Clinical utility of Mini Nutritional Assessment Short Form (MNA-SF) in predicting stroke-associated pneumonia in acute stroke patients

Clinical utility of Mini Nutritional Assessment Short Form (MNA-SF) in predicting stroke-associated pneumonia in acute stroke patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Clinical utility of Mini Nutritional Assessment Short Form (MNA-SF) in predicting stroke-associated pneumonia in acute stroke patients Xiong Liao, Xiang Zhu, Tianpan Cai, Qifan Fen, Jimin Wu, Xueting Lin, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8556078/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 17 You are reading this latest preprint version Abstract Objective 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. Methods 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. Results 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 14.53, 95% CI 7.75–27.23), 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%. Conclusions 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. Health sciences/Biomarkers Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Health sciences/Risk factors stroke-associated pneumonia Mini Nutritional Assessment Short Form malnutrition logistic regression ROC analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. 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 outcomes [ 1 ]. 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 admission [ 2 – 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 infection [ 4 ]. Reduced dietary intake, swallowing dysfunction, immobilization, and acute metabolic stress may rapidly impair nutritional status after stroke [ 5 – 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 ratio [ 7 ], micronutrient levels [ 8 – 10 ], and composite indices like the Geriatric Nutritional Risk Index [ 11 ]. 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 practice [ 12 ]. 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 index [ 13 – 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. 2. Materials and methods 2.1 Study Population This prospective, single-center 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 hours 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 ). 2.2 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, incorporating clinical symptoms, radiographic findings, and laboratory indicators. Pneumonia present before stroke onset was excluded. SAP diagnoses were independently confirmed by two experienced neurologists blinded to patients’ nutritional data. 2.3 Data Collection Demographic and clinical data included age, sex, body mass index (BMI), hypertension, diabetes mellitus, smoking and drinking history, stroke subtype, and National Institutes of Health Stroke Scale (NIHSS) score at admission. 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. 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. 2.4 Statistical Analysis 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 tests. Multivariable logistic regression models 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. 3. Results 3.1. Basic characteristics of patients 3.1 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). 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). 3.2 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; Figure 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 = 14.53, 95% CI 7.75–27.23, P <0.001). This association remained robust after adjustment for demographic, clinical, laboratory, and anthropometric factors across all models (Table 2). Forest plot analysis (Figure 3) further showed that higher hemoglobin and chest circumference were independently associated with reduced SAP risk, whereas elevated globulin levels increased SAP risk. 3.3 Predictive Performance of Nutritional Indicators ROC analysis identified 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 3; Figure 4). The discriminative performance of MNA-SF was significantly superior to that of globulin, hemoglobin, and chest circumference ( P <0.001 for all comparisons). 4. 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 hospitalization [ 15 ]. As expected, patients who developed SAP were older and had higher stroke severity scores, both of which are well-established risk factors [ 16 ]. 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 hemorrhage [ 17 – 18 ]. 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 composition [ 19 – 21 ]. 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. Previous studies have linked objective nutritional indices, such as the Geriatric Nutritional Risk Index and serum protein levels, to SAP risk [ 22 ]. While these markers provide valuable information, they primarily reflect biochemical or inflammatory status and require invasive blood sampling [ 23 ]. 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. 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 patients [ 24 – 25 ]. Stroke-related complications such as dysphagia and gastrointestinal dysfunction can further exacerbate nutritional decline, creating a cycle that increases infection risk [ 26 – 27 ]. 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 outcomes [ 28 – 30 ]. 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. 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. Declarations Disclosures: None declared. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. 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) . Consent for publication Not applicable. Competing interests Not applicable. Sources of Funding This work was supported by the Science and Technology Research Project of Jiangxi Provincial Department of Education (grant number GJJ210149) Financial: Authors' contributions Xiong Liao and Xiang Zhu had a major role in the acquisition of data as well as writing the manuscript. Xiang Zhu made substantial contributions to the conception of the work as well as contributions to the draft and revisions of the work. Xiong Liao had a major role in analysis of the data. Tianpan Cai and Qifan Fen made substantial contributions to the conception of the work and interpretation of data. Jimin Wu and Xueting Lin was a substantial contributor to designing the work as well as interpretation of the data. Yuanan Lu for manuscript preparation, data verification and presentation., and Lei Wu revised the work in its entirety. All authors read and approved the final manuscript. 