Construction and Validation of a Convenient Death Prediction Model for Pediatric Pneumonia Patients in Intensive Care Units

Construction and Validation of a Convenient Death Prediction Model for Pediatric Pneumonia Patients in Intensive Care Units | 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 Construction and Validation of a Convenient Death Prediction Model for Pediatric Pneumonia Patients in Intensive Care Units Chuan-Fei Wu, Xue-Li Cheng, Xiao-Tian Bian, Guo-Cheng Jiang, Mei-Tong Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5294454/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background : Pneumonia is one of the major diseases threatening the safety of children's lives; however, there are currently few convenient tools available to predict the death risk of children with pneumonia. We explored the risk factors for mortality in pediatric intensive care unit (PICU) patients with pneumonia and developed and validated a mortality risk prediction model. Methods : A research cohort was established using a public database from a pediatric intensive care unit, including data from 467 cases. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for mortality in pneumonia patients, and a prediction model was constructed based on these risk levels, resulting in a nomogram. Results : 1. A total of 351 cases were included for modeling, with 69 in-hospital deaths and 282 in-hospital survivors identified as outcomes. 2. The analysis identified independent risk factors for mortality in pneumonia patients as age in months, white blood cell count, CRP, potassium ion concentration, total bilirubin and application of glucocorticoids. 3. The area under the curve (AUC) for the prediction model was 0.765 (95% CI: 0.705-0.825), with a sensitivity of 0.813 and specificity of 0.578; internal validation demonstrated that the model has good consistency. Conclusion : A convenient model for predicting the mortality risk of children with pneumonia in PICU has been developed, showing a reasonable level of accuracy. Prediction Model Pneumonia PICU Children Figures Figure 1 Figure 2 1 Introduction Pneumonia is one of the leading causes of hospitalization among children worldwide. While most children with pneumonia have a good prognosis, there are significant differences in outcomes due to various factors, with death being the most severe consequence. Statistics show that in 2016, there were 652,527 deaths from pneumonia among children under five years old (95% UI 586,475–720,612), accounting for 13.1% of the total deaths in this age group (95% UI 11.8–14.3) [ 1 , 2 ] . With the rapid advancement of critical care medicine and the continuous improvement of life support technologies, more children's lives are being saved. However, even in PICU, pneumonia remains a significant threat to children's lives. An analysis of the disease spectrum of deaths in the PICU at Shengjing Hospital affiliated with China Medical University found that pneumonia accounted for 27.07% of PICU deaths [ 3 ] . Due to the development of medical technology and the uneven distribution of medical resources, studies in developed countries report mortality rates for severe pneumonia cases ranging from 8.2–13.5% [ 4 , 5 ] . However, in low-income areas or countries with scarce medical resources, the mortality rate is even higher, reaching 30% [ 6 ] . In fact, there has been prior research on predicting the risk of death due to pneumonia, with commonly used tools including pediatric mortality risk [ 7 ] , pediatric mortality index [ 8 ] , and pediatric critical illness score [ 9 ] . However, these scoring tools have certain limitations in clinical application, such as requiring multiple indicators for completion or lacking timeliness, as results are only available 24 hours after admission. This makes them less user-friendly for physicians in countries or regions with weak healthcare systems. Currently, there are few accessible early warning tools for predicting the prognosis of children with pneumonia in PICU, especially regarding mortality risk. Our study aims to develop a death prediction model for PICU patients with pneumonia that is easy to use and has low barriers, thereby providing risk references for clinicians in lower-tier PICUs. 2 Materials and Methods 2.1 Data Sources All data for this study were sourced from the public database of the Paediatric Intensive Care (PIC) [ 10 ] , which is currently the only specialized pediatric critical care database. It was organized and constructed by the National Clinical Research Center for Child Health of Zhejiang University’s School of Medicine and includes clinical data from all pediatric patients admitted to various ICUs at Zhejiang University Children’s Hospital from 2010 to 2018, totaling 13,499 hospitalization records for 12,881 distinct pediatric patients. 2.2 Inclusion Criteria First, cases with a primary diagnosis of pneumonia were included, diagnosed based on the International Classification of Diseases (ICD) codes, specifically ICD-10 code J18.900 and its related codes. Second, cases admitted to either PICUs or general ICUs were included. Finally, neonates and cases with incomplete clinical data were excluded. A total of 467 cases were ultimately included in this study, with three-quarters allocated to the modeling group and the remaining quarter to the validation group. 2.3 Data Acquisition The PIC database contains 16 tables, all stored in comma-separated values (CSV) format for easy loading into a relational database. The tables connect patient data through unique identifiers: SUBJECT-ID, HADM-ID, and ICUSTAY-ID. This project utilized PostgreSQL to construct the SQL database, extracting data based on the identifiers across tables. 2.4 Indicator Selection and Outcome Indicators This study incorporated various data types: basic demographic data (e.g., age, sex), laboratory test results (e.g., complete blood counts, biochemical tests), treatment data, and other essential clinical information. All data were collected within 24 hours of admission. We excluded variables with missing data exceeding 10% and some variables related to special inspections to avoid high missing rates, which could complicate the predictive modeling process. The outcome indicators are based on the case records in the database, where in-hospital death is considered an occurrence of the outcome, and in-hospital survival is regarded as a non-occurrence of the outcome. 2.5 Statistical Analysis All statistical analyses were conducted using Empower Stats software based on R programming. To facilitate clinical application, continuous variables were converted into categorical data through curve fitting, applying the best cutoff values based on the clinical context. A multivariate logistic regression analysis was performed to identify risk factors for mortality among children with pneumonia in the PICU and to develop a predictive model for mortality risk based on the multivariate analysis results. The model’s effectiveness was evaluated using the Receiver Operating Characteristic (ROC) curve. A p-value of < 0.05 was considered statistically significant. 2.6 Ethics This study complies with medical ethics standards and has been approved by the Ethics Committee of Yiwu Maternity and Child Health Hospital (Approval No: A000109). Because this study is retrospective, a waiver for informed consent was obtained. Clinical trial number: Not applicable. 3 Results 3.1 Characteristics of the Population A total of 467 cases were included in this study, with 75% randomly assigned to the modeling group (351 cases) and 25% to the validation group (116 cases). In the modeling group, 282 patients survived (80.3%), comprising 120 females (42.6%) and 162 males (57.4%), while 69 patients died in the hospital (19.7%), including 32 females (46.4%) and 37 males (53.6%). During the preliminary analysis, conventional study variables did not reveal significant correlations. Therefore, we employed curve fitting to determine the correlation trends between study variables and outcome variables, followed by ROC diagnostic testing to identify the optimal cutoff values, converting continuous variables into categorical variables accordingly. Notably, white blood cell counts demonstrated a distinct segmented trend with respect to the outcome variable. Table 1 presents the clinical characteristics and common laboratory test results of the modeling cases, with inter-group comparisons based on outcome determination. Differences in variables such as age in months, white blood cell count, neutrophil count, C-reactive protein, albumin, potassium ion concentration, sodium ion concentration, total bilirubin, and application of glucocorticoids were statistically significant (P < 0.05). 3.2 Model Establishment Logistic regression analysis was used to examine the risk factors associated with mortality outcomes. A forward selection method was employed to evaluate the predictive efficacy of the variables while controlling for confounding factors. Ultimately, six variables were included as risk factors: age in months, white blood cell count, CRP, potassium ion concentration, total bilirubin, application of glucocorticoids, which formed the predictive model (Table 2 ). The fitted regression equation is logit (mortality risk) = -2.700–0.984*(age in months) + 0.913*(white blood cell count) + 1.384*(CRP) + 0.715*(potassium ion concentration) + 0.799*(application of glucocorticoids) . 3.3 Nomogram The nomogram for the final predictive model is shown in Fig. 1 . The total score is calculated based on the scores corresponding to each variable. A vertical line is drawn from the bottom to the top of the total score line to determine the predicted probability of mortality. 