Prospective Comparison of pSOFA and a Biomarker-Enhanced Modified pSOFA Model for Predicting In-Hospital Mortality, Length of PICU Stay, and Mechanical Ventilation Duration in Children Presenting to the Paediatric Emergency Department

Prospective Comparison of pSOFA and a Biomarker-Enhanced Modified pSOFA Model for Predicting In-Hospital Mortality, Length of PICU Stay, and Mechanical Ventilation Duration in Children Presenting to the Paediatric Emergency Department | 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 Prospective Comparison of pSOFA and a Biomarker-Enhanced Modified pSOFA Model for Predicting In-Hospital Mortality, Length of PICU Stay, and Mechanical Ventilation Duration in Children Presenting to the Paediatric Emergency Department Murugan Thimiri Palani, Amanda Grace Sajem, Varun Anand, Santosh Kumar Rathia, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8339610/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective: To evaluate and compare the predictive performance of the Paediatric Sequential Organ Failure Assessment (pSOFA) score and a biomarker-enhanced modified pSOFA model (pSOFA + CRP, Procalcitonin, Lactate) for ( 1 ) in-hospital mortality, and ( 2 ) secondary outcomes including length of PICU stay and duration of mechanical ventilation among children presenting to the Emergency Department (ED). Methods: A prospective observational study will be conducted among consecutive paediatric ED patients. pSOFA and modified pSOFA scores will be calculated on admission. Associations with mortality, PICU length of stay, and mechanical ventilation duration will be evaluated using ROC analysis, multivariable regression, and correlation statistics. Results: pSOFA showed excellent discrimination (AUC 0.853), while the modified model improved prediction (AUC 0.920). Secondary outcome analysis will explore the relationship between both models and ( 1 ) PICU length of stay and ( 2 ) mechanical ventilation duration. Conclusion: The modified pSOFA model may improve prognostic accuracy for mortality and could provide enhanced prediction of PICU resource utilization. pSOFA modified pSOFA paediatric sepsis biomarkers mortality prediction PICU stay mechanical ventilation emergency department Figures Figure 1 Figure 2 INTRODUCTION Early recognition of severe illness in children presenting to the Emergency Department is essential for timely intervention and improved outcomes. Children often present with subtle or rapidly evolving physiological derangements, making risk stratification particularly challenging. The Paediatric Sequential Organ Failure Assessment (pSOFA) score was adapted from the adult SOFA score to quantify organ dysfunction in paediatric populations and has demonstrated strong validity for predicting mortality and the need for intensive care( 1 ). However, organ dysfunction scores capture only part of the disease process. The underlying inflammatory and metabolic derangements that precede clinical deterioration may not be fully reflected in bedside physiological measurements. Biomarkers such as CRP, Procalcitonin, and Lactate are frequently obtained in emergency and critical care settings and may offer additional prognostic value beyond organ dysfunction alone( 2 ). Procalcitonin, in particular, has been shown to correlate with bacterial infection severity, while Lactate is a well-established marker of impaired perfusion and tissue hypoxia( 3 – 5 ). Despite encouraging signals, robust paediatric data evaluating biomarker-enhanced pSOFA models are scarce. Few studies have tested these approaches prospectively or within the same cohort, leaving uncertainty about their incremental value. To address this unmet need, we designed this study to assess whether integrating biomarkers with pSOFA improves early risk prediction in children with sepsis. OBJECTIVES Primary Objective To prospectively compare the discriminative accuracy of pSOFA versus a biomarker-enhanced modified pSOFA model (pSOFA + CRP + Procalcitonin + Lactate) for predicting in-hospital mortality among children presenting to the paediatric emergency department. Secondary Objectives To compare the ability of pSOFA vs Modified pSOFA to predict length of PICU stay. To compare the ability of pSOFA vs Modified pSOFA to predict duration of mechanical ventilation in critically ill children. Methods Study Design A prospective observational cohort study will be conducted to evaluate and compare the performance of the Paediatric Sequential Organ Failure Assessment (pSOFA) score and a biomarker-enhanced modified pSOFA model (pSOFA + CRP + Procalcitonin + Lactate) in predicting clinical outcomes among children presenting to the Paediatric Emergency Department (ED). Study Setting The study was conducted in the Paediatric Emergency Department of a tertiary-care hospital from India with dedicated paediatric intensive care services. Study Population Inclusion Criteria · Children aged 1 month to 18 years. · Presenting to the paediatric ED with suspected sepsis, systemic infection, shock, altered sensorium, respiratory distress, or any condition requiring organ dysfunction assessment. · Blood tests for CRP, Procalcitonin, and Lactate drawn as part of routine clinical care. · Admission to hospital or PICU for further management. · Informed consent obtained from parent or guardian. Exclusion Criteria · Children with pre-existing end-of-life care plans or palliative intent. · Missing data preventing calculation of pSOFA or modified pSOFA scores. · Transfers from another hospital with >24 hrs of prior admission. Data Collection Upon presentation to the ED, the following data will be collected: 1. Clinical and Laboratory Variables · pSOFA score calculated at ED arrival using the components: o Respiratory (PaO₂/FiO₂ or SpO₂/FiO₂) o Cardiovascular (vasopressor use, MAP) o Coagulation (platelet count) o Liver function (bilirubin) o Neurologic function (GCS) o Renal function (creatinine, urine output) 2. Biomarkers for Modified pSOFA · C-reactive protein (CRP) (mg/L) - CRP is an acute-phase reactant that rises in inflammatory states. Elevated CRP reflects systemic inflammation but lacks specificity. It is widely used due to availability and low cost. · Procalcitonin (ng/mL) - Procalcitonin correlates with bacterial infection severity and systemic inflammatory response. Prior studies show strong performance as a sepsis biomarker. · Lactate (mmol/L) - Lactate is a marker of impaired perfusion and oxygen delivery. High levels indicate circulatory failure and correlate with mortality risk. All measured at the time of initial ED evaluation. 3. Outcome Variables Primary outcome: · In-hospital mortality (coded as 1 = death, 0 = survival) Secondary outcomes: · Length of PICU stay (days) · Duration of mechanical ventilation (days) These outcomes will be recorded from electronic hospital records. Statistical Analysis Descriptive Statistics Continuous variables were summarized using mean ± SD, median, interquartile range (IQR), and ranges. Categorical variables were reported as frequencies and proportions. Univariate ROC Analysis ROC curves were generated for each predictor—pSOFA, CRP, Procalcitonin, and Lactate—to assess their individual discriminatory capacity. AUC values were interpreted as: · 0.7–0.79: acceptable · 0.8–0.89: good · 0.9+: excellent Cutoff points maximizing sensitivity and specificity were identified using the Youden Index. Multivariable Model Construction A logistic regression model was constructed using: · pSOFA · CRP · Procalcitonin · Lactate All variables were entered simultaneously, reflecting their combined early diagnostic utility. Regression outputs included: · Regression coefficients · Adjusted odds ratios (ORs) · 95% confidence intervals · p-values A predictor was considered statistically significant if p < 0.05. Model Discrimination A combined model ROC curve was generated. The AUC was compared to pSOFA’ s AUC to assess whether adding biomarkers improved prediction. AUC improvement suggests: · Better separation between survivors and non-survivors · Additional prognostic information beyond organ dysfunction The optimal probability cutoff was again identified using the Youden Index. Sample-Size Planning Prior to study initiation, we planned the sample size using the Obuchowski method for comparing paired ROC curves, as both pSOFA and the biomarker-augmented model were to be evaluated in the same patients(6). This approach assumes a binormal distribution for test results, accounts for the correlation between paired AUCs, and estimates the variance of the difference in AUCs to determine the required number of events. Published paediatric studies have reported pSOFA AUCs in the range of 0.82–0.86 for mortality prediction(1,7). In the absence of directly comparable biomarker-enhanced pSOFA models in similar populations, we prospectively specified an absolute AUC improvement of 0.07 (from approximately 0.84 to 0.91) as a clinically relevant effect size. This assumption was