.
Exclusion criteria included: transient ischemic attack; active infection or fever within 2 weeks prior to admission; history of prior stroke, cancer, or immunosuppressive therapy; pneumonia prior to stroke onset; or incomplete clinical data (Fig. 1).
Fig. 1.
Flow of participants. SAP, stroke-associated pneumonia.
Diagnosis of stroke-associated pneumonia
SAP was diagnosed within 7 days after stroke onset according to the modified Centers for Disease Control and Prevention criteria for hospital-acquired pneumonia, defined as the presence of at least 1 qualifying systemic inflammatory manifestation (fever, abnormal white blood cell count, or unexplained altered mental status in patients aged ≥ 70 years), plus at least 2 qualifying respiratory clinical manifestations, combined with new or progressive infectious pulmonary lesions on chest imaging. Pneumonia present before stroke onset was excluded. SAP diagnoses were independently confirmed by two experienced neurologists blinded to patients’ nutritional data.
Data collection
This study used consecutive convenience sampling. Sample size was determined based on the event-per-variable ratio recommended for predictive modeling and previous similar studies.
Demographic and clinical data included age, sex, body mass index (BMI), hypertension, diabetes mellitus, smoking and drinking history, stroke subtype, National Institutes of Health Stroke Scale (NIHSS) score at admission and feeding route. Fasting venous blood samples were collected on the morning following admission. Laboratory parameters included neutrophil count, lymphocyte count, platelet count, hemoglobin, serum creatinine, albumin, globulin, and albumin-to-globulin ratio (A/G).
Nutritional status was assessed using the Chinese version of the MNA-SF. Scores were categorized as follows: (1) Normal nutrition: ≥12 points; (2) At risk of malnutrition: 8–11 points; and (3) Malnutrition: ≤7 points. Assessments were performed by two trained nutritionists. For patients with impaired consciousness or cognitive deficits, MNA-SF was assessed via proxy respondents (family members or caregivers). We exclusively used the MNA-SF rather than the full MNA version for nutritional screening in this acute stroke cohort, as the MNA-SF is the recommended tool for rapid nutritional screening in acute settings, the full-version assessment is time-consuming and unfeasible to complete in patients with impaired consciousness, performing the full assessment only for those with a screening score ≤ 11 points as required by the standard MNA protocol was not achievable in our study population, and the MNA-SF has been fully validated and widely applied in stroke populations. MNA-SF was completed within 24 h of admission to avoid reverse temporality.
Anthropometric measurements—including mid-upper arm circumference, chest circumference, waist circumference, and calf circumference—were obtained using standardized tape measures (precision 0.2 cm). Mid-upper arm and calf circumferences were used as proxies for muscle mass, while chest and waist circumferences reflected thoracic and abdominal body composition.
Statistical analysis
Shapiro–Wilk test was used to evaluate the normality of continuous variables. Continuous variables were analyzed using Student’s t-test or Mann–Whitney U test and are presented as mean ± standard deviation or median (interquartile range), as appropriate. Categorical variables were compared using chi-square test.
Multivariable logistic regression models and Firth’s logistic regression were constructed to assess the association between MNA-SF score and SAP, with progressive adjustment for demographic, clinical, laboratory, and anthropometric covariates. Results are reported as odds ratios (ORs) with 95% confidence intervals (CIs). Associations were visualized using forest plots.
ROC curve analysis was conducted to evaluate predictive performance. AUROC values, optimal cutoff points, Youden index, sensitivity, specificity, PPV, and NPV were calculated. AUROCs were compared using the Z-test. Statistical analyses were performed using R (version 3.5.1) and MedCalc (version 13.0). A two-tailed P value < 0.05 was considered statistically significant.
Results
Basic characteristics of patients
Baseline characteristics
Among the 317 patients included, 86 (27.13%) developed SAP. Baseline characteristics are summarized in Table 1. The mean age was 64.9 years, and 218 patients (68.8%) were male. Compared with non-SAP patients, those with SAP were older (P < 0.001), had higher NIHSS scores (P < 0.001), and were more likely to have hemorrhagic stroke (P = 0.005) (Table 2). SAP patients had significantly lower MNA-SF scores and BMI (P < 0.001 and P = 0.001, respectively). Laboratory analysis revealed lower hemoglobin, albumin, and A/G levels, and higher neutrophil and globulin levels in SAP patients (P < 0.001). Anthropometric measurements—including mid-upper arm, chest, waist, and calf circumferences—were also significantly lower in the SAP group (P < 0.001).
