{"paper_id":"36bafff7-c01b-4c8b-bcc3-6432c3f6cdf4","body_text":"Prognostic Nutritional Index as a novel biomarker for predicting prognosis in sepsis-associated encephalopathy: A multicenter retrospective cohort study | 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 Research Article Prognostic Nutritional Index as a novel biomarker for predicting prognosis in sepsis-associated encephalopathy: A multicenter retrospective cohort study Lina Zhao, Chao Qi, Qinghe Yan, Yuehao Shen, Dongxue Huang, Haiying Liu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8037637/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Sepsis-associated encephalopathy (SAE) still has a high mortality rate, and there is a lack of effective biomarkers to assess the prognosis of SAE. This study aims to explore the relationship between prognostic nutritional index (PNI) and the prognosis of patients with SAE. Methods This study is a multicenter cohort study, data from 2008–2019. The primary outcome was 28-day all-cause mortality in the SAE population. To explore the prognostic relationship between PNI and SAE patients, the multivariable Logistic regression, propensity score matching, inverse probability weighting were conducted to adjust confounders. In this study, the generalized additive model (GAM), Kaplan-Meier curve, receiver operating characteristic curve (ROC) curve and other methods were used to analyze the relationship between PNI and the 28-day mortality rate of SAE patients. The results of this study were validated by external data. Results Among 3,202 SAE patients, multivariable analysis identified PNI as an independent predictor of 28-day mortality (OR: 0.85, 95% CI: 0.77–0.93) of original cohort. GAM of original cohort showed that a PNI of 34 was the optimal prognostic threshold for SAE patients. The Kaplan-Meier curves of both the original cohort and the external validation cohort showed that the 28-day mortality rate of SAE patients with PNI lower than 34 was significantly lower than that of patients with PIN higher than 34 ( P < 0.001). ROC analysis showed superior predictive performance in original cohort (AUC: 0.879; sensitivity: 0.878; specificity: 0.880) versus external validation cohort (AUC: 0.724; sensitivity: 0.878; specificity: 0.569). Stratified analysis of the results of the study showed that elevated PNI correlated with higher Glasgow Coma Scale scores ( P < 0.001). Conclusions This large-scale multicenter study establishes the PNI as an independent predictor of 28-day mortality in patients with sepsis-associated encephalopathy (SAE). We identified that SAE patients with PNI < 34 exhibited significantly higher 28-day mortality rates and worse neurological function. Prognostic Nutritional Index sepsis-associated encephalopathy 28-day mortality Glasgow Coma Scale Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction SAE is clinically defined as diffuse cerebral dysfunction secondary to systemic inflammatory responses in sepsis patients, in the absence of direct central nervous system (CNS) infection 1 . Previous studies have shown that the incidence of SAE is as high as 50–70% 2,3 . Critically, progression to SAE elevates sepsis mortality rates to approximately 50%, a marked increase compared to septic patients without encephalopathy 2 , 3 . Currently, there is still a lack of effective indicators biomarkers to evaluate the prognosis of patients with SAE. Therefore, it is particularly important to explore the early evaluation of the prognosis of SAE patients and give effective interventions to reduce the mortality rate of SAE patients. The PNI, calculated as serum albumin (g/dL) × 10 + total lymphocyte count (× 10⁹/L) × 5, integrates two pivotal pathophysiological pathways in SAE: hypoalbuminemia reflects persistent systemic inflammation and blood-brain barrier disruption, while lymphopenia indicates immunosuppression and impaired microbial clearance 4 – 6 . Serum albumin, an endogenous neuroprotective agent, binds endotoxins and free radicals that contribute to neuroinflammation, lower serum albumin levels were independently associated with increased 90-day mortality in sepsis-associated encephalopathy patients 7 .In patients with delirium, higher albumin levels were associated with shorter hospital stays 8 . Concurrently, lymphocyte depletion correlates with secondary infections and failure to resolve systemic inflammation, both known drivers of SAE progression 9 , 10 . This dual biomarker synergy explains PNI's may be superior prognostic performance over isolated measures. We hypothesize that the PNI demonstrates superior prognostic accuracy compared to the conventional SOFA or SAPS II score in predicting prognosis of SAE patients. In this multicenter study, we evaluate the prognostic value of the PNI for 28-day mortality in patients with SAE, and independent validation cohorts validate the results of the exploration. 2 Materials and methods 2.1 Study Settings This research leveraged the open-source medical information from the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.0) database and multicenter database electronic Intensive Care Unit Collaborative Research Database (eICU-CRD v2.0). MIMIC-IV is a comprehensive repository that encompasses patient data from Beth Israel Deaconess Medical Center, spanning the years 2008 to 2019 11 . The eICU-CRD aggregates ICU admission data from 208 U.S. hospitals during 2014-2015, representing a large-scale multicenter cohort of critically ill patients. The study protocol was reviewed and approved by the Institutional Review Board (IR No. 33690380), with all researchers completing mandatory human subjects protection training through the Collaborative Institutional Training Initiative program. The above databases have been approved by the Massachusetts Institute of Technology Review Committee. The raw data were extracted by employing structure query language (SQL) with Navicat and further processed using the R software. 2.2 Patients All enrolled patients met the Sepsis-3 diagnostic criteria 12 . SAE was defined by GCS < 15 or delirium at the presence of sepsis 13-16 . For patients undergoing sedation or surgery, GCS scores before sedation or surgery were extracted. We excluded patients with: (1) primary neurological conditions that could independently impair consciousness (traumatic brain injury, acute ischemic/hemorrhagic stroke, active epilepsy, or intracranial infection); (2) pre-existing severe organ dysfunction (Child-Pugh class C liver cirrhosis or end-stage renal disease requiring dialysis); (3) acute life-threatening comorbidities (post-cardiac arrest status); (4) substance abuse disorders (chronic alcoholism or illicit drug use documented in medical records); (5) severe metabolic derangements (hyponatremia < 120 mmol/L, persistent hyperglycemia > 180 mg/dL, or hypoglycemia < 54 mg/dL despite correction); (6) insufficient observation time (ICU death/discharge within 24 hours of admission); (7) incomplete neurological assessments (missing GCS documentation); (8) unavailable nutritional-inflammatory data (missing albumin or lymphocyte counts for PNI calculation); and (9) age < 18 years. 2.3 Data Collection We collected comprehensive clinical data, including: (1) demographic characteristics (age and sex); (2) 28-day mortality outcomes; (3) comorbidities classified using the International Classification of Diseases, Ninth Revision (ICD-9) criteria; (4) mean vital sign measurements prior to SAE diagnosis; (5) mean laboratory measurements prior to SAE diagnosis, first available laboratory results following ICU admission; and (6) infection sites and causative microorganisms. Disease severity was assessed using standardized scoring systems recorded within 24 hours of ICU admission: the Simplified Acute Physiology Score II (SAPS II), Logistic Organ Dysfunction System (LODS), Systemic Inflammatory Response Syndrome (SIRS) criteria, Sequential Organ Failure Assessment (SOFA) score, Charlson Comorbidity Index (CCI), and Glasgow Coma Scale (GCS). 