Development and Validation of a PSS Criteria-Based Prediction Model for 28-Day Mortality in Pediatric Sepsis

Development and Validation of a PSS Criteria-Based Prediction Model for 28-Day Mortality in Pediatric Sepsis | 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 Development and Validation of a PSS Criteria-Based Prediction Model for 28-Day Mortality in Pediatric Sepsis Wang yan, Ruan peisen, Wang jiapei, Zhen yao, Chen hehe This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7625240/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 ObjectiveTo construct and clinically validate a prognostic nomogram for 28-day mortality prediction in pediatric sepsis cases meeting PSS diagnostic standards. Methods This study retrospectively enrolled 254 pediatric sepsis patients who met the diagnostic criteria for pediatric sepsis syndrome (PSS) and were admitted to the Pediatric Intensive Care Unit (PICU) of Women and Children's Hospital of Ningbo University from June 2022 to December 2024. The patients were randomly divided into a training set (n=178) and a test set (n=76) at a 7:3 ratio. Demographic data (age, sex), PICU length of stay, infection sites, and routine laboratory parameters within 24 hours of PICU admission were collected, and the comparability of clinical characteristics between the two cohorts was assessed. Based on 28-day outcomes, the patients were classified into survival and non-survival groups. Univariate and multivariate logistic regression analyses were performed on the training set to identify risk factors for mortality and establish a nomogram prediction model. The predictive performance, accuracy, and clinical utility of the nomogram were evaluated in both the training and validation sets using receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA). Results In the training cohort, univariate and multivariate logistic regression analyses of the clinical and laboratory parameters identified IL-6, DD , and FIB as independent risk factors for 28-day mortality in pediatric sepsis patients (P < 0.05). A nomogram prediction model constructed using these three variables demonstrated superior predictive performance compared to individual indicators (IL-6, DD, or FIB), with an AUC of 0.834, sensitivity of 0.810, and specificity of 0.801.ROC curve analysis revealed that the nomogram model achieved an AUC of 0.883 (95% CI: 0.802–0.964) in the training set and 0.834 (95% CI: 0.758–0.911) in the test set, indicating good discriminative ability. The calibration curve, assessed by the Hosmer-Lemeshow goodness-of-fit test, showed no significant deviation between predicted and observed outcomes in either the training cohort (P = 0.369) or the validation cohort (P = 0.798), confirming good model fit. Furthermore, decision curve analysis (DCA) demonstrated that the model had favorable clinical utility. Conclusion The nomogram incorporating IL-6, DD, and FIB represents a reliable tool for early risk stratification of 28-day mortality in pediatric sepsis patients meeting PSS criteria, potentially assisting clinicians in optimizing timely interventions and improving prognosis. Phoenix sepsis score sepsis nomogram interleukin-6 fibrinogen D-dimer Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background The incidence of sepsis reaches its zenith in early childhood and represents a predominant cause of pediatric mortality globally. Epidemiological data demonstrate that sepsis impacts approximately 50 million individuals worldwide annually, with half of these cases occurring in children under 19 years of age, resulting in over 3 million pediatric deaths [1] . This condition not only impairs the physical health of children but may also exert long-term detrimental effects on cognitive function, thereby imposing a substantial burden on healthcare infrastructures and socioeconomic frameworks. A U.S.-based survey indicated that hospitalization expenditures for pediatric sepsis surged by nearly 25% from 2005 to 2016, constituting 18% of aggregate pediatric hospitalization costs [2] . The majority of sepsis-related fatalities in children transpire within the initial days of hospitalization, underscoring the imperative for early identification of high-risk patients to facilitate timely bundle therapies and organ support. Effective identification instruments must exhibit high sensitivity to enable early clinical detection while remaining sufficiently straightforward, particularly in low- and middle-income nations, to curtail the misuse of medical resources and the adverse effects of excessive treatment. The 2024 International Pediatric Sepsis Consensus introduced the Phoenix Sepsis Score (PSS). Nonetheless, there is a paucity of research on predictive models for 28-day mortality risk in pediatric sepsis patients meeting PSS criteria. Consequently, this study conducted a retrospective analysis of clinical data from pediatric sepsis patients with a PSS score of ≥ 2 at our institution. Utilizing a logistic regression model, we identified independent predictors of 28-day mortality, developed a predictive model, and validated it, with the objective of offering a clinical reference tool. Materials and Methods Study Population A retrospective cohort study was conducted, including 254 pediatric sepsis patients admitted to the Pediatric Intensive Care Unit (PICU) at the Women and Children's Hospital of Ningbo University from June 2022 to December 2024.Inclusion Criteria:(1)PICU admission duration exceeding 24 hours.(2)Diagnosis of sepsis according to the 2024 International Consensus on Pediatric Sepsis [3] .(3)Age range: >1 month to < 18 years.Exclusion Criteria:(1)PICU admission duration less than 24 hours.(2) Patients with hematologic malignancies (e.g., leukemia, lymphoma) or solid tumors (e.g., neuroblastoma, hepatoblastoma) in the end-stage of disease, or those with other severe chronic conditions with a life expectancy of less than 28 days (e.g., end-stage organ failure).(3)Postoperative status following cardiac surgery or severe traumatic injury.(4)Neonates and preterm infants admitted during the perinatal period. Data Collection and Grouping The enrolled pediatric patients were randomly allocated into a training set (n = 178) and a test set (n = 76) in a 7:3 ratio.Each cohort was stratified into survival and non-survival groups based on 28-day outcomes. Demographic and clinical data were collected, including age, sex, duration of PICU admission, infection site, and the necessity of mechanical ventilation. Laboratory parameters assessed within the first 24 hours of PICU admission included:Hematologic Parameters: White blood cell count (WBC), platelet count (PLT), neutrophil count (NEUT), and lymphocyte count (LYM).Hepatic Function Indicators: Albumin (ALB), alanine aminotransferase (ALT), and total bilirubin (TIBL).Renal Function Indicators: Blood urea nitrogen (BUN) and serum creatinine (Cr).Cardiac Biomarkers: Cardiac troponin I (cTnI) and brain natriuretic peptide (BNP).Coagulation Parameters: Prothrombin time (PT), activated partial thromboplastin time (APTT), international normalized ratio (INR), fibrinogen (FIB), and D-dimer (DD).Inflammatory Biomarkers: Lactate (LAC), procalcitonin (PCT), C-reactive protein (CRP), interleukin-6 (IL-6), and interleukin-10 (IL-10). A small proportion of missing data was present among the laboratory indicators collected in this study. For variables with a missing rate of less than 5%, preprocessing was performed using mean/median imputation (for normally distributed variables) or multiple imputation methods such as Random Forest-based imputation. All analyses were conducted based on the processed complete dataset. The