informed by prior work suggesting that multimodal, biomarker-integrated models can achieve AUCs approaching or exceeding 0.90 in sepsis risk stratification, albeit in heterogeneous settings and populations(8). Using the Obuchowski method with: · α = 0.05 (two-sided), · power = 80%, · assumed correlation between paired AUCs = 0.6, · target ΔAUC = 0.07, we estimated that 82 deaths and 82 survivors would be required. Allowing for an anticipated mortality of 30–35%, this translated into a target enrolment of approximately 230–240 children. Our final cohort comprised 204 children, including 74 deaths (36.3%), which closely approximated the planned number of events for the primary AUC comparison. Secondary Outcome Analyses A. Length of PICU Stay · Spearman correlation with pSOFA and modified pSOFA · Linear regression models · ROC curves for predicting prolonged PICU stay (>6 days) · Comparison of AUCs between pSOFA and modified pSOFA B. Duration of Mechanical Ventilation · Spearman correlation with pSOFA and modified pSOFA · Linear regression for predictors of ventilation duration · ROC curves for predicting prolonged ventilation (>72 hrs) · AUC comparison using DeLong test RESULTS Study Population A total of 204 children were included in the analysis. Among them: Survivors : 130 Deaths : 74 (36.3%) All 204 children had complete pSOFA, CRP, Procalcitonin, Lactate, PICU length of stay, and mechanical ventilation data available for analysis. Baseline Characteristics and Descriptive Statistics The distributions of pSOFA, CRP, Procalcitonin, and Lactate are summarized in Table 1 . Table 1 Descriptive Statistics of Predictor Variables (N = 204) Variable Count Mean SD Min 25% Median 75% Max pSOFA 204 8.436 3.677 1.0 6.0 8.0 12.0 18.0 CRP (mg/L) 204 57.800 60.879 3.0 13.6 26.5 112.175 311.4 Procalcitonin (ng/mL) 204 9.313 11.437 0.5 2.3 9.5 11.6 136.3 Lactate (mmol/L) 204 4.740 2.491 1.2 3.2 4.2 6.2 14.0 Primary Outcome: In-Hospital Mortality 1. ROC Curve Analysis pSOFA (Fig. 1 ) demonstrated excellent discriminative ability: AUC : 0.853 Sensitivity : 0.74 Specificity : 0.85 Optimal cutoff : 10.0 (Youden) Figure 1 - ROC Curve for pSOFA (AUC = 0.85) The modified pSOFA model (Fig. 2 ) incorporating CRP, Procalcitonin, and Lactate showed improved performance: AUC : 0.920 Sensitivity : 0.824 Specificity : 0.869 Optimal probability threshold : 0.382 2. Univariate Predictor Performance pSOFA: AUC 0.853 • CRP: AUC 0.779 Procalcitonin: AUC 0.805 Lactate: AUC 0.751 3. Multivariable Logistic Regression Table 2 Multivariable Logistic Regression Predictor Coefficient OR 95% CI p-value pSOFA 0.442 1.556 1.297–1.867 < 0.001 CRP 0.000 1.000 0.991–1.009 0.937 Procalcitonin 0.196 1.216 1.124–1.316 < 0.001 Lactate 0.103 1.109 0.927–1.326 0.258 pSOFA and Procalcitonin remained strong independent predictors of mortality. CRP and Lactate were not statistically significant predictors after adjustment. (Table 2 ) Secondary Outcome 1: Length of PICU Stay Descriptive statistics Among the 204 children, PICU length of stay was: Mean : 6.85 days Median : 6 days IQR : 4–10 days Range : 2–16 days Correlation with predictors Spearman correlations between PICU LOS and predictors: pSOFA : ρ = −0.27 , p < 0.001 CRP : ρ = −0.35 , p < 0.001 Procalcitonin : ρ = −0.37 , p < 0.001 Lactate : ρ = −0.25 , p < 0.001 Table 3 Descriptive Statistics for PICU Length of Stay and Mechanical Ventilation Duration (N = 204) Variable Mean Median SD IQR (25–75%) Min Max PICU Length of Stay (days) 6.85 6 3.54 4–10 2 16 Mechanical Ventilation Duration (days) 3.26 3 2.09 2–4 0 12 Table 4 Spearman Correlations of PICU LOS and Mechanical Ventilation Duration with Predictors Predictor Correlation with PICU LOS (ρ) p-value Correlation with MV Duration (ρ) p-value pSOFA −0.2708 < 0.001 0.4120 < 0.001 CRP −0.3525 < 0.001 0.3725 < 0.001 Procalcitonin −0.3739 < 0.001 0.1909 0.006 Lactate −0.2511 < 0.001 0.2879 < 0.001 Higher illness severity and biomarker levels were associated with shorter PICU stay, likely reflecting early mortality in the sickest children. (Tables 3 & 4 ) Logistic regression for prolonged PICU stay Prolonged PICU stay was defined as > 6 days (above the median). Model 1 – pSOFA only OR per 1-point increase in pSOFA: 0.89 95% CI: 0.82–0.96 p = 0.003 Model 2 – Modified pSOFA (pSOFA + CRP + Procalcitonin + Lactate) None of the individual predictors were statistically significant for prolonged LOS after adjustment: pSOFA OR ≈ 0.97 (95% CI 0.87–1.08), p = 0.56 CRP OR ≈ 1.00 (95% CI 0.99–1.00), p = 0.44 Procalcitonin OR ≈ 0.96 (95% CI 0.92–1.00), p = 0.07 Lactate OR ≈ 0.90 (95% CI 0.78–1.04), p = 0.15 ROC analysis – prolonged PICU stay (> 6 days) pSOFA only model : AUC = 0.62 Modified pSOFA model : AUC = 0.72 Table 5 Logistic Regression for Prolonged PICU Stay (> 6 days) Predictor OR 95% CI p-value Model 1: pSOFA Only pSOFA 0.89 0.82–0.96 0.003 Model 2: Modified pSOFA pSOFA 0.97 0.87–1.08 0.56 CRP 1.00 0.99–1.00 0.44 Procalcitonin 0.96 0.92–1.00 0.07 Lactate 0.90 0.78–1.04 0.15 So, for predicting prolonged PICU stay , the modified model shows better discrimination than pSOFA alone, but the overall AUC remains in the fair range and is influenced by the competing effect of early death. (Table 5 ) Secondary Outcome 2: Duration of Mechanical Ventilation Descriptive statistics Mechanical ventilation duration in the cohort: Mean : 3.26 days Median : 3 days IQR : 2–4 days Range : 0–12 days 0 days include children who did not receive mechanical ventilation. Correlation with predictors Spearman correlations with MV duration: pSOFA : ρ = 0.41 , p < 0.001 CRP : ρ = 0.37 , p < 0.001 Procalcitonin : ρ = 0.19 , p = 0.006 Lactate : ρ = 0.29 , p < 0.001 Higher pSOFA and biomarker levels were associated with longer duration of mechanical ventilation. Logistic regression for prolonged mechanical ventilation Prolonged mechanical ventilation was defined as > 3 days (above the median). Model 1 – pSOFA only OR per 1-point increase in pSOFA: 1.25 95% CI: 1.15–1.37 p < 0.001 Model 2 – Modified pSOFA (pSOFA + CRP + Procalcitonin + Lactate) pSOFA OR: 1.14 (95% CI 1.02–1.29), p = 0.025 CRP OR: ~1.00 (95% CI 1.00–1.01), p = 0.28 Procalcitonin OR: ~1.01 (95% CI 0.98–1.04), p = 0.61 Lactate OR: 1.15 (95% CI 0.99–1.34), p = 0.06 pSOFA remained an independent predictor of prolonged mechanical ventilation after adjustment. (Table 6 ) Table 6 Logistic Regression for Prolonged Mechanical Ventilation (> 3 days) Predictor OR 95% CI p-value Model 1: pSOFA Only pSOFA 1.25 1.15–1.37 3 days) pSOFA only model : AUC = 0.72 Modified pSOFA model : AUC = 0.76 For predicting prolonged mechanical ventilation , both models show good discriminative ability, with a modest improvement when CRP, Procalcitonin, and Lactate are added. (Table 7 ) Table 7 ROC AUC Comparison for Secondary Outcomes Outcome pSOFA AUC Modified pSOFA AUC Prolonged PICU Stay (> 6 days) 0.618 0.716 Prolonged Mechanical Ventilation (> 3 days) 0.717 0.762 DISCUSSION In this prospective comparison study, both the original pSOFA score and a biomarker-enhanced modified pSOFA model demonstrated significant utility in predicting adverse outcomes among children presenting to the Paediatric Emergency Department. Consistent with prior validation studies, pSOFA alone showed excellent discrimination for in-hospital mortality, with an AUC of 0.853. (Fig. 1 ) This aligns closely with the original paediatric SOFA adaptation study by Matics and Sanchez-Pinto, which reported an AUC of approximately 0.82–0.86 when validating the score in critically ill children( 1 , 9 ), and with subsequent international work highlighting the strong relationship between organ dysfunction burden and mortality risk( 2 , 9 ). The addition of CRP, Procalcitonin, and Lactate significantly improved predictive performance, increasing the AUC to 0.920. (Fig. 2 ) This finding is consistent with literature demonstrating that combining clinical scores with inflammatory and perfusion biomarkers enhances early prognostic precision. Multiple studies—including those evaluating Procalcitonin and Lactate in paediatric sepsis—have shown that these markers carry independent predictive value for mortality and organ dysfunction( 3 , 5 ). The strong independent effect of Procalcitonin in our cohort supports existing evidence regarding its association with severe bacterial infection and progression to multi-organ dysfunction( 4 ). While CRP and Lactate were not independently significant in adjusted models, their inclusion contributed to overall model performance, as reflected by the improved AUC. Comparison With Existing Mortality Prediction Tools The mortality performance of the modified pSOFA model (AUC 0.920) exceeds that reported for several established paediatric organ dysfunction scores. For example, the Paediatric Logistic Organ Dysfunction Score (PELOD-2) typically demonstrates AUC values ranging from 0.88–0.91 in ICU settings( 7 ), whereas our model maintained high discrimination in the ED setting, where physiological variability is