2.4 Statistical Analysis Continuous variables were expressed as mean ± standard deviation or median (interquartile range), and categorical variables as frequencies (percentages). Multivariable Logistic regression analysis was employed to evaluate independent predictors of 28-day mortality in SAE patients, adjusting for potential confounding variables. A GAM was applied to explore linear associations and identify optimal thresholds. KM survival curves were generated to compare 28-day survival probabilities between PNI-stratified groups in both MIMIC and eICU cohorts. ROC curves were constructed to assess the predictive performance of PNI for mortality. Boxplot analysis was conducted to examine the relationship between PNI values and GCS scores. All statistical analyses were performed using R software, with P < 0.05 considered statistically significant. 3 Results 3.1 Baseline characteristics A total of 3,202 patients with SAE were included based on predefined criteria (Supplementary Figure 1). Participants were stratified into non-survivors ( n = 1,062) and survivors ( n = 2,140) according to 28-day mortality. Non-survivors were significantly older and demonstrated profoundly impaired nutritional-inmunological status, characterized by substantially lower serum albumin, reduced lymphocyte counts, and critically depressed PNI levels. Furthermore, non-survivors exhibited exacerbated organ dysfunction and higher disease severity, evidenced by markedly elevated SOFA, SAPS II, and LODS scores compared to survivors. All baseline variables are presented in Table 1. 3.2 Association between PNI and 28-day mortality in SAE The GAM demonstrated a significant linear relationship between PNI and 28-day mortality in SAE (Figure 1). A knot value of about 34 was identified, with mortality risk decreasing significantly when PNI exceeded this threshold (Figure 1). Multivariate Logistic regression analysis demonstrated that higher PNI was significantly associated with reduced 28-day mortality in SAE patients (adjusted OR: 0.85, 95% CI: 0.77-0.93, P < 0.001). Consistent findings were observed across alternative analytical approaches: propensity score-matched analysis (OR: 0.87, 95% CI: 0.81-0.93, P < 0.001), IPW (OR: 0.82, 95% CI: 0.78-0.86, P < 0.001), and doubly robust estimation (OR: 0.81, 95% CI: 0.76-0.86, P < 0.001) (Table 2). 3.3 Predictive performance of prognostic models for 28-day mortality in SAE Kaplan-Meier survival analysis demonstrated significantly prolonged 28-day survival in SAE patients with PNI > 34 compared to those with PNI ≤ 34 in both MIMIC ( P < 0.001) and EICU ( P < 0.001) cohorts (Figure 2). Receiver operating characteristic (ROC) curve analysis demonstrated that the PNI exhibited excellent discriminative ability for predicting 28-day mortality in SAE patients, with area under the curve (AUC) values of 0.879 (95% CI: 0.867-0.891) in the MIMIC IV database and 0.724 (95% CI: 0.707-0.741) in the eICU database (Figure 3). Model 4 (PNI + SOFA + SAPS II) showed superior predictive performance, with AUCs of 0.907 (MIMIC) and 0.766 (EICU). 3.4 The relationship between PNI and GCS score Boxplot analysis revealed a significant inverse correlation between PNI levels and GCS severity categories ( GCS grade 1： GCS score 3-8； GCS grade 2： GCS score 9-12; GCS grade 3：GCS score 13-14) in both the MIMIC-IV database ( P < 0.001) and eICU database ( P < 0.001) cohorts (Figure 4). The PNI values progressively decreased with increasing GCS severity: 38.2 (34.5-42.1) in grade 1 versus 45.6 (41.2-49.8) in grade 3 ( P < 0.001). 4 Discussion Our findings demonstrate that SAE patients had a 28-day mortality rate of 33.2%. The PNI as a significant predictor of 28-day mortality of SAE superior than SOFA score and SAPS II score, with PNI levels below 34 showing a linear correlation with increased mortality risk. Stratified analysis show that progressively lower PNI levels were associated with worsening degrees of consciousness impairment. The multicenter analysis demonstrates unabated high 28-day mortality rates among SAE patients, consistent with established clinical evidence 2,3 . Despite substantial research efforts, the current lack of sensitive prognostic biomarkers impedes early risk stratification and targeted interventions for SAE. Our findings reinforce the critical need to identify reliable indicators that can dynamically predict outcomes and guide therapeutic escalation in this vulnerable population. We established the PNI as a novel independent predictor of SAE mortality, outperforming conventional severity scores (SOFA/SAPS II) in both discrimination and clinical utility. The optimal threshold (PNI = 34) robustly stratified mortality risk across cohorts. The mechanisms underlying PNI's predictive value in SAE likely involve dual pathways: (1) Albumin were associated with SAE and were supported by medium- to high-quality evidence 6 . Use of albumin decreased the risk of sepsis-associated delirium 5 . Hypoalbuminemia exacerbates blood-brain barrier disruption via oxidative stress and endothelial dysfunction (2) Lymphocytopenia reflects impaired immunomodulation, worsening neuroinflammation. The lymphocyte population comprises three principal immunophenotypically distinct subsets: T lymphocytes (T cells), B lymphocytes (B cells), and natural killer cells (NK cells). T cells constitute a major subset of lymphocytes, representing one of the principal cellular components within the lymphoid lineage. V-domain immunoglobulin suppressor of T cell activation (VISTA) has emerged as a crucial player in the pathogenesis of neurological disorders 17 . CD86 in CD3 + CD56 + natural killer T (NKT) cells is an independent risk factor of SAE 18 . Mechanistically, PNI integrates hypoalbuminemia-driven blood-brain barrier disruption and lymphopenia-mediated neuroinflammation – core pathways in SAE pathogenesis 19-23 . We therefore recommend serial PNI monitoring in SAE management, with prompt nutritional-immunomodulatory therapy when PNI falls below 34. Stratified analysis demonstrated a significant correlation between higher PNI and improved GCS scores indicating PNI's capacity to reflect SAE-induced consciousness impairment severity. This association may be attributed to the potential roles of albumin in attenuating oxidative neuronal injury and lymphocytes in controlling CNS infections, with both processes possibly influencing neurological recovery pathways. Clinicians should utilize PNI trends to anticipate neurological trajectory and personalize neuroprotective strategies (e.g., antioxidant supplementation, infection source control) in comatose SAE patients. Limitations Several study limitations warrant acknowledgment. First, the retrospective design inherently carries potential selection bias, notwithstanding comprehensive statistical adjustment. Second, while validated across two independent databases, the generalizability of the established PNI threshold (34) requires prospective verification. Third, residual confounding from unmeasured variables (e.g., nutritional supplementation regimens, underlying comorbidities) may potentially influence clinical outcomes. Finally, the pathophysiological mechanisms mediating the observed PNI-GCS association remain hypothetical and necessitate further mechanistic investigation. Conclusions This multicenter study identified the PNI as a robust and independent predictor of 28-day mortality in patients with SAE, demonstrating an optimal prognostic threshold of 34. The consistent predictive performance across diverse analytical methodologies and independent validation cohorts substantiates its clinical utility for risk stratification in SAE management. Abbreviations CCI: Charlson Comorbidity Index CI: confidence interval eICU-CRD: electronic Intensive Care Unit Collaborative Research Database GAM: generalized additive model GCS: Glasgow Coma Scale INR: international normalized ratio IPW: inverse probability weighting KM: Kaplan-Meier LODS: Logistic Organ Dysfunction System MIMIC-IV: Medical Information Mart for Intensive Care IV OR: odds ratio PNI: Prognostic Nutritional Index PSM: propensity score matching PT: prothrombin time PTT: partial thromboplastin time ROC: receiver operating characteristic SAE: sepsis-associated encephalopathy SAPS II: Simplified Acute Physiology Score II SIRS: Systemic Inflammatory