A Clinical Risk Score to Predict the Critical Illness in Respiratory Syncytial Virus Co- detected With Streptococcus Pneumoniae Hospitalized Children

A Clinical Risk Score to Predict the Critical Illness in Respiratory Syncytial Virus Co- detected With Streptococcus Pneumoniae Hospitalized Children | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article A Clinical Risk Score to Predict the Critical Illness in Respiratory Syncytial Virus Co- detected With Streptococcus Pneumoniae Hospitalized Children Lu Li, Xiaohan Huang, Ximing Xu, Enmei Liu, Yu Deng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5314862/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 Purpose To develop and validate a clinical score at hospital admission for predicting which patients will develop critical illness. Methods A retrospective study was conducted in the Children’s Hospital of Chongqing Medical University from January 2012 to December 2021. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator and logistic regression to construct a predictive risk score. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from our hospital and another hospital were used to validate the score. Results The study included 1516 patients totally, including training cohort 1039 patients，validation cohort 477 patients, and external cohort 122 patients. From 35 potential predictors, 11 variables were independent predictive factors and were included in the risk score: neonate hospitalization，chronic pulmonary disease，congenital heart disease，immune deficiency disease，anhelation，disorders of consciousness，assisted respiration，lymph decreased，RBC decreased，CRP increased，pulmonary atelectasis. The mean AUC in the training cohort was 0.853, the AUC in the validation cohort was 0.848，and the external cohort was 0.967. Conclusion In this study, the risk score based on characteristics of RSV-infected patients co-detected with S.pn at the time of admission to the hospital was developed that may help predict a patient’s risk of developing critical illness. Health sciences/Diseases/Respiratory tract diseases Biological sciences/Microbiology/Pathogens Biological sciences/Microbiology/Virology Respiratory syncytial virus Streptococcus pneumoniae risk scores critical illness children Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Respiratory syncytial virus (RSV) is the most common viral cause of acute lower respiratory tract infection (ALRI) in infants and young children [ 1 , 2 ], with annual winter outbreaks placing a significant burden on global health systems. In 2019, it was estimated that 33 million cases of RSV-associated acute lower respiratory tract infection occurred in children aged 0–60 months, resulting in 3.6 million hospitalisations and 101,400 deaths worldwide [ 3 ]. Approximately half of the deaths occurred in infants under six months of age, with the majority occurring in developing countries. Despite a notable reduction in the hospitalization burden of RSV-associated ALRI in children younger than 5 years during the initial phase of the pandemic, a resurgence in hospitalization rates to pre-pandemic levels was observed in high-income regions by March 2022[ 4 ]. RSV infection is a common occurrence in children, with almost all children experiencing at least one episode before reaching the age of two. Despite the advent of several vaccines in recent years, their clinical application remains constrained, and there are currently no specific medications available for the widespread treatment or prevention of RSV infection in children. Streptococcus pneumoniae (S.pn) is a frequently occurring bacterium in the nasopharynx of children under the age of five years [ 5 ], and is one of the most commonly detected bacteria in children with respiratory syncytial virus infection[ 6 ], which increases the severity of disease and mortality in children. S.pn is the predominant airway-dominant organism in children under the age of two who are first infected with RSV [ 7 ]. Its presence is associated with an increased risk of hospitalization, disease severity, and a robust inflammatory immune response[ 8 ]. Furthermore, co-infection with S.pn in preterm infants hospitalised for RSV was associated with prolonged hospitalisation and increased intensive care unit admissions[ 9 ]. Although S.pn is asymptomatically colonised in the upper respiratory tract, viral infections can alter the local environment [ 10 ], thereby enhancing the adherence of pathogenic bacteria to the nasopharyngeal epithelium and facilitating invasion of the lower respiratory tract [ 11 ], where it can cause pneumonia, bacteremia and even meningitis [ 12 ]. The early identification of patients with respiratory syncytial virus infection co-detected with Streptococcus pneumoniae who may develop critical illness is of significant importance, as it may facilitate the delivery of optimal treatment and the optimization of resource utilization. Nevertheless, no studies have hitherto reported the risk factors for the development of critical illness in children infected with RSV and co-detected with S. pneumoniae. The objective of this study was to construct a risk prediction score based on the Southwest cohort of Chinese children with RSV infection, with the aim of identifying patients at the time of hospital admission who are likely to develop critical illness. Materials and methods Data Sources and Processing This retrospective cohort study was conducted at the Children's Hospital of Chongqing Medical University (CHCMU), a tertiary-care and regional teaching hospital located in southwestern China. The study analysed patients who had been hospitalised with laboratory-confirmed RSV infection between 1 January 2012 and 31 December 2021. The inclusion criteria included: (1) diagnosis of community-acquired pneumonia (CAP)[13] and age < 5 years old (no neonates); (2) Nasopharyngeal aspirates (NPAs) were obtained immediately following hospital admission and sent to the laboratory for standardized processing and bacterial culture for S.pn; and (3) In instances where multiple RSV-positive samples were obtained from a single patient over the course of the study, only the initial RSV-positive sample was incorporated into the analysis. The exclusion criteria included: (1) bacterial infections [14, 15]，a positive bacterial culture will determine the diagnosis, provided that one or more of the following conditions are met: A C-reactive protein (CRP) level exceeding 80 mg/L, a white blood cell count greater than 15 × 10⁹/L, or a procalcitonin concentration above 0.5 μg/L; (2) Infections with adenovirus, influenza A/B virus, parainfluenza virus, or other viral pneumonias may also be present; (3) Mycoplasma or Chlamydia infections, whooping cough, or fungal pneumonia; (4) NPA cultures containing fungus or unknown bacteria; and (5) incomplete medical records. The data set comprised two distinct cohorts: the training cohort, comprising data from January 2012 to December 2018, was used to construct the risk score; the validation cohort, comprising data from January 2019 to December 2021, was used to validate the risk score; and the external validation cohort, comprising data from January to December 2021 of Zhengzhou Hospital in Henan Province, was used to validate the risk score according to the aforementioned inclusion and exclusion criteria. Potential Predictive Variables The following patient characteristics at hospital admission were considered as potential predictive variables: clinical signs and symptoms, imaging results, laboratory findings, demographic variables, and medical history. The demographic variables collected for the purposes of this study included the subject's sex, age (divided into the following categories: <1 month, <6 months, <2 years, and <5 years), delivery mode, and feeding method. The medical history included information on premature birth, a history of eczema and/or food protein allergy, neonatal hospitalisation, a previous lower respiratory tract infection, and the presence of wheezing. The underlying diseases included congenital heart disease, chronic lung disease, congenital airway developmental abnormalities, immunodeficiencies, and antibiotic use prior to the current visit. The clinical signs and symptoms were recorded as categorical and continuous variables, including the patient's initial body temperature, respiratory rate, mental status, cyanosis, and the presence of any signs of assisted respiration (such as triple concave signs, nasal flaring, or nodding). Additionally, the presence of pulmonary rales on admission and the number of days spent in hospital were also documented. The imaging results included the presence of abnormalities on chest X-ray (CXR), the severity of these abnormalities, and the presence of abnormalities on chest computed tomography (CT) imaging. Laboratory findings included routine blood tests (white blood cell, neutrophil, lymphocyte, platelet, erythrocyte, and haemoglobin counts), CRP levels, and biochemical indices (gamma-glutamyl transferase, gamma glutaminase, urea, creatinine, and lactate dehydrogenase). Outcomes A critical illness was defined as any condition meeting one or more of the following criteria: severe pneumonia (diagnostic criteria based on the Diagnostic and Treatment Guidelines for Community-Acquired Pneumonia in Children, 2019 edition[13]), oxygen therapy, invasive mechanical ventilation, admission to the ICU, and death. This composite endpoint was adopted in order to assess the severity of the disease in question, given that such outcomes are also observed in other serious infectious diseases[16, 17]. Variable Selection and Score Construction All data were subjected to analysis using the statistical software package SPSS v27.0 (SPSS, Inc., Chicago, IL, USA). Missing data were completed using random forests. As previously outlined, a total of 35 variables were included in the selection process. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to mitigate the risk of collinearity among variables obtained from the same patient and to prevent overfitting. Binary variables were imputed using logistic regression. L1-penalized least absolute shrinkage and selection regression was employed for multivariable analyses, with 10-fold cross-validation utilized for internal validation. The R package "glmnet" statistical software (R Foundation) was employed for the purpose of performing the LASSO regression. Subsequently, the variables identified by LASSO regression analysis were entered into logistic regression models. Those that were consistently statistically significant were then used to construct the risk score. A line graph was plotted using the R function "nomogram" to visualise the risk scores. Assessment of Accuracy A calibration curve was plotted to evaluate the consistency between the predicted and actual probabilities of the risk score. This was achieved by using the "calibrate" function in R software to plot the risk score after 1000 resamples, and obtaining the C-statistic. Furthermore, the area under the receiver operating characteristic curve (AUC) was employed to evaluate the accuracy of the risk score. To validate the generalisability of the risk score, we plotted a calibration curve and a receiver-operator characteristic curve for internal and external validation data, respectively. A decision curve analysis (DCA) was subsequently conducted to evaluate the clinical utility of the risk score. The horizontal axis of the DCA graph represents the "threshold probability," which is the probability of mortality for patient i. When a specific value is reached by the various evaluation methods, patient i is defined as positive, indicating the need for intervention when the probability of mortality (denoted as Pi) reaches a certain threshold (denoted as Pt). The vertical axis represents the net benefit rate, which is calculated by subtracting the disadvantages from the advantages. Results Characteristics of the Training Cohort A total of 1,039 patients were included in the training cohort, of whom 287 (27.6%) were identified as having critical illness and 752 (72.4%) were classified as having non-critical illness. The median (interquartile range) age of