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 Disclosures None References Sarmadi, S., Sanaie, N. & Zare-Kaseb, A. Global prevalence of stroke-associated pneumonia: a systematic review and meta-analysis of cross-sectional studies. Top. Stroke Rehabil :1–17 (2025). Guven, M. E., Tekan, U. Y., Pirdal, B. Z. & Orken, D. N. Understanding definitive and probable stroke-associated pneumonia: Risk factors and clinical outcomes. Clin. Neurol. Neurosurg. 249 , 108770 (2025). Zhang, S., Huang, J. & Zhou, X. A Meta-analysis of the Risk Factors for Stroke-associated Pneumonia. J. Coll. Physicians Surgeons–Pakistan: JCPSP . 33 (7), 799–803 (2023). Li, D. et al. Association between malnutrition and stroke-associated pneumonia in patients with ischemic stroke. BMC Neurol. 23 (1), 290 (2023). Mehta, A. et al. 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Immunol. 13 , 868973 (2022). Tables Table 1. Comparison of the baseline data 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 <0.001 Gender, n (%) 0.001 0.969 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 0.001 Clinical data NIHSS score,median (Q 25 –Q 75 ) 3(1-7） 3(1-4) 12.5(6.7-21.2) -10.451 <0.001 Hypertension, n (%) 191(60.3%) 144(62.3%) 47(54.7) 1.546 0.214 Diabetes,n(%) 80(25.2%) 62(26.8%) 18(20.9%) 1.160 0.281 Smoking,n(%) 163(51.4%) 115(49.8%) 48(55.8%) 0.912 0.339 Drinking,n(%) 138(43.5%) 100(43.3%) 38(44.2%) 0.020 0.886 Stroke type,n(%) 8.031 0.005 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(Q 25 –Q 75 ) 13(11-14) 14(13-14) 10(9-11) -9.636 <0.001 MNA-SF score,n(%) 103.277 <0.001 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%) Laboratory data Neutrophil(×109 /L),median(Q 25 –Q 75 ) 5.04(3.77-6.90) 4.53(3.61-5.72) 7.23(5.18-9.27) -6.704 <0.001 Lymphocyte(×109 /L),median(Q 25 –Q 75 ) 1.46(1.12-1.82) 1.52(1.19-1.89) 1.19(0.92-1.52) -4.955 <0.001 Platelet(×109 /L),median(Q 25 –Q 75 ) 213(178-252) 212(179-249) 216(165-259) -0.252 0.801 Hemoglobin(g/L),median(Q 25 –Q 75 ) 129(119-142) 131(121-145) 126(110-136) -3.625 <0.001 Creatinine (umol/L),median(Q 25 –Q 75 ) 76(64-88) 76(66-88) 75(62-89) -0.428 0.669 Albumin(g/L),median(Q 25 –Q 75 ) 38(36-41) 39(37-41) 37(34-40) -3.247 0.001 Globulin(g/L),median(Q 25 –Q 75 ) 26(24-28) 26(23-27) 27(25-31) -3.812 <0.001 A/G,median(Q 25 –Q 75 ) 1.53(1.37-1.68) 1.53(1.44-1.69) 1.45(1.21-1.65) -3.669 <0.001 Anthropometric parameters Mid-upper arm circumference(cm),median(Q 25 –Q 75 ) 26(25-28) 27(25-29) 25(24-26) -5.409 <0.001 Chest circumference(cm),median(Q 25 –Q 75 ) 90(85-95) 91(85-97) 85(83-90) -5.087 <0.001 Waist circumference(cm),Mean±SD 90.0±8.6 91.2±8.4 87.1±8.2 3.939 <0.001 Calf circumferenc(cm),median(Q 25 –Q 75 ) 31(29-34) 32(30-34) 29(28-31) -5.099 <0.001 SAP = stroke-associated pneumonia; BMI = body mass index; SD = standard deviation; NIHSS = National Institutes of Health Stroke Scale; MNA-SF = Mini Nutritional Assessment Short-Form; A/G =albumin to globulin ratio. Table 2 . 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 4.48（0.81-24.63） 0.084 2.42（0.34-16.94） 0.373 3.67（0.49-27.25） 0.204 Malnutrition risk (8–11) 14.53（7.75-27.23） <0.001 5.36（2.22-12.89） <0.001 3.57（1.34-9.47） 0.011 4.72（1.61-13.85） 0.005 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 and NIHSS score. Model2: adjusted for covariates from Model 1 and further adjusted for Neutrophil, Lymphocyte, Platelet,Hemoglobin, Creatinine, Albumin, Globulin, A/G . Model3:adjusted for covariates from Model2 and further adjusted for Mid-upper arm circumference,Chest circumference,Waist circumference,Calf circumferenc. Table 3. Results of ROC analysis about nutrition parameters with stroke-associated pneumonia(SAP) . AUC (95%CI) Cutoff 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 circumferenc 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 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8556078","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":619934469,"identity":"9c8a941a-20cd-4b3b-9773-eeae2fd4feb4","order_by":0,"name":"Xiong Liao","email":"","orcid":"","institution":"The First Affiliated Hospital of Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Xiong","middleName":"","lastName":"Liao","suffix":""},{"id":619934470,"identity":"417e5626-ba5b-4d14-9327-b6e0decc01c0","order_by":1,"name":"Xiang Zhu","email":"","orcid":"","institution":"Jiangxi Provincial Key Laboratory of Preventive Medicine,Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Xiang","middleName":"","lastName":"Zhu","suffix":""},{"id":619934471,"identity":"77726b3b-0d05-4ca6-8577-ae10e72794af","order_by":2,"name":"Tianpan Cai","email":"","orcid":"","institution":"Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Tianpan","middleName":"","lastName":"Cai","suffix":""},{"id":619934472,"identity":"93021cfc-15f0-410f-8d7b-bf4e9e2ed8db","order_by":3,"name":"Qifan Fen","email":"","orcid":"","institution":"Nanchang university","correspondingAuthor":false,"prefix":"","firstName":"Qifan","middleName":"","lastName":"Fen","suffix":""},{"id":619934473,"identity":"5f80be14-a186-4632-a4ba-c051d89893f7","order_by":4,"name":"Jimin Wu","email":"","orcid":"","institution":"Nanchang university","correspondingAuthor":false,"prefix":"","firstName":"Jimin","middleName":"","lastName":"Wu","suffix":""},{"id":619934474,"identity":"db63abb9-e203-42e2-b591-32def85522e6","order_by":5,"name":"Xueting Lin","email":"","orcid":"","institution":"Jiangxi Provincial Key Laboratory of Preventive Medicine,Nanchang University","correspondingAuthor":false,"prefix":"","firstName":"Xueting","middleName":"","lastName":"Lin","suffix":""},{"id":619934475,"identity":"9cbb04d6-22be-46b9-86f1-872dec4def29","order_by":6,"name":"Melanie Zhang","email":"","orcid":"","institution":"University of Hawaii at Manoa","correspondingAuthor":false,"prefix":"","firstName":"Melanie","middleName":"","lastName":"Zhang","suffix":""},{"id":619934476,"identity":"ac22ff7f-8d6a-4942-86bf-9c263a887569","order_by":7,"name":"Yuanan Lu","email":"","orcid":"","institution":"University of Hawaii at Manoa","correspondingAuthor":false,"prefix":"","firstName":"Yuanan","middleName":"","lastName":"Lu","suffix":""},{"id":619934477,"identity":"b20e8d08-0c8f-4cac-b3e3-f13917ac4fce","order_by":8,"name":"Lei Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsklEQVRIiWNgGAWjYBACPgbGBgaGCghHgigtbGAtZ0jTAgSMbSRpYW9uk+add0fe4ADzwds8DHZ5hLXwHARq2fbMcMMBtmRrHobkYsJaJBLbbvNuO5xgcIDHTJqH4UBiA0Et8g+BWuaAtPB/I1KLBCNQSwPYFjYitfAktv+cc+yw4czDbMaWcwySCWvhZz/+2OBNzWF5vuPND2+8qbAjrAUBmEGEAfHqR8EoGAWjYBTgAQAAjjZkPTZVCQAAAABJRU5ErkJggg==","orcid":"","institution":"Jiangxi Provincial Key Laboratory of Preventive Medicine,Nanchang University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-01-09 02:53:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8556078/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8556078/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106544726,"identity":"c02284c3-c005-4dec-bec4-038a6435ba1c","added_by":"auto","created_at":"2026-04-09 16:42:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31521,"visible":true,"origin":"","legend":"\u003cp\u003eFlow of participants. SAP, stroke-associated pneumonia.