{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T06:12:28Z","timestamp":1778825548645,"version":"3.51.4"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T00:00:00Z","timestamp":1778803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell. Infect. Microbiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Respiratory syncytial virus (RSV) bronchiolitis is a leading cause of pediatric intensive care unit (PICU) admissions worldwide, with septic shock representing a severe and potentially fatal complication. Early identification of high-risk patients remains challenging in clinical practice. This study aimed to develop and validate a reliable prediction model for septic shock in children admitted to PICU with RSV bronchiolitis using readily available clinical and laboratory parameters.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective cohort study was conducted on 224 pediatric patients with RSV bronchiolitis admitted to PICU. Demographic characteristics, clinical features, and laboratory findings upon admission were systematically collected. Given the low event rate, Firth logistic regression was employed for univariate and multivariate analyses to mitigate small-sample bias. Model performance was rigorously evaluated using 5-fold cross-validation, bootstrap resampling, calibration metrics (Hosmer-Lemeshow test, Brier score, calibration intercept and slope), and discrimination measures (area under the receiver operating characteristic curve). Development of web calculator based on Shiny app to facilitate clinical implementation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Four independent predictors of septic shock were identified through multivariate Firth logistic regression: fungal co-infection (adjusted odds ratio [aOR]: 9.01, 95% confidence interval [CI]: 2.26-36.49,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0= .003), elevated admission glucose (aOR: 1.23, 95% CI: 1.04-1.48,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0= .02), decreased antithrombin III (aOR: 0.96, 95% CI: 0.94-0.99,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0= .004), and elevated interleukin-6 (aOR: 1.00, 95% CI: 1.00-1.01,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0= .008). The final model demonstrated excellent discrimination with an area under the curve (AUC) of 0.892 (95% CI: 0.870-0.914) in 5-fold cross-validation and 0.906 (95% CI: 0.838-0.949) in bootstrap validation. Calibration was optimal (Hosmer-Lemeshow \u03c7\u00b2=5.991,\n                      <jats:italic>P<\/jats:italic>\n                      \u00a0= .648; Brier score=0.065; calibration slope=0.957; calibration intercept=0.002). At the optimal threshold of 0.154, the model achieved sensitivity of 75.0%, specificity of 90.5%, and overall accuracy of 88.8%.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed and internally validated a prediction model incorporating fungal co-infection, admission glucose, antithrombin III, and interleukin-6 for early identification of septic shock risk in pediatric RSV bronchiolitis patients. The model demonstrated good discrimination (AUC 0.892) in this single-center cohort. However, given the modest sample size, external validation in larger, multicenter populations is essential before clinical implementation. This tool may support early risk stratification and clinical decision-making, though its impact on patient outcomes requires further evaluation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical trial registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>http:\/\/www.chictr.org.cn\/index.aspx<\/jats:ext-link>\n                      , identifier ChiCTR2200057182.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcimb.2026.1743976","type":"journal-article","created":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T05:44:21Z","timestamp":1778823861000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Development and internal validation of a clinical prediction model for septic shock in pediatric respiratory syncytial virus bronchiolitis based on routine blood biomarkers and concomitant fungal infection"],"prefix":"10.3389","volume":"16","author":[{"given":"Junyu","family":"Dong","sequence":"first","affiliation":[{"name":"Pediatric Intensive Care Unit, Luoyang Maternal and Child Health Hospital","place":["Luoyang, Henan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yikang","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Intensive Care Unit, Clinical Medical College & Affiliated Hospital of Chengdu University","place":["Chengdu, Sichuan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingwen","family":"Ni","sequence":"additional","affiliation":[{"name":"Pediatric Intensive Care Unit, Luoyang Maternal and Child Health Hospital","place":["Luoyang, Henan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mengxin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Pediatric Intensive Care Unit, Luoyang Maternal and Child Health Hospital","place":["Luoyang, Henan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihui","family":"Du","sequence":"additional","affiliation":[{"name":"Pediatric Intensive Care Unit, Luoyang Maternal and Child Health Hospital","place":["Luoyang, Henan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenan","family":"Fang","sequence":"additional","affiliation":[{"name":"Pediatric Intensive Care Unit, Luoyang Maternal and Child Health Hospital","place":["Luoyang, Henan, China"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,5,15]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"974","DOI":"10.1016\/s1473-3099(24)00176-2","article-title":"Global, regional, and national incidence and mortality burden of non-COVID-19 lower respiratory infections and aetiologies, 1990-2021: a systematic analysis from the Global Burden of Disease Study 2021","volume":"24","author":"Bender","year":"2024","journal-title":"Lancet Infect. 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