3.4 Predictive Efficacy and Validation of the Model In this study, internal validation was performed using ROC diagnostic testing to assess predictive efficacy, and the ROC curve was plotted (Fig. 2 ). The specific parameters for predictive efficacy in the modeling group and validation group are provided in Table 3 . The validation results indicate that the predictive efficacy of the established model in the modeling group has an AUC of 0.765 (CI: 0.705–0.825), with a sensitivity of 81.25% and specificity of 57.82%, demonstrating a certain level of predictive accuracy. The validation group showed similar performance, with an AUC of 0.734 (CI: 0.597–0.871), indicating good consistency. 4 Discussion This study analyzed pneumonia cases in the PICU to explore the factors associated with mortality due to pneumonia. Multivariate analysis identified age in months, white blood cell count, CRP, potassium ion concentration, total bilirubin, application of glucocorticoids as independent risk factors for mortality in pediatric pneumonia patients. Based on these identified independent risk factors, a user-friendly predictive model for mortality in PICU pneumonia patients was established, accompanied by a nomogram. 4.1 Factors Related to the Mortality Risk in Pediatric Pneumonia Patients Pediatric pneumonia cases involve complex infection situations with significant individual differences, influenced by numerous factors affecting outcomes. Risk factors for mortality can encompass various aspects including the child's basic condition, clinical manifestations during illness, and laboratory test results. These risk factors differ in their degree of influence. It is crucial to identify a few that are easily accessible, user-friendly, exhibit significant independent effects, and can provide predictive capabilities. This study conducted univariate and multivariate analyses to identify independent risk factors for mortality in children with pneumonia in the PICU. The correlation between white blood cell count and mortality risk in pneumonia patients showed a distinct segmented trend. The analysis indicated that a white blood cell count within the range of (0.55–15.55) * 10^9/L represents a relatively safe zone, with values below or above this threshold being associated with increased mortality risk. A study on pneumonia patients in the PICU also corroborated these findings, although it did not discuss the critical values for white blood cell count [ 6 ] . Similarly, Toshihiro Sakakibara et al established a lower limit of 0.4*10 9 /L for white blood cell count, which aligns closely with our findings [ 11 ] . We believe that both elevated and reduced white blood cell counts suggest a severe inflammatory response, with low counts potentially indicating immunosuppression, thereby raising the risk of mortality [ 12 ] . Age is typically a factor affecting disease progression and severity. In adult pneumonia cases, age is generally considered a risk factor, however, in pediatric cases, it often serves as a protective factor. To gain a more precise understanding of the impact of age on mortality risk, we recorded age in months. Our analyses indicated that age in months correlates with mortality in severe pneumonia cases and is the only protective risk factor identified in the model. This conclusion is supported by previous studies [ 13 , 14 ] , including a four-year prospective study which identified younger age as a risk factor for increased mortality, defining young age as being under 12 months [ 15 ] , whereas our cutoff was established at 14.5 months, which is quite close. C-reactive protein (CRP) is an acute-phase protein produced during inflammatory responses, typically associated with inflammation and helpful in predicting outcomes in pediatric pneumonia [ 16 , 17 ] . Yanhui Wang et al confirmed that high CRP levels are an independent risk factor for poor outcomes in pneumonia [ 18 ] . A multicenter case-control study demonstrated good accuracy for distinguishing pneumonia cases in children at a critical CRP value of 37.1 mg/L [ 19 ] , which is very close to our conclusion of 33.5 mg/L. The application of glucocorticoids was the only medication information included in our study, and contrary to our initial hypothesis, it emerged as a risk factor rather than a protective one. Glucocorticoids are generally believed to have anti-inflammatory effects that can yield positive outcomes in pneumonia treatment [ 20 ] , indicating that glucocorticoids might reduce ICU stay and mortality risk [ 21 ] . However, this view remains contentious, with guidelines from the U.S. in 2020 and the UK in 2015 advising against their use in pneumonia patients [ 22 , 23 ] . We hypothesize that the use of glucocorticoids may be somewhat helpful in preventing the progression of disease, but is not decisive and cannot alter severe outcomes such as death. Previous studies on the use of glucocorticoids in the treatment of pneumonia have shown that they may slow disease progression or reduce the need for mechanical ventilation, but no correlation with mortality risk or adverse prognoses has been established [ 24 ] .Electrolyte imbalances are common complications of pneumonia in children. In our study, a potassium level exceeding 4.45 mg/L significantly increased the risk of mortality in pneumonia patients. A retrospective study on pneumonia cases noted that electrolyte disturbances are risk factors affecting the prognosis of pneumonia, with hyperkalemia being more common than hypokalemia [ 25 ] . This may be because elevated potassium levels typically impact cardiac function, leading to worse outcomes [ 26 ] . Research on the correlation between total bilirubin and mortality is limited. In a study on pneumonia caused by influenza, bilirubin was identified as an independent risk factor for mortality in children [ 27 ] . Additionally, a multicenter case-control study also demonstrated a relationship between total bilirubin levels and the risk of death in pediatric pneumonia cases [ 28 ] . In addition to the relevant indicators included in the predictive model of this study, there are many other indicators or influencing factors that can predict the risk of mortality in children with pneumonia, such as severity scores, environmental factors, underlying diseases, and certain biochemical indicators [ 29 , 30 ] . However, some of these indicators are not routine examination items in primary healthcare institutions, some assessments have significant subjective biases, and some indicators have a high rate of missing values in databases, making it difficult for frontline healthcare workers to complete assessments in a short time. Therefore, these variables were not included in the statistical analysis of this study. 4.2 Comparison of This Predictive Model with Other Studies There have long been prediction models developed for mortality in ICU severe cases. One model, based on the MIMIC database, was created to assess the risk of death from COVID-19 and achieved an AUC of 0.760 (95% CI: 0.739–0.781) for predicting mortality within one year [ 29 ] . In a study on children, Stuart Haggie et al developed a clinical scoring system for pediatric pneumonia patients, yielding an area under the curve (AUC) of 0.78 [ 31 ] ,which is comparable to our predictive efficacy. Other research integrated various severity scores and major complications into their predictive models, raising the AUC to 0.85 [ 32 ] . Over the past decade, while emerging blood and radiological methods have been studied to enhance the speed and accuracy of diagnoses [ 33 ] , this has raised the entry barriers for lower-tier healthcare institutions. However, it is in resource-limited areas and low-to middle-income countries (LMIC) where the burden of childhood pneumonia is particularly heavy [ 30 ] . Therefore, we sacrificed a certain degree of predictive performance to enhance the convenience of the model. We believe our design achieves a good balance between benefits and drawbacks. The area under the curve (AUC) of the model constructed in this study is 0.765, indicating acceptable predictive performance. While ensuring that the predictive capability remains substantial, we have effectively lowered the accessibility barrier for using the predictive model. 4.3 Advantages of This Study The predictive model we developed is highly targeted. As pneumonia is one of the most common diseases in children and has the highest mortality rate in the PICU, there are currently few predictive models developed specifically for pediatric cases in the PICU. Our constructed model is user-friendly and low cost, consisting of only six routine variables. This significantly reduces the number of variables compared to common risk scoring methods in the ICU, greatly enhancing usability. All variables we employed are routine information and commonly tested items, allowing immediate testing results to be obtained quickly. We utilized a nomogram presentation style, enabling rapid acquisition of predictive results in clinical settings, which provides higher clinical practicality compared to exploring risk factors. Our model has a low threshold for use, facilitating rapid risk assessment and treatment in primary PICUs and economically underdeveloped regions, allowing timely referral of high-risk pneumonia cases to higher-level PICUs or medical centers, and enabling tailored clinical plans for low-risk patients, thereby achieving precision medicine. 