Response Syndrome SMD: standardized mean differences SOFA: Sequential Organ Failure Assessment SQL: Structured Query Language Declarations Ethics approval and consent to participate : The use of the MIMIC-IV and eICU-CRD databases was approved by the institutional review boards (IRBs) of the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC). All methods were carried out in accordance with the Declaration of Helsinki and relevant institutional guidelines and regulations. Informed consent was waived by the IRBs of MIT and BIDMC because the study did not impact clinical care, and all protected health information was de-identified. Clinical Trial: Not applicable Consent for Publication : The authors confirm that they have reviewed and approved the final version of the manuscript and consent to its publication in BMC Neurolpgy. Availability of data and materials: If the reason is reasonable, the original data can be requested from the corresponding author. Conflict of Interest: The authors declare no competing interests. Funding: This work was supported by Joint Funds of the Natural Science Foundation of Tianjin (No. 25JCLMJC00350). Author Contributions Dr LNZ had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Concept and design: LNZ, CQ, KLX. Acquisition, analysis, or interpretation of data: All authors. Drafting of the manuscript: LNZ, CQ, KLX. Critical review of the manuscript for important intellectual content: All authors. Data collection and statistical analysis: CQ, YHS, HYL, XGL, DXH, QHY. Obtained funding: LNZ. Administrative, technical, or material support: YL, YHS, HYL, XGL, DXH, QHY. Supervision: YL, KLX. Acknowledgments Not applicable References Song Z, Chen H, Xu W, et al. The hexapeptide functionalized gold nanoparticles protect against sepsis-associated encephalopathy by forming specific protein corona and regulating macrophage activation. Mater Today Bio. 2025;32:101704. Chen L, Luo S, Liu T, et al. Growth differentiation factor 15 aggravates sepsis-induced cognitive and memory impairments by promoting microglial inflammatory responses and phagocytosis. J Neuroinflammation. 2025;22(1):44. Zeng QQ, Wang J, Yue RC, et al. 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Tables Table 1 Baseline characteristics and outcomes of s epsis-associated encephalopathy patients Original cohort Match cohort Survival group ( n = 2140) Non-survival group ( n = 1062) P Survival group ( n = 965) Non-survival group ( n = 965) P Baseline variables Age (years) (median[IQR]) 71.00 [59.00,81.00] 68.00 [59.00, 77.00] < 0.001 69.00 [58.00, 78.00] 68.00 [60.00, 78.00] 0.885 Gender,M (%) 1205 (56.3) 681 ( 64.1) < 0.001 594 (61.6) 611 (63.3) 0.452 Laboratory parameters (median [IQR]) Albumin (g/dL) 3.50 [2.90,3.90] 1.60 [1.10,2.10] < 0.001 3.50 [2.90,4.00] 1.60 [1.10, 2.10] < 0.001 Lymphocyte (× 10⁹/L) 1.85 [1.42, 2.42] 1.13 [0.73, 1.66] < 0.001 1.79 [1.38, 2.42] 1.13 [0.73, 1.64] < 0.001 PNI 44.15 [38.60,49.56] 22.43 [17.85, 28.89] < 0.001 44.75 [38.70, 50.42] 22.40 [17.85,29.40] < 0.001 Critical illness score (median [IQR]) CCI 5.00 [3.00, 7.00] 5.00 [3.00, 6.00] 0.055 5.00 [3.00,7.00] 5.00 [3.00, 6.00] 0.055 GCS 8.00 [6.00, 9.00] 3.00 [3.00,3.00] < 0.001 8.00 [6.00, 9.00] 3.00 [3.00, 3.00] < 0.001 SOFA 6.00 [4.00, 8.00] 7.00 [6.00, 9.00] < 0.001 6.00 [4.00, 8.00] 7.00 [6.00, 9.00] < 0.001 SAPS II 41.00 [32.75, 51.00] 54.00 [40.00, 63.00] < 0.001 42.00 [33.00, 51.00] 54.00 [40.00, 63.00] < 0.001 LODS 5.00 [3.00, 8.00] 8.00 [6.00, 9.00] < 0.001 6.00 [4.00, 8.00] 7.00 [6.00, 9.00] < 0.001 SIRS 3.00 [2.00, 3.00] 3.00 [2.00, 3.00] 0.005 3.00 [2.00, 3.00] 3.00 [2.00, 3.00] 0.121 Clinical outcome Ventdurations (n(%)) 1379 (64.4) 924 ( 87.0) < 0.001 839 (86.9) 829 ( 85.9) 0.550 Vasopressin (n(%)) 214 (10.0) 172 ( 16.2) < 0.001 153 (15.9) 151 ( 15.6) 0.950 Length of stay (median [IQR]) 3.90 [1.70, 9.20] 5.30 [2.30, 10.10] < 0.001 3.80 [1.50, 10.30] 5.20 [2.30, 10.20] < 0.001 PNI = 10 × Albumin (g/dL) + 5 × Lymphocyte (× 10⁹/L); CCI: Charlson Comorbidity Index; GCS: Glasgow Coma Scale; SOFA: Sequential Organ Failure Assessment; SAPS II: Simplified Acute Physiology Score II; LODS: Logistic Organ Dysfunction System; SIRS: Systemic Inflammatory Response Syndrome Table 2 Association between Prognostic Nutritional Index and 28-day mortality in sepsis-associated encephalopathy Models OR CI P 2.5% 97.5% Multivariate Logistic analysis* 0.85 0.77 0.80 < 0.001 Propensity score matching* 0.77 0.81 0.88 < 0.001 Inverse probability weighting* 0.80 0.78 0.81 < 0.001 Doubly robust with all coariates# 0.10 exp(coef) [confint] p 0.09 exp(coef) [confint] p 0.12 exp(coef) [confint] p < 0.001 exp(coef) [confint] p *Analysis was conducted using the continuous variable of PNI# The doubly robust method requires converting the continuous PNI into a binary categorical variable using a predefined cut-off value for analysis; OR: odds ratio; CI: confidence interval; P < 0.05, statistically significant. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.doc Supplementary Material eTable 1 Baseline characteristics of sepsis-associated encephalopathy patients eTable 2 Multivariable Logistic analysis of factors associated with 28-day mortality in patients with sepsis-associated encephalopathy eFigure 1 Flow chart for patient selection. MIMIC-IV: Medical Information Mart for Intensive Care IV; eICU-CRD: electronic Intensive Care Unit Collaborative Research Database eFigure 2 The SMD of propensity-matched. SMD: standardized mean differences eFigure 3 Forest plot of multivariable Logistic regression analysis influencing the primary outcomes of SAE patients. The OR point estimates for each predictor variable are represented by red dots. Horizontal lines denote the corresponding 95% CI. SAE: sepsis-associated encephalopathy; OR: odds ratio; CI: confidence interval. 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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-8037637\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":550659284,\"identity\":\"0542c894-8494-4176-a65d-0b7a4ebbb873\",\"order_by\":0,\"name\":\"Lina Zhao\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tianjin Medical University General Hospital\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Lina\",\"middleName\":\"\",\"lastName\":\"Zhao\",\"suffix\":\"\"},{\"id\":550659285,\"identity\":\"e86c396d-e47d-4cae-bcd6-138b0703630c\",\"order_by\":1,\"name\":\"Chao 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13:42:19\",\"extension\":\"html\",\"order_by\":19,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":91764,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8037637/v1/a3c61e135775eeb3b41344a0.html\"},{\"id\":97366740,\"identity\":\"4960d1c5-a04b-4efd-a604-ab59758de3f9\",\"added_by\":\"auto\",\"created_at\":\"2025-12-03 16:04:58\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":705934,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eLinear association between PNI and 28-day mortality in SAE. SAE: sepsis-associated encephalopathy\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8037637/v1/cc4e01f29d9c397dceb7ed6c.png\"},{\"id\":97367758,\"identity\":\"5e55611e-5c9f-4ee3-8bed-4b5dcecec7aa\",\"added_by\":\"auto\",\"created_at\":\"2025-12-03 16:20:35\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":947082,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eKM survival curves of 28-day mortality in SAE patients and PNI\\u003c/p\\u003e\\n\\u003cp\\u003elevels. Patients were stratified into two groups based on the PNI, GROUP1:\\u003c/p\\u003e\\n\\u003cp\\u003ePNI ≤ 34; GROUP2: PNI \\u0026gt; 34. Figure 2A: Analysis of the MIMIC-IV\\u003c/p\\u003e\\n\\u003cp\\u003ecohort; Figure 2B: Analysis of the eICU-CRD cohort. KM: Kaplan-Meier; SAE: sepsis-associated encephalopathy; PNI: Prognostic Nutritional Index; MIMIC-IV: Medical Information Mart for Intensive Care IV; eICU-CRD: electronic\\u003c/p\\u003e\\n\\u003cp\\u003eIntensive Care Unit Collaborative Research Database\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8037637/v1/a52d11211284a8eb97784bfa.png\"},{\"id\":97366913,\"identity\":\"b53abccc-516a-4d72-8300-f6b9abd5d8dd\",\"added_by\":\"auto\",\"created_at\":\"2025-12-03 16:13:24\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":2168275,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003ePredictive performance of prognostic models for 28-day mortality in SAE. Model 1 = PNI; Model 2 = SOFA; Model 3 = SAPS II; Model 4 =\\u003c/p\\u003e\\n\\u003cp\\u003ePNI + SOFA + SAPS II. Figure 3A: ROC curves for predicting 28-day mortality in SAE of MIMIC-IV cohort; Figure 3B: ROC curves for predicting 28-day mortality in SAE of eICU-CRD cohort; Figure 3C: Comparison of predictive\\u003c/p\\u003e\\n\\u003cp\\u003eperformance for 28-day mortality in SAE of MIMIC-IV cohort; Figure 3D:\\u003c/p\\u003e\\n\\u003cp\\u003eComparison of predictive performance for 28-day mortality in SAE of\\u003c/p\\u003e\\n\\u003cp\\u003eeICU-CRD cohort. SAE: sepsis-associated encephalopathy; PNI: Prognostic\\u003c/p\\u003e\\n\\u003cp\\u003eNutritional Index; SOFA: Sequential Organ Failure Assessment; SAPS II:\\u003c/p\\u003e\\n\\u003cp\\u003eSimplified Acute Physiology Score; ROC: receiver operating characteristic;\\u003c/p\\u003e\\n\\u003cp\\u003eMIMIC-IV: Medical Information Mart for Intensive Care IV; eICU-CRD:\\u003c/p\\u003e\\n\\u003cp\\u003eelectronic Intensive Care Unit Collaborative Research Database.