greater and illness trajectories are less established. This supports the potential role of biomarker-enhanced tools in early triage when decision-making is time-critical. PICU Length of Stay (LOS) as a Secondary Outcome The secondary analysis evaluating PICU LOS revealed an inverse correlation between severity markers (pSOFA and biomarkers) and length of stay. This likely reflects a competing-risk phenomenon, where patients with the highest severity scores tend to experience early mortality, thereby shortening calculated LOS. Similar patterns have been documented in other paediatric sepsis cohorts, where early deaths reduce median LOS in the sickest subgroup( 10 ). The modified pSOFA model improved discrimination for predicting prolonged PICU stay (AUC 0.716 vs 0.618), though performance remained modest, suggesting that LOS is multifactorial and influenced by factors beyond initial severity, such as comorbidity profiles, therapeutic responsiveness, and institutional practices. Mechanical Ventilation Duration Both pSOFA and modified pSOFA scores demonstrated meaningful associations with mechanical ventilation duration, with correlations strongest for pSOFA (ρ = 0.412, p < 0.001). Logistic regression confirmed pSOFA as a strong independent predictor of prolonged ventilation (OR 1.25 per point increase). These findings are consistent with prior sepsis and respiratory failure literature showing that early organ dysfunction scores correlate with ventilator days and respiratory support requirements(12). The modified model improved AUC from 0.717 to 0.762, suggesting incremental prognostic utility. Biomarkers, especially Lactate, approached significance in the multivariable model, underscoring their physiologic relevance in predicting ventilation needs related to shock and tissue hypoperfusion. Clinical Implications The results highlight three important clinical implications: Early ED-based pSOFA scoring is reliable and can be implemented even before ICU admission. Biomarker augmentation (CRP, Procalcitonin, Lactate) substantially improves prediction, particularly for mortality and mechanical ventilation duration. The modified pSOFA model may serve as an effective tool for resource allocation, early triage decisions, PICU bed prioritization, and anticipating ventilatory requirements. Given the increasing emphasis on early sepsis recognition and rapid escalation pathways, a combined biomarker-score model may offer significant advantages, especially in high-volume emergency settings. Strengths and Limitations A major strength of the study is its prospective design and complete availability of biomarker and outcome data for all participants. Additionally, inclusion of both mortality and PICU resource-utilization measures allows comprehensive assessment of clinical severity. Key limitations include being a single-center study, which may limit external generalizability, and the potential influence of institutional treatment protocols on LOS and ventilation duration. The competing-risk effect on LOS also complicates interpretation and should be explored further using survival models in future studies. Conclusion Both pSOFA and the biomarker-enhanced modified pSOFA model effectively predict in-hospital mortality in children presenting to the ED, with the modified model exhibiting superior discriminative performance. Secondary analyses demonstrate meaningful associations between initial severity and mechanical ventilation duration, and moderate associations with PICU LOS. These findings support integrating biomarker-enhanced scoring into ED assessment pathways to improve prognostication and resource planning. Abbreviations pSOFA Paediatric Sequential Organ Failure Assessment PICU Paediatric Intensive Care Unit CRP C-reactive protein ED Emergency Department ROC Receiver Operating Characteristic AUC Area Under the Curve Declarations Ethics approval and consent to participate The study was reviewed and approved by the Institutional Ethics Committee, All India Institute of Medical Sciences (AIIMS), Raipur, Chhattisgarh, India (IEC Proposal No. AIIMSRPR/IEC/2023/1327 , approval date 18 March 2023 ). The study was conducted in accordance with the ethical standards of the institutional ethics committee and in compliance with the Declaration of Helsinki . Written informed consent was obtained from the parents or legal guardians of all participating children prior to enrolment. Consent for publication Not applicable. Availability of data and materials The datasets generated and analysed during the current study are included in this published article as an Appendix . Additional details, if required, are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding The study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions Murugan Thimiri Palani conceived the study, developed the study design, and coordinated its execution. Murugan Thimiri Palani and Amanda Grace Sajem were responsible for patient enrolment, clinical assessment, and prospective data collection in the paediatric emergency department. Melwin Dany Samuel performed data cleaning and statistical analysis, interpreted the results, and drafted the initial version of the manuscript. Santosh Kumar Rathia and Varun Anand contributed to critical review of the study methodology, interpretation of results, and revision of the manuscript for important intellectual content. Murugan Thimiri Palani also provided overall supervision of the project. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work. Acknowledgements The authors thank the patients and their families for participating in the study, and the staff of the Paediatric Emergency Department and Paediatric Intensive Care Unit for their support during data collection. References Matics TJ, Sanchez-Pinto LN. Adaptation and Validation of a Pediatric Sequential Organ Failure Assessment Score and Evaluation of the Sepsis-3 Definitions in Critically Ill Children. JAMA Pediatr. 2017;171(10):e172352. Weiss SL, Peters MJ, Alhazzani W, Agus MSD, Flori HR, Inwald DP, et al. Surviving Sepsis Campaign International Guidelines for the Management of Septic Shock and Sepsis-Associated Organ Dysfunction in Children. Pediatr Crit Care Med. 2020;21(2):e52. Becker KL, Snider R, Nylen ES. Procalcitonin in sepsis and systemic inflammation: a harmful biomarker and a therapeutic target. Br J Pharmacol. 2010;159(2):253–64. Schuetz P, Birkhahn R, Sherwin R, Jones AE, Singer A, Kline JA, et al. Serial Procalcitonin Predicts Mortality in Severe Sepsis Patients: Results From the Multicenter Procalcitonin MOnitoring SEpsis (MOSES) Study. Crit Care Med. 2017;45(5):781–9. Nguyen HB, Rivers EP, Knoblich BP, Jacobsen G, Muzzin A, Ressler JA, et al. Early lactate clearance is associated with improved outcome in severe sepsis and septic shock. Crit Care Med. 2004;32(8):1637–42. Obuchowski NA. Sample size calculations in studies of test accuracy. Stat Methods Med Res. 1998;7(4):371–92. Leteurtre S, Martinot A, Duhamel A, Proulx F, Grandbastien B, Cotting J et al. Validation of the paediatric logistic organ dysfunction (PELOD) score: prospective, observational, multicentre study. Lancet Lond Engl 2003 July 19;362(9379):192–7. Lee CW, Kou H, wei, Chou HS, Chou H, huan, Huang SF, Chang CH, et al. A combination of SOFA score and biomarkers gives a better prediction of septic AKI and in-hospital mortality in critically ill surgical patients: a pilot study. World J Emerg Surg WJES. 2018 Sept;10:13:41. Schlapbach LJ, Straney L, Alexander J, MacLaren G, Festa M, Schibler A, et al. Mortality related to invasive infections, sepsis, and septic shock in critically ill children in Australia and New Zealand, 2002–13: a multicentre retrospective cohort study. Lancet Infect Dis. 2015;15(1):46–54. Ruth A, McCracken CE, Fortenberry JD, Hall M, Simon HK, Hebbar KB. Pediatric severe sepsis: current trends and outcomes from the Pediatric Health Information Systems database. Pediatr Crit Care Med J Soc Crit Care Med World Fed Pediatr Intensive Crit Care Soc. 2014;15(9):828–38. Lu F, Qin H, Li AM. The Correlation Between Mechanical Ventilation Duration, Pediatric Sequential Organ Failure Assessment Score, and Blood Lactate Level in Children in Pediatric Intensive Care. Front Pediatr [Internet]. 2022 Mar 14 [cited 2025 Dec 8];10. Available from: https://www.frontiersin.org/journals/pediatrics/articles/ 10.3389/fped.2022.767690/full Additional Declarations No competing interests reported. Supplementary Files Appendix.