core variables (IL-6, DD, FIB) included in the final model construction demonstrated 100% completeness, with no missing data. Statistical Methods Statistical analyses were conducted using SPSS 26.0 software. Categorical variables were analyzed using Pearson’s chi-square test or Fisher’s exact test, expressed as χ², with results presented as frequencies (%). Continuous variables were initially tested for normality using the Shapiro-Wilk test. Normally distributed data were reported as mean ± standard deviation (Mean ± SD) and compared using the independent t-test. Non-normally distributed data were expressed as median (interquartile range, IQR) [M (Q25, Q75)] and compared using the Mann-Whitney U test.Univariate logistic regression was performed to identify significant variables, which were subsequently incorporated into a multivariate logistic regression model. Significant variables from the multivariate analysis were used to develop a nomogram. The model’s predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Model accuracy and clinical utility were assessed using the Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA). Internal validation was conducted using 1000 bootstrap resamples. A P-value < 0.05 was considered statistically significant. Results Comparison of Clinical Characteristics and Laboratory Parameters of the Study Population ( Table 1 ) A total of 254 pediatric patients with sepsis who met the aforementioned criteria were included in this study, with an overall 28-day mortality rate of 22.8% (58/254). These patients were randomly allocated into a training set (n = 178), which included 42 non-survivors (23.6%) and 136 survivors (76.4%), and a validation set (n = 76), which included 16 non-survivors (21.1%) and 60 survivors (78.9%). No statistically significant differences were observed in the clinical characteristics or laboratory indicators between the two sets (P > 0.05, Table 1 ). Among the 58 non-surviving patients, 15 (25.9%) succumbed within 72 hours following PICU admission. The in-hospital mortality rate was consistent with the 28-day mortality rate at 22.8% (58/254), as all documented death events occurred during the hospitalization period, which aligns with the primary endpoint of 28-day outcome in this study. Univariate Analysis of Clinical Characteristics and Laboratory Parameters ( Table 2 ) In the training cohort, no significant differences were found between the non-survival and survival groups of pediatric sepsis patients regarding gender, age, duration of PICU admission, or infection site (P > 0.05). However, the non-survival group exhibited significantly elevated levels of PLT, LYM, ALT, BUN, Cr, cTnI, BNP, PT, APTT, INR, FIB, DD, PCT, CRP, IL-6, and IL-10 compared to the survival group (P < 0.05). Multivariate Logistic Regression Analysis ( Table 3 ) Significant laboratory parameters from the univariate analysis were incorporated into the logistic regression model. Iterative multivariate logistic regression identified IL-6, DD, and FIB as independent predictors of 28-day mortality in pediatric sepsis patients (P < 0.05), optimizing the balance between model fit and the number of covariates. The complete results of the multivariate logistic regression analysis are presented in Table 3 , which includes the regression coefficient (β), standard error (SE), Z statistic, P value, odds ratio (OR), and 95% confidence interval (CI) for all variables retained in the final model. Nomogram Prediction Model Based on Multivariate Logistic Regression Analysis ( Fig. 1 ) A nomogram was developed using the independent risk factors derived from multivariate logistic regression analysis. The score for each variable was determined by aligning the patient’s value with the top point scale. The sum of these scores yielded a total score, which was mapped to the bottom axis to predict the probability of 28-day mortality in pediatric sepsis patients. Higher total scores correlated with an increased risk of sepsis-related mortality. Evaluation and Validation of the Nomogram Model The nomogram model outperformed individual predictors (IL-6, DD, and FIB) in predictive accuracy (AUC = 0.834, sensitivity = 0.810, specificity = 0.801) (Figure 2 ). ROC curve analysis yielded AUC values of 0.883 (95% CI: 0.802–0.964) in the training set and 0.834 (95% CI: 0.758–0.911) in the test set (Figure 3 ), confirming strong discriminative performance. Calibration plots and the Hosmer-Lemeshow test indicated good model fit, with P-values of 0.369 (training set) and 0.798 (test set), reflecting excellent agreement between predicted and observed outcomes (Figure 4 ). Decision curve analysis (DCA) demonstrated that the nomogram model provided higher clinical net benefit than the "treat-all" or "treat-none" approaches in both cohorts (Figure 5). Table 2 Univariate analysis of survivor and non-survivor groups in pediatric sepsis patients within the training cohort Index Survivor group (n = 136) Nor-survivor group (n = 42) t/X²/Z pvalue Gender (Male/Female) 79/57 24/18 0.012 0.914 Age[mon,M(Q 25 ,Q 75 )] 31.5(4,90.5) 30.5(12,84) 0.641 0.522 Site of infection 1.995 0.864 Lungs 100(73.5%) 35(83.3%) lymph gland 1(0.7%) 0(0%) Urinary 3(2.2%) 0(0%) Soft tissue 3(2.2%) 0(0%) Digestive 15(11%) 3(7.1%) Cental system 14(10.3%) 4(9.5%) Hospital stays[Day,M(Q 25 ,Q 75 )] 11(7,15) 7.5(3,13) -0.618 0.541 LAC 2.2(1.8,3.3) 3.85(1.5,6.9) 1.32 0.187 PCT 0.764(0.326,3.568) 3.237(0.502,22.1) 2.545 0.011 WBC 10.85(7.7,17.9) 11.85(8.6,15.6) 0.406 0.685 PLT 295.5(207,393) 237.5(177,348) 2.037 0.042 CRP 32.65(5.95,90) 11.15(1.1,40.5) 2.649 0.058 IL6 37.8(9.795,189.5) 1240.5(128.6,2380) 5.738 0.000 IL10 21(7.8,44.5) 80.5(33.76,333.5) 5.156 0.000 ALB 37.6(34.25,41.25) 35.7(32,39.4) -1.967 0.051 ALT 21(14,39.5) 109(43,205) 5.42 0.000 TiBL 9.05(5.85,18.85) 7.6(5.2,13.9) -1.221 0.222 BUN 3.45(2.6,5.2) 5.35(4.3,7.8) 3.992 0.000 Cr 43(32,53) 56(43,75) 3.617 0.000 CTNI 0.021(0.017,0.03) 0.122(0.025,1.044) 5.473 0.000 BNP 176(29,1254.5) 538(81.9,2213) 1.992 0.046 PT 13.7(12.6,15.15) 16.35(14.7,19.1) 5.912 0.000 APTT 32.3(28.65,36.1) 36(31.2,47.9) 2.922 0.003 INR 1.185(1.08,1.31) 1.43(1.28,1.66) 5.856 0.000 FIB 347(270.5,505) 209.5(155,287) -5.074 0.000 DD 1045(365,2555) 8210(2450,24480) 5.994 0.000 NEUT 7.9(4.65,13.4) 6.55(4,12.6) -1.305 0.192 LYM 1.8(1,3.2) 3.8(1.8,7.6) 3.805 0.000 WBC:White blood cells; PLT:platelet count; NEUT:Neutrophil; LYM:Lymphocyte; ALB:Albumin; ALT:Glutamic-pyruvic transaminase; TiBL:total bilirubin; BUN:blood urea nitrogen; Cr:creatinine;CTNI:Cardiac troponin I; BNP:brain natriuretic peptide; PT:Prothrombin time; APTT:activated partial thromboplastin time; INR:International Normalized Ratio; FIB:Fibrinogen ; DD:D-dimer; LAC:Lactate; PCT:procalcitonin; CRP:c-reactive protein; IL-6:interleukin-6; IL-10:interleukin-10 Table 3 Logistic Analysis of Independent Risk Factors for 28-Day Mortality in Pediatric Sepsis Patients in the Training Cohort. Index β se z statis p value OR 95%_CI IL6 0.000 0.000 2.941 0.003 1.000 1 ~ 1 FIB -0.006 0.002 -3.583 0.000 0.994 0.991 ~ 0.997 DD 0.000 0.000 2.302 0.021 1.000 1 ~ 1 IL-6:interleukin-6; FIB:Fibrinogen; DD:D-dimer Discussion Sepsis remains a leading cause of childhood mortality, with its pathophysiology centered on a self-perpetuating vicious cycle of dysregulated inflammation and concomitant coagulation dysfunction [1, 4] . The Phoenix Sepsis Score (PSS), as the most recent international consensus standard, provides a novel organ dysfunction-based framework for diagnosing pediatric sepsis [3] . Based on the PSS criteria, this retrospective study identified IL-6, D-dimer (DD), and fibrinogen (FIB) as independent risk factors for 28-day