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8556078/v1/f5d1144f1a796beac58d588c.png"},{"id":106725755,"identity":"ced925a0-4deb-4c77-83b5-6195d89e5b8e","added_by":"auto","created_at":"2026-04-12 18:33:54","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":40053,"visible":true,"origin":"","legend":"\u003cp\u003ePercentage of SAP subcategorized groups by MNA-SF score in stroke patients.P \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8556078/v1/7a7e6030547b5cfa02edf823.png"},{"id":106544729,"identity":"ab1d6937-bc16-4692-8d96-8e3fb5c12b4f","added_by":"auto","created_at":"2026-04-09 16:42:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78434,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the association between nutritional indicators and stroke-associated pneumonia (SAP).An OR \u0026gt;1 meant an increased risk of SAP, and an OR \u0026lt;1 meant the opposite.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8556078/v1/54f63efc96859352ba77c2cb.png"},{"id":106724943,"identity":"a06118cc-678f-4a8e-aaf1-560a4fda0755","added_by":"auto","created_at":"2026-04-12 18:30:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":56438,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of area under the receiver operating characteristic curve (AUROC) values among MNA-SF and nutrition indicators of SAP. MNA-SF vs. Globulin, \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001; MNA-SF vs. Hemoglobin, \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001; MNA-SF vs. Chest circumference, \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8556078/v1/5aa9c724b3c1ba563295ddc1.png"},{"id":106960186,"identity":"fe760f5f-7ec0-47f9-a0a3-79fe4dbfc95f","added_by":"auto","created_at":"2026-04-15 09:19:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1154893,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8556078/v1/98f0e214-8608-4db8-b444-f70ed5085ffb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical utility of Mini Nutritional Assessment Short Form (MNA-SF) in predicting stroke-associated pneumonia in acute stroke patients","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eStroke-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 outcomes [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although several clinical risk factors for SAP\u0026mdash;such as advanced age, stroke severity, and dysphagia\u0026mdash;have been well established, many of these factors are not readily modifiable at hospital admission [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Identifying additional, modifiable risk factors is therefore important for improving early prevention strategies.\u003c/p\u003e \u003cp\u003eMalnutrition is common in patients with acute stroke and has been increasingly recognized as an important contributor to adverse clinical outcomes, including infection [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Reduced dietary intake, swallowing dysfunction, immobilization, and acute metabolic stress may rapidly impair nutritional status after stroke [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003ePrevious studies have explored the relationship between nutritional status and SAP using laboratory-based indicators such as serum albumin, albumin-to-globulin ratio [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], micronutrient levels [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and composite indices like the Geriatric Nutritional Risk Index [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eThe Mini Nutritional Assessment Short Form (MNA-SF) is a validated, non-invasive nutritional screening tool widely used in clinical nutrition practice [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. 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 index [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eTherefore, 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.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Population\u003c/h2\u003e \u003cp\u003e This prospective, single-center 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.\u003c/p\u003e \u003cp\u003e \u003cb\u003eInclusion criteria\u003c/b\u003e were: a) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; b) stroke confirmed by brain CT or MRI; and c) hospital admission within 48 hours of stroke onset.\u003c/p\u003e \u003cp\u003e \u003cb\u003eExclusion criteria\u003c/b\u003e 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.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Diagnosis of Stroke-Associated Pneumonia\u003c/h2\u003e \u003cp\u003eSAP was diagnosed within 7 days after stroke onset according to the modified Centers for Disease Control and Prevention criteria for hospital-acquired pneumonia, incorporating clinical symptoms, radiographic findings, and laboratory indicators. Pneumonia present before stroke onset was excluded. SAP diagnoses were independently confirmed by two experienced neurologists blinded to patients\u0026rsquo; nutritional data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Collection\u003c/h2\u003e \u003cp\u003eDemographic and clinical data included age, sex, body mass index (BMI), hypertension, diabetes mellitus, smoking and drinking history, stroke subtype, and National Institutes of Health Stroke Scale (NIHSS) score at admission. 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).\u003c/p\u003e \u003cp\u003eNutritional status was assessed using the Chinese version of the MNA-SF. Scores were categorized as follows: 1) Normal nutrition: \u0026ge;12 points; 2) At risk of malnutrition: 8\u0026ndash;11 points; and 3) Malnutrition: \u0026le;7 points. Assessments were performed by two trained nutritionists.\u003c/p\u003e \u003cp\u003eAnthropometric measurements\u0026mdash;including mid-upper arm circumference, chest circumference, waist circumference, and calf circumference\u0026mdash;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.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eContinuous variables were analyzed using Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test or Mann\u0026ndash;Whitney \u003cem\u003eU\u003c/em\u003e test and are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or median (interquartile range), as appropriate. Categorical variables were compared using chi-square tests.