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 20 Mar, 2026 Editor assigned by journal 04 Feb, 2026 Editor invited by journal 27 Jan, 2026 Submission checks completed at journal 23 Jan, 2026 First submitted to journal 23 Jan, 2026 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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Samuel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFklEQVRIiWNgGAWjYFAC5gaGBAZmEOMAmM/HDpfBpYURpoUtAcxng6vEpwUiy2OApgUH0G1vbJN4uMNa3lwi5+OnGzWH5diYmY99+MBgJ8/AznsAmxazMwfbJBLPpBvunJG7WTrn2GFjNma25JkzGJING5j5ErBquZHYBkSHGTfcyN3GnNuQltjGzGPMzMPADPQg1KnoWu4/BGux33Aj5xlISz1Yyx+GetxabjCCtSQCtbABtdgksIG0MDAcxq3lTGL7j8S29OQNZ54ZA/1iY9gG9Atjj8FxIAOHluOHDxv+bLO23XA8+eHnnBoJeX725sMMPyqq5fn5z2DVggACKMEDVMyGXz0Q8B8gqGQUjIJRMApGKAAAPkRY6d+iWxEAAAAASUVORK5CYII=","orcid":"","institution":"Christian Medical College \u0026 Hospital","correspondingAuthor":true,"prefix":"","firstName":"Melwin","middleName":"Dany","lastName":"Samuel","suffix":""}],"badges":[],"createdAt":"2025-12-11 19:08:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8339610/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8339610/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105321134,"identity":"c8fd9974-282b-4775-8af4-84793645049d","added_by":"auto","created_at":"2026-03-24 17:20:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":183775,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve for pSOFA (AUC = 0.85)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8339610/v1/ad532cc84f96fa5ffa86634c.png"},{"id":105321136,"identity":"70e04ce0-074c-4964-86cf-f94911fb09ad","added_by":"auto","created_at":"2026-03-24 17:20:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":211766,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC Curve for pSOFA + CRP + Procalcitonin (AUC = 0.92)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8339610/v1/0b873d2d0dc8985ec160a2cd.png"},{"id":105564802,"identity":"5f3b94f0-562d-4ae7-8b04-6d3edceb9e5a","added_by":"auto","created_at":"2026-03-27 12:50:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2112814,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8339610/v1/f01b02f7-562f-4885-9108-4f1cbe2ad9ee.pdf"},{"id":105321135,"identity":"6f3cb71c-710d-4623-98db-34493710d4d3","added_by":"auto","created_at":"2026-03-24 17:20:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1499692,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8339610/v1/c6ea242ad43525408fee1f03.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Prospective Comparison of pSOFA and a Biomarker-Enhanced Modified pSOFA Model for Predicting In-Hospital Mortality, Length of PICU Stay, and Mechanical Ventilation Duration in Children Presenting to the Paediatric Emergency Department","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eEarly recognition of severe illness in children presenting to the Emergency Department is essential for timely intervention and improved outcomes. Children often present with subtle or rapidly evolving physiological derangements, making risk stratification particularly challenging. The Paediatric Sequential Organ Failure Assessment (pSOFA) score was adapted from the adult SOFA score to quantify organ dysfunction in paediatric populations and has demonstrated strong validity for predicting mortality and the need for intensive care(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, organ dysfunction scores capture only part of the disease process. The underlying inflammatory and metabolic derangements that precede clinical deterioration may not be fully reflected in bedside physiological measurements. Biomarkers such as CRP, Procalcitonin, and Lactate are frequently obtained in emergency and critical care settings and may offer additional prognostic value beyond organ dysfunction alone(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Procalcitonin, in particular, has been shown to correlate with bacterial infection severity, while Lactate is a well-established marker of impaired perfusion and tissue hypoxia(\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite encouraging signals, robust paediatric data evaluating biomarker-enhanced pSOFA models are scarce. Few studies have tested these approaches prospectively or within the same cohort, leaving uncertainty about their incremental value. To address this unmet need, we designed this study to assess whether integrating biomarkers with pSOFA improves early risk prediction in children with sepsis.\u003c/p\u003e\n\u003ch3\u003eOBJECTIVES\u003c/h3\u003e\n\u003cp\u003ePrimary Objective\u003c/p\u003e \u003cp\u003eTo prospectively compare the discriminative accuracy of pSOFA versus a biomarker-enhanced modified pSOFA model (pSOFA\u0026thinsp;+\u0026thinsp;CRP\u0026thinsp;+\u0026thinsp;Procalcitonin\u0026thinsp;+\u0026thinsp;Lactate) for predicting in-hospital mortality among children presenting to the paediatric emergency department.\u003c/p\u003e \u003cp\u003eSecondary Objectives\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo compare the ability of pSOFA vs Modified pSOFA to predict length of PICU stay.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eTo compare the ability of pSOFA vs Modified pSOFA to predict duration of mechanical ventilation in critically ill children.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eStudy Design\u003c/p\u003e\n\u003cp\u003eA prospective observational cohort study will be conducted to evaluate and compare the performance of the Paediatric Sequential Organ Failure Assessment (pSOFA) score and a biomarker-enhanced modified pSOFA model (pSOFA + CRP + Procalcitonin + Lactate) in predicting clinical outcomes among children presenting to the Paediatric Emergency Department (ED).\u003c/p\u003e\n\u003cp\u003eStudy Setting\u003c/p\u003e\n\u003cp\u003eThe study was conducted in the Paediatric Emergency Department of a tertiary-care hospital from India with dedicated paediatric intensive care services.\u003c/p\u003e\n\u003cp\u003eStudy Population\u003c/p\u003e\n\u003cp\u003eInclusion Criteria\u003c/p\u003e\n\u003cp\u003e\u0026middot; Children aged 1 month to 18 years.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Presenting to the paediatric ED with suspected sepsis, systemic infection, shock, altered sensorium, respiratory distress, or any condition requiring organ dysfunction assessment.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Blood tests for CRP, Procalcitonin, and Lactate drawn as part of routine clinical care.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Admission to hospital or PICU for further management.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Informed consent obtained from parent or guardian.\u003c/p\u003e\n\u003cp\u003eExclusion Criteria\u003c/p\u003e\n\u003cp\u003e\u0026middot; Children with pre-existing end-of-life care plans or palliative intent.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Missing data preventing calculation of pSOFA or modified pSOFA scores.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Transfers from another hospital with \u0026gt;24 hrs of prior admission.\u003c/p\u003e\n\u003cp\u003eData Collection\u003c/p\u003e\n\u003cp\u003eUpon presentation to the ED, the following data will be collected:\u003c/p\u003e\n\u003cp\u003e1. Clinical and Laboratory Variables\u003c/p\u003e\n\u003cp\u003e\u0026middot; pSOFA score calculated at ED arrival using the components:\u003c/p\u003e\n\u003cp\u003eo Respiratory (PaO₂/FiO₂ or SpO₂/FiO₂)\u003c/p\u003e\n\u003cp\u003eo Cardiovascular (vasopressor use, MAP)\u003c/p\u003e\n\u003cp\u003eo Coagulation (platelet count)\u003c/p\u003e\n\u003cp\u003eo Liver function (bilirubin)\u003c/p\u003e\n\u003cp\u003eo Neurologic function (GCS)\u003c/p\u003e\n\u003cp\u003eo Renal function (creatinine, urine output)\u003c/p\u003e\n\u003cp\u003e2. Biomarkers for Modified pSOFA\u003c/p\u003e\n\u003cp\u003e\u0026middot; C-reactive protein (CRP) (mg/L) - CRP is an acute-phase reactant that rises in inflammatory states. Elevated CRP reflects systemic inflammation but lacks specificity. It is widely used due to availability and low cost.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Procalcitonin (ng/mL) - Procalcitonin correlates with bacterial infection severity and systemic inflammatory response. Prior studies show strong performance as a sepsis biomarker.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Lactate (mmol/L) - Lactate is a marker of impaired perfusion and oxygen delivery. High levels indicate circulatory failure and correlate with mortality risk.\u003c/p\u003e\n\u003cp\u003eAll measured at the time of initial ED evaluation.\u003c/p\u003e\n\u003cp\u003e3. Outcome Variables\u003c/p\u003e\n\u003cp\u003ePrimary outcome:\u003c/p\u003e\n\u003cp\u003e\u0026middot; In-hospital mortality (coded as 1 = death, 0 = survival)\u003c/p\u003e\n\u003cp\u003eSecondary outcomes:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Length of PICU stay (days)\u003c/p\u003e\n\u003cp\u003e\u0026middot; Duration of mechanical ventilation (days)\u003c/p\u003e\n\u003cp\u003eThese outcomes will be recorded from electronic hospital records.\u003c/p\u003e\n\u003cp\u003eStatistical Analysis\u003c/p\u003e\n\u003cp\u003eDescriptive Statistics\u003c/p\u003e\n\u003cp\u003eContinuous variables were summarized using mean \u0026plusmn; SD, median, interquartile range (IQR), and ranges. Categorical variables were reported as frequencies and proportions.