mortality in septic children. These three biomarkers were combined to develop and validate a nomogram prediction model. During internal validation, the model demonstrated favorable discrimination, calibration, and clinical usefulness. The selected biomarkers are not independent; they accurately capture the core mechanism of “immunothrombotic” cross-talk in sepsis. IL-6 is a pivotal inflammatory cytokine in sepsis and organ dysfunction. It rises rapidly following infection, activating inflammatory cascades and stimulating hepatic production of acute-phase proteins, including FIB [5, 6] . However, excessive inflammation damages endothelial cells, leading to tissue factor exposure and initiation of coagulation. D-dimer, a degradation product of cross-linked fibrin, serves as direct evidence of coagulation activation and secondary hyperfibrinolysis. Markedly elevated levels strongly indicate widespread microthrombosis and consumption of coagulation factors [7, 8] .he dynamic changes in FIB reflect the progression of this process: levels may be elevated or normal in early sepsis as an acute-phase reactant, but decline sharply as disseminated intravascular coagulation (DIC) evolves, due to ongoing microvascular consumption and impaired hepatic synthesis [9, 10] . Thus, the combination of high IL-6 (reflecting hyperinflammation), high DD (indicating active coagulation/fibrinolysis), and low FIB (suggesting exhaustion of clotting substrate) clearly portrays a critically ill child in a state of inflammatory storm and decompensated coagulopathy—a profile strongly associated with poor outcome. Compared to any single biomarker, the three-parameter model (AUC: 0.834) offers a more comprehensive and stable risk assessment, effectively overcoming the limitations of individual variables that are susceptible to multiple confounding influences [11] . Our findings align with yet extend previous research. Multiple studies have separately established the value of IL-6 in evaluating sepsis-related inflammation [5] , the role of DD in predicting sepsis mortality [8, 12] , and the prognostic significance of low FIB [10, 13] . The novelty of our study lies in the integration of these three markers—representing inflammation, fibrinolysis, and coagulation consumption, respectively—within a pediatric sepsis cohort defined by the new PSS criteria, and in quantifying their combined predictive power. Compared with complex multi-organ scores, our model relies on only three laboratory parameters, offering greater simplicity and rapid availability. Unlike traditional inflammatory markers such as PCT and CRP, the inclusion of a coagulation dimension may provide higher specificity for prognostic assessment beyond diagnostic purposes. The ultimate value of a prediction model lies in its ability to inform clinical decisions and improve outcomes [14] . The present nomogram is visually intuitive, allowing clinicians to quickly estimate an individual’s mortality probability based on laboratory results obtained within 24 hours of admission via graphical interpretation. This offers a practical tool for early identification of high-risk children. For clinical implementation, the model could be integrated into electronic clinical workflows—for example, through automated extraction of IL-6, DD, and FIB values from the hospital information system, with real-time risk calculation and prompt notification at the point of care. A mobile version could also be developed for bedside use. Identification of high-risk patients may trigger enhanced monitoring and earlier aggressive intervention. Several limitations should be acknowledged. First, this was a single-center retrospective study. Although internal validation was performed using training/test splitting, external validation is lacking, and generalizability to other regions and populations requires further confirmation. Future multi-center prospective validation is warranted. Second, the sample size, though sufficient to identify strong predictors, was relatively limited and may have constrained the ability to detect weaker prognostic factors. Third, not all potential prognostic biomarkers (e.g., proadrenomedullin, von Willebrand factor) were included. Future studies could compare our model with those incorporating newer markers. Finally, we focused on short-term (28-day) mortality and did not evaluate the model’s performance in predicting long-term outcomes such as neurodevelopmental sequelae. In summary, using the PSS criteria, we developed and validated a nomogram incorporating IL-6, DD, and FIB to predict 28-day mortality in children with sepsis. The model demonstrates good predictive accuracy and translates core pathophysiological mechanisms into an accessible, visual clinical tool. Despite its limitations, it provides a valuable reference for early risk stratification, forming a basis for individualized and preemptive management strategies that may improve outcomes in pediatric sepsis. The ultimate clinical utility should be further evaluated in prospective studies and real-world implementation. Abbreviations PSS, Phoenix sepsis score. WBC, White blood cells. PLT, platelet count. NEUT, Neutrophil. LYM, Lymphocyte. ALB, Albumin. ALT, Glutamic-pyruvic transaminase. TiBL, Total bilirubin. BUN, Blood urea nitrogen. Cr, Creatinine. CTNI, Cardiac troponin I. BNP, Brain natriuretic peptide. PT, Prothrombin time. APTT, Activated partial thromboplastin time. INR, International normalized ratio. FIB, Fibrinogen. DD, D-dimer. LAC, Lactate. PCT, Procalcitonin. CRP, C-reactive protein. IL-6, Interleukin-6. IL-10, Interleukin-10. Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Women and Children’s Hospital of Ningbo University (approval no. NBFE-2025-KY-173). The need for informed consent was waived by the Ethics Committee due to the retrospective nature of the study. For participants under the age of 16, consent to participate was obtained from their parents or legal guardians. This study was conducted in accordance with the principles of the Declaration of Helsinki. Consent for publication All authors have approved the final version of the manuscript and its submission to BMC Pediatrics. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by Ningbo Medical and Health Brand Discipline (PPXK2024-06), Medical and Health Technology Plan Projects of Zhejiang (2023RC085, 2023KY1117, and 2025KY273), Ningbo Health Technology Plan Project (2023Y23, 2024Y15), Ningbo Public Welfare Science and Technology Project (2024S145), Zhejiang Province Traditional Chinese medicine science and technology project (2025ZL122), Ningbo Clinical Research Center for Children’s Health and Diseases (2019A21002), Ningbo Key discipline of Pediatrics (2022-B17), Ningbo Clinical Research Center Member for Emergency and Critical Diseases under Grant (2024L003), and the Innovation Project of Distinguished Medical Team in Ningbo (2022020405). Authors' contributions W.Y.was responsible for the conceptualization and design of this study, as well as the manuscript drafting. R. P.was in charge of data acquisition and analysis. W.J.performed the interpretation of complex data and statistical analysis. Z.Y. undertook the revision of the manuscript. C. H.provided research ideas and guided the research project. Acknowledgements Not applicable. References Watson RS, Carrol ED, Carter MJ, et al. The burden and contemporary epidemiology of sepsis in children. Lancet Child Adolesc Health[J]. 2024;8(9)DOI:10.1016/S2352-4642(24)00140-8. Rudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet[J]. 2020;395(10219)DOI:10.1016/S0140-6736(19)32989-7. Schlapbach LJ, Watson RS, Sorce LR, et al. International Consensus Criteria for Pediatric Sepsis and Septic Shock. JAMA[J]. 