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8037637/v1/3760949397f1cbf15973915d.png\"},{\"id\":97257848,\"identity\":\"ce63a869-9ab1-4687-8a08-a37ccb272b34\",\"added_by\":\"auto\",\"created_at\":\"2025-12-02 13:42:19\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":1190053,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eThe relationship between PNI and GCS grade. GCS grade 1： GCS score 3-8； GCS grade 2： GCS score 9-12; GCS grade 3： GCS score 13-14. Figure 4A: Analysis of the MIMIC-IV cohort; Figure 4B: Analysis of the\\u003c/p\\u003e\\n\\u003cp\\u003eeICU-CRD cohort.\\u003c/p\\u003e\\n\\u003cp\\u003ePNI: Prognostic Nutritional Index; GCS: Glasgow Coma Scale; MIMIC-IV: Medical Information Mart for Intensive Care IV; eICU-CRD: electronic Intensive Care Unit Collaborative Research Database.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"Figure4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8037637/v1/0d97317360aa7970d0e35210.png\"},{\"id\":101849062,\"identity\":\"1e4edece-43fc-4900-a6ce-6f2076aa794c\",\"added_by\":\"auto\",\"created_at\":\"2026-02-04 09:43:28\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":5759516,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8037637/v1/0af7e5dd-9492-46e4-8dab-2dd089a3e55d.pdf\"},{\"id\":97257866,\"identity\":\"5477a1f6-8e96-4fc5-8992-8eff192eae43\",\"added_by\":\"auto\",\"created_at\":\"2025-12-02 13:42:21\",\"extension\":\"doc\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":77905920,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSupplementary Material\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eeTable 1\\u003c/strong\\u003e Baseline characteristics of sepsis-associated encephalopathy patients\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eeTable 2\\u003c/strong\\u003e Multivariable Logistic analysis of factors associated with 28-day mortality in patients with sepsis-associated encephalopathy\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eeFigure 1\\u003c/strong\\u003e Flow chart for patient selection. MIMIC-IV: Medical Information Mart for Intensive Care IV; eICU-CRD: electronic Intensive Care Unit\\u003c/p\\u003e\\n\\u003cp\\u003eCollaborative Research Database\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eeFigure 2\\u003c/strong\\u003e The SMD of propensity-matched. SMD: standardized mean differences\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eeFigure 3\\u003c/strong\\u003e Forest plot of multivariable Logistic regression analysis influencing the primary outcomes of SAE patients. The OR point estimates for each predictor variable are represented by red dots. Horizontal lines denote the corresponding 95% CI. SAE: sepsis-associated encephalopathy; OR: odds ratio; CI: confidence interval.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"SupplementaryMaterial.doc\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8037637/v1/1f4fe17b9b4c33993d4d467d.doc\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"\\u003cp\\u003ePrognostic Nutritional Index as a novel biomarker for predicting prognosis in sepsis-associated encephalopathy: A multicenter retrospective cohort study\\u003c/p\\u003e\",\"fulltext\":[{\"header\":\"1 Introduction\",\"content\":\"\\u003cp\\u003eSAE is clinically defined as diffuse cerebral dysfunction secondary to systemic inflammatory responses in sepsis patients, in the absence of direct central nervous system (CNS) infection\\u003csup\\u003e\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e. Previous studies have shown that the incidence of SAE is as high as 50\\u0026ndash;70%\\u003csup\\u003e2,3\\u003c/sup\\u003e. Critically, progression to SAE elevates sepsis mortality rates to approximately 50%, a marked increase compared to septic patients without encephalopathy\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e. Currently, there is still a lack of effective indicators biomarkers to evaluate the prognosis of patients with SAE. Therefore, it is particularly important to explore the early evaluation of the prognosis of SAE patients and give effective interventions to reduce the mortality rate of SAE patients.\\u003c/p\\u003e\\u003cp\\u003eThe PNI, calculated as serum albumin (g/dL) \\u0026times; 10\\u0026thinsp;+\\u0026thinsp;total lymphocyte count (\\u0026times; 10⁹/L) \\u0026times; 5, integrates two pivotal pathophysiological pathways in SAE: hypoalbuminemia reflects persistent systemic inflammation and blood-brain barrier disruption, while lymphopenia indicates immunosuppression and impaired microbial clearance\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR5\\\" citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e\\u003c/sup\\u003e. Serum albumin, an endogenous neuroprotective agent, binds endotoxins and free radicals that contribute to neuroinflammation, lower serum albumin levels were independently associated with increased 90-day mortality in sepsis-associated encephalopathy patients\\u003csup\\u003e\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u003c/sup\\u003e.In patients with delirium, higher albumin levels were associated with shorter hospital stays\\u003csup\\u003e\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e\\u003c/sup\\u003e. Concurrently, lymphocyte depletion correlates with secondary infections and failure to resolve systemic inflammation, both known drivers of SAE progression\\u003csup\\u003e\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e\\u003cp\\u003eThis dual biomarker synergy explains PNI's may be superior prognostic performance over isolated measures. We hypothesize that the PNI demonstrates superior prognostic accuracy compared to the conventional SOFA or SAPS II score in predicting prognosis of SAE patients. In this multicenter study, we evaluate the prognostic value of the PNI for 28-day mortality in patients with SAE, and independent validation cohorts validate the results of the exploration.\\u003c/p\\u003e\"},{\"header\":\"2 Materials and methods\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e2.1 Study Settings\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThis research leveraged the open-source medical information from the Medical Information Mart for Intensive Care IV (MIMIC-IV 2.0) database and multicenter database electronic Intensive Care Unit Collaborative Research Database\\u0026nbsp;(eICU-CRD v2.0). MIMIC-IV is a comprehensive repository that encompasses patient data from Beth Israel Deaconess Medical Center, spanning the years 2008 to 2019\\u003csup\\u003e11\\u003c/sup\\u003e. The eICU-CRD aggregates ICU admission data from 208 U.S. hospitals during 2014-2015, representing a large-scale multicenter cohort of critically ill patients. The study protocol was reviewed and approved by the Institutional Review Board (IR No. 33690380), with all researchers completing mandatory human subjects protection training through the Collaborative Institutional Training Initiative program. The above databases have been approved by the Massachusetts Institute of Technology Review Committee. The raw data were extracted by employing structure query language (SQL) with Navicat and further processed using the R software.