Table 1.
Sociodemographic characteristics between the stroke-associated pneumonia (SAP) and non-SAP groups.
| Variables | Total(n = 317) | Non-SAP (n = 231) | SAP(n = 86) | Statistic | p-value |
|---|---|---|---|---|---|
| Demographic characteristics | |||||
| Age (years), Mean ± SD | 64.9 ± 11.4 | 62.9 ± 11.0 | 70.3 ± 10.5 | -5.387 | < 0.001a |
| Gender, n (%) | 0.001 | 0.969b | |||
| Male | 218 (68.8%) | 159 (68.8%) | 59 (68.6%) | ||
| Female | 99 (31.2%) | 72 (31.2%) | 27 (31.4%) | ||
| BMI (kg/m2), Mean ± SD | 23.8 ± 3.3 | 24.2 ± 3.2 | 22.8 ± 3.1 | 3.370 | 0.001a |
| Height (m) | 1.64 ± 0.07 | 1.63 ± 0.08 | 1.64 ± 0.07 | -1.005 | 0.316a |
| Weight (kg) | 64 ± 11 | 65 ± 12 | 61 ± 11 | 2.595 | 0.010a |
SAP, stroke-associated pneumonia; BMI, body mass index; SD, standard deviation. a: Student’s t-test; b: chi-square test.
Table 2.
Comparison of the clinical and nutritional parameters between the stroke-associated pneumonia(SAP)and non-SAP groups.
| Variables | Total(n = 317) | Non-SAP (n = 231) | SAP(n = 86) | Statistic | p-value |
|---|---|---|---|---|---|
| Clinical data | |||||
| NIHSS score, median (Q25–Q75) | 3(1–7) | 3(1–4) | 12.5(6.7–21.2) | − 10.451 | < 0.001c |
| Hypertension, n (%) | 191(60.3%) | 144(62.3%) | 47(54.7) | 1.546 | 0.214b |
| Diabetes, n (%) | 80(25.2%) | 62(26.8%) | 18(20.9%) | 1.160 | 0.281b |
| Smoking, n (%) | 163(51.4%) | 115(49.8%) | 48(55.8%) | 0.912 | 0.339b |
| Drinking, n (%) | 138(43.5%) | 100(43.3%) | 38(44.2%) | 0.020 | 0.886b |
| Stroke type, n (%) | 8.031 | 0.005b | |||
| Ischemic stroke | 275(86.8%) | 208(90.0%) | 67(77.9%) | ||
| Hemorrhagic stroke | 42(13.2%) | 23(10.0%) | 19(22.1%) | ||
| MNA-SF score, median (Q25–Q75) | 13(11–14) | 14(13–14) | 10(9–11) | − 9.636 | < 0.001c |
| MNA-SF score, n(%) | 103.277 | < 0.001b | |||
| Malnourished (0–7) | 17(5.4%) | 4(1.7%) | 13(15.1%) | ||
| Malnutrition risk (8–11) | 88(27.8%) | 35(15.2%) | 53(61.6%) | ||
| Normal (12–14) | 212(66.9%) | 192(83.1%) | 20(23.3%) | ||
| Feeding route, n(%) | 55.631 | < 0.001b | |||
| Oral intake | 282(89.0%) | 224(97.0%) | 58(67.4%) | ||
| Enteral nutrition | 35(11.0%) | 7(3.0%) | 28(32.6%) | ||
| Laboratory data | |||||
| Neutrophil(×109 /L), median(Q25–Q75) | 5.04(3.77–6.90) | 4.53(3.61–5.72) | 7.23(5.18–9.27) | − 6.704 | < 0.001c |
| Lymphocyte(×109 /L), median(Q25–Q75) | 1.46(1.12–1.82) | 1.52(1.19–1.89) | 1.19(0.92–1.52) | − 4.955 | < 0.001c |
| Platelet(×109 /L), median(Q25–Q75) | 213(178–252) | 212(179–249) | 216(165–259) | − 0.252 | 0.801c |
| Hemoglobin(g/L), median(Q25–Q75) | 129(119–142) | 131(121–145) | 126(110–136) | − 3.625 | < 0.001c |
| Creatinine (umol/L), median(Q25–Q75) | 76(64–88) | 76(66–88) | 75(62–89) | − 0.428 | 0.669c |
| Albumin(g/L), median(Q25–Q75) | 38(36–41) | 39(37–41) | 37(34–40) | − 3.247 | 0.001c |
| Globulin(g/L), median(Q25–Q75) | 26(24–28) | 26(23–27) | 27(25–31) | − 3.812 | < 0.001c |
| A/G, median(Q25–Q75) | 1.53(1.37–1.68) | 1.53(1.44–1.69) | 1.45(1.21–1.65) | − 3.669 | < 0.001c |
| Anthropometric parameters | |||||
| Mid-upper arm circumference(cm), median(Q25–Q75) | 26(25–28) | 27(25–29) | 25(24–26) | − 5.409 | < 0.001c |
| Chest circumference(cm), median(Q25–Q75) | 90(85–95) | 91(85–97) | 85(83–90) | − 5.087 | < 0.001c |
| Waist circumference(cm), Mean ± SD | 90.0 ± 8.6 | 91.2 ± 8.4 | 87.1 ± 8.2 | 3.939 | < 0.001a |
| Calf circumferenc(cm), median(Q25–Q75) | 31(29–34) | 32(30–34) | 29(28–31) | − 5.099 | < 0.001c |
SAP, stroke-associated pneumonia; NIHSS, National Institutes of Health Stroke Scale; MNA-SF, Mini Nutritional Assessment Short-Form; A/G, albumin to globulin ratio. a: Student’s t-test; b: chi-square test; c: Mann–Whitney U test.