4.4 Limitations of This Study Of course, our study has certain limitations. Firstly, our predictive model is based on a critical care medicine database, but the final sample size included in the analysis was only 467 cases, making it challenging to fully control for bias. Secondly, while our model showed high consistency during validation, the internal validation method reduced the generalizability of the model, and the observational study design was not conducive to exploring the mechanisms of the disease. Furthermore, to ensure that the model could be conveniently used in lower-tier PICUs, the selection of included variables was limited, resulting in an overall simple variable set and excluding some special or auxiliary variables, which affected the model's predictive power. Finally, the data quality of the database somewhat restricted the development of our predictive model. 4.5 Summary We developed a predictive model for mortality risk in children with pneumonia in the PICU using the PIC database. The model consists of six variables: age in months, white blood cell count, CRP, potassium ion concentration, total bilirubin and application of glucocorticoids. Our predictive model demonstrates good predictive performance and certain external applicability. Declarations Author Contribution CF W responsible for research design, data analysis and interpretation, preparation of all charts, and drafting sections of manuscripts.XLC involved in the design of this research, data analysis and interpretation, and contributed to drafting parts of the paper.XTB involved in the design of this research, data analysis and interpretation.GC J involved in the design of this research, preparation of some charts. MTL involved in data analysis and contributed to drafting parts of the paper.All authors reviewed the manuscript. Data availability: The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. References Estimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990-2016: a systematic analysis for the global burden of disease study 2016. Lancet Infect Dis. 2018; 18:1191-1210. 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Tables Table 1: Basic Information of the Study Population Variables Survival Death t/ χ 2 / Z P N（%） 282（80.3） 69（19.7） Lymphocyte count（*10^9/L） 2.4 (1.5-4.4) 2.2 (1.1-4.6) -0.846 0.398 Neutrophil percentage（%） 61.5 (41.2-74.7) 63.8 (46.1-77.0) -0.944 0.345 Lymphocyte percentage（%） 31.2 (17.4-47.6) 28.9 (17.2-41.6) 0.676 0.499 Hemoglobin（g/L） 105.2 ± 19.7 101.9 ± 18.2 1.280 0.201 Neutrophil/lymphocyte ratio（%） 1.9 (0.9-4.4) 2.6 (1.1-4.5) -0.894 0.371 Eosinophil percentage（%） 0.5 (0.2-1.4) 0.6 (0.2-1.0) 0.456 0.566 Base excess（mmol/L） 0.2 (-2.7-3.6) -0.6 (-4.7-4.3) -1.157 0.247 Creatinine （μmol/L） 37.0 (29.0-46.3) 38.0 (28.4-51.5) 1.126 0.224 Creatine kinase isoenzyme（U/L） 29.0 (19.0-43.0) 26.0 (18.5-44.0) -0.669 0.504 Lactate dehydrogenase（U/L） 383.0 (296.5-561.5) 422.0 (300.0-683.5) -1.110 0.267 Alanine aminotransferase （U/L） 26.0 (17.0-44.0) 25.0 (19.0-42.0) -0.285 0.743 Aspartate aminotransferase （U/L） 47.0 (34.0-81.0) 52.0 (37.5-104.5) -0.967 0.333 Gender 0.330 0.648 Female 120 (42.6%) 32 (46.4%) Male 162 (57.4%) 37 (53.6%) Age（month） 7.648 0.006 ≤14.5 175 (62.1%) 55 (79.7%) ＞14.5 107 (37.9%) 14 (20.3%) White blood cell count（*10^9/L） 11.941 11.5 29 (10.3%) 16 (23.2%) Platelet count（*10^9/L） 3.399 0.065 ≤357 175 (62.1%) 51 (73.9%) >357 107 (37.9%) 18 (26.1%) C-reactive protein（mg/L） 8.647 0.003 ≤33.5 248 (87.9%) 51 (73.9%) >33.5 34 (12.1%) 18 (26.1%) Albumin （g/L） 5.531 0.019 ≤30.5 29 (10.4%) 14 (20.9%) >30.5 251 (89.6%) 53 (79.1%) Lactic acid（mmol/L） 0.108 0.776 ≤5.5 267 (95.0%) 63 (94.0%) >5.5 14 (5.0%) 4 (6.0%) PH 3.939 0.047 ≤7.5 272 (96.5%) 65 (94.2%) >7.5 10 (3.5%) 4 (5.8%) Potassium ion concentration（mmol/L） 8.340 0.004 ≤4.45 216 (76.6%) 41 (59.4%) >4.45 66 (23.4%) 28 (40.6%) Sodium ion concentration （mmol/L） 5.556 0.018 ≤138 165 (58.5%) 51 (73.9%) >138 117 (41.5%) 18 (26.1%) Total bilirubin（μmol/L） 8.314 0.004 ≤7.15 158 (57.5%) 24 (37.5%) >7.15 117 (42.5%) 40 (62.5%) Application of glucocorticoids 4.534 0.033 No 186 (66.0%) 36 (52.2%) Yes 96 (34.0%) 33 (47.8%) Table 2: Multivariate logistic regression analysis results of factors affecting outcomes in children with pneumonia in the PICU. Intercept and variable β Standard error Waldχ2 Odds ratio 95%CI P Intercept -2.700 0.362 55.645 0.067 0.033-0.137 <0.001 Age（month） -0.984 0.378 6.790 0.373 0.178-0.784 0.009 White blood cell count（*10^9/L） 0.913 0.352 6.726 2.493 1.250-4.971 0.009 C-reactive protein（mg/L） 1.384 0.406 11.602 3.991 1.800-8.851 <0.001 Potassium ion concentration（mmol/L） 0.715 0.319 5.034 2.044 1.095-3.819 0.025 Total bilirubin（μmol/L） 0.953 0.316 9.118 2.594 1.397-4.816 0.005 Application of glucocorticoids 0.799 0.316 6.403 2.222 1.197-4.126 0.011 Table 3 Comparison of Modelling and Validation Population Parameters Test items AUC 95%CI Optimal Threshold Specificity Sensitivity Accuracy Positive likelihood ratio Negative likelihood ratio Diagnostic odds ratio Modeling group 0.765 0.705-0.825 -1.767 0.578 0.813 0.622 1.926 0.324 5.939 Validation group 0.734 0.597-0.871 -1.123 0.835 0.647 0.806 3.926 0.422 9.288 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5294454","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372174883,"identity":"9f32bfd3-073a-4269-b8b0-d3104f814777","order_by":0,"name":"Chuan-Fei Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYBACxobDBx98qJBglmdvIFILc+OxZMMZZ2zYDXsOEKmFvfmMmjRnWxo/w40EIrXwtp1hNmZgOyzNOPPxxhsMNTbRBLVI9pw9+LiA57Axu3RasQXDsbTcBkJaDGecSzaeIXE4mXF2jpkEMDAIa7G//8ZMmsfgcH3DzTNEamFsOAPUkpDGzHCDh2gtoEA+YMNs2AP0SwIxfgFH5cd/oKg8vPHGhxobwlqQgYFEAinKIVpI1TEKRsEoGAUjAwAAscZExmAhrWoAAAAASUVORK5CYII=","orcid":"","institution":"Yiwu Maternal and Children Hospital","correspondingAuthor":true,"prefix":"","firstName":"Chuan-Fei","middleName":"","lastName":"Wu","suffix":""},{"id":372174885,"identity":"78c11fa5-2881-426d-b8d2-705736ac7815","order_by":1,"name":"Xue-Li Cheng","email":"","orcid":"","institution":"Yiwu Maternal and Children Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xue-Li","middleName":"","lastName":"Cheng","suffix":""},{"id":372174888,"identity":"140cb4b4-2bde-4ff7-8e35-237e644c953a","order_by":2,"name":"Xiao-Tian Bian","email":"","orcid":"","institution":"Yiwu Maternal and Children Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiao-Tian","middleName":"","lastName":"Bian","suffix":""},{"id":372174890,"identity":"ad349745-dd5c-4efe-9079-c8e2b7eec1fd","order_by":3,"name":"Guo-Cheng Jiang","email":"","orcid":"","institution":"Yiwu Maternal and Children Hospital","correspondingAuthor":false,"prefix":"","firstName":"Guo-Cheng","middleName":"","lastName":"Jiang","suffix":""},{"id":372174894,"identity":"9074fa5a-a382-4005-ac53-c8f084fdb9a1","order_by":4,"name":"Mei-Tong Liu","email":"","orcid":"","institution":"Yiwu Maternal and Children Hospital","correspondingAuthor":false,"prefix":"","firstName":"Mei-Tong","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2024-10-19 12:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5294454/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5294454/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69244938,"identity":"34ed0c33-d093-471d-95de-1bc313bc0999","added_by":"auto","created_at":"2024-11-18 10:54:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":32012,"visible":true,"origin":"","legend":"\u003cp\u003eRisk Prediction Nomogram for Mortality in Paediatric Patients with Pneumonia in the PICU\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5294454/v1/3d422a0704a0c3b4570424e3.png"},{"id":69244939,"identity":"780d1b9f-19fa-4d4c-bd63-45f6c42a7028","added_by":"auto","created_at":"2024-11-18 10:54:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":16401,"visible":true,"origin":"","legend":"\u003cp\u003eROC Curves for the Modelling and Validation Populations\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5294454/v1/ca36ceb30c52eb99351abbb7.png"},{"id":84598320,"identity":"4b50cb21-7201-40b9-938c-c8fc7e198cda","added_by":"auto","created_at":"2025-06-14 07:46:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":836356,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5294454/v1/e1daedbf-d401-4cf0-8ee0-b218475a3bb4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Construction and Validation of a Convenient Death Prediction Model for Pediatric Pneumonia Patients in Intensive Care Units","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003ePneumonia is one of the leading causes of hospitalization among children worldwide. While most children with pneumonia have a good prognosis, there are significant differences in outcomes due to various factors, with death being the most severe consequence. Statistics show that in 2016, there were 652,527 deaths from pneumonia among children under five years old (95% UI 586,475\u0026ndash;720,612), accounting for 13.1% of the total deaths in this age group (95% UI 11.8\u0026ndash;14.3)\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. With the rapid advancement of critical care medicine and the continuous improvement of life support technologies, more children's lives are being saved. However, even in PICU, pneumonia remains a significant threat to children's lives. An analysis of the disease spectrum of deaths in the PICU at Shengjing Hospital affiliated with China Medical University found that pneumonia accounted for 27.07% of PICU deaths\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Due to the development of medical technology and the uneven distribution of medical resources, studies in developed countries report mortality rates for severe pneumonia cases ranging from 8.2\u0026ndash;13.5%\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. However, in low-income areas or countries with scarce medical resources, the mortality rate is even higher, reaching 30% \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. In fact, there has been prior research on predicting the risk of death due to pneumonia, with commonly used tools including pediatric mortality risk\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, pediatric mortality index\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, and pediatric critical illness score\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. However, these scoring tools have certain limitations in clinical application, such as requiring multiple indicators for completion or lacking timeliness, as results are only available 24 hours after admission. This makes them less user-friendly for physicians in countries or regions with weak healthcare systems. Currently, there are few accessible early warning tools for predicting the prognosis of children with pneumonia in PICU, especially regarding mortality risk. Our study aims to develop a death prediction model for PICU patients with pneumonia that is easy to use and has low barriers, thereby providing risk references for clinicians in lower-tier PICUs.