\u003c/p\u003e\n\u003cp\u003eUnivariate ROC Analysis\u003c/p\u003e\n\u003cp\u003eROC curves were generated for each predictor\u0026mdash;pSOFA, CRP, Procalcitonin, and Lactate\u0026mdash;to assess their individual discriminatory capacity. AUC values were interpreted as:\u003c/p\u003e\n\u003cp\u003e\u0026middot; 0.7\u0026ndash;0.79: acceptable\u003c/p\u003e\n\u003cp\u003e\u0026middot; 0.8\u0026ndash;0.89: good\u003c/p\u003e\n\u003cp\u003e\u0026middot; 0.9+: excellent\u003c/p\u003e\n\u003cp\u003eCutoff points maximizing sensitivity and specificity were identified using the Youden Index.\u003c/p\u003e\n\u003cp\u003eMultivariable Model Construction\u003c/p\u003e\n\u003cp\u003eA logistic regression model was constructed using:\u003c/p\u003e\n\u003cp\u003e\u0026middot; pSOFA\u003c/p\u003e\n\u003cp\u003e\u0026middot; CRP\u003c/p\u003e\n\u003cp\u003e\u0026middot; Procalcitonin\u003c/p\u003e\n\u003cp\u003e\u0026middot; Lactate\u003c/p\u003e\n\u003cp\u003eAll variables were entered simultaneously, reflecting their combined early diagnostic utility.\u003c/p\u003e\n\u003cp\u003eRegression outputs included:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Regression coefficients\u003c/p\u003e\n\u003cp\u003e\u0026middot; Adjusted odds ratios (ORs)\u003c/p\u003e\n\u003cp\u003e\u0026middot; 95% confidence intervals\u003c/p\u003e\n\u003cp\u003e\u0026middot; p-values\u003c/p\u003e\n\u003cp\u003eA predictor was considered statistically significant if p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eModel Discrimination\u003c/p\u003e\n\u003cp\u003eA combined model ROC curve was generated. The AUC was compared to pSOFA\u0026rsquo; s AUC to assess whether adding biomarkers improved prediction.\u003c/p\u003e\n\u003cp\u003eAUC improvement suggests:\u003c/p\u003e\n\u003cp\u003e\u0026middot; Better separation between survivors and non-survivors\u003c/p\u003e\n\u003cp\u003e\u0026middot; Additional prognostic information beyond organ dysfunction\u003c/p\u003e\n\u003cp\u003eThe optimal probability cutoff was again identified using the Youden Index.\u003c/p\u003e\n\u003cp\u003eSample-Size Planning\u003c/p\u003e\n\u003cp\u003ePrior to study initiation, we planned the sample size using the Obuchowski method for comparing paired ROC curves, as both pSOFA and the biomarker-augmented model were to be evaluated in the same patients(6). This approach assumes a binormal distribution for test results, accounts for the correlation between paired AUCs, and estimates the variance of the difference in AUCs to determine the required number of events.\u003c/p\u003e\n\u003cp\u003ePublished paediatric studies have reported pSOFA AUCs in the range of 0.82\u0026ndash;0.86 for mortality prediction(1,7). In the absence of directly comparable biomarker-enhanced pSOFA models in similar populations, we prospectively specified an absolute AUC improvement of 0.07 (from approximately 0.84 to 0.91) as a clinically relevant effect size. This assumption was informed by prior work suggesting that multimodal, biomarker-integrated models can achieve AUCs approaching or exceeding 0.90 in sepsis risk stratification, albeit in heterogeneous settings and populations(8).\u003c/p\u003e\n\u003cp\u003eUsing the Obuchowski method with:\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u0026alpha; = 0.05 (two-sided),\u003c/p\u003e\n\u003cp\u003e\u0026middot; power = 80%,\u003c/p\u003e\n\u003cp\u003e\u0026middot; assumed correlation between paired AUCs = 0.6,\u003c/p\u003e\n\u003cp\u003e\u0026middot; target \u0026Delta;AUC = 0.07,\u003c/p\u003e\n\u003cp\u003ewe estimated that 82 deaths and 82 survivors would be required. Allowing for an anticipated mortality of 30\u0026ndash;35%, this translated into a target enrolment of approximately 230\u0026ndash;240 children. Our final cohort comprised 204 children, including 74 deaths (36.3%), which closely approximated the planned number of events for the primary AUC comparison.\u003c/p\u003e\n\u003cp\u003eSecondary Outcome Analyses\u003c/p\u003e\n\u003cp\u003eA. Length of PICU Stay\u003c/p\u003e\n\u003cp\u003e\u0026middot; Spearman correlation with pSOFA and modified pSOFA\u003c/p\u003e\n\u003cp\u003e\u0026middot; Linear regression models\u003c/p\u003e\n\u003cp\u003e\u0026middot; ROC curves for predicting prolonged PICU stay (\u0026gt;6 days)\u003c/p\u003e\n\u003cp\u003e\u0026middot; Comparison of AUCs between pSOFA and modified pSOFA\u003c/p\u003e\n\u003cp\u003eB. Duration of Mechanical Ventilation\u003c/p\u003e\n\u003cp\u003e\u0026middot; Spearman correlation with pSOFA and modified pSOFA\u003c/p\u003e\n\u003cp\u003e\u0026middot; Linear regression for predictors of ventilation duration\u003c/p\u003e\n\u003cp\u003e\u0026middot; ROC curves for predicting prolonged ventilation (\u0026gt;72 hrs)\u003c/p\u003e\n\u003cp\u003e\u0026middot; AUC comparison using DeLong test\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy Population\u003c/h2\u003e \u003cp\u003eA total of \u003cb\u003e204 children\u003c/b\u003e were included in the analysis.\u003c/p\u003e \u003cp\u003eAmong them:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSurvivors\u003c/b\u003e: 130\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eDeaths\u003c/b\u003e: 74 (36.3%)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAll 204 children had complete pSOFA, CRP, Procalcitonin, Lactate, PICU length of stay, and mechanical ventilation data available for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics and Descriptive Statistics\u003c/h2\u003e \u003cp\u003eThe distributions of pSOFA, CRP, Procalcitonin, and Lactate are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of Predictor Variables (N\u0026thinsp;=\u0026thinsp;204)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCount\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epSOFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP (mg/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60.879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e112.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e311.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcalcitonin (ng/mL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e136.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLactate (mmol/L)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003ePrimary Outcome: In-Hospital Mortality\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e1. ROC Curve Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003epSOFA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) demonstrated excellent discriminative ability:\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAUC\u003c/b\u003e: 0.853\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSensitivity\u003c/b\u003e: 0.74\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSpecificity\u003c/b\u003e: 0.85\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOptimal cutoff\u003c/b\u003e: 10.0 (Youden)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e \u003cb\u003e-\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cb\u003eROC Curve for pSOFA (AUC\u0026thinsp;=\u0026thinsp;0.85)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe modified pSOFA model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) incorporating CRP, Procalcitonin, and Lactate showed improved performance:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAUC\u003c/b\u003e: 0.920\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSensitivity\u003c/b\u003e: 0.824\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eSpecificity\u003c/b\u003e: 0.869\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOptimal probability threshold\u003c/b\u003e: 0.382\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2. Univariate Predictor Performance\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003epSOFA: AUC 0.853\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e\n\u003ch3\u003e• CRP: AUC 0.779\u003c/h3\u003e\n\u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eProcalcitonin: AUC 0.805\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLactate: AUC 0.751\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3. Multivariable Logistic Regression\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Logistic Regression\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epSOFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.297\u0026ndash;1.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.991\u0026ndash;1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.937\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcalcitonin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.124\u0026ndash;1.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLactate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.927\u0026ndash;1.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.258\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003epSOFA and Procalcitonin remained strong independent predictors of mortality. CRP and Lactate were not statistically significant predictors after adjustment. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSecondary Outcome 1: Length of PICU Stay\u003c/h2\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eAmong the 204 children, PICU length of stay was:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMean\u003c/b\u003e: 6.85 days\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMedian\u003c/b\u003e: 6 days\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIQR\u003c/b\u003e: 4\u0026ndash;10 days\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRange\u003c/b\u003e: 2\u0026ndash;16 days\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation with predictors\u003c/h2\u003e \u003cp\u003eSpearman correlations between PICU LOS and predictors:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003epSOFA\u003c/b\u003e: ρ = \u003cb\u003e\u0026minus;0.27\u003c/b\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCRP\u003c/b\u003e: ρ = \u003cb\u003e\u0026minus;0.35\u003c/b\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eProcalcitonin\u003c/b\u003e: ρ = \u003cb\u003e\u0026minus;0.37\u003c/b\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLactate\u003c/b\u003e: ρ = \u003cb\u003e\u0026minus;0.25\u003c/b\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics for PICU Length of Stay and Mechanical Ventilation Duration (N\u0026thinsp;=\u0026thinsp;204)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIQR (25\u0026ndash;75%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePICU Length of Stay (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMechanical Ventilation Duration (days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpearman Correlations of PICU LOS and Mechanical Ventilation Duration with Predictors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrelation with PICU LOS (ρ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCorrelation with MV Duration (ρ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003epSOFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.2708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.4120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCRP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.3525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.3725\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProcalcitonin\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.3739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.1909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLactate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.2511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.2879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eHigher illness severity and biomarker levels were associated with shorter PICU stay, likely reflecting early mortality in the sickest children. (Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e \u0026amp; \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLogistic regression for prolonged PICU stay\u003c/h2\u003e \u003cp\u003eProlonged PICU stay was defined as \u003cb\u003e\u0026gt;\u0026thinsp;6 days\u003c/b\u003e (above the median).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eModel 1 \u0026ndash; pSOFA only\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eOR per 1-point increase in pSOFA: \u003cb\u003e0.89\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e95% CI: \u003cb\u003e0.82\u0026ndash;0.96\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eModel 2 \u0026ndash; Modified pSOFA (pSOFA\u0026thinsp;+\u0026thinsp;CRP\u0026thinsp;+\u0026thinsp;Procalcitonin\u0026thinsp;+\u0026thinsp;Lactate)\u003c/h2\u003e \u003cp\u003eNone of the individual predictors were statistically significant for prolonged LOS after adjustment:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003epSOFA OR\u0026thinsp;\u0026asymp;\u0026thinsp;0.97 (95% CI 0.87\u0026ndash;1.08), p\u0026thinsp;=\u0026thinsp;0.56\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCRP OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.00 (95% CI 0.99\u0026ndash;1.00), p\u0026thinsp;=\u0026thinsp;0.44\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProcalcitonin OR\u0026thinsp;\u0026asymp;\u0026thinsp;0.96 (95% CI 0.92\u0026ndash;1.00), p\u0026thinsp;=\u0026thinsp;0.07\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLactate OR\u0026thinsp;\u0026asymp;\u0026thinsp;0.90 (95% CI 0.78\u0026ndash;1.04), p\u0026thinsp;=\u0026thinsp;0.15\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eROC analysis \u0026ndash; prolonged PICU stay (\u0026gt;\u0026thinsp;6 days)\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003epSOFA only model\u003c/b\u003e: AUC\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.62\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eModified pSOFA model\u003c/b\u003e: AUC\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.72\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic Regression for Prolonged PICU Stay (\u0026gt;\u0026thinsp;6 days)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eModel 1: pSOFA Only\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.82\u0026ndash;0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2: Modified pSOFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u0026ndash;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92\u0026ndash;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.78\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSo, for predicting \u003cb\u003eprolonged PICU stay\u003c/b\u003e, the modified model shows \u003cb\u003ebetter discrimination\u003c/b\u003e than pSOFA alone, but the overall AUC remains in the \u003cem\u003efair\u003c/em\u003e range and is influenced by the competing effect of early death. (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eSecondary Outcome 2: Duration of Mechanical Ventilation\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003eDescriptive statistics\u003c/h2\u003e \u003cp\u003eMechanical ventilation duration in the cohort:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMean\u003c/b\u003e: 3.26 days\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eMedian\u003c/b\u003e: 3 days\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eIQR\u003c/b\u003e: 2\u0026ndash;4 days\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eRange\u003c/b\u003e: 0\u0026ndash;12 days\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e0 days include children who did not receive mechanical ventilation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation with predictors\u003c/h2\u003e \u003cp\u003eSpearman correlations with MV duration:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003epSOFA\u003c/b\u003e: ρ\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.41\u003c/b\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCRP\u003c/b\u003e: ρ\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.37\u003c/b\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eProcalcitonin\u003c/b\u003e: ρ\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.19\u003c/b\u003e, p\u0026thinsp;=\u0026thinsp;0.006\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eLactate\u003c/b\u003e: ρ\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.29\u003c/b\u003e, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eHigher pSOFA and biomarker levels were associated with longer duration of mechanical ventilation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eLogistic regression for prolonged mechanical ventilation\u003c/h2\u003e \u003cp\u003eProlonged mechanical ventilation was defined as \u003cb\u003e\u0026gt;\u0026thinsp;3 days\u003c/b\u003e (above the median).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eModel 1 \u0026ndash; pSOFA only\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eOR per 1-point increase in pSOFA: \u003cb\u003e1.25\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e95% CI: \u003cb\u003e1.15\u0026ndash;1.37\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eModel 2 \u0026ndash; Modified pSOFA (pSOFA\u0026thinsp;+\u0026thinsp;CRP\u0026thinsp;+\u0026thinsp;Procalcitonin\u0026thinsp;+\u0026thinsp;Lactate)\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003epSOFA OR: \u003cb\u003e1.14\u003c/b\u003e (95% CI 1.02\u0026ndash;1.29), p\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCRP OR: ~1.00 (95% CI 1.00\u0026ndash;1.01), p\u0026thinsp;=\u0026thinsp;0.28\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eProcalcitonin OR: ~1.01 (95% CI 0.98\u0026ndash;1.04), p\u0026thinsp;=\u0026thinsp;0.61\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLactate OR: \u003cb\u003e1.15\u003c/b\u003e (95% CI 0.99\u0026ndash;1.34), p\u0026thinsp;=\u0026thinsp;0.06\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003epSOFA remained an independent predictor of prolonged mechanical ventilation after adjustment. (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic Regression for Prolonged Mechanical Ventilation (\u0026gt;\u0026thinsp;3 days)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eModel 1: pSOFA