patients in the cohort was 11.0 (6.0-18.0) months, with 653 patients (62.8%) being male and 443 (42.6%) having undergone vaginal delivery. Additionally, 469 patients (45.1%) were breastfed. The top three medical histories were lower respiratory tract infection (329, 31.7%), wheeze (329, 31.7%), and neonatal hospitalisation (151, 14.5%). The most common underlying disease was congenital heart disease (126, 12.1%). Prior to the current visit, 864 (82.3%) patients had used antibiotics. The most prevalent symptoms were cyanosis (653, 62.8%), wheezing (626, 60.3%), and anhelation (435, 41.9%) ( Table 1 ). The laboratory findings of the training cohort are presented in Table 1 . Predictor Selection A total of 35 variables, measured at the time of hospital admission (see Table 1), were included in the LASSO regression analysis. Following the LASSO regression selection process, 14 variables were identified as significant predictors of critical illness. These included neonate hospitalisation, chronic pulmonary disease, congenital heart disease, immune deficiency disease, antibiotic use, anhelation, disorders of consciousness, assisted respiration, an increase in white blood cells (WBC), an increase in neutrophils (Neut), a decrease in lymphocytes (Lymph), a decrease in red blood cells (RBC), an increase in C-reactive protein (CRP), and pulmonary atelectasis. The inclusion of these 14 variables in a logistic regression model resulted in the identification of 11 variables that were independently statistically significant predictors of critical illness and were subsequently incorporated into the risk score. These variables included neonate hospitalization (OR, 2.06; 95% CI, 1.276-5.38； P =0.003)，chronic pulmonary disease（OR, 4.901; 95% CI, 1.310-22.121; P =0.025），congenital heart disease（OR, 2.357; 95% CI, 1.428-3.888; P =0.001），immune deficiency disease（OR, 12.632; 95% CI, 1.721-116.829; P =0.015），anhelation（OR, 2.641；95% CI, 1.810-3.863; P <0.001），disorders of consciousness（OR, 19.041；95% CI, 1.849-426.476; P =0.019），assisted respiration（OR, 8.807；95% CI, 6.050-12.960; P <0.001），Lymph decreased（OR, 2.467；95% CI, 1.190-5.176; P =0.016），RBC decreased（OR, 2.018；95% CI, 1.158-3.496; P =0.013），CRP increased（OR, 1.828；95% CI, 1.199-2.784; P =0.005），pulmonary atelectasis（OR, 10.348；95% CI, 4.086-27.873; P <0.001）（ Table 2 ）. Construction of the Risk Score The critical illness risk score was constructed based on the coefficients derived from the logistic model, and a nomogram was plotted based on that risk score ( Figure 1 ). The objective is to facilitate the speculation by clinicians on the likelihood (with 95% CI) of a child developing into a critical illness based on 11 variables during the course of their hospitalisation. The C-statistic of the calibration curve and the AUC based on data from the training cohort were 0.853 (95% CI, 0.825-0.880) ( Figure 2 ). The DCA demonstrated that the intervention based on the risk score had high clinical utility ( Figure 3 ). Validation of the Risk Score The validation cohort comprised 477 patients with a median (interquartile range) age of 10.0 (5.0-19.0) months, 299 (62.7%) of whom were male, and 123 of whom developed the critical illness. The variables employed in the risk score for the validation cohort are presented in Table 3 . The validation cohort exhibited a comparable degree of accuracy in risk score assessment to that observed in the training cohort. The C-statistic and AUC values for the validation cohort were 0.848 (95% CI, 0.806-0.891) ( Figure 4 ), which align with the findings observed in the training cohort. The external validation cohort comprised 122 patients, 24 of whom were diagnosed with critical illness. The median age of this group was 12.0 months (interquartile range [IQR]: 6.4-22.0 months), and 71 patients (58.2%) were male. The variables employed in the risk score for the external validation cohort are presented in Table 4 . However, the accuracy of the risk score in the external validation cohort was not consistent with that of the training cohort. The C statistic and AUC for the training cohort were 0.967 (95% CI, 0.933-1.000). It was proposed that the negative predictive value was more accurate, indicating that the risk score is more suitable for assessing the high likelihood that a child will progress to a non-critical illness. Discussion In this study, we developed and validated a clinical risk score to predict the development of critical illness among hospitalized children infected with RSV and co-detected with S. pneumoniae. The performance of this risk score was satisfactory, with an accuracy based on AUCs of 0.85 in both the development and validation cohorts. The nomogram may be employed by clinicians to estimate the risk of a child who has been hospitalised developing a critical illness. The 11 variables required for the prediction of the risk of developing critical illness are typically readily available at the time of hospital admission. In the event that the patient's estimated risk of developing a critical illness is deemed to be low, the clinician may opt to implement a monitoring strategy. Conversely, in instances where the estimated risk is identified as being high, the clinician may consider pursuing a more aggressive course of treatment or even admission to the ICU. It was our intention to avoid the creation of arbitrary categories of risk, such as low-, moderate-, and high-risk groups. We believe that clinicians are best equipped to make informed decisions regarding the risk associated with each individual patient, taking into account the specific circumstances and conditions present in their local or regional context. For instance, in regions with convenient access to clinical and supportive care, patient outcomes may be enhanced by opting to administer more assertive care to patients with moderate risk profiles. In contrast, in areas with high case volume and/or limited resources, a decision may be made to provide less aggressive care to moderate-risk patients in order to optimise the availability of IUC beds and ventilators [ 16 ]. The risk score incorporates a number of factors, including the hospitalization of neonates, the presence of chronic pulmonary disease, congenital heart disease, immune deficiency disease, anhelation, disorders of consciousness, the use of assisted respiration, lymphocyte count, red blood cell count, C-reactive protein levels, and the presence of pulmonary atelectasis. In comparison to other viruses, respiratory syncytial virus (RSV) is associated with an elevated risk of disease severity in infants and young children[ 18 ]. The principal risk factors for severe RSV infection include prematurity [ 19 ], chronic lung disease[ 20 ], congenital heart disease and immunodeficiency[ 21 ]. Nevertheless, the majority of infants who are hospitalized with RSV infection do not have any underlying risk factors that would predispose them to developing a severe disease [ 22 ]. Moreover, while viral factors, dysregulated host immune responses [ 23 ] and genetic predisposition [ 24 ] contribute to the severity of RSV infection, they do not fully account for the variability in clinical presentation and outcome. Recent findings indicate that the composition of the nasopharyngeal microbiota is associated with an increased risk of respiratory infections and a greater severity of acute respiratory symptoms [ 25 ]. RSV has been demonstrated to create favourable conditions for S.pn infection, primarily through the damaging of ciliary cells in the airway, the enhancement of local inflammatory responses, and the promotion of bacterial receptor expression[ 26 ]. Glycoprotein G, which is expressed by RSV in infected host cells, can act as a receptor for S. pneumoniae, thereby promoting binding and adhesion to epithelial cells and increasing invasiveness [ 27 ]. It has been demonstrated that RSV G proteins bind directly to S.pn penicillin-binding protein 1A, thereby enhancing bacterial adhesion and virulence gene expression and promoting the colonization and invasive infection of host cells[ 28 ]. It is noteworthy that the incidence of hospitalisation due to RSV infection declined following the introduction of the pneumococcal conjugate vaccine in paediatric populations [ 29 ]. Although studies had reported that RSV vaccine reduces the risk of severe illness in children [ 30 ] and immunization against S.pn significantly reduced the incidence of invasive pneumococcal disease [ 31 ], both the RSV vaccine and S.pn vaccine only provided single protection [ 32 , 33 ] whose protection against coinfections were limited. There is currently no specific drug available for the treatment of respiratory syncytial virus (RSV) infection. Beta-lactam antibiotics remain the recommended first-line treatment for Streptococcus pneumoniae infections [ 34 ]. Nevertheless, the identification of S. pneumoniae in a clinical setting did not necessarily indicate the presence of a bacterial infection. It is also possible that the bacterium was in a state of colonization. Consequently, the indiscriminate use of antibiotics has accelerated the emergence of drug-resistant pathogens [ 35 ], thereby reducing the efficacy of treatments and complicating patient care. The early prediction of patients who may progress to a critical illness is of significant importance, as it allows for the timely and aggressive provision of care, thereby optimising the limited medical resources available. The present study is limited by a small sample size for the construction of the risk score and a relatively small sample size for validation. The data used for score development and validation are exclusively from CHCMU, which may restrict the applicability of the risk score in other regions of China. It would be beneficial to conduct further validation studies of the critical illness risk score in areas outside of the southwest. It is anticipated that further studies will be conducted in the future to assess the applicability of the risk score. In this study, we developed a risk score to estimate the risk of developing critical illness among patients infected with RSV who were co-detected with S.pn. This was based on 11 variables that are commonly measured on admission to the hospital. The estimation of the risk of critical illness may assist in the identification of patients who are and are not likely to develop critical illness, thereby facilitating the provision of appropriate treatment and optimising the utilisation of medical resources. Abbreviations ALT Alanine Aminotransferase, ALRI Acute Lower Respiratory Tract Infections AUC Area Under Curve, AST Aspartate Aminotransferase CAP Community-Acquired Pneumonia, CI Confidence Interval. CRP C-reactive protein, Cr Creatinine CT Computed Tomographic, CXR Chest X-radiography CHCMU Children’s Hospital of Chongqing Medical University DCA Decision Curve Analysis Hb Hemoglobin LASSO Least Absolute Shrinkage and Selection Operator LDH Lactate Dehydrogenase, Lymph lymphocyte Mo Month NPAs Nasopharyngeal Aspirates, Neut Neutrophil OR Odds Ratio PLT Platelet RBC Red Blood Cell, RSV Respiratory syncytial virus S.pn Streptococcus pneumoniae WBC, White Blood Cell Declarations Funding This work was funded by a National key Research and Development Program of China (2022YFC2704900); Natural Science Foundation of Chongqing, China(cstc2019jycj-msxmX0858); CQMU Program for Youth Innovation in Future Medicine. Competing Interests The funding bodies had no part in the design, conduct, analysis or interpretation of this study or the decision to submit this paper for publication. All authors either have or do not have a commercial or other association that might pose a conflict of interest. Author Contributions All authors approved the final manuscript as submitted. Lu Li cleaned the data used in this study, performed the analyses, wrote the first draft of the manuscript, and critically reviewed the manuscript for intellectual content. Xiaohan Huang cleaned portions of the data used in this study, drafted parts of the manuscript, and critically reviewed the manuscript for intellectual content. (Author Lu Li and Author Xiao Han Huang contributed equally to this manuscript.) Ximing Xu provided statistical input on conduct and interpretation of the results and critically reviewed the manuscript for intellectual content. Enmei Liu provided clinical and microbiological input on interpretation of results and critically reviewed the manuscript for intellectual content. Yu Deng conceptualized and secured funding for the study, provided clinical input on interpretation of the results, and critically reviewed the manuscript for intellectual content. Ethics approval The study was approved by the Ethics Committee of Children's Hospital affiliated to Chongqing Medical University. All methods were performed in accordance with the relevant guidelines and regulations. Consent to participate Due to the retrospective nature of the study, the Ethics Committee of Children's Hospital affiliated to Chongqing Medical University waived the need of obtaining informed consent. Consent to publish N/A Acknowledgments We thank the Zhengzhou hospital for support for this study. Data availability The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. References Boccalini S, Bonito B, Salvati C, Del Riccio M, Stancanelli E, Bruschi M et al. 