\u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Sources\u003c/h2\u003e \u003cp\u003eAll data for this study were sourced from the public database of the Paediatric Intensive Care (PIC) \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, which is currently the only specialized pediatric critical care database. It was organized and constructed by the National Clinical Research Center for Child Health of Zhejiang University\u0026rsquo;s School of Medicine and includes clinical data from all pediatric patients admitted to various ICUs at Zhejiang University Children\u0026rsquo;s Hospital from 2010 to 2018, totaling 13,499 hospitalization records for 12,881 distinct pediatric patients.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Inclusion Criteria\u003c/h2\u003e \u003cp\u003eFirst, cases with a primary diagnosis of pneumonia were included, diagnosed based on the International Classification of Diseases (ICD) codes, specifically ICD-10 code J18.900 and its related codes. Second, cases admitted to either PICUs or general ICUs were included. Finally, neonates and cases with incomplete clinical data were excluded. A total of 467 cases were ultimately included in this study, with three-quarters allocated to the modeling group and the remaining quarter to the validation group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data Acquisition\u003c/h2\u003e \u003cp\u003eThe PIC database contains 16 tables, all stored in comma-separated values (CSV) format for easy loading into a relational database. The tables connect patient data through unique identifiers: SUBJECT-ID, HADM-ID, and ICUSTAY-ID. This project utilized PostgreSQL to construct the SQL database, extracting data based on the identifiers across tables.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Indicator Selection and Outcome Indicators\u003c/h2\u003e \u003cp\u003eThis study incorporated various data types: basic demographic data (e.g., age, sex), laboratory test results (e.g., complete blood counts, biochemical tests), treatment data, and other essential clinical information. All data were collected within 24 hours of admission. We excluded variables with missing data exceeding 10% and some variables related to special inspections to avoid high missing rates, which could complicate the predictive modeling process. The outcome indicators are based on the case records in the database, where in-hospital death is considered an occurrence of the outcome, and in-hospital survival is regarded as a non-occurrence of the outcome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses were conducted using Empower Stats software based on R programming. To facilitate clinical application, continuous variables were converted into categorical data through curve fitting, applying the best cutoff values based on the clinical context. A multivariate logistic regression analysis was performed to identify risk factors for mortality among children with pneumonia in the PICU and to develop a predictive model for mortality risk based on the multivariate analysis results. The model\u0026rsquo;s effectiveness was evaluated using the Receiver Operating Characteristic (ROC) curve. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Ethics\u003c/h2\u003e \u003cp\u003e This study complies with medical ethics standards and has been approved by the Ethics Committee of Yiwu Maternity and Child Health Hospital (Approval No: A000109). Because this study is retrospective, a waiver for informed consent was obtained. Clinical trial number: Not applicable.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of the Population\u003c/h2\u003e \u003cp\u003eA total of 467 cases were included in this study, with 75% randomly assigned to the modeling group (351 cases) and 25% to the validation group (116 cases). In the modeling group, 282 patients survived (80.3%), comprising 120 females (42.6%) and 162 males (57.4%), while 69 patients died in the hospital (19.7%), including 32 females (46.4%) and 37 males (53.6%). During the preliminary analysis, conventional study variables did not reveal significant correlations. Therefore, we employed curve fitting to determine the correlation trends between study variables and outcome variables, followed by ROC diagnostic testing to identify the optimal cutoff values, converting continuous variables into categorical variables accordingly. Notably, white blood cell counts demonstrated a distinct segmented trend with respect to the outcome variable. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the clinical characteristics and common laboratory test results of the modeling cases, with inter-group comparisons based on outcome determination. Differences in variables such as age in months, white blood cell count, neutrophil count, C-reactive protein, albumin, potassium ion concentration, sodium ion concentration, total bilirubin, and application of glucocorticoids were statistically significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Model Establishment\u003c/h2\u003e \u003cp\u003eLogistic regression analysis was used to examine the risk factors associated with mortality outcomes. A forward selection method was employed to evaluate the predictive efficacy of the variables while controlling for confounding factors. Ultimately, six variables were included as risk factors: age in months, white blood cell count, CRP, potassium ion concentration, total bilirubin, application of glucocorticoids, which formed the predictive model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The fitted regression equation is logit (mortality risk) = -2.700\u0026ndash;0.984*(age in months)\u0026thinsp;+\u0026thinsp;0.913*(white blood cell count)\u0026thinsp;+\u0026thinsp;1.384*(CRP)\u0026thinsp;+\u0026thinsp;0.715*(potassium ion concentration)\u0026thinsp;+\u0026thinsp;0.799*(application of glucocorticoids) .\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Nomogram\u003c/h2\u003e \u003cp\u003eThe nomogram for the final predictive model is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The total score is calculated based on the scores corresponding to each variable. A vertical line is drawn from the bottom to the top of the total score line to determine the predicted probability of mortality.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Predictive Efficacy and Validation of the Model\u003c/h2\u003e \u003cp\u003eIn this study, internal validation was performed using ROC diagnostic testing to assess predictive efficacy, and the ROC curve was plotted (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The specific parameters for predictive efficacy in the modeling group and validation group are provided in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The validation results indicate that the predictive efficacy of the established model in the modeling group has an AUC of 0.765 (CI: 0.705\u0026ndash;0.825), with a sensitivity of 81.25% and specificity of 57.82%, demonstrating a certain level of predictive accuracy. The validation group showed similar performance, with an AUC of 0.734 (CI: 0.597\u0026ndash;0.871), indicating good consistency.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study analyzed pneumonia cases in the PICU to explore the factors associated with mortality due to pneumonia. Multivariate analysis identified age in months, white blood cell count, CRP, potassium ion concentration, total bilirubin, application of glucocorticoids as independent risk factors for mortality in pediatric pneumonia patients. Based on these identified independent risk factors, a user-friendly predictive model for mortality in PICU pneumonia patients was established, accompanied by a nomogram.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Factors Related to the Mortality Risk in Pediatric Pneumonia Patients\u003c/h2\u003e \u003cp\u003ePediatric pneumonia cases involve complex infection situations with significant individual differences, influenced by numerous factors affecting outcomes. Risk factors for mortality can encompass various aspects including the child's basic condition, clinical manifestations during illness, and laboratory test results. These risk factors differ in their degree of influence. It is crucial to identify a few that are easily accessible, user-friendly, exhibit significant independent effects, and can provide predictive capabilities.