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.2 Patients\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eAll enrolled patients met the Sepsis-3 diagnostic criteria\\u003csup\\u003e12\\u003c/sup\\u003e. SAE was defined by GCS \\u0026lt; 15 or delirium at the presence of sepsis\\u003csup\\u003e13-16\\u003c/sup\\u003e. For patients undergoing sedation or surgery, GCS scores before sedation or surgery were extracted. We excluded patients with: (1) primary neurological conditions that could independently impair consciousness (traumatic brain injury, acute ischemic/hemorrhagic stroke, active epilepsy, or intracranial infection); (2) pre-existing severe organ dysfunction (Child-Pugh class C liver cirrhosis or end-stage renal disease requiring dialysis); (3) acute life-threatening comorbidities (post-cardiac arrest status); (4) substance abuse disorders (chronic alcoholism or illicit drug use documented in medical records); (5) severe metabolic derangements (hyponatremia \\u0026lt; 120 mmol/L, persistent hyperglycemia \\u0026gt; 180 mg/dL, or hypoglycemia \\u0026lt; 54 mg/dL despite correction); (6) insufficient observation time (ICU death/discharge within 24 hours of admission); (7) incomplete neurological assessments (missing GCS documentation); (8) unavailable nutritional-inflammatory data (missing albumin or lymphocyte counts for PNI calculation); and (9) age \\u0026lt; 18 years.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.3 Data Collection\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eWe collected comprehensive clinical data, including: (1) demographic characteristics (age and sex); (2) 28-day mortality outcomes; (3) comorbidities classified using the International Classification of Diseases, Ninth Revision (ICD-9) criteria; (4) mean vital sign measurements prior to SAE diagnosis; (5) mean laboratory measurements prior to SAE diagnosis, first available laboratory results following ICU admission; and (6) infection sites and causative microorganisms. Disease severity was assessed using standardized scoring systems recorded within 24 hours of ICU admission: the Simplified Acute Physiology Score II (SAPS II), Logistic Organ Dysfunction System (LODS), Systemic Inflammatory Response Syndrome (SIRS) criteria, Sequential Organ Failure Assessment (SOFA) score, Charlson Comorbidity Index (CCI), and Glasgow Coma Scale (GCS).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e2.4 Statistical Analysis\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eContinuous variables were expressed as mean \\u0026plusmn; standard deviation or median (interquartile range), and categorical variables as frequencies (percentages). Multivariable Logistic regression analysis was employed to evaluate independent predictors of 28-day mortality in SAE patients, adjusting for potential confounding variables. A GAM was applied to explore linear associations and identify optimal thresholds. KM survival curves were generated to compare 28-day survival probabilities between PNI-stratified groups in both MIMIC and eICU cohorts. ROC curves were constructed to assess the predictive performance of PNI for mortality. Boxplot analysis was conducted to examine the relationship between PNI values and GCS scores. All statistical analyses were performed using R software, with \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.05 considered statistically significant.\\u003c/p\\u003e\"},{\"header\":\"3 Results\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003e3.1 Baseline characteristics\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eA total of 3,202 patients with SAE were included based on predefined criteria (Supplementary Figure 1). Participants were stratified into non-survivors (\\u003cem\\u003en\\u0026nbsp;\\u003c/em\\u003e= 1,062) and survivors (\\u003cem\\u003en\\u0026nbsp;\\u003c/em\\u003e= 2,140) according to 28-day mortality. Non-survivors were significantly older and demonstrated profoundly impaired nutritional-inmunological status, characterized by substantially lower serum albumin, reduced lymphocyte counts, and critically depressed PNI levels. Furthermore, non-survivors exhibited exacerbated organ dysfunction and higher disease severity, evidenced by markedly elevated SOFA, SAPS II, and LODS scores compared to survivors. All baseline variables are presented in Table 1.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.2 Association between PNI and 28-day mortality in SAE\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe GAM demonstrated a significant linear relationship between PNI and 28-day mortality in SAE (Figure 1). A knot value of about 34 was identified, with mortality risk decreasing significantly when PNI exceeded this threshold (Figure 1). Multivariate Logistic regression analysis demonstrated that higher PNI was significantly associated with reduced 28-day mortality in SAE patients (adjusted OR: 0.85, 95% CI: 0.77-0.93, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001). Consistent findings were observed across alternative analytical approaches: propensity score-matched analysis (OR: 0.87, 95% CI: 0.81-0.93, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001), IPW (OR: 0.82, 95% CI: 0.78-0.86, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001), and doubly robust estimation (OR: 0.81, 95% CI: 0.76-0.86, \\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001) (Table 2).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.3 Predictive performance of prognostic models for 28-day mortality in\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eSAE\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eKaplan-Meier survival analysis demonstrated significantly prolonged 28-day survival in SAE patients with PNI \\u0026gt; 34 compared to those with PNI \\u0026le; 34 in both MIMIC (\\u003cem\\u003eP\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.001) and EICU (\\u003cem\\u003eP\\u003c/em\\u003e\\u003cem\\u003e\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.001) cohorts (Figure 2). Receiver operating characteristic (ROC) curve analysis demonstrated that the PNI exhibited excellent discriminative ability for predicting 28-day mortality in SAE patients, with area under the curve (AUC) values of 0.879 (95% CI: 0.867-0.891) in the MIMIC IV database and 0.724 (95% CI: 0.707-0.741) in the eICU database (Figure 3). Model 4 (PNI + SOFA + SAPS II) showed superior predictive performance, with AUCs of 0.907 (MIMIC) and 0.766 (EICU).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003e3.4\\u003c/strong\\u003e \\u003cstrong\\u003eThe relationship between PNI and GCS score\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eBoxplot analysis revealed a significant inverse correlation between PNI levels and GCS severity categories (\\u0026nbsp;GCS grade 1：\\u0026nbsp;GCS score 3-8；\\u0026nbsp;GCS grade 2：\\u0026nbsp;GCS score 9-12; GCS grade 3：GCS score 13-14) in both the MIMIC-IV database (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001) and eICU database (\\u003cem\\u003eP\\u003c/em\\u003e \\u0026lt; 0.001) cohorts (Figure 4). The PNI values progressively decreased with increasing GCS severity: 38.2 (34.5-42.1) in grade 1 versus 45.6 (41.2-49.8) in grade 3 (\\u003cem\\u003eP\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.001).\\u003c/p\\u003e\"},{\"header\":\"4 Discussion\",\"content\":\"\\u003cp\\u003eOur findings demonstrate that SAE patients had a 28-day mortality rate of 33.2%. The PNI as a significant predictor of 28-day mortality of SAE superior than SOFA score and SAPS II score, with PNI levels below 34 showing a linear correlation with increased mortality risk. Stratified analysis show that progressively lower PNI levels were associated with worsening degrees of consciousness impairment.