\u003c/p\u003e \u003cp\u003eMultivariable logistic regression models 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.\u003c/p\u003e \u003cp\u003eROC 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 \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1. Basic characteristics of patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Baseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 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 (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001), had higher NIHSS scores (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001), and were more likely to have hemorrhagic stroke (\u003cem\u003eP\u003c/em\u003e = 0.005). SAP patients had significantly lower MNA-SF scores and BMI (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001 and \u003cem\u003eP\u003c/em\u003e = 0.001, respectively). Laboratory analysis revealed lower hemoglobin, albumin, and A/G levels, and higher neutrophil and globulin levels in SAP patients (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001). Anthropometric measurements—including mid-upper arm, chest, waist, and calf circumferences—were also significantly lower in the SAP group (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Association Between MNA-SF Score and SAP\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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; \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001; Figure 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 = 14.53, 95% CI 7.75–27.23, \u003cem\u003eP\u003c/em\u003e \u0026lt;0.001). This association remained robust after adjustment for demographic, clinical, laboratory, and anthropometric factors across all models (Table 2).\u003c/p\u003e\n\u003cp\u003eForest plot analysis (Figure 3) further showed that higher hemoglobin and chest circumference were independently associated with reduced SAP risk, whereas elevated globulin levels increased SAP risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Predictive Performance of Nutritional Indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC analysis identified 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 3; Figure 4). The discriminative performance of MNA-SF was significantly superior to that of globulin, hemoglobin, and chest circumference (\u003cem\u003eP\u003c/em\u003e \u0026lt;0.001 for all comparisons).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn 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.\u003c/p\u003e \u003cp\u003eThe 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 hospitalization [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. As expected, patients who developed SAP were older and had higher stroke severity scores, both of which are well-established risk factors [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. 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 hemorrhage [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Notably, SAP patients had lower body mass index and reduced anthropometric measurements, suggesting that compromised nutritional reserves may contribute to vulnerability to infection.\u003c/p\u003e \u003cp\u003eMalnutrition 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 composition [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In our study, MNA-SF scores were significantly lower in patients who developed SAP, and being classified as \u0026ldquo;at risk of malnutrition\u0026rdquo; 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.\u003c/p\u003e \u003cp\u003ePrevious studies have linked objective nutritional indices, such as the Geriatric Nutritional Risk Index and serum protein levels, to SAP risk [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While these markers provide valuable information, they primarily reflect biochemical or inflammatory status and require invasive blood sampling [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. 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.\u003c/p\u003e \u003cp\u003eThe 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 patients [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Stroke-related complications such as dysphagia and gastrointestinal dysfunction can further exacerbate nutritional decline, creating a cycle that increases infection risk [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. 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 outcomes [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom 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.\u003c/p\u003e \u003cp\u003eSeveral 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.\u003c/p\u003e \u003cp\u003eIn 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.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDisclosures: None declared.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe 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) .\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSources of Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Science and Technology Research Project of Jiangxi Provincial Department of Education (grant number GJJ210149)\u003c/p\u003e\n\u003cp\u003eFinancial:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiong Liao and Xiang Zhu had a major role in the acquisition of data as well as writing the manuscript. Xiang Zhu made substantial contributions to the conception of the work as well as contributions to the draft and revisions of the work. Xiong Liao had a major role in analysis of the data. Tianpan Cai and Qifan Fen made substantial contributions to the conception of the work and interpretation of data. Jimin Wu and Xueting Lin was a substantial contributor to designing the work as well as interpretation of the data. Yuanan Lu for manuscript preparation, data verification and presentation., and Lei Wu revised the work in its entirety. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSarmadi, S., Sanaie, N. \u0026amp; Zare-Kaseb, A. Global prevalence of stroke-associated pneumonia: a systematic review and meta-analysis of cross-sectional studies. \u003cem\u003eTop. Stroke Rehabil\u003c/em\u003e :1\u0026ndash;17 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuven, M. E., Tekan, U. Y., Pirdal, B. Z. \u0026amp; Orken, D. N. Understanding definitive and probable stroke-associated pneumonia: Risk factors and clinical outcomes. \u003cem\u003eClin. Neurol. Neurosurg.