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Microorganisms Associated With Pneumonia in Children <5 Years of Age in Developing and Emerging Countries: The GABRIEL Pneumonia Multicenter, Prospective, Case-Control Study. Clin Infect Dis. 2017;65(4):604-12. doi:10.1093/cid/cix378. Man WH, Scheltema NM, Clerc M, van Houten MA, Nibbelke EE, Achten NB et al. Infant respiratory syncytial virus prophylaxis and nasopharyngeal microbiota until 6 years of life: a subanalysis of the MAKI randomised controlled trial. Lancet Respir Med. 2020;8(10):1022-31. doi:10.1016/s2213-2600(19)30470-9. Farrar JL, Childs L, Ouattara M, Akhter F, Britton A, Pilishvili T et al. Systematic Review and Meta-Analysis of the Efficacy and Effectiveness of Pneumococcal Vaccines in Adults. Pathogens. 2023;12(5). doi:10.3390/pathogens12050732. McLean HQ, Petrie JG, Hanson KE, Meece JK, Rolfes MA, Sylvester GC et al. Interim Estimates of 2022-23 Seasonal Influenza Vaccine Effectiveness - Wisconsin, October 2022-February 2023. MMWR Morb Mortal Wkly Rep. 2023;72(8):201-5. doi:10.15585/mmwr.mm7208a1. Hsiao A, Hansen J, Timbol J, Lewis N, Isturiz R, Alexander-Parrish R et al. Incidence and Estimated Vaccine Effectiveness Against Hospitalizations for All-Cause Pneumonia Among Older US Adults Who Were Vaccinated and Not Vaccinated With 13-Valent Pneumococcal Conjugate Vaccine. JAMA Netw Open. 2022;5(3):e221111. doi:10.1001/jamanetworkopen.2022.1111. Smith AM. Quantifying the therapeutic requirements and potential for combination therapy to prevent bacterial coinfection during influenza. J Pharmacokinet Pharmacodyn. 2017;44(2):81-93. doi:10.1007/s10928-016-9494-9. English BK. Limitations of beta-lactam therapy for infections caused by susceptible Gram-positive bacteria. J Infect. 2014;69 Suppl 1:S5-9. doi:10.1016/j.jinf.2014.07.010. Tables Table 1. Demographics and Clinical Characteristics Among Patients in the Training Cohort Who Did or Did Not Develop Critical Illness. characteristic Training cohort, total Critical illness Yes No No. 1039 287 752 Male, No. (%) 653（62.8） 174（60.6） 479（63.7） Age, median（IQR）, mo 11.0（6.0-18.0） 10.0（4.0-16.0） 11.0（6.0-19.0） ＜6mo 249（24.0） 92（32.1） 157（20.9） ＜12mo 300（28.9） 73（25.4） 227（30.2） ＜24mo 305（29.4） 82（28.6） 223（29.7） ＜60mo 185（17.8） 40 （13.9） 145（19.3） Vaginal delivery, No. (%) 42.6（443） 129（44.9） 314（41.8） Breast feeding, No. (%) 45.1（469） 116（40.4） 353（46.9） Medical history, No. (%) Premature 107（10.3） 46（16.0） 61（8.1） Food protein allergy 50（4.8） 17（5.9） 33（4.4） Eczema 188（18.1） 47（16.4） 141（18.8） Neonate hospitalization 151（14.5） 65（22.6） 86（11.4） Lower respiratory tract infection 329（31.7） 92（32.1） 237（31.5） Wheeze 273（26.3） 70（24.4） 203（27.0） Underling diseases, No. (%) Congenital airway malformations 89（8.6） 30（10.5） 59（7.8） Chronic pulmonary disease 16（1.5） 12（4.2） 4（0.5） Congenital heart disease 126（12.1） 58（20.2） 68（9.0） Immune deficiency disease 6（0.6） 4（1.4） 2（0.3） History of antibiotic, No. (%) 864（83.2） 227（79.1） 637（84.7） Symptom/Signs, No. (%) Fever 229（22.0） 73（25.4） 156（20.7） Anhelation 435（41.9） 201（70.0） 234（31.1） Disorders of consciousness 5（0.5） 4（1.4） 1（0.1） Cyanosis 653（62.8） 214（74.6） 439（58.4） Assisted respiration 315（30.3） 199（699.3） 116（15.4） Wheezing rale 626（60.3） 204（71.1） 422（56.1） Blood routine, No. (%) WBC↑ 69（6.6） 13（4.5） 56（7.4） Neut↑ 210（20.2） 91（31.7） 119（15.8） Lymph↓ 118（11.4） 56（19.5） 62（8.2） RBC↓ 94（9.0） 42（14.6） 52（6.9） Hb↓ 144（13.9） 53（15.8） 91（12.1） PLT↑ 119（11.5） 25（8.7） 94（12.5） CRP＞10mg/L, No. (%) 233（22.4） 87（30.3） 146（119.4） Comorbidity, No. (%) AST↑ 157（15.1） 46（16.0） 111（14.8） ALT↑ 67（6.4） 22（7.7） 45（6.0） LDH↑ 190（18.3） 41（14.3） 149（19.8） Urea↑ 8（0.8） 5（1.7） 3（0.4） Cr↑ 22（2.1） 10（3.5） 12（1.6） Pulmonary consolidation 45（4.3） 23（8.0） 22（2.9） Pulmonary atelectasis 40（3.8） 30（10.5） 10（1.3） Notes: ↑, increase; ↓, decrease. No. (%), where No. is the total number of patients with available data. Table 2. Multivariable Logistic Regression Model for Predicting Development of Critical Illness in 1039 RSV-infected Hospitalized Patients Co-detected with S.pn Variables Odds rations (95% CI) P value Neonate hospitalization 2.063(1.276-3.323) 0.003 Chronic pulmonary disease 4.901(1.310-22.121) 0.025 Congenital heart disease 2.357(1.428-3.888) 0.001 Immune deficiency disease 12.632(1.721-116.829) 0.015 Anhelation 2.641(1.810-3.863) <0.001 Disorders of consciousness 19.041(1.849-426.476) 0.019 Assisted respiration 8.807(6.050-12.960) <0.001 Lymph↓ 2.467(1.190-5.176) 0.016 RBC↓ 2.018(1.158-3.496) 0.013 CRP＞10mg/L 1.828(1.199-2.784) 0.005 Pulmonary atelectasis 10.348(4.086-27.873) <0.001 NOTE: Odds ratio (OR) and 95% confidence interval (CI) were also calculated. All tests were two-tailed, and P < 0.05 was considered statistically significant. Table 3. Demographics and Clinical Characteristics of Patients in Validation Cohorts characteristic No. (%) Validation cohort, total Critical illness Yes No No. 477 123 354 Male 299（62.7） 76（61.8） 223（63.0） Age, median（IQR）,mo 10.0（5.0-19.0） 7.0（4.0-15.0） 11.0（6.0-22.0） ＜6mo 983（47.1） 49（39.8） 75（21.2） ＜12mo 507（24.3） 34（27.6） 108（30.5） ＜24mo 369（17.7） 27（22.0） 91（25.7） ＜60mo 226（10.8） 13（10.6） 80（22.6） Neonate hospitalization, 73（15.3） 29（23.6） 44（12.4） Chronic pulmonary disease 9（1.9） 9（7.3） 0（0） Congenital heart disease 62（13.0） 23（18.7） 39（11.0） Immune deficiency disease 0（0） 0（0） 0（0） Dyspnea 200（41.9） 85（69.1） 115（32.5） Unconsciousness 4（0.8） 4（3.3） 0（0） Assisted respiration 140（29.4） 91（74.0） 49（13.8） Lymph↓ 37（7.8） 22（17.9） 15（4.2） RBC↓ 42（8.8） 18（14.6） 24（6.8） CRP↑ 115（24.1） 36（29.3） 79（22.3） Pulmonary atelectasis 12（2.5） 4（3.3） 8(23.0) Notes: No. (%), where No. is the total number of patients with available data. Table 4. Demographics and Clinical Characteristics of Patients in External Validation Cohorts characteristic No. (%) External validation, Critical illness total Yes No No. 122 24 98 Male 71（58.2） 58.3（14） 58.2 (57) Age, median（IQR）,mo 12.0（6.4-22.0） 7.2（4.2-14.5） 13.0（8.0-23.3） ＜6mo 29（23.8） 11（45.8） 18 (18.4) ＜12mo 35（28.7） 7（29.2） 28 (28.6) ＜24mo 33（27.0） 3（12.5） 30 (30.6) ＜60mo 25（20.5） 3（12.5） 22 (22.4) Neonate hospitalization, 29（23.8） 13（54.2） 16 (16.3) Chronic pulmonary disease 3（2.5） 0（0） 3 (3.1) Congenital heart disease 13（10.7） 6（25.0） 7 (7.1) Immune deficiency disease 0（0） 0（0） 0(0) Dyspnea 10（8.2） 5（20.8） 5 (5.1) Unconsciousness 1（0.8） 0（0） 1 (1.0) Assisted respiration 28（23.0） 24（100.0） 4 (4.1) Lymph↓ 13 (10.7) 4（16.7） 9 (9.2) RBC↓ 14 (11.5) 4（16.7） 10（10.2） CRP↑ 38 (31.1) 9 (37.5) 29（29.6） Pulmonary atelectasis 0（0） 0（0） 0(0) Notes: No. (%), where No. is the total number of patients with available data. 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-5314862","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":382449490,"identity":"e2ff2456-f6ea-4e31-a051-8261c7c6ffa9","order_by":0,"name":"Lu Li","email":"","orcid":"","institution":"Children's Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Li","suffix":""},{"id":382449491,"identity":"80132e69-a29c-4a18-8303-c4999cfd7f21","order_by":1,"name":"Xiaohan Huang","email":"","orcid":"","institution":"Children's Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohan","middleName":"","lastName":"Huang","suffix":""},{"id":382449493,"identity":"260bbccc-79eb-4fce-b1e4-51e79d254244","order_by":2,"name":"Ximing Xu","email":"","orcid":"","institution":"Children's Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ximing","middleName":"","lastName":"Xu","suffix":""},{"id":382449494,"identity":"be898831-e238-47b1-b673-44148492a17f","order_by":3,"name":"Enmei Liu","email":"","orcid":"","institution":"Children's Hospital of Chongqing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Enmei","middleName":"","lastName":"Liu","suffix":""},{"id":382449499,"identity":"52054849-29d7-44d8-abdd-4b798873f32c","order_by":4,"name":"Yu Deng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYDCCAzAGM/PBBx8YLHhI0MLOlmw4g0GCFC38PGbCPAwShHXwHe89/Jrnz+F8fma2NGabGgkZgxu5Bxh+VGzDqUXyzLk0a962w5Yzm5mPPc45JsFjcCMvgbHnzG2cWgxu5JgZ8zYcNjA4zJZunMMG0pJjwMzYhkfL/TdmxkCHGdgf5jGTtvhHjJYbPMaPediAtjADtTC2EaFF8kyOGePctnQDicPAQO7tk+CRPPPG4CA+v/AdP2P84c0fawP+/sMHH/z4ZmPPdzzH8MGPCtxagIBNCiX6FA4gRRYOwPzxBzJXvoGA+lEwCkbBKBhxAAD9MVai8sVyXAAAAABJRU5ErkJggg==","orcid":"","institution":"Children's Hospital of Chongqing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Deng","suffix":""}],"badges":[],"createdAt":"2024-10-23 01:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5314862/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5314862/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71054157,"identity":"3fe4e085-ee99-404f-a5a5-3979a9df4fc4","added_by":"auto","created_at":"2024-12-10 16:02:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104170,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe Nomogram for Predicting Critical Illness Among RSV-infected Children Co-detected with S.pn\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5314862/v1/c1e20c3d4ac7700b0a1297fc.png"},{"id":71053266,"identity":"b720f68e-2f49-4344-a640-9a35359fb76c","added_by":"auto","created_at":"2024-12-10 15:54:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105604,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel accuracy evaluation diagram. (A) The calibration curve is used to evaluate the consistency between the predicted probability and the actual probability of the model. (B)The area under the receiver-operator characteristic (ROC) curve (AUC) of predicting critical illness among RSV-infected patients c0-detected with S.pn. (Training set, N=1039)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5314862/v1/c11ced4fc1bdf7059af72e1b.png"},{"id":71053264,"identity":"e1af7b9f-abdf-438c-b2a4-07a26b92c74c","added_by":"auto","created_at":"2024-12-10 15:54:10","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":64450,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision Curve Analysis of the risk score.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5314862/v1/36897688cd7443c72654c91b.png"},{"id":71053267,"identity":"75ce1aa7-66a7-4210-be31-93c46e09e7b3","added_by":"auto","created_at":"2024-12-10 15:54:10","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":119547,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe calibration curve and ROC curve of RSV-GRAM in a validation independent cohort. (Validation set, N=477)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5314862/v1/1778f2855bedb3f8dcebe1c8.png"},{"id":71056487,"identity":"65d87796-e6b5-42b5-8ff9-8ceaed6244d3","added_by":"auto","created_at":"2024-12-10 16:18:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1205276,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5314862/v1/45595a66-3c2c-4492-8de7-d3a15df2b482.