2024;331(8)DOI:10.1001/jama.2024.0179. Tsantes AG, Parastatidou S, Tsantes EA, et al. Sepsis-Induced Coagulopathy: An Update on Pathophysiology, Biomarkers, and Current Guidelines. Life (Basel)[J]. 2023;13(2)DOI:10.3390/life13020350. Gan K, Chen Y, Tao L, et al. Diagnostic value of circulating IL-6 in adult sepsis: a meta-analysis. Minerva Anestesiol[J]. 2024;90(11)DOI:10.23736/S0375-9393.24.18100-X. Hamilton FW, Thomas M, Arnold D, et al. Therapeutic potential of IL6R blockade for the treatment of sepsis and sepsis-related death: A Mendelian randomisation study. PLoS Med[J]. 2023;20(1)DOI:10.1371/journal.pmed.1004174. Chinese Society on T, Hemostasis. [Chinese expert consensus on D-dimer laboratory testing and clinical application]. Zhonghua Yi Xue Za Zhi[J]. 2023;103(35)DOI:10.3760/cma.j.cn112137-20230721-00066. Han YQ, Yan L, Zhang L, et al. Performance of D-dimer for predicting sepsis mortality in the intensive care unit. Biochem Med (Zagreb)[J]. 2021;31(2)DOI:10.11613/BM.2021.020709. Mori K, Tsujita Y, Yamane T, et al. Decreasing Plasma Fibrinogen Levels in the Intensive Care Unit Are Associated with High Mortality Rates In Patients With Sepsis-Induced Coagulopathy. Clin Appl Thromb Hemost[J]. 2022;28DOI:10.1177/10760296221101386. Schupp T, Weidner K, Rusnak J, et al. Fibrinogen reflects severity and predicts outcomes in patients with sepsis and septic shock. Blood Coagul Fibrinolysis[J]. 2023;34(3)DOI:10.1097/MBC.0000000000001197. McElvaney OJ, Curley GF, Rose-John S, et al. Interleukin-6: obstacles to targeting a complex cytokine in critical illness. Lancet Respir Med[J]. 2021;9(6)DOI:10.1016/S2213-2600(21)00103-X. Wang G, Liu J, Xu R, et al. Elevated plasma D-dimer levels are associated with the poor prognosis of critically ill children. Front Pediatr[J]. 2022;10DOI:10.3389/fped.2022.1001893. Tang X, Shao L, Dou J, et al. Fibrinogen as a Prognostic Predictor in Pediatric Patients with Sepsis: A Database Study. Mediators Inflamm[J]. 2020;2020DOI:10.1155/2020/9153620. Su L, Liu S, Long Y, et al. Chinese experts' consensus on the application of intensive care big data. Front Med (Lausanne)[J]. 2023;10DOI:10.3389/fmed.2023.1174429. Table 1 Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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1","display":"","copyAsset":false,"role":"figure","size":163151,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram for predicting 28-day mortality risk in pediatric patients with sepsis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7625240/v1/c073e353d2eaa393f3d875ce.png"},{"id":94473440,"identity":"c2cd4b74-c59a-459c-bb92-e68bb0bd0dfb","added_by":"auto","created_at":"2025-10-27 15:44:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":157570,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves of the nomogram model versus individual risk factors for predicting 28-day mortality in pediatric patients with sepsis.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7625240/v1/bafe01fbb8f7be224c947a48.png"},{"id":94473433,"identity":"4b9c1af4-6bc8-409a-b456-ca6fefca79b1","added_by":"auto","created_at":"2025-10-27 15:44:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":248933,"visible":true,"origin":"","legend":"\u003cp\u003eThe ROC curves of the nomogram model in the training and validation cohort.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7625240/v1/741502406ea8d8d3ae7882d0.png"},{"id":94473461,"identity":"a845ee13-0d18-4dea-ae67-251d3fb065c5","added_by":"auto","created_at":"2025-10-27 15:44:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":440612,"visible":true,"origin":"","legend":"\u003cp\u003eThe calibration curves of the nomogram model in the training and validation cohort.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7625240/v1/e5125fcc99d5627e1c973aad.png"},{"id":94473623,"identity":"1a254fb3-d6ec-425b-98bc-cf1bc037575a","added_by":"auto","created_at":"2025-10-27 15:45:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":270043,"visible":true,"origin":"","legend":"\u003cp\u003eThe DCA curves of the nomogram model in the training and validation cohort.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7625240/v1/e786d877246af0db05b89725.png"},{"id":103716348,"identity":"8e9e63ab-0fa0-4e4c-a3aa-672dbaa726bc","added_by":"auto","created_at":"2026-03-02 05:56:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2059333,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7625240/v1/c1eeae12-8a89-4e66-8f5a-913a7e700e4b.pdf"},{"id":94473591,"identity":"3b62c6fa-b1c7-4103-bf7b-c2ea1b3f22a1","added_by":"auto","created_at":"2025-10-27 15:44:55","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":20259,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7625240/v1/8d482b0fff907e37d7a2879d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDevelopment and Validation of a PSS Criteria-Based Prediction Model for 28-Day Mortality in Pediatric Sepsis\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eThe incidence of sepsis reaches its zenith in early childhood and represents a predominant cause of pediatric mortality globally. Epidemiological data demonstrate that sepsis impacts approximately 50\u0026nbsp;million individuals worldwide annually, with half of these cases occurring in children under 19 years of age, resulting in over 3\u0026nbsp;million pediatric deaths\u003csup\u003e[1]\u003c/sup\u003e. This condition not only impairs the physical health of children but may also exert long-term detrimental effects on cognitive function, thereby imposing a substantial burden on healthcare infrastructures and socioeconomic frameworks. A U.S.-based survey indicated that hospitalization expenditures for pediatric sepsis surged by nearly 25% from 2005 to 2016, constituting 18% of aggregate pediatric hospitalization costs\u003csup\u003e[2]\u003c/sup\u003e. The majority of sepsis-related fatalities in children transpire within the initial days of hospitalization, underscoring the imperative for early identification of high-risk patients to facilitate timely bundle therapies and organ support. Effective identification instruments must exhibit high sensitivity to enable early clinical detection while remaining sufficiently straightforward, particularly in low- and middle-income nations, to curtail the misuse of medical resources and the adverse effects of excessive treatment. The 2024 International Pediatric Sepsis Consensus introduced the Phoenix Sepsis Score (PSS). Nonetheless, there is a paucity of research on predictive models for 28-day mortality risk in pediatric sepsis patients meeting PSS criteria. Consequently, this study conducted a retrospective analysis of clinical data from pediatric sepsis patients with a PSS score of \u0026ge;\u0026thinsp;2 at our institution. Utilizing a logistic regression model, we identified independent predictors of 28-day mortality, developed a predictive model, and validated it, with the objective of offering a clinical reference tool.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cb\u003eStudy Population\u003c/b\u003e A retrospective cohort study was conducted, including 254 pediatric sepsis patients admitted to the Pediatric Intensive Care Unit (PICU) at the Women and Children's Hospital of Ningbo University from June 2022 to December 2024.Inclusion Criteria:(1)PICU admission duration exceeding 24 hours.(2)Diagnosis of sepsis according to the 2024 International Consensus on Pediatric Sepsis\u003csup\u003e[3]\u003c/sup\u003e.(3)Age range: \u0026gt;1 month to \u0026lt;\u0026thinsp;18 years.Exclusion Criteria:(1)PICU admission duration less than 24 hours.