ArticleItalian journal of pediatrics2024
Developing a nomogram for predicting acute complicated course in pediatric acute hematogenous osteomyelitis.
Article in Italian journal of pediatrics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Developing a nomogram based on admission AGR and CLR to predict subsequent surgical intervention in pediatric acute hematogenous osteomyelitis.Scientific reports · 2026Article
- Admission albumin and C-reactive protein-to-lymphocyte ratio are associated with MRI-defined abscess in children with acute hematogenous osteomyelitis: a two-center retrospective study.Frontiers in cellular and infection microbiology · 2026Observational
- A decade-long real-world cohort (2016-2025): development of an individualized risk-stratification nomogram and evaluation of clinical utility for recurrent respiratory tract infections in children.Frontiers in pediatrics · 2026Article
- Clinical features and sequelae of pediatric osteomyelitis following surgical treatment: a 10-year retrospective study.BMC infectious diseases · 2025Article
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Authors and funding
6 authors.
Funding
Abstract
backgroundThe objective of this study was to develop and validate a nomogram for predicting the risk of an acute complicated course in pediatric patients with Acute Hematogenous Osteomyelitis (AHO).
methodsA predictive model was developed based on a dataset of 82 pediatric AHO patients. Clinical data, imaging findings, and laboratory results were systematically collected for all patients. Subsequently, biomarker indices were calculated based on the laboratory results to facilitate a comprehensive evaluation. Univariate and multivariate logistic regression analyses were conducted to identify factors influencing early adverse outcomes in AHO. A nomogram model was constructed based on independent factors and validated internally through bootstrap methods. The discriminative ability, calibration, and clinical utility of the nomogram model were assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA), respectively. The developed nomogram model was compared with previously published A-score and Gouveia scoring systems.
resultsLogistic regression analysis identified delayed source control, suppurative arthritis, albumin on admission, and platelet to lymphocyte ratio (PLR) as independent predictors of early adverse outcomes in pediatric AHO patients. The logistic regression model was formulated as: Log(P) = 7. 667-1.752 × delayed source control - 1.956 × suppurative arthritis - 0.154 × albumin on admission + 0.009 × PLR. The nomogram's AUC obtained through Bootstrap validation was 0.829 (95% CI: 0.740-0.918). Calibration plots showed good agreement between predictions and observations. Decision curve analysis demonstrated that the model achieved net benefits across all threshold probabilities. The predictive efficacy of our nomogram model for acute complicated course in pediatric AHO patients surpassed that of the A-score and Gouveia scores.
conclusionsA predictive model for the acute complicated course of pediatric AHO was established based on four variables: delayed source control, suppurative arthritis, albumin on admission, and PLR. This model is practical, easy to use for clinicians, and can aid in guiding clinical treatment decisions.
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