\\u003c/p\\u003e\\n\\u003cp\\u003eThe multicenter analysis demonstrates unabated high 28-day mortality rates among SAE patients, consistent with established clinical evidence\\u003csup\\u003e2,3\\u003c/sup\\u003e. Despite substantial research efforts, the current lack of sensitive prognostic biomarkers impedes early risk stratification and targeted interventions for SAE. Our findings reinforce the critical need to identify reliable indicators that can dynamically predict outcomes and guide therapeutic escalation in this vulnerable population.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eWe established the PNI as a novel independent predictor of SAE mortality, outperforming conventional severity scores (SOFA/SAPS II) in both discrimination and clinical utility. The optimal threshold (PNI = 34) robustly stratified mortality risk across cohorts. The mechanisms underlying PNI\\u0026apos;s predictive value in SAE likely involve dual pathways: (1) Albumin were associated with SAE and were supported by medium- to high-quality evidence\\u003csup\\u003e6\\u003c/sup\\u003e. Use of albumin decreased the risk of sepsis-associated delirium\\u003csup\\u003e5\\u003c/sup\\u003e\\u003csub\\u003e.\\u0026nbsp;\\u003c/sub\\u003eHypoalbuminemia exacerbates blood-brain barrier disruption via oxidative stress and endothelial dysfunction (2) Lymphocytopenia reflects impaired immunomodulation, worsening neuroinflammation. The lymphocyte population comprises three principal immunophenotypically distinct subsets: T lymphocytes (T cells), B lymphocytes (B cells), and natural killer cells (NK cells).\\u0026nbsp;T cells constitute a major subset of lymphocytes, representing one of the principal cellular components within the lymphoid lineage. V-domain immunoglobulin suppressor of T cell activation (VISTA) has emerged as a crucial player in the pathogenesis of neurological disorders\\u003csup\\u003e17\\u003c/sup\\u003e.\\u0026nbsp;CD86 in CD3 + CD56 + natural killer T (NKT) cells is an independent risk factor of SAE\\u003csup\\u003e18\\u003c/sup\\u003e\\u003csub\\u003e.\\u003c/sub\\u003e\\u003csup\\u003e\\u0026nbsp;\\u003c/sup\\u003eMechanistically, PNI integrates hypoalbuminemia-driven blood-brain barrier disruption and lymphopenia-mediated neuroinflammation \\u0026ndash; core pathways in SAE pathogenesis\\u003csup\\u003e19-23\\u003c/sup\\u003e. We therefore recommend serial PNI monitoring in SAE management, with prompt nutritional-immunomodulatory therapy when PNI falls below 34.\\u003c/p\\u003e\\n\\u003cp\\u003eStratified analysis demonstrated a significant correlation between higher PNI and improved GCS scores indicating PNI\\u0026apos;s capacity to reflect SAE-induced consciousness impairment severity. This association may be attributed to the potential roles of albumin in attenuating oxidative neuronal injury and lymphocytes in controlling CNS infections, with both processes possibly influencing neurological recovery pathways. Clinicians should utilize PNI trends to anticipate neurological trajectory and personalize neuroprotective strategies (e.g., antioxidant supplementation, infection source control) in comatose SAE patients.\\u003c/p\\u003e\"},{\"header\":\"Limitations\",\"content\":\"\\u003cp\\u003eSeveral study limitations warrant acknowledgment. First, the retrospective design inherently carries potential selection bias, notwithstanding comprehensive statistical adjustment. Second, while validated across two independent databases, the generalizability of the established PNI threshold (34) requires prospective verification. Third, residual confounding from unmeasured variables (e.g., nutritional supplementation regimens, underlying comorbidities) may potentially influence clinical outcomes. Finally, the pathophysiological mechanisms mediating the observed PNI-GCS association remain hypothetical and necessitate further mechanistic investigation.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\"},{\"header\":\"Conclusions\",\"content\":\"\\u003cp\\u003eThis multicenter study identified the PNI as a robust and independent predictor of 28-day mortality in patients with SAE, demonstrating an optimal prognostic threshold of 34. The consistent predictive performance across diverse analytical methodologies and independent validation cohorts substantiates its clinical utility for risk stratification in SAE management.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cbr\\u003e\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cp\\u003eCCI: Charlson Comorbidity Index\\u003c/p\\u003e\\n\\u003cp\\u003eCI: confidence interval\\u003c/p\\u003e\\n\\u003cp\\u003eeICU-CRD: electronic Intensive Care Unit Collaborative Research Database\\u003c/p\\u003e\\n\\u003cp\\u003eGAM: generalized additive model\\u003c/p\\u003e\\n\\u003cp\\u003eGCS: Glasgow Coma Scale\\u003c/p\\u003e\\n\\u003cp\\u003eINR: international normalized ratio\\u003c/p\\u003e\\n\\u003cp\\u003eIPW: inverse probability weighting\\u003c/p\\u003e\\n\\u003cp\\u003eKM: Kaplan-Meier\\u003c/p\\u003e\\n\\u003cp\\u003eLODS:\\u0026nbsp;Logistic Organ Dysfunction System\\u003c/p\\u003e\\n\\u003cp\\u003eMIMIC-IV: Medical Information Mart for Intensive Care IV\\u003c/p\\u003e\\n\\u003cp\\u003eOR: odds ratio\\u003c/p\\u003e\\n\\u003cp\\u003ePNI: Prognostic Nutritional Index\\u003c/p\\u003e\\n\\u003cp\\u003ePSM: propensity score matching\\u003c/p\\u003e\\n\\u003cp\\u003ePT: prothrombin time\\u003c/p\\u003e\\n\\u003cp\\u003ePTT: partial thromboplastin time\\u003c/p\\u003e\\n\\u003cp\\u003eROC: receiver operating characteristic\\u003c/p\\u003e\\n\\u003cp\\u003eSAE: sepsis-associated encephalopathy\\u003c/p\\u003e\\n\\u003cp\\u003eSAPS II: Simplified Acute Physiology Score II\\u003c/p\\u003e\\n\\u003cp\\u003eSIRS: Systemic Inflammatory Response Syndrome\\u003c/p\\u003e\\n\\u003cp\\u003eSMD: standardized mean differences\\u003c/p\\u003e\\n\\u003cp\\u003eSOFA: Sequential Organ Failure Assessment\\u003c/p\\u003e\\n\\u003cp\\u003eSQL: Structured Query Language\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e: The use of the MIMIC-IV and eICU-CRD databases was approved by the institutional review boards (IRBs) of the Massachusetts Institute of Technology (MIT) and Beth Israel Deaconess Medical Center (BIDMC). All methods were carried out in accordance with the Declaration of Helsinki and relevant institutional guidelines and regulations. Informed consent was waived by the IRBs of MIT and BIDMC because the study did not impact clinical care, and all protected health information was de-identified.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eClinical Trial:\\u003c/strong\\u003eNot applicable\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for Publication\\u003c/strong\\u003e: The authors confirm that they have reviewed and approved the final version of the manuscript and consent to its publication in\\u0026nbsp;\\u003cem\\u003eBMC Neurolpgy.\\u003c/em\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAvailability of data and materials:\\u003c/strong\\u003eIf the reason is reasonable, the original data can be requested from the corresponding author.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of Interest:\\u0026nbsp;\\u003c/strong\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u0026nbsp;\\u003c/strong\\u003eThis work was supported by Joint Funds of the Natural Science Foundation of Tianjin (No. 25JCLMJC00350).\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthor Contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eDr LNZ had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eConcept and design: LNZ, CQ, KLX.\\u003c/p\\u003e\\n\\u003cp\\u003eAcquisition, analysis, or interpretation of data: All authors.\\u003c/p\\u003e\\n\\u003cp\\u003eDrafting of the manuscript: LNZ, CQ, KLX.\\u003c/p\\u003e\\n\\u003cp\\u003eCritical review of the manuscript for important intellectual content: All authors.\\u003c/p\\u003e\\n\\u003cp\\u003eData collection and statistical analysis: CQ, YHS, HYL, XGL, DXH, QHY.\\u003c/p\\u003e\\n\\u003cp\\u003eObtained funding: LNZ.\\u003c/p\\u003e\\n\\u003cp\\u003eAdministrative, technical, or material support: YL, YHS, HYL, XGL, DXH, QHY.\\u003c/p\\u003e\\n\\u003cp\\u003eSupervision: YL, KLX.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eSong Z, Chen H, Xu W, et al. 