(2) Patients with hematologic malignancies (e.g., leukemia, lymphoma) or solid tumors (e.g., neuroblastoma, hepatoblastoma) in the end-stage of disease, or those with other severe chronic conditions with a life expectancy of less than 28 days (e.g., end-stage organ failure).(3)Postoperative status following cardiac surgery or severe traumatic injury.(4)Neonates and preterm infants admitted during the perinatal period.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection and Grouping\u003c/b\u003e The enrolled pediatric patients were randomly allocated into a training set (n\u0026thinsp;=\u0026thinsp;178) and a test set (n\u0026thinsp;=\u0026thinsp;76) in a 7:3 ratio.Each cohort was stratified into survival and non-survival groups based on 28-day outcomes. Demographic and clinical data were collected, including age, sex, duration of PICU admission, infection site, and the necessity of mechanical ventilation. Laboratory parameters assessed within the first 24 hours of PICU admission included:Hematologic Parameters: White blood cell count (WBC), platelet count (PLT), neutrophil count (NEUT), and lymphocyte count (LYM).Hepatic Function Indicators: Albumin (ALB), alanine aminotransferase (ALT), and total bilirubin (TIBL).Renal Function Indicators: Blood urea nitrogen (BUN) and serum creatinine (Cr).Cardiac Biomarkers: Cardiac troponin I (cTnI) and brain natriuretic peptide (BNP).Coagulation Parameters: Prothrombin time (PT), activated partial thromboplastin time (APTT), international normalized ratio (INR), fibrinogen (FIB), and D-dimer (DD).Inflammatory Biomarkers: Lactate (LAC), procalcitonin (PCT), C-reactive protein (CRP), interleukin-6 (IL-6), and interleukin-10 (IL-10). A small proportion of missing data was present among the laboratory indicators collected in this study. For variables with a missing rate of less than 5%, preprocessing was performed using mean/median imputation (for normally distributed variables) or multiple imputation methods such as Random Forest-based imputation. All analyses were conducted based on the processed complete dataset. The core variables (IL-6, DD, FIB) included in the final model construction demonstrated 100% completeness, with no missing data.\u003c/p\u003e\u003cp\u003e\u003cb\u003eStatistical Methods\u003c/b\u003e Statistical analyses were conducted using SPSS 26.0 software. Categorical variables were analyzed using Pearson\u0026rsquo;s chi-square test or Fisher\u0026rsquo;s exact test, expressed as χ\u0026sup2;, with results presented as frequencies (%). Continuous variables were initially tested for normality using the Shapiro-Wilk test. Normally distributed data were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD) and compared using the independent t-test. Non-normally distributed data were expressed as median (interquartile range, IQR) [M (Q25, Q75)] and compared using the Mann-Whitney U test.Univariate logistic regression was performed to identify significant variables, which were subsequently incorporated into a multivariate logistic regression model. Significant variables from the multivariate analysis were used to develop a nomogram. The model\u0026rsquo;s predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Model accuracy and clinical utility were assessed using the Hosmer-Lemeshow test, calibration curves, and decision curve analysis (DCA). Internal validation was conducted using 1000 bootstrap resamples. A P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eComparison of Clinical Characteristics and Laboratory Parameters of the Study Population (\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e A total of 254 pediatric patients with sepsis who met the aforementioned criteria were included in this study, with an overall 28-day mortality rate of 22.8% (58/254). These patients were randomly allocated into a training set (n\u0026thinsp;=\u0026thinsp;178), which included 42 non-survivors (23.6%) and 136 survivors (76.4%), and a validation set (n\u0026thinsp;=\u0026thinsp;76), which included 16 non-survivors (21.1%) and 60 survivors (78.9%). No statistically significant differences were observed in the clinical characteristics or laboratory indicators between the two sets (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among the 58 non-surviving patients, 15 (25.9%) succumbed within 72 hours following PICU admission. The in-hospital mortality rate was consistent with the 28-day mortality rate at 22.8% (58/254), as all documented death events occurred during the hospitalization period, which aligns with the primary endpoint of 28-day outcome in this study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eUnivariate Analysis of Clinical Characteristics and Laboratory Parameters (\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e In the training cohort, no significant differences were found between the non-survival and survival groups of pediatric sepsis patients regarding gender, age, duration of PICU admission, or infection site (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, the non-survival group exhibited significantly elevated levels of PLT, LYM, ALT, BUN, Cr, cTnI, BNP, PT, APTT, INR, FIB, DD, PCT, CRP, IL-6, and IL-10 compared to the survival group (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003cb\u003eMultivariate Logistic Regression Analysis (\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e Significant laboratory parameters from the univariate analysis were incorporated into the logistic regression model. Iterative multivariate logistic regression identified IL-6, DD, and FIB as independent predictors of 28-day mortality in pediatric sepsis patients (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), optimizing the balance between model fit and the number of covariates. The complete results of the multivariate logistic regression analysis are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, which includes the regression coefficient (β), standard error (SE), Z statistic, P value, odds ratio (OR), and 95% confidence interval (CI) for all variables retained in the final model.\u003c/p\u003e\u003cp\u003e\u003cb\u003eNomogram Prediction Model Based on Multivariate Logistic Regression Analysis (\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e A nomogram was developed using the independent risk factors derived from multivariate logistic regression analysis. The score for each variable was determined by aligning the patient\u0026rsquo;s value with the top point scale. The sum of these scores yielded a total score, which was mapped to the bottom axis to predict the probability of 28-day mortality in pediatric sepsis patients. Higher total scores correlated with an increased risk of sepsis-related mortality.\u003c/p\u003e\u003cp\u003e\u003cb\u003eEvaluation and Validation of the Nomogram Model\u003c/b\u003e The nomogram model outperformed individual predictors (IL-6, DD, and FIB) in predictive accuracy (AUC\u0026thinsp;=\u0026thinsp;0.834, sensitivity\u0026thinsp;=\u0026thinsp;0.810, specificity\u0026thinsp;=\u0026thinsp;0.801) (Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). ROC curve analysis yielded AUC values of 0.883 (95% CI: 0.802\u0026ndash;0.964) in the training set and 0.834 (95% CI: 0.758\u0026ndash;0.911) in the test set (Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), confirming strong discriminative performance. Calibration plots and the Hosmer-Lemeshow test indicated good model fit, with P-values of 0.369 (training set) and 0.798 (test set), reflecting excellent agreement between predicted and observed outcomes (Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Decision curve analysis (DCA) demonstrated that the nomogram model provided higher clinical net benefit than the \"treat-all\" or \"treat-none\" approaches in both cohorts (Figure 5).