\u003c/em\u003e \u003cb\u003e249\u003c/b\u003e, 108770 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, S., Huang, J. \u0026amp; Zhou, X. A Meta-analysis of the Risk Factors for Stroke-associated Pneumonia. \u003cem\u003eJ. Coll. Physicians Surgeons\u0026ndash;Pakistan: JCPSP\u003c/em\u003e. \u003cb\u003e33\u003c/b\u003e (7), 799\u0026ndash;803 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, D. et al. Association between malnutrition and stroke-associated pneumonia in patients with ischemic stroke. \u003cem\u003eBMC Neurol.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (1), 290 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMehta, A. et al. 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Nutritional Improvement Correlates with Recovery of Activities of Daily Living among Malnourished Elderly Stroke Patients in the Convalescent Stage: A Cross-Sectional Study. \u003cem\u003eJ. Acad. Nutr. Diet.\u003c/em\u003e \u003cb\u003e116\u003c/b\u003e (5), 837\u0026ndash;843 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArsava, E. M. et al. Admission chest CT findings and risk assessment for stroke-associated pneumonia. \u003cem\u003eActa Neurol. Belg.\u003c/em\u003e ;1\u0026ndash;7. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan, J. et al. ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage. \u003cem\u003eJ. Transl Med.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (1), 193 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParra-Romero, G. et al. Hemorrhagic stroke associated to COVID-19 infection in Mexico General Hospital. Accidente cerebrovascular hemorr\u0026aacute;gico asociado a infecci\u0026oacute;n por COVID-19 en Hospital General de M\u0026eacute;xico. \u003cem\u003eCir. Cir.\u003c/em\u003e \u003cb\u003e89\u003c/b\u003e (4), 435\u0026ndash;442 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSyahrul, S. et al. Hemorrhagic and ischemic stroke in patients with coronavirus disease 2019: incidence, risk factors, and pathogenesis - a systematic review and meta-analysis. \u003cem\u003eF1000Res\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 34 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, H. et al. Nutritional Status According to the Short-Form Mini Nutritional Assessment (MNA-SF) and Clinical Characteristics as Predictors of Length of Stay, Mortality, and Readmissions Among Older Inpatients in China: A National Study. \u003cem\u003eFront. Nutr.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 815578 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaluźniak-Szymanowska, A. et al. Diagnostic Performance and Accuracy of the MNA-SF against GLIM Criteria in Community-Dwelling Older Adults from Poland. \u003cem\u003eNutrients\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (7), 2183 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHsueh, S. W. et al. A comparison of the MNA-SF, MUST, and NRS-2002 nutritional tools in predicting treatment incompletion of concurrent chemoradiotherapy in patients with head and neck cancer. \u003cem\u003eSupport Care Cancer\u003c/em\u003e. \u003cb\u003e29\u003c/b\u003e (9), 5455\u0026ndash;5462 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDai, C., Yan, D., Xu, M., Huang, Q. \u0026amp; Ren, W. Geriatric Nutritional Risk Index is related to the risk of stroke-associated pneumonia. \u003cem\u003eBrain Behav.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (8), e2718 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSabbouh, T. \u0026amp; Torbey, M. T. Malnutrition in Stroke Patients: Risk Factors, Assessment, and Management. \u003cem\u003eNeurocrit Care\u003c/em\u003e. \u003cb\u003e29\u003c/b\u003e (3), 374\u0026ndash;384 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, Q., Wu, M., Tang, Q., Yan, P. \u0026amp; Zhu, L. Age-Related Alterations in Immune Function and Inflammation: Focus on Ischemic Stroke. \u003cem\u003eAging Dis.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (3), 1046\u0026ndash;1074 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYong, H. Y. F., Ganesh, A. \u0026amp; Camara-Lemarroy, C. Gastrointestinal Dysfunction in Stroke. \u003cem\u003eSemin Neurol.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e (4), 609\u0026ndash;625 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBond, V. E., Doeltgen, S., Kleinig, T. \u0026amp; Murray, J. Dysphagia-related acute stroke complications: A retrospective observational cohort study. \u003cem\u003eJ. stroke Cerebrovasc. diseases: official J. Natl. Stroke Association\u003c/em\u003e. \u003cb\u003e32\u003c/b\u003e (6), 107123 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBai, J. et al. Stroke-Associated Pneumonia and the Brain-Gut-Lung Axis: A Systematic Literature Review. \u003cem\u003eneurologist\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e (4), 237\u0026ndash;250 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUno, C. et al. Nutritional status change and activities of daily living in elderly pneumonia patients admitted to acute care hospital: A retrospective cohort study from the Japan Rehabilitation Nutrition Database. \u003cem\u003eNutrition\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 110613 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEckart, A. et al. Relationship of Nutritional Status, Inflammation, and Serum Albumin Levels During Acute Illness: A Prospective Study. \u003cem\u003eAm. J. Med.\u003c/em\u003e \u003cb\u003e133\u003c/b\u003e (6), 713\u0026ndash;722e7 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhan, S. R. et al. Serum Immunoglobulins, Pneumonia Risk, and Lung Function in Middle-Aged and Older Individuals: A Population-Based Cohort Study. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 868973 (2022).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Comparison of the baseline data between the stroke-associated pneumonia(SAP)and non-SAP groups\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003eTotal(n=317)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003eNon-SAP (n=231)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003eSAP(n=86)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003eStatistic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDemographic characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eAge (years), Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e64.9\u0026plusmn;11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e62.9\u0026plusmn;11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e70.3\u0026plusmn;10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-5.387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eGender, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e218 (68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e159 (68.