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The expression of CD86 in CD3(+)CD56(+) NKT cells is associated with the occurrence and prognosis of sepsis-associated encephalopathy in sepsis patients: a prospective observational cohort study. Immunol Res. 2023;71(6):929-940.\\u003c/li\\u003e\\n\\u003cli\\u003eSaito M, Fujinami Y, Ono Y, et al. Infiltrated regulatory T cells and Th2 cells in the brain contribute to attenuation of sepsis-associated encephalopathy and alleviation of mental impairments in mice with polymicrobial sepsis. Brain Behav Immun. 2021;92:25-38.\\u003c/li\\u003e\\n\\u003cli\\u003eZhang Y, Xin J, Zhao D, et al. Magnesium hexacyanoferrate mitigates sepsis-associated encephalopathy through inhibiting microglial activation and neuronal cuproptosis. Biomaterials. 2025;321:123279.\\u003c/li\\u003e\\n\\u003cli\\u003eRen C, Yao RQ, Zhang H, et al. Sepsis-associated encephalopathy: a vicious cycle of immunosuppression. J Neuroinflammation. 2020;17(1):14.\\u003c/li\\u003e\\n\\u003cli\\u003eHeming N, Mazeraud A, Verdonk F, et al. Neuroanatomy of sepsis-associated encephalopathy. Crit Care. 2017;21(1):65.\\u003c/li\\u003e\\n\\u003cli\\u003eLuo RY, Luo C, Zhong F, et al. ProBDNF promotes sepsis-associated encephalopathy in mice by dampening the immune activity of meningeal CD4(+) T cells. J Neuroinflammation. 2020;17(1):169.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"Tables\",\"content\":\"\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"100%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\" valign=\\\"top\\\" style=\\\"width: 582px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eTable\\u003c/strong\\u003e\\u003cstrong\\u003e\\u0026nbsp;1 Baseline characteristics and outcomes of\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003es\\u003c/strong\\u003e\\u003cstrong\\u003eepsis-associated encephalopathy\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003epatients\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\" valign=\\\"top\\\" style=\\\"width: 255px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eOriginal\\u0026nbsp;\\u003c/strong\\u003e\\u003cstrong\\u003ecohort\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"3\\\" valign=\\\"top\\\" style=\\\"width: 224px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eMatch cohort\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003eSurvival group (\\u003cem\\u003en\\u0026nbsp;\\u003c/em\\u003e= 2140)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003eNon-survival group (\\u003cem\\u003en\\u0026nbsp;\\u003c/em\\u003e= 1062)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003eSurvival group (\\u003cem\\u003en\\u0026nbsp;\\u003c/em\\u003e= 965)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003eNon-survival group (\\u003cem\\u003en\\u0026nbsp;\\u003c/em\\u003e= 965)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\" valign=\\\"top\\\" style=\\\"width: 582px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eBaseline variables\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eAge (years)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003cp\\u003e(median[IQR])\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e71.00 [59.00,81.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e68.00 [59.00, 77.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e69.00 [58.00, 78.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e68.00 [60.00, 78.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e0.885\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eGender,M (%)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e1205 (56.3)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e681 ( 64.1)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e594 (61.6)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e611 (63.3)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e0.452\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\" valign=\\\"top\\\" style=\\\"width: 582px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eLaboratory parameters (median [IQR])\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eAlbumin (g/dL)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e3.50 [2.90,3.90]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.60 [1.10,2.10]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e3.50 [2.90,4.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e1.60 [1.10, 2.10]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eLymphocyte (\\u0026times; 10⁹/L)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e1.85 [1.42, 2.42]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e1.13 [0.73, 1.66]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e1.79 [1.38, 2.42]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e1.13 [0.73, 1.64]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003ePNI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e44.15 [38.60,49.56]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e22.43 [17.85, 28.89]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e44.75 [38.70, 50.42]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e22.40 [17.85,29.40]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"7\\\" valign=\\\"top\\\" style=\\\"width: 582px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCritical illness score\\u003c/strong\\u003e\\u003cstrong\\u003e(median [IQR])\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eCCI\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e5.00 [3.00, 7.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e5.00 [3.00, 6.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.055\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e5.00 [3.00,7.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e5.00 [3.00, 6.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e0.055\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eGCS\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e8.00 [6.00, 9.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e3.00 [3.00,3.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e8.00 [6.00, 9.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e3.00 [3.00, 3.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eSOFA\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e6.00 [4.00, 8.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e7.00 [6.00, 9.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e6.00 [4.00, 8.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e7.00 [6.00, 9.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eSAPS II\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e41.00 [32.75, 51.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e54.00 [40.00, 63.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e42.00 [33.00, 51.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e54.00 [40.00, 63.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eLODS\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e5.00 [3.00, 8.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e8.00 [6.00, 9.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e6.00 [4.00, 8.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e7.00 [6.00, 9.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eSIRS\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e3.00 [2.00, 3.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e3.00 [2.00, 3.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e0.005\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e3.00 [2.00, 3.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e3.00 [2.00, 3.00]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e0.121\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd colspan=\\\"4\\\" valign=\\\"top\\\" style=\\\"width: 358px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eClinical outcome\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eVentdurations (n(%))\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e1379 (64.4)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e924 ( 87.