\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eUnivariate analysis of survivor and non-survivor groups in pediatric sepsis patients within the training cohort\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIndex\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eSurvivor group (n\u0026thinsp;=\u0026thinsp;136)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eNor-survivor group (n\u0026thinsp;=\u0026thinsp;42)\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003et/X\u0026sup2;/Z\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003epvalue\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender (Male/Female)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79/57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24/18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge[mon,M(Q\u003csub\u003e25\u003c/sub\u003e,Q\u003csub\u003e75\u003c/sub\u003e)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31.5(4,90.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.5(12,84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.522\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSite of\u0026nbsp;infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLungs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100(73.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elymph gland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUrinary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoft tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0(0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDigestive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(11%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(7.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCental system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(9.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHospital stays[Day,M(Q\u003csub\u003e25\u003c/sub\u003e,Q\u003csub\u003e75\u003c/sub\u003e)]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(7,15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.5(3,13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.2(1.8,3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.85(1.5,6.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.764(0.326,3.568)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.237(0.502,22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.85(7.7,17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.85(8.6,15.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e295.5(207,393)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e237.5(177,348)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.65(5.95,90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.15(1.1,40.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.649\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.8(9.795,189.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1240.5(128.6,2380)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(7.8,44.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80.5(33.76,333.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.6(34.25,41.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.7(32,39.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(14,39.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109(43,205)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTiBL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.05(5.85,18.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.6(5.2,13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBUN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.45(2.6,5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.35(4.3,7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(32,53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e56(43,75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCTNI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021(0.017,0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.122(0.025,1.044)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176(29,1254.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e538(81.9,2213)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13.7(12.6,15.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.35(14.7,19.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAPTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.3(28.65,36.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36(31.2,47.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eINR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.185(1.08,1.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.43(1.28,1.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e347(270.5,505)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e209.5(155,287)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1045(365,2555)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8210(2450,24480)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNEUT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.9(4.65,13.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.55(4,12.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLYM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.8(1,3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8(1.8,7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eWBC:White blood cells; PLT:platelet count; NEUT:Neutrophil; LYM:Lymphocyte; ALB:Albumin; ALT:Glutamic-pyruvic transaminase; TiBL:total bilirubin; BUN:blood urea nitrogen; Cr:creatinine;CTNI:Cardiac troponin I; BNP:brain natriuretic peptide; PT:Prothrombin time; APTT:activated partial thromboplastin time; INR:International Normalized Ratio; FIB:Fibrinogen ; DD:D-dimer; LAC:Lactate; PCT:procalcitonin; CRP:c-reactive protein; IL-6:interleukin-6; IL-10:interleukin-10\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLogistic Analysis of Independent Risk Factors for 28-Day Mortality in Pediatric Sepsis Patients in the Training Cohort.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eIndex\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ese\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ez statis\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ep value\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eOR\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e95%_CI\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIL6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;~\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFIB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.994\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003e0.991\u0026thinsp;~\u0026thinsp;0.997\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026thinsp;~\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eIL-6:interleukin-6; FIB:Fibrinogen; DD:D-dimer\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eSepsis remains a leading cause of childhood mortality, with its pathophysiology centered on a self-perpetuating vicious cycle of dysregulated inflammation and concomitant coagulation dysfunction\u003csup\u003e[1, 4]\u003c/sup\u003e. The Phoenix Sepsis Score (PSS), as the most recent international consensus standard, provides a novel organ dysfunction-based framework for diagnosing pediatric sepsis\u003csup\u003e[3]\u003c/sup\u003e. Based on the PSS criteria, this retrospective study identified IL-6, D-dimer (DD), and fibrinogen (FIB) as independent risk factors for 28-day mortality in septic children. These three biomarkers were combined to develop and validate a nomogram prediction model. During internal validation, the model demonstrated favorable discrimination, calibration, and clinical usefulness.