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e59 (68.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e99 (31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e72 (31.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e27 (31.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eBMI(kg/m2), Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e23.8\u0026plusmn;3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e24.2\u0026plusmn;3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e22.8\u0026plusmn;3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e3.370\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eNIHSS score,median (Q\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e3(1-7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e3(1-4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e12.5(6.7-21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-10.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eHypertension, n (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e191(60.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e144(62.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e47(54.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eDiabetes,n(%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e80(25.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e62(26.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e18(20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e1.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.281\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eSmoking,n(%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e163(51.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e115(49.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e48(55.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.339\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eDrinking,n(%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e138(43.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e100(43.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e38(44.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eStroke type,n(%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e8.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eIschemic stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e275(86.8%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e208(90.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e67(77.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eHemorrhagic stroke\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e42(13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e23（10.0%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e19（22.1%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003e\u0026nbsp;MNA-SF score,median(Q\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e13(11-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e14(13-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e10(9-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-9.636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003e\u0026nbsp;MNA-SF score,n(%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e103.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eMalnourished (0\u0026ndash;7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e17(5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e4(1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e13(15.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eMalnutrition risk (8\u0026ndash;11)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e88(27.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e35(15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e53(61.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003eNormal (12-14)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e212(66.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e192(83.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e20(23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 359px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory data\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eNeutrophil(\u0026times;109 /L),median(Q\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e5.04(3.77-6.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e4.53(3.61-5.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e7.23(5.18-9.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-6.704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eLymphocyte(\u0026times;109 /L),median(Q\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e1.46(1.12-1.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e1.52(1.19-1.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.19(0.92-1.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-4.955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003ePlatelet(\u0026times;109 /L),median(Q\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e213(178-252)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e212(179-249)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e216(165-259)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.801\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eHemoglobin(g/L),median(Q\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e129(119-142)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e131(121-145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e126(110-136)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-3.625\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eCreatinine (umol/L),median(Q\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e76(64-88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e76(66-88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e75(62-89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eAlbumin(g/L),median(Q\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e38(36-41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e39(37-41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e37(34-40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-3.247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eGlobulin(g/L),median(Q\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e26(24-28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e26(23-27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e27(25-31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-3.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eA/G,median(Q\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e1.53(1.37-1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e1.53(1.44-1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e1.45(1.21-1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-3.