\u003c/p\u003e \u003cp\u003eThis study conducted univariate and multivariate analyses to identify independent risk factors for mortality in children with pneumonia in the PICU. The correlation between white blood cell count and mortality risk in pneumonia patients showed a distinct segmented trend. The analysis indicated that a white blood cell count within the range of (0.55\u0026ndash;15.55) * 10^9/L represents a relatively safe zone, with values below or above this threshold being associated with increased mortality risk. A study on pneumonia patients in the PICU also corroborated these findings, although it did not discuss the critical values for white blood cell count\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Similarly, Toshihiro Sakakibara et al established a lower limit of 0.4*10\u003csup\u003e9\u003c/sup\u003e/L for white blood cell count, which aligns closely with our findings\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. We believe that both elevated and reduced white blood cell counts suggest a severe inflammatory response, with low counts potentially indicating immunosuppression, thereby raising the risk of mortality\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Age is typically a factor affecting disease progression and severity. In adult pneumonia cases, age is generally considered a risk factor, however, in pediatric cases, it often serves as a protective factor. To gain a more precise understanding of the impact of age on mortality risk, we recorded age in months. Our analyses indicated that age in months correlates with mortality in severe pneumonia cases and is the only protective risk factor identified in the model. This conclusion is supported by previous studies\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, including a four-year prospective study which identified younger age as a risk factor for increased mortality, defining young age as being under 12 months\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e, whereas our cutoff was established at 14.5 months, which is quite close. C-reactive protein (CRP) is an acute-phase protein produced during inflammatory responses, typically associated with inflammation and helpful in predicting outcomes in pediatric pneumonia\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Yanhui Wang et al confirmed that high CRP levels are an independent risk factor for poor outcomes in pneumonia\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. A multicenter case-control study demonstrated good accuracy for distinguishing pneumonia cases in children at a critical CRP value of 37.1 mg/L\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, which is very close to our conclusion of 33.5 mg/L. The application of glucocorticoids was the only medication information included in our study, and contrary to our initial hypothesis, it emerged as a risk factor rather than a protective one. Glucocorticoids are generally believed to have anti-inflammatory effects that can yield positive outcomes in pneumonia treatment\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e, indicating that glucocorticoids might reduce ICU stay and mortality risk\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. However, this view remains contentious, with guidelines from the U.S. in 2020 and the UK in 2015 advising against their use in pneumonia patients\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. We hypothesize that the use of glucocorticoids may be somewhat helpful in preventing the progression of disease, but is not decisive and cannot alter severe outcomes such as death. Previous studies on the use of glucocorticoids in the treatment of pneumonia have shown that they may slow disease progression or reduce the need for mechanical ventilation, but no correlation with mortality risk or adverse prognoses has been established\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e.Electrolyte imbalances are common complications of pneumonia in children. In our study, a potassium level exceeding 4.45 mg/L significantly increased the risk of mortality in pneumonia patients. A retrospective study on pneumonia cases noted that electrolyte disturbances are risk factors affecting the prognosis of pneumonia, with hyperkalemia being more common than hypokalemia\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. This may be because elevated potassium levels typically impact cardiac function, leading to worse outcomes\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. Research on the correlation between total bilirubin and mortality is limited. In a study on pneumonia caused by influenza, bilirubin was identified as an independent risk factor for mortality in children\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Additionally, a multicenter case-control study also demonstrated a relationship between total bilirubin levels and the risk of death in pediatric pneumonia cases\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn addition to the relevant indicators included in the predictive model of this study, there are many other indicators or influencing factors that can predict the risk of mortality in children with pneumonia, such as severity scores, environmental factors, underlying diseases, and certain biochemical indicators\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. However, some of these indicators are not routine examination items in primary healthcare institutions, some assessments have significant subjective biases, and some indicators have a high rate of missing values in databases, making it difficult for frontline healthcare workers to complete assessments in a short time. Therefore, these variables were not included in the statistical analysis of this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Comparison of This Predictive Model with Other Studies\u003c/h2\u003e \u003cp\u003eThere have long been prediction models developed for mortality in ICU severe cases. One model, based on the MIMIC database, was created to assess the risk of death from COVID-19 and achieved an AUC of 0.760 (95% CI: 0.739\u0026ndash;0.781) for predicting mortality within one year\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. In a study on children, Stuart Haggie et al developed a clinical scoring system for pediatric pneumonia patients, yielding an area under the curve (AUC) of 0.78\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e,which is comparable to our predictive efficacy. Other research integrated various severity scores and major complications into their predictive models, raising the AUC to 0.85\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. Over the past decade, while emerging blood and radiological methods have been studied to enhance the speed and accuracy of diagnoses\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e, this has raised the entry barriers for lower-tier healthcare institutions. However, it is in resource-limited areas and low-to middle-income countries (LMIC) where the burden of childhood pneumonia is particularly heavy\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Therefore, we sacrificed a certain degree of predictive performance to enhance the convenience of the model. We believe our design achieves a good balance between benefits and drawbacks. The area under the curve (AUC) of the model constructed in this study is 0.765, indicating acceptable predictive performance. While ensuring that the predictive capability remains substantial, we have effectively lowered the accessibility barrier for using the predictive model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Advantages of This Study\u003c/h2\u003e \u003cp\u003eThe predictive model we developed is highly targeted. As pneumonia is one of the most common diseases in children and has the highest mortality rate in the PICU, there are currently few predictive models developed specifically for pediatric cases in the PICU. Our constructed model is user-friendly and low cost, consisting of only six routine variables. This significantly reduces the number of variables compared to common risk scoring methods in the ICU, greatly enhancing usability. All variables we employed are routine information and commonly tested items, allowing immediate testing results to be obtained quickly. We utilized a nomogram presentation style, enabling rapid acquisition of predictive results in clinical settings, which provides higher clinical practicality compared to exploring risk factors. Our model has a low threshold for use, facilitating rapid risk assessment and treatment in primary PICUs and economically underdeveloped regions, allowing timely referral of high-risk pneumonia cases to higher-level PICUs or medical centers, and enabling tailored clinical plans for low-risk patients, thereby achieving precision medicine.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Limitations of This Study\u003c/h2\u003e \u003cp\u003eOf course, our study has certain limitations. Firstly, our predictive model is based on a critical care medicine database, but the final sample size included in the analysis was only 467 cases, making it challenging to fully control for bias. Secondly, while our model showed high consistency during validation, the internal validation method reduced the generalizability of the model, and the observational study design was not conducive to exploring the mechanisms of the disease. Furthermore, to ensure that the model could be conveniently used in lower-tier PICUs, the selection of included variables was limited, resulting in an overall simple variable set and excluding some special or auxiliary variables, which affected the model's predictive power. Finally, the data quality of the database somewhat restricted the development of our predictive model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Summary\u003c/h2\u003e \u003cp\u003eWe developed a predictive model for mortality risk in children with pneumonia in the PICU using the PIC database. The model consists of six variables: age in months, white blood cell count, CRP, potassium ion concentration, total bilirubin and application of glucocorticoids. Our predictive model demonstrates good predictive performance and certain external applicability.