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Clinical Risk Score to Predict the Critical Illness in Respiratory Syncytial Virus Co- detected With Streptococcus Pneumoniae Hospitalized Children","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRespiratory syncytial virus (RSV) is the most common viral cause of acute lower respiratory tract infection (ALRI) in infants and young children [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], with annual winter outbreaks placing a significant burden on global health systems. In 2019, it was estimated that 33\u0026nbsp;million cases of RSV-associated acute lower respiratory tract infection occurred in children aged 0\u0026ndash;60 months, resulting in 3.6\u0026nbsp;million hospitalisations and 101,400 deaths worldwide [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Approximately half of the deaths occurred in infants under six months of age, with the majority occurring in developing countries. Despite a notable reduction in the hospitalization burden of RSV-associated ALRI in children younger than 5 years during the initial phase of the pandemic, a resurgence in hospitalization rates to pre-pandemic levels was observed in high-income regions by March 2022[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. RSV infection is a common occurrence in children, with almost all children experiencing at least one episode before reaching the age of two. Despite the advent of several vaccines in recent years, their clinical application remains constrained, and there are currently no specific medications available for the widespread treatment or prevention of RSV infection in children.\u003c/p\u003e \u003cp\u003eStreptococcus pneumoniae (S.pn) is a frequently occurring bacterium in the nasopharynx of children under the age of five years [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and is one of the most commonly detected bacteria in children with respiratory syncytial virus infection[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which increases the severity of disease and mortality in children. S.pn is the predominant airway-dominant organism in children under the age of two who are first infected with RSV [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Its presence is associated with an increased risk of hospitalization, disease severity, and a robust inflammatory immune response[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Furthermore, co-infection with S.pn in preterm infants hospitalised for RSV was associated with prolonged hospitalisation and increased intensive care unit admissions[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although S.pn is asymptomatically colonised in the upper respiratory tract, viral infections can alter the local environment [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], thereby enhancing the adherence of pathogenic bacteria to the nasopharyngeal epithelium and facilitating invasion of the lower respiratory tract [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], where it can cause pneumonia, bacteremia and even meningitis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe early identification of patients with respiratory syncytial virus infection co-detected with Streptococcus pneumoniae who may develop critical illness is of significant importance, as it may facilitate the delivery of optimal treatment and the optimization of resource utilization. Nevertheless, no studies have hitherto reported the risk factors for the development of critical illness in children infected with RSV and co-detected with S. pneumoniae. The objective of this study was to construct a risk prediction score based on the Southwest cohort of Chinese children with RSV infection, with the aim of identifying patients at the time of hospital admission who are likely to develop critical illness.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eData Sources and Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective cohort study was conducted at the Children\u0026apos;s Hospital of Chongqing Medical University (CHCMU), a tertiary-care and regional teaching hospital located in southwestern China. The study analysed patients who had been hospitalised with laboratory-confirmed RSV infection between 1 January 2012 and 31 December 2021. The inclusion criteria included: (1) diagnosis of community-acquired pneumonia (CAP)[13] and age \u0026lt; 5 years old (no neonates); (2) Nasopharyngeal aspirates (NPAs) were obtained immediately following hospital admission and sent to the laboratory for standardized processing and bacterial culture for S.pn; and (3) In instances where multiple RSV-positive samples were obtained from a single patient over the course of the study, only the initial RSV-positive sample was incorporated into the analysis. The exclusion criteria included: (1) bacterial infections [14, 15]，a positive bacterial culture will determine the diagnosis, provided that one or more of the following conditions are met: A C-reactive protein (CRP) level exceeding 80 mg/L, a white blood cell count greater than 15 \u0026times; 10⁹/L, or a procalcitonin concentration above 0.5 \u0026mu;g/L; (2) Infections with adenovirus, influenza A/B virus, parainfluenza virus, or other viral pneumonias may also be present; (3) Mycoplasma or Chlamydia infections, whooping cough, or fungal pneumonia; (4) NPA cultures containing fungus or unknown bacteria; and (5) incomplete medical records. The data set comprised two distinct cohorts: the training cohort, comprising data from January 2012 to December 2018, was used to construct the risk score; the validation cohort, comprising data from January 2019 to December 2021, was used to validate the risk score; and the external validation cohort, comprising data from January to December 2021 of Zhengzhou Hospital in Henan Province, was used to validate the risk score according to the aforementioned inclusion and exclusion criteria. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePotential Predictive Variables \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following patient characteristics at hospital admission were considered as potential predictive variables: clinical signs and symptoms, imaging results, laboratory findings, demographic variables, and medical history. The demographic variables collected for the purposes of this study included the subject\u0026apos;s sex, age (divided into the following categories: \u0026lt;1 month, \u0026lt;6 months, \u0026lt;2 years, and \u0026lt;5 years), delivery mode, and feeding method. The medical history included information on premature birth, a history of eczema and/or food protein allergy, neonatal hospitalisation, a previous lower respiratory tract infection, and the presence of wheezing. The underlying diseases included congenital heart disease, chronic lung disease, congenital airway developmental abnormalities, immunodeficiencies, and antibiotic use prior to the current visit. The clinical signs and symptoms were recorded as categorical and continuous variables, including the patient\u0026apos;s initial body temperature, respiratory rate, mental status, cyanosis, and the presence of any signs of assisted respiration (such as triple concave signs, nasal flaring, or nodding). Additionally, the presence of pulmonary rales on admission and the number of days spent in hospital were also documented. The imaging results included the presence of abnormalities on chest X-ray (CXR), the severity of these abnormalities, and the presence of abnormalities on chest computed tomography (CT) imaging. Laboratory findings included routine blood tests (white blood cell, neutrophil, lymphocyte, platelet, erythrocyte, and haemoglobin counts), CRP levels, and biochemical indices (gamma-glutamyl transferase, gamma glutaminase, urea, creatinine, and lactate dehydrogenase). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA critical illness was defined as any condition meeting one or more of the following criteria: severe pneumonia (diagnostic criteria based on the Diagnostic and Treatment Guidelines for Community-Acquired Pneumonia in Children, 2019 edition[13]), oxygen therapy, invasive mechanical ventilation, admission to the ICU, and death. This composite endpoint was adopted in order to assess the severity of the disease in question, given that such outcomes are also observed in other serious infectious diseases[16, 17].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVariable Selection and Score Construction \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were subjected to analysis using the statistical software package SPSS v27.0 (SPSS, Inc., Chicago, IL, USA). Missing data were completed using random forests. As previously outlined, a total of 35 variables were included in the selection process. Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed to mitigate the risk of collinearity among variables obtained from the same patient and to prevent overfitting. Binary variables were imputed using logistic regression. L1-penalized least absolute shrinkage and selection regression was employed for multivariable analyses, with 10-fold cross-validation utilized for internal validation. The R package \u0026quot;glmnet\u0026quot; statistical software (R Foundation) was employed for the purpose of performing the LASSO regression. Subsequently, the variables identified by LASSO regression analysis were entered into logistic regression models. Those that were consistently statistically significant were then used to construct the risk score. A line graph was plotted using the R function \u0026quot;nomogram\u0026quot; to visualise the risk scores. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of Accuracy \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA calibration curve was plotted to evaluate the consistency between the predicted and actual probabilities of the risk score. This was achieved by using the \u0026quot;calibrate\u0026quot; function in R software to plot the risk score after 1000 resamples, and obtaining the C-statistic. Furthermore, the area under the receiver operating characteristic curve (AUC) was employed to evaluate the accuracy of the risk score. To validate the generalisability of the risk score, we plotted a calibration curve and a receiver-operator characteristic curve for internal and external validation data, respectively. A decision curve analysis (DCA) was subsequently conducted to evaluate the clinical utility of the risk score. The horizontal axis of the DCA graph represents the \u0026quot;threshold probability,\u0026quot; which is the probability of mortality for patient i. When a specific value is reached by the various evaluation methods, patient i is defined as positive, indicating the need for intervention when the probability of mortality (denoted as Pi) reaches a certain threshold (denoted as Pt). The vertical axis represents the net benefit rate, which is calculated by subtracting the disadvantages from the advantages.