Association between MNA-SF score and SAP
The proportions of malnutrition and malnutrition risk were significantly higher among SAP patients compared with non-SAP patients (15.1% vs. 1.7% and 61.6% vs. 15.2%, respectively; P < 0.001; Fig. 2). Using normal nutritional status (MNA-SF ≥ 12) as the reference, multivariable logistic regression demonstrated that malnutrition risk (MNA-SF 8–11) was independently associated with SAP (OR 3.94, 95% CI 1.26–12.32, P = 0.018). This association remained robust after adjustment for demographic, clinical, laboratory, and anthropometric factors across all models (Table 3).
Fig. 2.
Percentage of SAP subcategorized groups by MNA-SF score in stroke patients. P < 0.001.
Table 3.
Multivariate logistic analysis for the association between MNA-SF score and stroke-associated pneumonia(SAP) risk.
| Unadjusted | Model1 | Model2 | Model3 | |||||
|---|---|---|---|---|---|---|---|---|
| OR(95% CI) | P-value | OR(95% CI) | P-value | OR(95% CI) | P-value | OR(95% CI) | P-value | |
| MNA-SF score | ||||||||
| Malnourished (0–7) | 31.20 (9.28-104.79) | < 0.001 | 3.26 (0.55–19.26) | 0.192 | 1.96 (0.26–14.52) | 0.512 | 3.06 (0.39–24.34) | 0.290 |
| Malnutrition risk (8–11) | 14.53 (7.75–27.23) | < 0.001 | 4.44 (1.82–10.88) | 0.001 | 2.96 (1.07–8.21) | 0.038 | 3.94 (1.26–12.32) | 0.018 |
| Normal (12–14) | Reference | Reference | Reference | Reference | ||||
MNA-SF, Mini Nutritional Assessment Short-Form; OR, odds ratio;95%CI, 95% confidence interval.
Model1: adjusted for age, gender, BMI, hypertension, diabetes, smoking, drinking, stroke type, NIHSS score and feeding route.
Model2: adjusted for covariates from Model 1 and further adjusted for Neutrophil, Lymphocyte, Platelet, Hemoglobin, Creatinine, Albumin, Globulin, A/G and feeding route.
Model3:adjusted for covariates from Model2 and further adjusted for Mid-upper arm circumference, Chest circumference, Waist circumference, Calf circumferenc and feeding route.
Forest plot analysis (Fig. 3) further showed that higher hemoglobin and chest circumference were independently associated with reduced SAP risk, whereas elevated globulin levels increased SAP risk.
Fig. 3.
Forest plot of the association between nutritional indicators and stroke-associated pneumonia (SAP). An OR > 1 meant an increased risk of SAP, and an OR < 1 meant the opposite.
Predictive performance of nutritional indicators
To determine the optimal cutoff score for clinical screening, we used the ROC curve to identify an optimal MNA-SF cutoff value of 11 for SAP prediction, yielding an AUROC of 0.84 (95% CI 0.79–0.88), sensitivity of 76.7%, specificity of 83.1%, PPV of 62.9%, and NPV of 90.6% (Table 4; Fig. 4). The discriminative performance of MNA-SF was significantly superior to that of globulin, hemoglobin, and chest circumference (P < 0.001 for all comparisons).