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCF W responsible for research design, data analysis and interpretation, preparation of all charts, and drafting sections of manuscripts.XLC involved in the design of this research, data analysis and interpretation, and contributed to drafting parts of the paper.XTB involved in the design of this research, data analysis and interpretation.GC J involved in the design of this research, preparation of some charts. MTL involved in data analysis and contributed to drafting parts of the paper.All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData availability:\u003c/h2\u003e \u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eEstimates of the global, regional, and national morbidity, mortality, and aetiologies of lower respiratory infections in 195 countries, 1990-2016: a systematic analysis for the global burden of disease study 2016. Lancet Infect Dis. 2018; 18:1191-1210.\u003c/li\u003e\n\u003cli\u003eGlobal burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the global burden of disease study 2019. Lancet. 2020; 396:1204-1222.\u003c/li\u003e\n\u003cli\u003eLi JJ, Chen YF, Lin YX\u003cstrong\u003e.\u003c/strong\u003e [Investigation of disease spectrum in the picu of shengjing hospital of china medical university between 2005 and 2012]. Zhongguo Dang Dai Er Ke Za Zhi. 2013; 15:472-6.\u003c/li\u003e\n\u003cli\u003eKoh J, Wong JJ, Sultana R, Wong P, Mok YH, Lee JH\u003cstrong\u003e.\u003c/strong\u003e Risk factors for mortality in children with pneumonia admitted to the pediatric intensive care unit. Pediatr Pulm. 2017; 52:1076-1084.\u003c/li\u003e\n\u003cli\u003eRamachandran P, Nedunchelian K, Vengatesan A, Suresh S\u003cstrong\u003e.\u003c/strong\u003e Risk factors for mortality in community acquired pneumonia among children aged 1-59 months admitted in a referral hospital. Indian Pediatr. 2012; 49:889-95.\u003c/li\u003e\n\u003cli\u003eDivecha C, Tullu MS, Chaudhary S\u003cstrong\u003e.\u003c/strong\u003e Burden of respiratory illnesses in pediatric intensive care unit and predictors of mortality: experience from a low resource country. Pediatr Pulm. 2019; 54:1234-1241.\u003c/li\u003e\n\u003cli\u003ePollack MM, Patel KM, Ruttimann UE\u003cstrong\u003e.\u003c/strong\u003e Prism iii: an updated pediatric risk of mortality score. Crit Care Med. 1996; 24:743-52.\u003c/li\u003e\n\u003cli\u003eStraney L, Clements A, Parslow RC, Pearson G, Shann F, Alexander J, et al. Paediatric index of mortality 3: an updated model for predicting mortality in pediatric intensive care*. Pediatr Crit Care Me. 2013; 14:673-81.\u003c/li\u003e\n\u003cli\u003eZhang L, Huang H, Cheng Y, Xu L, Huang X, Pei Y, et al. [Predictive value of four pediatric scores of critical illness and mortality on evaluating mortality risk in pediatric critical patients]. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2018; 30:51-56.\u003c/li\u003e\n\u003cli\u003eZeng X, Yu G, Lu Y, Tan L, Wu X, Shi S, et al. Pic, a paediatric-specific intensive care database. Sci Data. 2020; 7:14.\u003c/li\u003e\n\u003cli\u003eSakakibara T, Shindo Y, Kobayashi D, Sano M, Okumura J, Murakami Y, et al. A prediction rule for severe adverse events in all inpatients with community-acquired pneumonia: a multicenter observational study. Bmc Pulm Med. 2022; 22:34.\u003c/li\u003e\n\u003cli\u003eOzbay S, Ayan M, Ozsoy O, Akman C, Karcioglu O\u003cstrong\u003e.\u003c/strong\u003e Diagnostic and prognostic roles of procalcitonin and other tools in community-acquired pneumonia: a narrative review. Diagnostics. 2023; 13.\u003c/li\u003e\n\u003cli\u003eEinloft PR, Garcia PC, Piva JP, Bruno F, Kipper DJ, Fiori RM\u003cstrong\u003e.\u003c/strong\u003e [A sixteen-year epidemiological profile of a pediatric intensive care unit, brazil]. Rev Saude Publ. 2002; 36:728-33.\u003c/li\u003e\n\u003cli\u003eEl-Nawawy A\u003cstrong\u003e.\u003c/strong\u003e Evaluation of the outcome of patients admitted to the pediatric intensive care unit in alexandria using the pediatric risk of mortality (prism) score. J Trop Pediatrics. 2003; 49:109-14.\u003c/li\u003e\n\u003cli\u003eZhang Q, Guo Z, Bai Z, MacDonald NE\u003cstrong\u003e.\u003c/strong\u003e A 4 year prospective study to determine risk factors for severe community acquired pneumonia in children in southern china. Pediatr Pulm. 2013; 48:390-7.\u003c/li\u003e\n\u003cli\u003eKoster MJ, Broekhuizen BD, Minnaard MC, Balemans WA, Hopstaken RM, de Jong PA, et al. Diagnostic properties of c-reactive protein for detecting pneumonia in children. Resp Med. 2013; 107:1087-93.\u003c/li\u003e\n\u003cli\u003ePrincipi N, Esposito S\u003cstrong\u003e.\u003c/strong\u003e Biomarkers in pediatric community-acquired pneumonia. Int J Mol Sci. 2017; 18.\u003c/li\u003e\n\u003cli\u003eWang Y, Zhang S, Li L, Xie J\u003cstrong\u003e.\u003c/strong\u003e The usefulness of serum procalcitonin, c-reactive protein, soluble triggering receptor expressed on myeloid cells 1 and clinical pulmonary infection score for evaluation of severity and prognosis of community-acquired pneumonia in elderly patients. Arch Gerontol Geriat. 2019; 80:53-57.\u003c/li\u003e\n\u003cli\u003eHigdon MM, Le T, O\u0026apos;Brien KL, Murdoch DR, Prosperi C, Baggett HC, et al. Association of c-reactive protein with bacterial and respiratory syncytial virus-associated pneumonia among children aged \u0026lt;5 years in the perch study. Clin Infect Dis. 2017; 64:S378-S386.\u003c/li\u003e\n\u003cli\u003eBriel M, Spoorenberg S, Snijders D, Torres A, Fernandez-Serrano S, Meduri GU, et al. Corticosteroids in patients hospitalized with community-acquired pneumonia: systematic review and individual patient data metaanalysis. Clin Infect Dis. 2018; 66:346-354.\u003c/li\u003e\n\u003cli\u003eCheema HA, Musheer A, Ejaz A, Paracha AA, Shahid A, Rehman M, et al. Efficacy and safety of corticosteroids for the treatment of community-acquired pneumonia: a systematic review and meta-analysis of randomized controlled trials. J Crit Care. 2024; 80:154507.\u003c/li\u003e\n\u003cli\u003ePletz MW, Blasi F, Chalmers JD, Dela CC, Feldman C, Luna CM, et al. International perspective on the new 2019 american thoracic society/infectious diseases society of america community-acquired pneumonia guideline: a critical appraisal by a global expert panel. Chest. 2020; 158:1912-1918.\u003c/li\u003e\n\u003cli\u003eLim WS, Smith DL, Wise MP, Welham SA\u003cstrong\u003e.\u003c/strong\u003e British thoracic society community acquired pneumonia guideline and the nice pneumonia guideline: how they fit together. Thorax. 2015; 70:698-700.\u003c/li\u003e\n\u003cli\u003eSaleem N, Kulkarni A, Snow T, Ambler G, Singer M, Arulkumaran N\u003cstrong\u003e.\u003c/strong\u003e Effect of corticosteroids on mortality and clinical cure in community-acquired pneumonia: a systematic review, meta-analysis, and meta-regression of randomized control trials. Chest. 2023; 163:484-497.\u003c/li\u003e\n\u003cli\u003eRavioli S, Gygli R, Funk GC, Exadaktylos A, Lindner G\u003cstrong\u003e.\u003c/strong\u003e Prevalence and impact on outcome of sodium and potassium disorders in patients with community-acquired pneumonia: a retrospective analysis. Eur J Intern Med. 2021; 85:63-67.\u003c/li\u003e\n\u003cli\u003eDesai AS, Liu J, Pfeffer MA, Claggett B, Fleg J, Lewis EF, et al. Incident hyperkalemia, hypokalemia, and clinical outcomes during spironolactone treatment of heart failure with preserved ejection fraction: analysis of the topcat trial. J Card Fail. 2018; 24:313-320.\u003c/li\u003e\n\u003cli\u003eBai Y, Guo Y, Gu L\u003cstrong\u003e.\u003c/strong\u003e Additional risk factors improve mortality prediction for patients hospitalized with influenza pneumonia: a retrospective, single-center case-control study. Bmc Pulm Med. 2023; 23:19.\u003c/li\u003e\n\u003cli\u003eCheng X, Wang H, Sun L, Ge W, Liu R, Qin H, et al. Construction and external validation of a scoring prediction model for mortality risk within 30 days of community-acquired pneumonia in children admitted to the pediatric intensive care unit: a multicenter retrospective case-control study. Medicine. 2024; 103:e37419.\u003c/li\u003e\n\u003cli\u003eWang B, Li Y, Tian Y, Ju C, Xu X, Pei S\u003cstrong\u003e.\u003c/strong\u003e Novel pneumonia score based on a machine learning model for predicting mortality in pneumonia patients on admission to the intensive care unit. Resp Med. 2023; 217:107363.\u003c/li\u003e\n\u003cli\u003eKapoor A, Awasthi S, Kumar YK\u003cstrong\u003e.\u003c/strong\u003e Predicting mortality and use of risc scoring system in hospitalized under-five children due to who defined severe community acquired pneumonia. J Trop Pediatrics. 2022; 68.\u003c/li\u003e\n\u003cli\u003eHaggie S, Barnes EH, Selvadurai H, Gunasekera H, Fitzgerald DA\u003cstrong\u003e.\u003c/strong\u003e Paediatric pneumonia: deriving a model to identify severe disease. Arch Dis Child. 2022; 107:491-496.\u003c/li\u003e\n\u003cli\u003ePan J, Bu W, Guo T, Geng Z, Shao M\u003cstrong\u003e.\u003c/strong\u003e Development and validation of an in-hospital mortality risk prediction model for patients with severe community-acquired pneumonia in the intensive care unit. Bmc Pulm Med. 2023; 23:303.