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of the Training Cohort\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 1,039 patients were included in the training cohort, of whom 287 (27.6%) were identified as having critical illness and 752 (72.4%) were classified as having non-critical illness. The median (interquartile range) age of patients in the cohort was 11.0 (6.0-18.0) months, with 653 patients (62.8%) being male and 443 (42.6%) having undergone vaginal delivery. Additionally, 469 patients (45.1%) were breastfed. The top three medical histories were lower respiratory tract infection (329, 31.7%), wheeze (329, 31.7%), and neonatal hospitalisation (151, 14.5%). The most common underlying disease was congenital heart disease (126, 12.1%). Prior to the current visit, 864 (82.3%) patients had used antibiotics. The most prevalent symptoms were cyanosis (653, 62.8%), wheezing (626, 60.3%), and anhelation (435, 41.9%) (\u003cstrong\u003eTable 1\u003c/strong\u003e). The laboratory findings of the training cohort are presented in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictor Selection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 35 variables, measured at the time of hospital admission (see Table 1), were included in the LASSO regression analysis. Following the LASSO regression selection process, 14 variables were identified as significant predictors of critical illness. These included neonate hospitalisation, chronic pulmonary disease, congenital heart disease, immune deficiency disease, antibiotic use, anhelation, disorders of consciousness, assisted respiration, an increase in white blood cells (WBC), an increase in neutrophils (Neut), a decrease in lymphocytes (Lymph), a decrease in red blood cells (RBC), an increase in C-reactive protein (CRP), and pulmonary atelectasis. The inclusion of these 14 variables in a logistic regression model resulted in the identification of 11 variables that were independently statistically significant predictors of critical illness and were subsequently incorporated into the risk score. These variables included neonate hospitalization (OR, 2.06; 95% CI, 1.276-5.38；\u003cem\u003eP\u003c/em\u003e=0.003)，chronic pulmonary disease（OR, 4.901; 95% CI, 1.310-22.121;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e=0.025），congenital heart disease（OR, 2.357; 95% CI, 1.428-3.888;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e=0.001），immune deficiency disease（OR, 12.632; 95% CI, 1.721-116.829;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e=0.015），anhelation（OR, 2.641；95% CI, 1.810-3.863;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt;0.001），disorders of consciousness（OR, 19.041；95% CI, 1.849-426.476;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e=0.019），assisted respiration（OR, 8.807；95% CI, 6.050-12.960;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt;0.001），Lymph decreased（OR, 2.467；95% CI, 1.190-5.176;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e=0.016），RBC decreased（OR, 2.018；95% CI, 1.158-3.496;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e=0.013），CRP increased（OR, 1.828；95% CI, 1.199-2.784;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e=0.005），pulmonary atelectasis（OR, 10.348；95% CI, 4.086-27.873;\u003cem\u003e\u0026nbsp;P\u003c/em\u003e\u0026lt;0.001）（\u003cstrong\u003eTable 2\u003c/strong\u003e）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of the Risk Score\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe critical illness risk score was constructed based on the coefficients derived from the logistic model, and a nomogram was plotted based on that risk score (\u003cstrong\u003eFigure 1\u003c/strong\u003e). The objective is to facilitate the speculation by clinicians on the likelihood (with 95% CI) of a child developing into a critical illness based on 11 variables during the course of their hospitalisation. The C-statistic of the calibration curve and the AUC based on data from the training cohort were 0.853 (95% CI, 0.825-0.880) (\u003cstrong\u003eFigure 2\u003c/strong\u003e). The DCA demonstrated that the intervention based on the risk score had high clinical utility (\u003cstrong\u003eFigure 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of the Risk Score\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe validation cohort comprised 477 patients with a median (interquartile range) age of 10.0 (5.0-19.0) months, 299 (62.7%) of whom were male, and 123 of whom developed the critical illness. The variables employed in the risk score for the validation cohort are presented in\u003cstrong\u003e\u0026nbsp;Table 3\u003c/strong\u003e. The validation cohort exhibited a comparable degree of accuracy in risk score assessment to that observed in the training cohort. The C-statistic and AUC values for the validation cohort were 0.848 (95% CI, 0.806-0.891) (\u003cstrong\u003eFigure 4\u003c/strong\u003e), which align with the findings observed in the training cohort. The external validation cohort comprised 122 patients, 24 of whom were diagnosed with critical illness. The median age of this group was 12.0 months (interquartile range [IQR]: 6.4-22.0 months), and 71 patients (58.2%) were male. The variables employed in the risk score for the external validation cohort are presented in \u003cstrong\u003eTable 4\u003c/strong\u003e. \u0026nbsp;However, the accuracy of the risk score in the external validation cohort was not consistent with that of the training cohort. The C statistic and AUC for the training cohort were 0.967 (95% CI, 0.933-1.000). It was proposed that the negative predictive value was more accurate, indicating that the risk score is more suitable for assessing the high likelihood that a child will progress to a non-critical illness.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed and validated a clinical risk score to predict the development of critical illness among hospitalized children infected with RSV and co-detected with S. pneumoniae. The performance of this risk score was satisfactory, with an accuracy based on AUCs of 0.85 in both the development and validation cohorts. The nomogram may be employed by clinicians to estimate the risk of a child who has been hospitalised developing a critical illness. The 11 variables required for the prediction of the risk of developing critical illness are typically readily available at the time of hospital admission. In the event that the patient's estimated risk of developing a critical illness is deemed to be low, the clinician may opt to implement a monitoring strategy. Conversely, in instances where the estimated risk is identified as being high, the clinician may consider pursuing a more aggressive course of treatment or even admission to the ICU. It was our intention to avoid the creation of arbitrary categories of risk, such as low-, moderate-, and high-risk groups. We believe that clinicians are best equipped to make informed decisions regarding the risk associated with each individual patient, taking into account the specific circumstances and conditions present in their local or regional context. For instance, in regions with convenient access to clinical and supportive care, patient outcomes may be enhanced by opting to administer more assertive care to patients with moderate risk profiles. In contrast, in areas with high case volume and/or limited resources, a decision may be made to provide less aggressive care to moderate-risk patients in order to optimise the availability of IUC beds and ventilators [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe risk score incorporates a number of factors, including the hospitalization of neonates, the presence of chronic pulmonary disease, congenital heart disease, immune deficiency disease, anhelation, disorders of consciousness, the use of assisted respiration, lymphocyte count, red blood cell count, C-reactive protein levels, and the presence of pulmonary atelectasis. In comparison to other viruses, respiratory syncytial virus (RSV) is associated with an elevated risk of disease severity in infants and young children[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The principal risk factors for severe RSV infection include prematurity [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], chronic lung disease[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], congenital heart disease and immunodeficiency[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Nevertheless, the majority of infants who are hospitalized with RSV infection do not have any underlying risk factors that would predispose them to developing a severe disease [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Moreover, while viral factors, dysregulated host immune responses [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and genetic predisposition [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] contribute to the severity of RSV infection, they do not fully account for the variability in clinical presentation and outcome. Recent findings indicate that the composition of the nasopharyngeal microbiota is associated with an increased risk of respiratory infections and a greater severity of acute respiratory symptoms [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRSV has been demonstrated to create favourable conditions for S.pn infection, primarily through the damaging of ciliary cells in the airway, the enhancement of local inflammatory responses, and the promotion of bacterial receptor expression[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Glycoprotein G, which is expressed by RSV in infected host cells, can act as a receptor for S. pneumoniae, thereby promoting binding and adhesion to epithelial cells and increasing invasiveness [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. It has been demonstrated that RSV G proteins bind directly to S.pn penicillin-binding protein 1A, thereby enhancing bacterial adhesion and virulence gene expression and promoting the colonization and invasive infection of host cells[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. It is noteworthy that the incidence of hospitalisation due to RSV infection declined following the introduction of the pneumococcal conjugate vaccine in paediatric populations [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Although studies had reported that RSV vaccine reduces the risk of severe illness in children [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and immunization against S.pn significantly reduced the incidence of invasive pneumococcal disease [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], both the RSV vaccine and S.pn vaccine only provided single protection [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] whose protection against coinfections were limited. There is currently no specific drug available for the treatment of respiratory syncytial virus (RSV) infection. Beta-lactam antibiotics remain the recommended first-line treatment for Streptococcus pneumoniae infections [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Nevertheless, the identification of S. pneumoniae in a clinical setting did not necessarily indicate the presence of a bacterial infection. It is also possible that the bacterium was in a state of colonization. Consequently, the indiscriminate use of antibiotics has accelerated the emergence of drug-resistant pathogens [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], thereby reducing the efficacy of treatments and complicating patient care. The early prediction of patients who may progress to a critical illness is of significant importance, as it allows for the timely and aggressive provision of care, thereby optimising the limited medical resources available.\u003c/p\u003e \u003cp\u003eThe present study is limited by a small sample size for the construction of the risk score and a relatively small sample size for validation. The data used for score development and validation are exclusively from CHCMU, which may restrict the applicability of the risk score in other regions of China. It would be beneficial to conduct further validation studies of the critical illness risk score in areas outside of the southwest. It is anticipated that further studies will be conducted in the future to assess the applicability of the risk score.\u003c/p\u003e \u003cp\u003eIn this study, we developed a risk score to estimate the risk of developing critical illness among patients infected with RSV who were co-detected with S.pn. This was based on 11 variables that are commonly measured on admission to the hospital. The estimation of the risk of critical illness may assist in the identification of patients who are and are not likely to develop critical illness, thereby facilitating the provision of appropriate treatment and optimising the utilisation of medical resources.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALT Alanine Aminotransferase, ALRI Acute Lower Respiratory Tract Infections\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC Area Under Curve, AST Aspartate Aminotransferase\u003c/p\u003e\n\u003cp\u003eCAP Community-Acquired Pneumonia, CI Confidence Interval.\u003c/p\u003e\n\u003cp\u003eCRP C-reactive protein, Cr Creatinine\u003c/p\u003e\n\u003cp\u003eCT Computed Tomographic, CXR Chest X-radiography\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCHCMU Children\u0026rsquo;s Hospital of Chongqing Medical University\u003c/p\u003e\n\u003cp\u003eDCA Decision Curve Analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHb Hemoglobin\u003c/p\u003e\n\u003cp\u003eLASSO Least Absolute Shrinkage and Selection Operator\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLDH Lactate Dehydrogenase, Lymph lymphocyte\u003c/p\u003e\n\u003cp\u003eMo Month\u003c/p\u003e\n\u003cp\u003eNPAs Nasopharyngeal Aspirates, Neut Neutrophil\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOR Odds Ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePLT Platelet\u003c/p\u003e\n\u003cp\u003eRBC Red Blood Cell, RSV Respiratory syncytial virus\u003c/p\u003e\n\u003cp\u003eS.pn Streptococcus pneumoniae\u003c/p\u003e\n\u003cp\u003eWBC, White Blood Cell\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This work was funded by a National key Research and Development Program of China (2022YFC2704900); Natural Science Foundation of Chongqing, China(cstc2019jycj-msxmX0858); CQMU Program for Youth Innovation in Future Medicine.