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnthropometric parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eMid-upper arm circumference(cm),median(Q\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e26(25-28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e27(25-29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e25(24-26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-5.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eChest circumference(cm),median(Q\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e90(85-95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e91(85-97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e85(83-90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-5.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eWaist circumference(cm),Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e90.0\u0026plusmn;8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e91.2\u0026plusmn;8.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e87.1\u0026plusmn;8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e3.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 359px;\"\u003e\n \u003cp\u003eCalf circumferenc(cm),median(Q\u003csub\u003e25\u003c/sub\u003e\u0026ndash;Q\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 112px;\"\u003e\n \u003cp\u003e31(29-34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 166px;\"\u003e\n \u003cp\u003e32(30-34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 128px;\"\u003e\n \u003cp\u003e29(28-31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 86px;\"\u003e\n \u003cp\u003e-5.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSAP = stroke-associated pneumonia; BMI = body mass index; SD = standard deviation; NIHSS = National Institutes of Health Stroke Scale; MNA-SF = Mini Nutritional Assessment Short-Form; A/G =albumin to globulin ratio.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Multivariate logistic analysis for the association between\u0026nbsp;MNA-SF score\u0026nbsp;and stroke-associated pneumonia(SAP) risk\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 221px;\"\u003e\n \u003cp\u003eUnadjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 177px;\"\u003e\n \u003cp\u003eModel1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 173px;\"\u003e\n \u003cp\u003eModel2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" colspan=\"2\" style=\"width: 172px;\"\u003e\n \u003cp\u003eModel3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 118px;\"\u003e\n \u003cp\u003eOR(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;MNA-SF score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalnourished\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(0\u0026ndash;7)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003e31.20（9.28-104.79）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003e4.48（0.81-24.63）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 56px;\"\u003e\n \u003cp\u003e0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003e2.42（0.34-16.94）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 118px;\"\u003e\n \u003cp\u003e3.67（0.49-27.25）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMalnutrition risk (8\u0026ndash;11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003e14.53（7.75-27.23）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003e5.36（2.22-12.89）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003e3.57（1.34-9.47）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 118px;\"\u003e\n \u003cp\u003e4.72（1.61-13.85）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal (12-14)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 160px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 56px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 38px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 121px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 118px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMNA-SF,Mini Nutritional Assessment Short-Form;OR,odds ratio;95%CI, 95% confidence interval.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel1: adjusted for age, gender, BMI,hypertension, diabetes, smoking,drinking,stroke type and\u0026nbsp;NIHSS score.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eModel2: adjusted for covariates from Model 1 and further adjusted for Neutrophil, Lymphocyte, Platelet,Hemoglobin, Creatinine, Albumin, Globulin,\u0026nbsp;A/G\u0026nbsp;.\u003c/p\u003e\n\u003cp\u003eModel3:adjusted for covariates from Model2 and further adjusted for Mid-upper arm circumference,Chest circumference,Waist circumference,Calf circumferenc.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Results of ROC analysis about nutrition parameters with stroke-associated pneumonia(SAP) .\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 147px;\"\u003e\n \u003cp\u003eAUC (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003eCutoff\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 105px;\"\u003e\n \u003cp\u003eYouden index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 103px;\"\u003e\n \u003cp\u003eSensitivity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 98px;\"\u003e\n \u003cp\u003eSpecificity (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003ePPV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003eNPV (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMNA-SF score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 147px;\"\u003e\n \u003cp\u003e0.837（0.792-0.876）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.598\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e76.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e83.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e62.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e90.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlbumin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e0.618（0.562-0.672）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e47.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e75.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e41.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e79.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGlobulin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e0.638（0.583-0.691）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e48.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e78.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e46.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e80.