\u003c/p\u003e\u003cp\u003eThe selected biomarkers are not independent; they accurately capture the core mechanism of \u0026ldquo;immunothrombotic\u0026rdquo; cross-talk in sepsis. IL-6 is a pivotal inflammatory cytokine in sepsis and organ dysfunction. It rises rapidly following infection, activating inflammatory cascades and stimulating hepatic production of acute-phase proteins, including FIB \u003csup\u003e[5, 6]\u003c/sup\u003e. However, excessive inflammation damages endothelial cells, leading to tissue factor exposure and initiation of coagulation. D-dimer, a degradation product of cross-linked fibrin, serves as direct evidence of coagulation activation and secondary hyperfibrinolysis. Markedly elevated levels strongly indicate widespread microthrombosis and consumption of coagulation factors\u003csup\u003e[7, 8]\u003c/sup\u003e.he dynamic changes in FIB reflect the progression of this process: levels may be elevated or normal in early sepsis as an acute-phase reactant, but decline sharply as disseminated intravascular coagulation (DIC) evolves, due to ongoing microvascular consumption and impaired hepatic synthesis\u003csup\u003e[9, 10]\u003c/sup\u003e. Thus, the combination of high IL-6 (reflecting hyperinflammation), high DD (indicating active coagulation/fibrinolysis), and low FIB (suggesting exhaustion of clotting substrate) clearly portrays a critically ill child in a state of inflammatory storm and decompensated coagulopathy\u0026mdash;a profile strongly associated with poor outcome. Compared to any single biomarker, the three-parameter model (AUC: 0.834) offers a more comprehensive and stable risk assessment, effectively overcoming the limitations of individual variables that are susceptible to multiple confounding influences\u003csup\u003e[11]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eOur findings align with yet extend previous research. Multiple studies have separately established the value of IL-6 in evaluating sepsis-related inflammation\u003csup\u003e[5]\u003c/sup\u003e, the role of DD in predicting sepsis mortality\u003csup\u003e[8, 12]\u003c/sup\u003e, and the prognostic significance of low FIB \u003csup\u003e[10, 13]\u003c/sup\u003e. The novelty of our study lies in the integration of these three markers\u0026mdash;representing inflammation, fibrinolysis, and coagulation consumption, respectively\u0026mdash;within a pediatric sepsis cohort defined by the new PSS criteria, and in quantifying their combined predictive power. Compared with complex multi-organ scores, our model relies on only three laboratory parameters, offering greater simplicity and rapid availability. Unlike traditional inflammatory markers such as PCT and CRP, the inclusion of a coagulation dimension may provide higher specificity for prognostic assessment beyond diagnostic purposes.\u003c/p\u003e\u003cp\u003eThe ultimate value of a prediction model lies in its ability to inform clinical decisions and improve outcomes\u003csup\u003e[14]\u003c/sup\u003e. The present nomogram is visually intuitive, allowing clinicians to quickly estimate an individual\u0026rsquo;s mortality probability based on laboratory results obtained within 24 hours of admission via graphical interpretation. This offers a practical tool for early identification of high-risk children. For clinical implementation, the model could be integrated into electronic clinical workflows\u0026mdash;for example, through automated extraction of IL-6, DD, and FIB values from the hospital information system, with real-time risk calculation and prompt notification at the point of care. A mobile version could also be developed for bedside use. Identification of high-risk patients may trigger enhanced monitoring and earlier aggressive intervention.\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged. First, this was a single-center retrospective study. Although internal validation was performed using training/test splitting, external validation is lacking, and generalizability to other regions and populations requires further confirmation. Future multi-center prospective validation is warranted. Second, the sample size, though sufficient to identify strong predictors, was relatively limited and may have constrained the ability to detect weaker prognostic factors. Third, not all potential prognostic biomarkers (e.g., proadrenomedullin, von Willebrand factor) were included. Future studies could compare our model with those incorporating newer markers. Finally, we focused on short-term (28-day) mortality and did not evaluate the model\u0026rsquo;s performance in predicting long-term outcomes such as neurodevelopmental sequelae.\u003c/p\u003e\u003cp\u003eIn summary, using the PSS criteria, we developed and validated a nomogram incorporating IL-6, DD, and FIB to predict 28-day mortality in children with sepsis. The model demonstrates good predictive accuracy and translates core pathophysiological mechanisms into an accessible, visual clinical tool. Despite its limitations, it provides a valuable reference for early risk stratification, forming a basis for individualized and preemptive management strategies that may improve outcomes in pediatric sepsis. The ultimate clinical utility should be further evaluated in prospective studies and real-world implementation.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePSS, Phoenix sepsis score.\u003c/p\u003e\n\u003cp\u003eWBC, White blood cells.\u003c/p\u003e\n\u003cp\u003ePLT, platelet count.\u003c/p\u003e\n\u003cp\u003eNEUT, Neutrophil.\u003c/p\u003e\n\u003cp\u003eLYM, Lymphocyte.\u003c/p\u003e\n\u003cp\u003eALB, Albumin.\u003c/p\u003e\n\u003cp\u003eALT, Glutamic-pyruvic transaminase.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTiBL, Total bilirubin.\u003c/p\u003e\n\u003cp\u003eBUN, Blood urea nitrogen.\u003c/p\u003e\n\u003cp\u003eCr, Creatinine.\u003c/p\u003e\n\u003cp\u003eCTNI, Cardiac troponin I.\u003c/p\u003e\n\u003cp\u003eBNP, Brain natriuretic peptide.\u003c/p\u003e\n\u003cp\u003ePT, Prothrombin time.\u003c/p\u003e\n\u003cp\u003eAPTT, Activated partial thromboplastin time.\u003c/p\u003e\n\u003cp\u003eINR, International normalized ratio.\u003c/p\u003e\n\u003cp\u003eFIB, Fibrinogen.\u003c/p\u003e\n\u003cp\u003eDD, D-dimer.\u003c/p\u003e\n\u003cp\u003eLAC, Lactate.\u003c/p\u003e\n\u003cp\u003ePCT, Procalcitonin.\u003c/p\u003e\n\u003cp\u003eCRP, C-reactive protein.\u003c/p\u003e\n\u003cp\u003eIL-6, Interleukin-6.\u003c/p\u003e\n\u003cp\u003eIL-10, Interleukin-10.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and consent to participate\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This study was approved by the Ethics Committee of Women and Children\u0026rsquo;s Hospital of Ningbo University (approval no. NBFE-2025-KY-173). The need for informed consent was waived by the Ethics Committee due to the retrospective nature of the study. For participants under the age of 16, consent to participate was obtained from their parents or legal guardians. This study was conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;All authors have approved the final version of the manuscript and its submission to BMC Pediatrics.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This work was supported by Ningbo Medical and Health Brand Discipline (PPXK2024-06), Medical and Health Technology Plan Projects of Zhejiang (2023RC085, 2023KY1117, and 2025KY273), Ningbo Health Technology Plan Project (2023Y23, 2024Y15), Ningbo Public Welfare Science and Technology Project (2024S145), Zhejiang Province Traditional Chinese medicine science and technology project (2025ZL122), Ningbo Clinical Research Center for Children\u0026rsquo;s Health and Diseases (2019A21002), Ningbo Key discipline of Pediatrics (2022-B17), Ningbo Clinical Research Center Member for Emergency and Critical Diseases under Grant (2024L003), and the Innovation Project of Distinguished Medical Team in Ningbo (2022020405).