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u0026nbsp;\u003c/strong\u003eThe funding bodies had no part in the design, conduct, analysis or interpretation of this study or the decision to submit this paper for publication. All authors either have or do not have a commercial or other association that might pose a conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u0026nbsp;\u003c/strong\u003eAll authors approved the final manuscript as submitted.\u003c/p\u003e\n\u003cp\u003eLu Li cleaned the data used in this study, performed the analyses, wrote the first draft of the manuscript, and critically reviewed the manuscript for intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eXiaohan Huang cleaned portions of the data used in this study, drafted parts of the manuscript, and critically reviewed the manuscript for intellectual content.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(Author Lu Li and Author Xiao Han Huang contributed equally to this manuscript.)\u003c/p\u003e\n\u003cp\u003eXiming Xu provided statistical input on conduct and interpretation of the results and critically reviewed the manuscript for intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEnmei Liu provided clinical and microbiological input on interpretation of results and critically reviewed the manuscript for intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eYu Deng conceptualized and secured funding for the study, provided clinical input on interpretation of the results, and critically reviewed the manuscript for intellectual content.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e The study was approved by the Ethics Committee of Children\u0026apos;s Hospital affiliated to Chongqing Medical University. All methods were performed in accordance with the relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e Due to the retrospective nature of the study, the Ethics Committee of Children\u0026apos;s Hospital affiliated to Chongqing Medical University waived the need of obtaining informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e N/A\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003eWe thank the Zhengzhou hospital for support for this study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The datasets used and/or analysed during the current study available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBoccalini S, Bonito B, Salvati C, Del Riccio M, Stancanelli E, Bruschi M et al. 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JAMA Intern Med. 2020;180(8):1081-9. doi:10.1001/jamainternmed.2020.2033.\u003c/li\u003e\n\u003cli\u003eMetlay JP, Waterer GW, Long AC, Anzueto A, Brozek J, Crothers K et al. Diagnosis and Treatment of Adults with Community-acquired Pneumonia. An Official Clinical Practice Guideline of the American Thoracic Society and Infectious Diseases Society of America. Am J Respir Crit Care Med. 2019;200(7):e45-e67. doi:10.1164/rccm.201908-1581ST.\u003c/li\u003e\n\u003cli\u003eHaddadin Z, Beveridge S, Fernandez K, Rankin DA, Probst V, Spieker AJ et al. Respiratory Syncytial Virus Disease Severity in Young Children. Clin Infect Dis. 2021;73(11):e4384-e91. doi:10.1093/cid/ciaa1612.\u003c/li\u003e\n\u003cli\u003eWang X, Li Y, Shi T, Bont LJ, Chu HY, Zar HJ et al. Global disease burden of and risk factors for acute lower respiratory infections caused by respiratory syncytial virus in preterm infants and young children in 2019: a systematic review and meta-analysis of aggregated and individual participant data. Lancet. 2024;403(10433):1241-53. doi:10.1016/s0140-6736(24)00138-7.\u003c/li\u003e\n\u003cli\u003eOppenlander KE, Chung AA, Clabaugh D. Respiratory Syncytial Virus Bronchiolitis: Rapid Evidence Review. Am Fam Physician. 2023;108(1):52-7. \u003c/li\u003e\n\u003cli\u003eResch B, Manzoni P, Lanari M. Severe respiratory syncytial virus (RSV) infection in infants with neuromuscular diseases and immune deficiency syndromes. Paediatr Respir Rev. 2009;10(3):148-53. doi:10.1016/j.prrv.2009.06.003.\u003c/li\u003e\n\u003cli\u003eGarc\u0026iacute;a CG, Bhore R, Soriano-Fallas A, Trost M, Chason R, Ramilo O et al. Risk factors in children hospitalized with RSV bronchiolitis versus non-RSV bronchiolitis. Pediatrics. 2010;126(6):e1453-60. doi:10.1542/peds.2010-0507.\u003c/li\u003e\n\u003cli\u003eMella C, Suarez-Arrabal MC, Lopez S, Stephens J, Fernandez S, Hall MW et al. Innate immune dysfunction is associated with enhanced disease severity in infants with severe respiratory syncytial virus bronchiolitis. J Infect Dis. 2013;207(4):564-73. doi:10.1093/infdis/jis721.\u003c/li\u003e\n\u003cli\u003eHeinonen S, Velazquez VM, Ye F, Mertz S, Acero-Bedoya S, Smith B et al. Immune profiles provide insights into respiratory syncytial virus disease severity in young children. Sci Transl Med. 2020;12(540). doi:10.1126/scitranslmed.aaw0268.\u003c/li\u003e\n\u003cli\u003eLuna PN, Hasegawa K, Ajami NJ, Espinola JA, Henke DM, Petrosino JF et al. The association between anterior nares and nasopharyngeal microbiota in infants hospitalized for bronchiolitis. Microbiome. 2018;6(1):2. doi:10.1186/s40168-017-0385-0.\u003c/li\u003e\n\u003cli\u003eHament JM, Aerts PC, Fleer A, Van Dijk H, Harmsen T, Kimpen JL et al. Enhanced adherence of Streptococcus pneumoniae to human epithelial cells infected with respiratory syncytial virus. Pediatr Res. 2004;55(6):972-8. doi:10.1203/01.Pdr.0000127431.11750.D9.\u003c/li\u003e\n\u003cli\u003eHament JM, Aerts PC, Fleer A, van Dijk H, Harmsen T, Kimpen JL et al. Direct binding of respiratory syncytial virus to pneumococci: a phenomenon that enhances both pneumococcal adherence to human epithelial cells and pneumococcal invasiveness in a murine model. Pediatr Res. 2005;58(6):1198-203. doi:10.1203/01.pdr.0000188699.55279.1b.\u003c/li\u003e\n\u003cli\u003eSmith CM, Sandrini S, Datta S, Freestone P, Shafeeq S, Radhakrishnan P et al. Respiratory syncytial virus increases the virulence of Streptococcus pneumoniae by binding to penicillin binding protein 1a. A new paradigm in respiratory infection. Am J Respir Crit Care Med. 2014;190(2):196-207. doi:10.1164/rccm.201311-2110OC.\u003c/li\u003e\n\u003cli\u003eB\u0026eacute;net T, S\u0026aacute;nchez Picot V, Messaoudi M, Chou M, Eap T, Wang J et al. Microorganisms Associated With Pneumonia in Children \u0026lt;5 Years of Age in Developing and Emerging Countries: The GABRIEL Pneumonia Multicenter, Prospective, Case-Control Study. Clin Infect Dis. 2017;65(4):604-12. doi:10.1093/cid/cix378.\u003c/li\u003e\n\u003cli\u003eMan WH, Scheltema NM, Clerc M, van Houten MA, Nibbelke EE, Achten NB et al. Infant respiratory syncytial virus prophylaxis and nasopharyngeal microbiota until 6 years of life: a subanalysis of the MAKI randomised controlled trial. Lancet Respir Med. 2020;8(10):1022-31. doi:10.1016/s2213-2600(19)30470-9.\u003c/li\u003e\n\u003cli\u003eFarrar JL, Childs L, Ouattara M, Akhter F, Britton A, Pilishvili T et al. Systematic Review and Meta-Analysis of the Efficacy and Effectiveness of Pneumococcal Vaccines in Adults. Pathogens. 2023;12(5). doi:10.3390/pathogens12050732.\u003c/li\u003e\n\u003cli\u003eMcLean HQ, Petrie JG, Hanson KE, Meece JK, Rolfes MA, Sylvester GC et al. Interim Estimates of 2022-23 Seasonal Influenza Vaccine Effectiveness - Wisconsin, October 2022-February 2023. MMWR Morb Mortal Wkly Rep. 2023;72(8):201-5. doi:10.15585/mmwr.mm7208a1.\u003c/li\u003e\n\u003cli\u003eHsiao A, Hansen J, Timbol J, Lewis N, Isturiz R, Alexander-Parrish R et al. Incidence and Estimated Vaccine Effectiveness Against Hospitalizations for All-Cause Pneumonia Among Older US Adults Who Were Vaccinated and Not Vaccinated With 13-Valent Pneumococcal Conjugate Vaccine. JAMA Netw Open. 2022;5(3):e221111. doi:10.1001/jamanetworkopen.2022.1111.\u003c/li\u003e\n\u003cli\u003eSmith AM. Quantifying the therapeutic requirements and potential for combination therapy to prevent bacterial coinfection during influenza. J Pharmacokinet Pharmacodyn. 2017;44(2):81-93. doi:10.1007/s10928-016-9494-9.\u003c/li\u003e\n\u003cli\u003eEnglish BK. Limitations of beta-lactam therapy for infections caused by susceptible Gram-positive bacteria. J Infect. 2014;69 Suppl 1:S5-9. doi:10.1016/j.jinf.2014.07.010.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e \u003cstrong\u003eDemographics and Clinical Characteristics Among Patients in the Training Cohort Who Did or Did Not Develop Critical Illness.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"688\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 246px;\"\u003e\n \u003cp\u003echaracteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 142px;\"\u003e\n \u003cp\u003eTraining cohort,\u003c/p\u003e\n \u003cp\u003etotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 300px;\"\u003e\n \u003cp\u003e\u0026nbsp;Critical illness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 142px;\"\u003e\n \u003cp\u003e1039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eMale, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e653（62.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e174（60.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e479（63.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAge, median（IQR）, mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e11.0（6.0-18.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e10.0（4.0-16.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e11.0（6.0-19.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e＜6mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e249（24.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e92（32.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e157（20.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e＜12mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e300（28.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e73（25.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e227（30.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e＜24mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e305（29.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e82（28.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e223（29.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003e＜60mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e185（17.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e40\u0026nbsp;（13.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e145（19.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eVaginal delivery, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e42.6（443）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e129（44.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e314（41.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eBreast feeding, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e45.1（469）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e116（40.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e353（46.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eMedical history, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003ePremature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e107（10.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e46（16.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e61（8.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFood protein allergy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e50（4.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e17（5.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e33（4.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eEczema\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e188（18.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e47（16.