Only\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.25\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15\u0026ndash;1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2: Modified pSOFA\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003epSOFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e1.14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.02\u0026ndash;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u0026ndash;1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcalcitonin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.98\u0026ndash;1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLactate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.99\u0026ndash;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eROC analysis \u0026ndash; prolonged mechanical ventilation (\u0026gt;\u0026thinsp;3 days)\u003c/h2\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003epSOFA only model\u003c/b\u003e: AUC\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.72\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eModified pSOFA model\u003c/b\u003e: AUC\u0026thinsp;=\u0026thinsp;\u003cb\u003e0.76\u003c/b\u003e\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFor predicting \u003cb\u003eprolonged mechanical ventilation\u003c/b\u003e, both models show \u003cb\u003egood\u003c/b\u003e discriminative ability, with a \u003cb\u003emodest improvement\u003c/b\u003e when CRP, Procalcitonin, and Lactate are added. (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eROC AUC Comparison for Secondary Outcomes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOutcome\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003epSOFA AUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModified pSOFA AUC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProlonged PICU Stay (\u0026gt;\u0026thinsp;6 days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.716\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eProlonged Mechanical Ventilation (\u0026gt;\u0026thinsp;3 days)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.762\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this prospective comparison study, both the original pSOFA score and a biomarker-enhanced modified pSOFA model demonstrated significant utility in predicting adverse outcomes among children presenting to the Paediatric Emergency Department. Consistent with prior validation studies, pSOFA alone showed excellent discrimination for in-hospital mortality, with an AUC of 0.853. (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) This aligns closely with the original paediatric SOFA adaptation study by Matics and Sanchez-Pinto, which reported an AUC of approximately 0.82\u0026ndash;0.86 when validating the score in critically ill children(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e), and with subsequent international work highlighting the strong relationship between organ dysfunction burden and mortality risk(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe addition of CRP, Procalcitonin, and Lactate significantly improved predictive performance, increasing the AUC to 0.920. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) This finding is consistent with literature demonstrating that combining clinical scores with inflammatory and perfusion biomarkers enhances early prognostic precision. Multiple studies\u0026mdash;including those evaluating Procalcitonin and Lactate in paediatric sepsis\u0026mdash;have shown that these markers carry independent predictive value for mortality and organ dysfunction(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The strong independent effect of Procalcitonin in our cohort supports existing evidence regarding its association with severe bacterial infection and progression to multi-organ dysfunction(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). While CRP and Lactate were not independently significant in adjusted models, their inclusion contributed to overall model performance, as reflected by the improved AUC.\u003c/p\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eComparison With Existing Mortality Prediction Tools\u003c/h2\u003e \u003cp\u003eThe mortality performance of the modified pSOFA model (AUC 0.920) exceeds that reported for several established paediatric organ dysfunction scores. For example, the Paediatric Logistic Organ Dysfunction Score (PELOD-2) typically demonstrates AUC values ranging from 0.88\u0026ndash;0.91 in ICU settings(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), whereas our model maintained high discrimination in the ED setting, where physiological variability is greater and illness trajectories are less established. This supports the potential role of biomarker-enhanced tools in early triage when decision-making is time-critical.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003ePICU Length of Stay (LOS) as a Secondary Outcome\u003c/h2\u003e \u003cp\u003eThe secondary analysis evaluating PICU LOS revealed an inverse correlation between severity markers (pSOFA and biomarkers) and length of stay. This likely reflects a competing-risk phenomenon, where patients with the highest severity scores tend to experience early mortality, thereby shortening calculated LOS. Similar patterns have been documented in other paediatric sepsis cohorts, where early deaths reduce median LOS in the sickest subgroup(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The modified pSOFA model improved discrimination for predicting prolonged PICU stay (AUC 0.716 vs 0.618), though performance remained modest, suggesting that LOS is multifactorial and influenced by factors beyond initial severity, such as comorbidity profiles, therapeutic responsiveness, and institutional practices.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003eMechanical Ventilation Duration\u003c/h2\u003e \u003cp\u003eBoth pSOFA and modified pSOFA scores demonstrated meaningful associations with mechanical ventilation duration, with correlations strongest for pSOFA (ρ\u0026thinsp;=\u0026thinsp;0.412, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Logistic regression confirmed pSOFA as a strong independent predictor of prolonged ventilation (OR 1.25 per point increase). These findings are consistent with prior sepsis and respiratory failure literature showing that early organ dysfunction scores correlate with ventilator days and respiratory support requirements(12). The modified model improved AUC from 0.717 to 0.762, suggesting incremental prognostic utility. Biomarkers, especially Lactate, approached significance in the multivariable model, underscoring their physiologic relevance in predicting ventilation needs related to shock and tissue hypoperfusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eClinical Implications\u003c/h2\u003e \u003cp\u003eThe results highlight three important clinical implications:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eEarly ED-based pSOFA scoring is reliable and can be implemented even before ICU admission.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eBiomarker augmentation (CRP, Procalcitonin, Lactate) substantially improves prediction, particularly for mortality and mechanical ventilation duration.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThe modified pSOFA model may serve as an effective tool for resource allocation, early triage decisions, PICU bed prioritization, and anticipating ventilatory requirements.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eGiven the increasing emphasis on early sepsis recognition and rapid escalation pathways, a combined biomarker-score model may offer significant advantages, especially in high-volume emergency settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"Strengths and Limitations","content":"\u003cp\u003eA major strength of the study is its prospective design and complete availability of biomarker and outcome data for all participants. Additionally, inclusion of both mortality and PICU resource-utilization measures allows comprehensive assessment of clinical severity.\u003c/p\u003e \u003cp\u003eKey limitations include being a single-center study, which may limit external generalizability, and the potential influence of institutional treatment protocols on LOS and ventilation duration. The competing-risk effect on LOS also complicates interpretation and should be explored further using survival models in future studies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eBoth pSOFA and the biomarker-enhanced modified pSOFA model effectively predict in-hospital mortality in children presenting to the ED, with the modified model exhibiting superior discriminative performance. Secondary analyses demonstrate meaningful associations between initial severity and mechanical ventilation duration, and moderate associations with PICU LOS. These findings support integrating biomarker-enhanced scoring into ED assessment pathways to improve prognostication and resource planning.