Table 4.
Results of ROC analysis about nutrition parameters with stroke-associated pneumonia(SAP) .
| AUC (95%CI) | Cut off | Youden index | Sensitivity (%) | Specificity (%) | PPV (%) | NPV (%) | P-value | |
|---|---|---|---|---|---|---|---|---|
| MNA-SF score | 0.837(0.792–0.876) | 11 | 0.598 | 76.74 | 83.12 | 62.9 | 90.6 | < 0.001 |
| Albumin | 0.618(0.562–0.672) | 36 | 0.230 | 47.67 | 75.32 | 41.8 | 79.5 | < 0.001 |
| Globulin | 0.638(0.583–0.691) | 27 | 0.276 | 48.84 | 78.79 | 46.2 | 80.5 | < 0.001 |
| A/G | 0.634(0.578–0.687) | 1.37 | 0.260 | 44.19 | 81.82 | 47.5 | 79.7 | < 0.001 |
| Hemoglobin | 0.632(0.577–0.686) | 126 | 0.208 | 54.65 | 66.23 | 37.6 | 79.7 | < 0.001 |
| Mid-upper arm circumference | 0.696(0.642–0.746) | 26 | 0.331 | 75.58 | 57.58 | 39.9 | 86.4 | < 0.001 |
| Chest circumference | 0.685(0.631–0.736) | 86 | 0.392 | 60.47 | 78.79 | 51.5 | 84.3 | < 0.001 |
| Waist circumference | 0.638(0.583–0.691) | 89 | 0.277 | 66.28 | 61.47 | 39.0 | 83.0 | < 0.001 |
| Calf circumference | 0.685(0.631–0.736) | 31 | 0.307 | 77.91 | 52.81 | 38.1 | 86.5 | < 0.001 |
ROC, Receiver operating characteristics; AUC, area under the ROC curve; PPV, positive predicted value; NPV, negative predicted value; MNA-SF, Mini Nutritional Assessment Short-Form; A/G, albumin to globulin ratio.
Fig. 4.
Comparison of area under the receiver operating characteristic curve (AUROC) values among MNA-SF and nutrition indicators of SAP. MNA-SF vs. Globulin, P < 0.001; MNA-SF vs. Hemoglobin, P < 0.001; MNA-SF vs. Chest circumference, P < 0.001.
Discussion
In this prospective real-world study, we demonstrated a strong and independent association between poor nutritional status, as assessed by the Mini Nutritional Assessment Short Form (MNA-SF), and the development of stroke-associated pneumonia (SAP). Our findings indicate that patients at risk of malnutrition had a markedly increased likelihood of SAP, and that MNA-SF showed superior predictive performance compared with commonly used laboratory biomarkers and anthropometric measures. These results highlight the importance of early, comprehensive nutritional screening in patients with acute stroke.
The incidence of SAP in our cohort (27.1%) is consistent with previously reported ranges and confirms that SAP remains a frequent complication during acute stroke hospitalization16. As expected, patients who developed SAP were older and had higher stroke severity scores, both of which are well-established risk factors17. We also observed a higher prevalence of SAP among patients with hemorrhagic stroke, which may reflect more severe neurological impairment and systemic inflammatory responses associated with intracerebral hemorrhage18,19. Notably, SAP patients had lower body mass index and reduced anthropometric measurements, suggesting that compromised nutritional reserves may contribute to vulnerability to infection.
Malnutrition is common after stroke and may arise from reduced oral intake, dysphagia, impaired mobility, and acute metabolic stress. The MNA-SF is a validated nutritional screening tool that captures multiple dimensions of nutritional risk, including recent weight loss, dietary intake, functional status, and body composition20–22. In our study, MNA-SF scores were significantly lower in patients who developed SAP, and being classified as “at risk of malnutrition” was independently associated with SAP even after adjustment for clinical severity, laboratory indices, and anthropometric variables. This finding suggests that global nutritional status, rather than isolated nutritional markers, plays a key role in susceptibility to post-stroke pneumonia. However, the sample size of malnutrition group in acute stroke patients is too small, which leads to insufficient statistical validity and does not show a significant correlation with pneumonia.