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e141（18.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNeonate hospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e151（14.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e65（22.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e86（11.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eLower respiratory tract infection\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e329（31.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e92（32.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e237（31.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eWheeze\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e273（26.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e70（24.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e203（27.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eUnderling diseases, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eCongenital airway malformations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e89（8.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e30（10.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e59（7.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eChronic pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e16（1.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e12（4.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e4（0.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eCongenital heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e126（12.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e58（20.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e68（9.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eImmune deficiency disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6（0.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4（1.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e2（0.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eHistory of antibiotic, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e864（83.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e227（79.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e637（84.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 246px;\"\u003e\n \u003cp\u003eSymptom/Signs, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eFever\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e229（22.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e73（25.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e156（20.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAnhelation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e435（41.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e201（70.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e234（31.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eDisorders of consciousness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e5（0.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4（1.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e1（0.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eCyanosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e653（62.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e214（74.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e439（58.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAssisted respiration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e315（30.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e199（699.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e116（15.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eWheezing rale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e626（60.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e204（71.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e422（56.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eBlood routine, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eWBC\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e69（6.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e13（4.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e56（7.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eNeut\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e210（20.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e91（31.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e119（15.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eLymph\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e118（11.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e56（19.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e62（8.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eRBC\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e94（9.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e42（14.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e52（6.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eHb\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e144（13.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e53（15.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e91（12.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003ePLT\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e119（11.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e25（8.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e94（12.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eCRP＞10mg/L, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e233（22.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e87（30.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e146（119.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eComorbidity, No. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eAST\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e157（15.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e46（16.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e111（14.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eALT\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e67（6.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e22（7.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e45（6.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eLDH\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e190（18.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e41（14.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e149（19.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eUrea\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e8（0.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e5（1.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e3（0.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003eCr\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e22（2.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e10（3.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e12（1.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003ePulmonary consolidation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e45（4.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e23（8.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e22（2.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 246px;\"\u003e\n \u003cp\u003ePulmonary atelectasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e40（3.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e30（10.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 168px;\"\u003e\n \u003cp\u003e10（1.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: \u0026uarr;, increase; \u0026darr;, decrease. No. (%), where No. is the total number of patients with available data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Multivariable Logistic Regression Model for Predicting Development of Critical Illness in 1039 RSV-infected Hospitalized Patients Co-detected with S.pn\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"449\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.3252%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.5301%;\"\u003e\n \u003cp\u003eOdds rations (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1448%;\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.3252%;\"\u003e\n \u003cp\u003eNeonate hospitalization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.5301%;\"\u003e\n \u003cp\u003e2.063(1.276-3.323)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1448%;\"\u003e\n \u003cp\u003e\u003cem\u003e0.003\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.3252%;\"\u003e\n \u003cp\u003eChronic pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.5301%;\"\u003e\n \u003cp\u003e4.901(1.310-22.121)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1448%;\"\u003e\n \u003cp\u003e\u003cem\u003e0.025\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.3252%;\"\u003e\n \u003cp\u003eCongenital heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.5301%;\"\u003e\n \u003cp\u003e2.357(1.428-3.888)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1448%;\"\u003e\n \u003cp\u003e\u003cem\u003e0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.3252%;\"\u003e\n \u003cp\u003eImmune deficiency disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.5301%;\"\u003e\n \u003cp\u003e12.632(1.721-116.829)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1448%;\"\u003e\n \u003cp\u003e\u003cem\u003e0.015\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.3252%;\"\u003e\n \u003cp\u003eAnhelation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.5301%;\"\u003e\n \u003cp\u003e2.641(1.810-3.863)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1448%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.3252%;\"\u003e\n \u003cp\u003eDisorders of consciousness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.5301%;\"\u003e\n \u003cp\u003e19.041(1.849-426.476)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1448%;\"\u003e\n \u003cp\u003e\u003cem\u003e0.019\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.3252%;\"\u003e\n \u003cp\u003eAssisted respiration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.5301%;\"\u003e\n \u003cp\u003e8.807(6.050-12.960)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1448%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.3252%;\"\u003e\n \u003cp\u003eLymph\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.5301%;\"\u003e\n \u003cp\u003e2.467(1.190-5.176)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1448%;\"\u003e\n \u003cp\u003e\u003cem\u003e0.016\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.3252%;\"\u003e\n \u003cp\u003eRBC\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.5301%;\"\u003e\n \u003cp\u003e2.018(1.158-3.496)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1448%;\"\u003e\n \u003cp\u003e\u003cem\u003e0.013\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.3252%;\"\u003e\n \u003cp\u003eCRP＞10mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.5301%;\"\u003e\n \u003cp\u003e1.828(1.199-2.784)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1448%;\"\u003e\n \u003cp\u003e\u003cem\u003e0.005\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 46.3252%;\"\u003e\n \u003cp\u003ePulmonary atelectasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38.5301%;\"\u003e\n \u003cp\u003e10.348(4.086-27.873)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.1448%;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNOTE: Odds ratio (OR) and 95% confidence interval (CI) were also calculated. All tests were two-tailed, and P \u0026lt; 0.05 was considered statistically significant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Demographics and Clinical Characteristics of Patients in Validation Cohorts\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 198px;\"\u003e\n \u003cp\u003echaracteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 387px;\"\u003e\n \u003cp\u003eNo. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 132px;\"\u003e\n \u003cp\u003eValidation cohort, total\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 255px;\"\u003e\n \u003cp\u003e\u0026nbsp;Critical illness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 198px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e299（62.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e76（61.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e223（63.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAge, median（IQR）,mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e10.0（5.0-19.