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.Y.was responsible for the conceptualization and design of this study, as well as the manuscript drafting. R. P.was in charge of data acquisition and analysis. W.J.performed the interpretation of complex data and statistical analysis. Z.Y. undertook the revision of the manuscript. C. H.provided research ideas and guided the research project.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWatson RS, Carrol ED, Carter MJ, et al. The burden and contemporary epidemiology of sepsis in children. Lancet Child Adolesc Health[J]. 2024;8(9)DOI:10.1016/S2352-4642(24)00140-8.\u003c/li\u003e\n\u003cli\u003eRudd KE, Johnson SC, Agesa KM, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet[J]. 2020;395(10219)DOI:10.1016/S0140-6736(19)32989-7.\u003c/li\u003e\n\u003cli\u003eSchlapbach LJ, Watson RS, Sorce LR, et al. International Consensus Criteria for Pediatric Sepsis and Septic Shock. JAMA[J]. 2024;331(8)DOI:10.1001/jama.2024.0179.\u003c/li\u003e\n\u003cli\u003eTsantes AG, Parastatidou S, Tsantes EA, et al. Sepsis-Induced Coagulopathy: An Update on Pathophysiology, Biomarkers, and Current Guidelines. Life (Basel)[J]. 2023;13(2)DOI:10.3390/life13020350.\u003c/li\u003e\n\u003cli\u003eGan K, Chen Y, Tao L, et al. Diagnostic value of circulating IL-6 in adult sepsis: a meta-analysis. Minerva Anestesiol[J]. 2024;90(11)DOI:10.23736/S0375-9393.24.18100-X.\u003c/li\u003e\n\u003cli\u003eHamilton FW, Thomas M, Arnold D, et al. Therapeutic potential of IL6R blockade for the treatment of sepsis and sepsis-related death: A Mendelian randomisation study. PLoS Med[J]. 2023;20(1)DOI:10.1371/journal.pmed.1004174.\u003c/li\u003e\n\u003cli\u003eChinese Society on T, Hemostasis. [Chinese expert consensus on D-dimer laboratory testing and clinical application]. Zhonghua Yi Xue Za Zhi[J]. 2023;103(35)DOI:10.3760/cma.j.cn112137-20230721-00066.\u003c/li\u003e\n\u003cli\u003eHan YQ, Yan L, Zhang L, et al. Performance of D-dimer for predicting sepsis mortality in the intensive care unit. Biochem Med (Zagreb)[J]. 2021;31(2)DOI:10.11613/BM.2021.020709.\u003c/li\u003e\n\u003cli\u003eMori K, Tsujita Y, Yamane T, et al. Decreasing Plasma Fibrinogen Levels in the Intensive Care Unit Are Associated with High Mortality Rates In Patients With Sepsis-Induced Coagulopathy. Clin Appl Thromb Hemost[J]. 2022;28DOI:10.1177/10760296221101386.\u003c/li\u003e\n\u003cli\u003eSchupp T, Weidner K, Rusnak J, et al. Fibrinogen reflects severity and predicts outcomes in patients with sepsis and septic shock. Blood Coagul Fibrinolysis[J]. 2023;34(3)DOI:10.1097/MBC.0000000000001197.\u003c/li\u003e\n\u003cli\u003eMcElvaney OJ, Curley GF, Rose-John S, et al. Interleukin-6: obstacles to targeting a complex cytokine in critical illness. Lancet Respir Med[J]. 2021;9(6)DOI:10.1016/S2213-2600(21)00103-X.\u003c/li\u003e\n\u003cli\u003eWang G, Liu J, Xu R, et al. Elevated plasma D-dimer levels are associated with the poor prognosis of critically ill children. Front Pediatr[J]. 2022;10DOI:10.3389/fped.2022.1001893.\u003c/li\u003e\n\u003cli\u003eTang X, Shao L, Dou J, et al. Fibrinogen as a Prognostic Predictor in Pediatric Patients with Sepsis: A Database Study. Mediators Inflamm[J]. 2020;2020DOI:10.1155/2020/9153620.\u003c/li\u003e\n\u003cli\u003eSu L, Liu S, Long Y, et al. Chinese experts\u0026apos; consensus on the application of intensive care big data. Front Med (Lausanne)[J]. 2023;10DOI:10.3389/fmed.2023.1174429.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table 1","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\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":"[email protected]","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":"Phoenix sepsis score, sepsis, nomogram, interleukin-6, fibrinogen, D-dimer","lastPublishedDoi":"10.21203/rs.3.rs-7625240/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7625240/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eObjectiveTo construct and clinically validate a prognostic nomogram for 28-day mortality prediction in pediatric sepsis cases meeting PSS diagnostic standards.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethods This study retrospectively enrolled 254 pediatric sepsis patients who met the diagnostic criteria for pediatric sepsis syndrome (PSS) and were admitted to the Pediatric Intensive Care Unit (PICU) of Women and Children's Hospital of Ningbo University from June 2022 to December 2024. The patients were randomly divided into a training set (n=178) and a test set (n=76) at a 7:3 ratio. Demographic data (age, sex), PICU length of stay, infection sites, and routine laboratory parameters within 24 hours of PICU admission were collected, and the comparability of clinical characteristics between the two cohorts was assessed. Based on 28-day outcomes, the patients were classified into survival and non-survival groups. Univariate and multivariate logistic regression analyses were performed on the training set to identify risk factors for mortality and establish a nomogram prediction model. The predictive performance, accuracy, and clinical utility of the nomogram were evaluated in both the training and validation sets using receiver operating characteristic (ROC) curve analysis, calibration curves, and decision curve analysis (DCA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults In the training cohort, univariate and multivariate logistic regression analyses of the clinical and laboratory parameters identified IL-6, DD , and FIB as independent risk factors for 28-day mortality in pediatric sepsis patients (P \u0026lt; 0.05). A nomogram prediction model constructed using these three variables demonstrated superior predictive performance compared to individual indicators (IL-6, DD, or FIB), with an AUC of 0.834, sensitivity of 0.810, and specificity of 0.801.ROC curve analysis revealed that the nomogram model achieved an AUC of 0.883 (95% CI: 0.802–0.964) in the training set and 0.834 (95% CI: 0.758–0.911) in the test set, indicating good discriminative ability. The calibration curve, assessed by the Hosmer-Lemeshow goodness-of-fit test, showed no significant deviation between predicted and observed outcomes in either the training cohort (P = 0.369) or the validation cohort (P = 0.798), confirming good model fit. Furthermore, decision curve analysis (DCA) demonstrated that the model had favorable clinical utility.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion The nomogram incorporating IL-6, DD, and FIB represents a reliable tool for early risk stratification of 28-day mortality in pediatric sepsis patients meeting PSS criteria, potentially assisting clinicians in optimizing timely interventions and improving prognosis.\u003c/p\u003e","manuscriptTitle":"Development and Validation of a PSS Criteria-Based Prediction Model for 28-Day Mortality in Pediatric Sepsis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 14:26:19","doi":"10.21203/rs.3.rs-7625240/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"21cb54e7-86f6-4885-9987-66907e609ab1","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T05:55:23+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 14:26:19","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7625240","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7625240","identity":"rs-7625240","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}