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA/G\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e0.634（0.578-0.687）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e44.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e81.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e79.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHemoglobin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e0.632（0.577-0.686）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e54.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e66.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e37.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e79.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMid-upper arm circumference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 147px;\"\u003e\n \u003cp\u003e0.696（0.642-0.746）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e75.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e57.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 68px;\"\u003e\n \u003cp\u003e39.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e86.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003echest circumference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e0.685（0.631-0.736）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.392\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e60.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e78.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e51.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e84.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ewaist circumference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e0.638（0.583-0.691）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.277\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e66.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e61.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e39.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e83.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" style=\"width: 206px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCalf circumferenc\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 147px;\"\u003e\n \u003cp\u003e0.685（0.631-0.736）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 64px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 105px;\"\u003e\n \u003cp\u003e0.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 103px;\"\u003e\n \u003cp\u003e77.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e52.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 68px;\"\u003e\n \u003cp\u003e38.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e86.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eROC, Receiver operating characteristics;\u0026nbsp;AUC, area under the ROC curve; PPV, positive predicted value;NPV,negative predicted value; MNA-SF,\u003c/p\u003e\n\u003cp\u003eMini Nutritional Assessment Short-Form; A/G,albumin to globulin ratio\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"stroke-associated pneumonia, Mini Nutritional Assessment Short Form, malnutrition, logistic regression, ROC analysis","lastPublishedDoi":"10.21203/rs.3.rs-8556078/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8556078/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eMalnutrition 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.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn 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.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSAP occurred in 86 patients (27.1%). Patients at risk of malnutrition (MNA-SF score 8\u0026ndash;11) had a significantly higher risk of SAP compared with those with normal nutritional status (adjusted OR up to 14.53, 95% CI 7.75\u0026ndash;27.23), 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\u0026ndash;0.88), outperforming blood-based nutritional biomarkers and anthropometric measures. The negative predictive value was 90.6%.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eLower 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.\u003c/p\u003e","manuscriptTitle":"Clinical utility of Mini Nutritional Assessment Short Form (MNA-SF) in predicting stroke-associated pneumonia in acute stroke patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 16:41:57","doi":"10.21203/rs.3.rs-8556078/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-09T06:39:53+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-09T00:02:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"41367681769450409597448498903327611209","date":"2026-04-08T16:48:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17690031649440086253824394969965191420","date":"2026-04-07T06:57:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"294872233367969691703285089930401894692","date":"2026-04-06T21:11:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"322633276643805219059499431292367948656","date":"2026-04-06T19:17:44+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-06T11:01:18+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"115656660601322963498832649970105740282","date":"2026-04-06T03:58:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"22908507330479161755219907332205550865","date":"2026-04-05T23:32:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"79365853076658312468515950844654931809","date":"2026-04-05T14:46:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"309859638837835759265402111088188354501","date":"2026-04-04T15:26:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"45278725740760669624555417303542053568","date":"2026-04-04T02:19:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-03T13:10:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-13T14:23:06+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-10T02:41:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-10T02:41:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-09T02:36:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"40cf5dfa-a1d4-4eea-adef-ea8aa913c6d1","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":65972940,"name":"Health sciences/Biomarkers"},{"id":65972941,"name":"Health sciences/Diseases"},{"id":65972942,"name":"Health sciences/Health care"},{"id":65972943,"name":"Health sciences/Medical research"},{"id":65972944,"name":"Health sciences/Neurology"},{"id":65972945,"name":"Biological sciences/Neuroscience"},{"id":65972946,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-22T08:56:47+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 16:41:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8556078","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8556078","identity":"rs-8556078","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}