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e7.0（4.0-15.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e11.0（6.0-22.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e＜6mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e983（47.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e49（39.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e75（21.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e＜12mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e507（24.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e34（27.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e108（30.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e＜24mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e369（17.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e27（22.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e91（25.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003e＜60mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e226（10.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e13（10.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e80（22.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eNeonate hospitalization,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e73（15.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e29（23.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e44（12.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eChronic pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e9（1.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e9（7.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0（0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eCongenital heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e62（13.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e23（18.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e39（11.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eImmune deficiency disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0（0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e0（0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0（0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eDyspnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e200（41.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e85（69.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e115（32.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eUnconsciousness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4（0.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4（3.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0（0）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eAssisted respiration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e140（29.4）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e91（74.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e49（13.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eLymph\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e37（7.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e22（17.9）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e15（4.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eRBC\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e42（8.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e18（14.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e24（6.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003eCRP\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e115（24.1）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e36（29.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e79（22.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 198px;\"\u003e\n \u003cp\u003ePulmonary atelectasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e12（2.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 123px;\"\u003e\n \u003cp\u003e4（3.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8(23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: No. (%), where No. is the total number of patients with available data.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Demographics and Clinical Characteristics of Patients in\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eExternal\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Validation Cohorts\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"586\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 24.6106%;\"\u003e\n \u003cp\u003echaracteristic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 416px;\"\u003e\n \u003cp\u003eNo. (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 161px;\"\u003e\n \u003cp\u003eExternal validation,\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 32.3907%;\"\u003e\n \u003cp\u003e\u0026nbsp;Critical illness\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 24.6106%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16.3542%;\"\u003etotal\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16.0366%;\"\u003eYes\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 16.3542%;\"\u003eNo\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.1403%;\"\u003e\n \u003cp\u003eNo.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 44.1403%;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e71（58.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e58.3（14）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e58.2 (57)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003eAge, median（IQR）,mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e12.0（6.4-22.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e7.2（4.2-14.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e13.0（8.0-23.3）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003e＜6mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e29（23.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e11（45.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e18 (18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003e＜12mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e35（28.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e7（29.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e28 (28.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003e＜24mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e33（27.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e3（12.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e30 (30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003e＜60mo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e25（20.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e3（12.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e22 (22.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003eNeonate hospitalization,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e29（23.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e13（54.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e16 (16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003eChronic pulmonary disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e3（2.5）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e0（0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e3 (3.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003eCongenital heart disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e13（10.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e6（25.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e7 (7.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003eImmune deficiency disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e0（0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e0（0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003eDyspnea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e10（8.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e5（20.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e5 (5.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003eUnconsciousness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e1（0.8）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e0（0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e1 (1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003eAssisted respiration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e28（23.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e24（100.0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e4 (4.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003e\u0026nbsp;Lymph\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e13 (10.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e4（16.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e9 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003eRBC\u0026darr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e14 (11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e4（16.7）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e10（10.2）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003eCRP\u0026uarr;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e38 (31.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e9 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e29（29.6）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 44.1403%;\"\u003e\n \u003cp\u003ePulmonary atelectasis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e0（0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.0366%;\"\u003e\n \u003cp\u003e0（0）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 16.3542%;\"\u003e\n \u003cp\u003e0(0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNotes: No. (%), where No. is the total number of patients with available data.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Respiratory syncytial virus, Streptococcus pneumoniae, risk scores, critical illness, children","lastPublishedDoi":"10.21203/rs.3.rs-5314862/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5314862/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e To develop and validate a clinical score at hospital admission for predicting which patients will develop critical illness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eA retrospective study was conducted in the Children’s Hospital of Chongqing Medical University from January 2012 to December 2021. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator and logistic regression to construct a predictive risk score. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from our hospital and another hospital were used to validate the score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThe study included 1516 patients totally, including training cohort 1039 patients，validation cohort 477 patients, and external cohort 122 patients. From 35 potential predictors, 11 variables were independent predictive factors and were included in the risk score: neonate hospitalization，chronic pulmonary disease，congenital heart disease，immune deficiency disease，anhelation，disorders of consciousness，assisted respiration，lymph decreased，RBC decreased，CRP increased，pulmonary atelectasis. The mean AUC in the training cohort was 0.853, the AUC in the validation cohort was 0.848，and the external cohort was 0.967.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e In this study, the risk score based on characteristics of RSV-infected patients co-detected with S.pn at the time of admission to the hospital was developed that may help predict a patient’s risk of developing critical illness.\u003c/p\u003e","manuscriptTitle":"A Clinical Risk Score to Predict the Critical Illness in Respiratory Syncytial Virus Co- detected With Streptococcus Pneumoniae Hospitalized Children","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-10 15:54:05","doi":"10.21203/rs.3.rs-5314862/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":"9346f86c-bd07-49eb-aebc-b4a081e5ee91","owner":[],"postedDate":"December 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":40731474,"name":"Health sciences/Diseases/Respiratory tract diseases"},{"id":40731475,"name":"Biological sciences/Microbiology/Pathogens"},{"id":40731477,"name":"Biological sciences/Microbiology/Virology"}],"tags":[],"updatedAt":"2024-12-10T15:54:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-10 15:54:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5314862","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5314862","identity":"rs-5314862","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}