\u003c/li\u003e\n\u003cli\u003eThomas J, Pociute A, Kevalas R, Malinauskas M, Jankauskaite L\u003cstrong\u003e.\u003c/strong\u003e Blood biomarkers differentiating viral versus bacterial pneumonia aetiology: a literature review. Ital J Pediatr. 2020; 46:4.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1: Basic Information of the Study Population\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"574\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.3086%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003eSurvival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003eDeath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003et/\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u0026chi;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e/\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eZ\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eN（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e282（80.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e69（19.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eLymphocyte count（*10^9/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e2.4 (1.5-4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e2.2 (1.1-4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e-0.846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eNeutrophil percentage（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e61.5 (41.2-74.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e63.8 (46.1-77.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e-0.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eLymphocyte percentage（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e31.2 (17.4-47.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e28.9 (17.2-41.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eHemoglobin（g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e105.2 \u0026plusmn; 19.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e101.9 \u0026plusmn; 18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e1.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eNeutrophil/lymphocyte ratio（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e1.9 (0.9-4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e2.6 (1.1-4.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e-0.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.371\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eEosinophil percentage（%）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e0.5 (0.2-1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e0.6 (0.2-1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eBase excess（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e0.2 (-2.7-3.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e-0.6 (-4.7-4.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e-1.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eCreatinine\u0026nbsp;（\u0026mu;mol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e37.0 (29.0-46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e38.0 (28.4-51.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e1.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eCreatine kinase isoenzyme（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e29.0 (19.0-43.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e26.0 (18.5-44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e-0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026nbsp;Lactate dehydrogenase（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;383.0 (296.5-561.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;422.0 (300.0-683.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e-1.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eAlanine aminotransferase\u0026nbsp;（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e26.0 (17.0-44.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e25.0 (19.0-42.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e-0.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eAspartate aminotransferase\u0026nbsp;（U/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e47.0 (34.0-81.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e52.0 (37.5-104.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e-0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.333\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e120 (42.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e32 (46.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e162 (57.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e37 (53.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eAge（month）\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e7.648\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026le;14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e175 (62.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e55 (79.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e＞14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e107 (37.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e14 (20.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eWhite blood cell count（*10^9/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e11.941\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e0.55-15.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e242 (85.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e47 (68.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e＜0.55 or\u0026nbsp;＞15.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e40 (14.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e22 (31.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eNeutrophil count\u0026nbsp;（*10^9/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e8.260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026le;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e253 (89.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e53 (76.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026gt;11.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e29 (10.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e16 (23.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003ePlatelet count（*10^9/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e3.399\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026le;357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e175 (62.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e51 (73.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026gt;357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e107 (37.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e18 (26.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eC-reactive protein（mg/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e8.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026le;33.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e248 (87.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e51 (73.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026gt;33.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e34 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e18 (26.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eAlbumin\u0026nbsp;（g/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e5.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026le;30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e29 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e14 (20.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026gt;30.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e251 (89.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e53 (79.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eLactic acid（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.776\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026le;5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e267 (95.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e63 (94.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026gt;5.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e14 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e4 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003ePH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e3.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026le;7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e272 (96.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e65 (94.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026gt;7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e10 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e4 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003ePotassium ion concentration（mmol/L）\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e8.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026le;4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e216 (76.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e41 (59.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026gt;4.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e66 (23.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e28 (40.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eSodium ion concentration （mmol/L）\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e5.556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026le;138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e165 (58.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e51 (73.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026gt;138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e117 (41.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e18 (26.