Our study confirmed that admission malnutrition is an independent risk factor for SAP in acute stroke patients, and this association can be explained by multiple interrelated biological and clinical mechanisms. First, malnutrition impairs systemic and respiratory mucosal immune function, the core pathway driving infection susceptibility, weakening the body’s ability to clear pathogenic bacteria and destroying the respiratory mechanical and immune barriers, thus increasing pneumonia risk23. Second, it exacerbates neurological impairment by worsening consciousness disturbance and bulbar palsy, elevating dysphagia and aspiration risk, the core pathological basis of SAP24. Third, malnutrition disrupts the intestinal barrier, drives persistent systemic inflammatory response, and amplifies pulmonary inflammatory injury after infection25. Finally, it reduces patients’ tolerance to clinical supportive treatment and treatment efficacy, prolongs hospital stay, and elevates the cumulative risk of nosocomial infections including SAP.
Previous studies have linked objective nutritional indices, such as the Geriatric Nutritional Risk Index and serum protein levels, to SAP risk26. While these markers provide valuable information, they primarily reflect biochemical or inflammatory status and require invasive blood sampling27. In contrast, MNA-SF is non-invasive, rapid, and easily implemented at the bedside. Our ROC analysis demonstrated that MNA-SF had significantly higher discriminative ability for SAP than globulin, hemoglobin, or chest circumference. The high negative predictive value further supports its usefulness for identifying patients at low risk, thereby assisting clinicians in prioritizing preventive interventions for those most vulnerable. In addition, we also determined that the optimal MNA-SF truncation value for SAP prediction is 11.
The association between malnutrition and pneumonia may be explained by impaired immune function, reduced respiratory muscle strength, and diminished ability to clear airway secretions in nutritionally compromised patients28,29. Stroke-related complications such as dysphagia and gastrointestinal dysfunction can further exacerbate nutritional decline, creating a cycle that increases infection risk30,31. Although this study was not designed to explore mechanistic pathways, our findings are consistent with prior evidence showing that improved nutritional status is associated with better infection-related outcomes32–34.
From a clinical nutrition perspective, our results underscore the value of routine nutritional screening at hospital admission for patients with acute stroke. Early identification of patients at nutritional risk provides an opportunity for timely nutritional support, which may help reduce the incidence of SAP and improve overall outcomes. Incorporating MNA-SF into standard stroke care pathways may represent a practical and cost-effective strategy to enhance infection prevention efforts.
Several limitations should be acknowledged. This was a single-center study with a moderate sample size, which may limit generalizability. Nutritional status was assessed only at admission, and changes during hospitalization were not evaluated. Additionally, certain treatment-related factors, such as dysphagia severity and enteral feeding, were not included and may influence both nutritional status and pneumonia risk. Future multicenter studies with longitudinal nutritional assessment are warranted to confirm these findings and to determine whether targeted nutritional interventions based on MNA-SF screening can reduce SAP incidence.
Conclusion
In conclusion, this study demonstrates that lower MNA-SF scores are strongly associated with stroke-associated pneumonia and outperform conventional nutritional indicators in predicting SAP risk. MNA-SF represents a simple, non-invasive, and clinically relevant tool for early nutritional risk assessment in stroke patients and may support more effective prevention strategies for post-stroke pneumonia.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors would like to acknowledge the technical assistance from Jiaxin Tu, Li-Fang Deng and Cheng Zhang, and the support from of the school administration.
Author contributions
X.L. and X.Z. had a major role in the acquisition of data as well as writing the manuscript. X.Z. made substantial contributions to the conception of the work as well as contributions to the draft and revisions of the work. X.L. had a major role in analysis of the data. T.C. and Q.F. made substantial contributions to the conception of the work and interpretation of data. J.W. and X.L. was a substantial contributor to designing the work as well as interpretation of the data. Y.L. for manuscript preparation, data verification and presentation., and L.W. revised the work in its entirety. All authors read and approved the final manuscript.
Funding
This work was supported by the Science and Technology Research Project of Jiangxi Provincial Department of Education (Grant number GJJ210149),and the Project of Jiangxi Provincial Medical Insurance(Grant number JXGZ2025-07-1209).
Data availability
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Declarations
Ethics approval and consent to participate
We obtained the approval of the Ethics Committee of the First Affiliated Hospital of Nanchang University(NO. 2021026). The institutional ethics committee reviewed the retrospective use of anonymous data for scientific purpose and waived the need to obtain informed written consent, and all methods were performed in accordance with the relevant guidelines and regulations (Declaration of Helsinki) .
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Xiong Liao and Xiang Zhu contributed equally to this work.
Contributor Information
Xiang Zhu, Email: 1826398377@qq.com.
Tianpan Cai, Email: ndyfy07856@ncu.edu.cn.
Lei Wu, Email: leiwu@ncu.edu.cn.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.