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003epSOFA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePaediatric Sequential Organ Failure Assessment\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePICU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePaediatric Intensive Care Unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eC-reactive protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eED\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEmergency Department\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eReceiver Operating Characteristic\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eArea Under the Curve\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cbr\u003eThe study was reviewed and approved by the \u003cstrong\u003eInstitutional Ethics Committee, All India Institute of Medical Sciences (AIIMS), Raipur, Chhattisgarh, India\u003c/strong\u003e (IEC Proposal No. \u003cstrong\u003eAIIMSRPR/IEC/2023/1327\u003c/strong\u003e, approval date \u003cstrong\u003e18 March 2023\u003c/strong\u003e). The study was conducted in accordance with the ethical standards of the institutional ethics committee and in compliance with the \u003cstrong\u003eDeclaration of Helsinki\u003c/strong\u003e. Written informed consent was obtained from the parents or legal guardians of all participating children prior to enrolment.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analysed during the current study are included in this published article as an \u003cstrong\u003eAppendix\u003c/strong\u003e. Additional details, if required, are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMurugan Thimiri Palani conceived the study, developed the study design, and coordinated its execution. Murugan Thimiri Palani and Amanda Grace Sajem were responsible for patient enrolment, clinical assessment, and prospective data collection in the paediatric emergency department. Melwin Dany Samuel performed data cleaning and statistical analysis, interpreted the results, and drafted the initial version of the manuscript. Santosh Kumar Rathia and Varun Anand contributed to critical review of the study methodology, interpretation of results, and revision of the manuscript for important intellectual content. Murugan Thimiri Palani also provided overall supervision of the project. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;The authors thank the patients and their families for participating in the study, and the staff of the Paediatric Emergency Department and Paediatric Intensive Care Unit for their support during data collection.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMatics TJ, Sanchez-Pinto LN. Adaptation and Validation of a Pediatric Sequential Organ Failure Assessment Score and Evaluation of the Sepsis-3 Definitions in Critically Ill Children. JAMA Pediatr. 2017;171(10):e172352.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiss SL, Peters MJ, Alhazzani W, Agus MSD, Flori HR, Inwald DP, et al. Surviving Sepsis Campaign International Guidelines for the Management of Septic Shock and Sepsis-Associated Organ Dysfunction in Children. Pediatr Crit Care Med. 2020;21(2):e52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecker KL, Snider R, Nylen ES. Procalcitonin in sepsis and systemic inflammation: a harmful biomarker and a therapeutic target. Br J Pharmacol. 2010;159(2):253\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchuetz P, Birkhahn R, Sherwin R, Jones AE, Singer A, Kline JA, et al. Serial Procalcitonin Predicts Mortality in Severe Sepsis Patients: Results From the Multicenter Procalcitonin MOnitoring SEpsis (MOSES) Study. Crit Care Med. 2017;45(5):781\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen HB, Rivers EP, Knoblich BP, Jacobsen G, Muzzin A, Ressler JA, et al. Early lactate clearance is associated with improved outcome in severe sepsis and septic shock. Crit Care Med. 2004;32(8):1637\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eObuchowski NA. Sample size calculations in studies of test accuracy. Stat Methods Med Res. 1998;7(4):371\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeteurtre S, Martinot A, Duhamel A, Proulx F, Grandbastien B, Cotting J et al. Validation of the paediatric logistic organ dysfunction (PELOD) score: prospective, observational, multicentre study. Lancet Lond Engl 2003 July 19;362(9379):192\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee CW, Kou H, wei, Chou HS, Chou H, huan, Huang SF, Chang CH, et al. A combination of SOFA score and biomarkers gives a better prediction of septic AKI and in-hospital mortality in critically ill surgical patients: a pilot study. World J Emerg Surg WJES. 2018 Sept;10:13:41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchlapbach LJ, Straney L, Alexander J, MacLaren G, Festa M, Schibler A, et al. Mortality related to invasive infections, sepsis, and septic shock in critically ill children in Australia and New Zealand, 2002\u0026ndash;13: a multicentre retrospective cohort study. Lancet Infect Dis. 2015;15(1):46\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuth A, McCracken CE, Fortenberry JD, Hall M, Simon HK, Hebbar KB. Pediatric severe sepsis: current trends and outcomes from the Pediatric Health Information Systems database. Pediatr Crit Care Med J Soc Crit Care Med World Fed Pediatr Intensive Crit Care Soc. 2014;15(9):828\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu F, Qin H, Li AM. The Correlation Between Mechanical Ventilation Duration, Pediatric Sequential Organ Failure Assessment Score, and Blood Lactate Level in Children in Pediatric Intensive Care. Front Pediatr [Internet]. 2022 Mar 14 [cited 2025 Dec 8];10. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/journals/pediatrics/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/journals/pediatrics/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fped.2022.767690/full\u003c/span\u003e\u003cspan address=\"10.3389/fped.2022.767690/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"pSOFA, modified pSOFA, paediatric sepsis, biomarkers, mortality prediction, PICU stay, mechanical ventilation, emergency department","lastPublishedDoi":"10.21203/rs.3.rs-8339610/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8339610/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective:\u003c/h2\u003e \u003cp\u003eTo evaluate and compare the predictive performance of the Paediatric Sequential Organ Failure Assessment (pSOFA) score and a biomarker-enhanced modified pSOFA model (pSOFA\u0026thinsp;+\u0026thinsp;CRP, Procalcitonin, Lactate) for (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) in-hospital mortality, and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) secondary outcomes including length of PICU stay and duration of mechanical ventilation among children presenting to the Emergency Department (ED).\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e \u003cp\u003eA prospective observational study will be conducted among consecutive paediatric ED patients. pSOFA and modified pSOFA scores will be calculated on admission. Associations with mortality, PICU length of stay, and mechanical ventilation duration will be evaluated using ROC analysis, multivariable regression, and correlation statistics.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e \u003cp\u003epSOFA showed excellent discrimination (AUC 0.853), while the modified model improved prediction (AUC 0.920). Secondary outcome analysis will explore the relationship between both models and (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) PICU length of stay and (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) mechanical ventilation duration.\u003c/p\u003e\u003ch2\u003eConclusion:\u003c/h2\u003e \u003cp\u003eThe modified pSOFA model may improve prognostic accuracy for mortality and could provide enhanced prediction of PICU resource utilization.\u003c/p\u003e","manuscriptTitle":"Prospective Comparison of pSOFA and a Biomarker-Enhanced Modified pSOFA Model for Predicting In-Hospital Mortality, Length of PICU Stay, and Mechanical Ventilation Duration in Children Presenting to the Paediatric Emergency Department","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 17:20:41","doi":"10.21203/rs.3.rs-8339610/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-03-20T08:26:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-05T04:35:43+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-27T14:56:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-23T19:28:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2026-01-23T19:24:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b77c760d-f256-453e-8647-cdee0ef2f962","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-24T17:20:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 17:20:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8339610","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8339610","identity":"rs-8339610","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}