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eTotal bilirubin（\u0026mu;mol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e8.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026le;7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e158 (57.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e24 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003e\u0026gt;7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e117 (42.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e40 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eApplication of glucocorticoids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e4.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eNo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e186 (66.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e36 (52.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40.4264%;\"\u003e\n \u003cp\u003eYes\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19.3749%;\"\u003e\n \u003cp\u003e96 (34.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21.0515%;\"\u003e\n \u003cp\u003e33 (47.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.8737%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3148%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: Multivariate logistic regression analysis results of factors affecting outcomes in children with pneumonia in the PICU.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"631\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.477%;\"\u003e\n \u003cp\u003eIntercept and variable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.66719%;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9572%;\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003eWald\u0026chi;2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7876%;\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9952%;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.477%;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.66719%;\"\u003e\n \u003cp\u003e-2.700\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9572%;\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e55.645\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7876%;\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9952%;\"\u003e\n \u003cp\u003e0.033-0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.477%;\"\u003e\n \u003cp\u003eAge（month）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.66719%;\"\u003e\n \u003cp\u003e-0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9572%;\"\u003e\n \u003cp\u003e0.378\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e6.790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7876%;\"\u003e\n \u003cp\u003e0.373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9952%;\"\u003e\n \u003cp\u003e0.178-0.784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.477%;\"\u003e\n \u003cp\u003eWhite blood cell count（*10^9/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.66719%;\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9572%;\"\u003e\n \u003cp\u003e0.352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e6.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7876%;\"\u003e\n \u003cp\u003e2.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9952%;\"\u003e\n \u003cp\u003e1.250-4.971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.477%;\"\u003e\n \u003cp\u003eC-reactive protein（mg/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.66719%;\"\u003e\n \u003cp\u003e1.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9572%;\"\u003e\n \u003cp\u003e0.406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e11.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7876%;\"\u003e\n \u003cp\u003e3.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9952%;\"\u003e\n \u003cp\u003e1.800-8.851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.477%;\"\u003e\n \u003cp\u003ePotassium ion concentration（mmol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.66719%;\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9572%;\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e5.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7876%;\"\u003e\n \u003cp\u003e2.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9952%;\"\u003e\n \u003cp\u003e1.095-3.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.477%;\"\u003e\n \u003cp\u003eTotal bilirubin（\u0026mu;mol/L）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.66719%;\"\u003e\n \u003cp\u003e0.953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9572%;\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e9.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7876%;\"\u003e\n \u003cp\u003e2.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9952%;\"\u003e\n \u003cp\u003e1.397-4.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.477%;\"\u003e\n \u003cp\u003eApplication of glucocorticoids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.66719%;\"\u003e\n \u003cp\u003e0.799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16.9572%;\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e6.403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.7876%;\"\u003e\n \u003cp\u003e2.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12.9952%;\"\u003e\n \u003cp\u003e1.197-4.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.55784%;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 3 Comparison of Modelling and Validation Population Parameters\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"774\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 113px;\"\u003e\n \u003cp\u003eTest items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 57px;\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 89px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003eOptimal Threshold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 75px;\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 74px;\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 64px;\"\u003e\n \u003cp\u003eAccuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003ePositive likelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003eNegative likelihood ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 74px;\"\u003e\n \u003cp\u003eDiagnostic odds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"42\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"42\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eModeling group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.705-0.825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-1.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.813\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e5.939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"42\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eValidation group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 57px;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0.597-0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e-1.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 64px;\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e3.926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e9.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"42\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Prediction Model, Pneumonia;PICU, Children","lastPublishedDoi":"10.21203/rs.3.rs-5294454/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5294454/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Pneumonia is one of the major diseases threatening the safety of children's lives; however, there are currently few convenient tools available to predict the death risk of children with pneumonia. We explored the risk factors for mortality in pediatric intensive care unit (PICU) patients with pneumonia and developed and validated a mortality risk prediction model.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: A research cohort was established using a public database from a pediatric intensive care unit, including data from 467 cases. Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for mortality in pneumonia patients, and a prediction model was constructed based on these risk levels, resulting in a nomogram.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: 1. A total of 351 cases were included for modeling, with 69 in-hospital deaths and 282 in-hospital survivors identified as outcomes. 2. The analysis identified independent risk factors for mortality in pneumonia patients as age in months, white blood cell count, CRP, potassium ion concentration, total bilirubin and application of glucocorticoids. 3. The area under the curve (AUC) for the prediction model was 0.765 (95% CI: 0.705-0.825), with a sensitivity of 0.813 and specificity of 0.578; internal validation demonstrated that the model has good consistency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: A convenient model for predicting the mortality risk of children with pneumonia in PICU has been developed, showing a reasonable level of accuracy.\u003c/p\u003e","manuscriptTitle":"Construction and Validation of a Convenient Death Prediction Model for Pediatric Pneumonia Patients in Intensive Care Units","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-18 10:54:28","doi":"10.21203/rs.3.rs-5294454/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"17cee1c0-913c-4521-9cf7-c75c9bcb9d26","owner":[],"postedDate":"November 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-14T07:38:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-18 10:54:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5294454","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5294454","identity":"rs-5294454","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}