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e839 (86.9)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e829 ( 85.9)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e0.550\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eVasopressin (n(%))\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e214 (10.0)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e172 ( 16.2)\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e153 (15.9)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e151 ( 15.6)\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\u003e\\n \\u003cp\\u003e0.950\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 103px;\\\"\\u003e\\n \\u003cp\\u003eLength of stay (median [IQR])\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 94px;\\\"\\u003e\\n \\u003cp\\u003e3.90 [1.70, 9.20]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 95px;\\\"\\u003e\\n \\u003cp\\u003e5.30 [2.30, 10.10]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd valign=\\\"top\\\" style=\\\"width: 66px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 83px;\\\"\\u003e\\n \\u003cp\\u003e3.80 [1.50, 10.30]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 80px;\\\"\\u003e\\n \\u003cp\\u003e5.20 [2.30, 10.20]\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 62px;\\\"\\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\\u003ePNI = 10 \\u0026times; Albumin (g/dL) + 5 \\u0026times; Lymphocyte (\\u0026times; 10⁹/L); CCI: Charlson Comorbidity Index; GCS: Glasgow Coma Scale; SOFA: Sequential Organ Failure Assessment; SAPS II: Simplified Acute Physiology Score II; LODS: Logistic Organ Dysfunction System; SIRS: Systemic Inflammatory Response Syndrome\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eTable 2\\u0026nbsp;\\u003c/strong\\u003eAssociation between Prognostic Nutritional Index and 28-day mortality \\u0026nbsp;in sepsis-associated encephalopathy\\u003c/p\\u003e\\n\\u003ctable border=\\\"1\\\" cellspacing=\\\"0\\\" cellpadding=\\\"0\\\" width=\\\"99%\\\"\\u003e\\n \\u003ctbody\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eModels\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eOR\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd colspan=\\\"2\\\" style=\\\"width: 25px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003eCI\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd rowspan=\\\"2\\\" style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u003cstrong\\u003e\\u003cem\\u003eP\\u003c/em\\u003e\\u003c/strong\\u003e\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e2.5%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e97.5%\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003ctr\\u003e\\n \\u003ctd style=\\\"width: 48px;\\\"\\u003e\\n \\u003cp\\u003eMultivariate Logistic analysis*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e0.85\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e0.77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e0.80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\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: 48px;\\\"\\u003e\\n \\u003cp\\u003ePropensity score matching*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e0.77\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e0.81\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e0.88\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\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: 48px;\\\"\\u003e\\n \\u003cp\\u003eInverse probability weighting*\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e0.80\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e0.78\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e0.81\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\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: 48px;\\\"\\u003e\\n \\u003cp\\u003eDoubly robust with all coariates#\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e0.10 \\u0026nbsp;exp(coef) [confint] p \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 11px;\\\"\\u003e\\n \\u003cp\\u003e0.09 \\u0026nbsp; \\u0026nbsp;exp(coef) [confint] p \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e0.12 \\u0026nbsp; \\u0026nbsp; exp(coef) [confint] p \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003ctd style=\\\"width: 13px;\\\"\\u003e\\n \\u003cp\\u003e\\u0026lt; 0.001 \\u0026nbsp; \\u0026nbsp; exp(coef) [confint] p \\u0026nbsp; \\u0026nbsp;\\u0026nbsp;\\u003c/p\\u003e\\n \\u003c/td\\u003e\\n \\u003c/tr\\u003e\\n \\u003c/tbody\\u003e\\n\\u003c/table\\u003e\\n\\u003cp\\u003e*Analysis was conducted using the continuous variable of PNI# The doubly robust method requires converting the continuous PNI into a binary categorical variable using a predefined cut-off value for analysis; OR: odds ratio; CI: confidence interval; \\u003cem\\u003eP\\u0026nbsp;\\u003c/em\\u003e\\u0026lt; 0.05, statistically significant.\\u0026nbsp;\\u003c/p\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Prognostic Nutritional Index, sepsis-associated encephalopathy, 28-day mortality, Glasgow Coma Scale\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8037637/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8037637/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003e\\u003cb\\u003eBackground\\u003c/b\\u003e Sepsis-associated encephalopathy (SAE) still has a high mortality rate, and there is a lack of effective biomarkers to assess the prognosis of SAE. This study aims to explore the relationship between prognostic nutritional index (PNI) and the prognosis of patients with SAE.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eMethods\\u003c/b\\u003e This study is a multicenter cohort study, data from 2008\\u0026ndash;2019. The primary outcome was 28-day all-cause mortality in the SAE population. To explore the prognostic relationship between PNI and SAE patients, the multivariable Logistic regression, propensity score matching, inverse probability weighting were conducted to adjust confounders. In this study, the generalized additive model (GAM), Kaplan-Meier curve, receiver operating characteristic curve (ROC) curve and other methods were used to analyze the relationship between PNI and the 28-day mortality rate of SAE patients. The results of this study were validated by external data.\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eResults\\u003c/b\\u003e Among 3,202 SAE patients, multivariable analysis identified PNI as an independent predictor of 28-day mortality (OR: 0.85, 95% CI: 0.77\\u0026ndash;0.93) of original cohort. GAM of original cohort showed that a PNI of 34 was the optimal prognostic threshold for SAE patients. The Kaplan-Meier curves of both the original cohort and the external validation cohort showed that the 28-day mortality rate of SAE patients with PNI lower than 34 was significantly lower than that of patients with PIN higher than 34 (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). ROC analysis showed superior predictive performance in original cohort (AUC: 0.879; sensitivity: 0.878; specificity: 0.880) versus external validation cohort (AUC: 0.724; sensitivity: 0.878; specificity: 0.569). Stratified analysis of the results of the study showed that elevated PNI correlated with higher Glasgow Coma Scale scores (\\u003cem\\u003eP\\u003c/em\\u003e\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001).\\u003c/p\\u003e\\u003cp\\u003e\\u003cb\\u003eConclusions\\u003c/b\\u003e This large-scale multicenter study establishes the PNI as an independent predictor of 28-day mortality in patients with sepsis-associated encephalopathy (SAE). We identified that SAE patients with PNI\\u0026thinsp;\\u0026lt;\\u0026thinsp;34 exhibited significantly higher 28-day mortality rates and worse neurological function.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Prognostic Nutritional Index as a novel biomarker for predicting prognosis in sepsis-associated encephalopathy: A multicenter retrospective cohort study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-12-02 13:42:14\",\"doi\":\"10.21203/rs.3.rs-8037637/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"1c024ca3-e9bb-411e-946f-04f8d8533d9a\",\"owner\":[],\"postedDate\":\"December 2nd, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-02-04T09:41:43+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-12-02 13:42:14\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8037637\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8037637\",\"identity\":\"rs-8037637\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}