دورية أكاديمية
Development and Validation of a Bayesian Network Predicting Intubation Following Hospital Arrival Among Injured Children.
| العنوان: | Development and Validation of a Bayesian Network Predicting Intubation Following Hospital Arrival Among Injured Children. |
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| المؤلفون: | Sullivan TM; Division of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, USA., Kim MS; Division of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, USA., Sippel GJ; Division of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, USA., Gestrich-Thompson WV; Division of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, USA., Melhado CG; Department of Surgery, University of California San Francisco, San Francisco, CA, USA., Griffin KL; Division of Pediatric Surgery, Nationwide Children's, Columbus, OH, USA., Moody SM; Division of Pediatric General and Thoracic Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA., Thakkar RK; Division of Pediatric Surgery, Nationwide Children's, Columbus, OH, USA., Kotagal M; Division of Pediatric General and Thoracic Surgery, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; Department of Surgery, University of Cincinnati College of Medicine, Cincinnati, OH, USA., Jensen AR; Department of Surgery, University of California San Francisco, San Francisco, CA, USA; Division of Pediatric Surgery, UCSF Benioff Children's Hospitals, San Francisco, CA, USA., Burd RS; Division of Trauma and Burn Surgery, Children's National Hospital, Washington, DC, USA. Electronic address: rburd@childrensnational.org. |
| المصدر: | Journal of pediatric surgery [J Pediatr Surg] 2024 Aug 31, pp. 161888. Date of Electronic Publication: 2024 Aug 31. |
| Publication Model: | Ahead of Print |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Saunders Country of Publication: United States NLM ID: 0052631 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1531-5037 (Electronic) Linking ISSN: 00223468 NLM ISO Abbreviation: J Pediatr Surg Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Philadelphia, PA : Saunders Original Publication: New York. |
| مستخلص: | Background: Inadequate airway management can contribute to preventable trauma deaths. Current machine learning tools for predicting intubation in trauma are limited to adult populations and include predictors not readily available at the time of patient arrival. We developed a Bayesian network to predict intubation in injured children and adolescents using observable data available upon or immediately after patient arrival. Methods: We obtained patient demographic, injury, resuscitation, and transportation characteristics from trauma registries from four American College of Surgeons-verified level 1 pediatric trauma centers from January 2010 through December 2021. We trained and validated a Bayesian network to predict emergent intubation after pediatric injury. We evaluated model performance using the area under the receiver operating and calibration curves. Results: The final model, TITAN (Timing of Intubation in Trauma Analysis Network), incorporated five factors: Glasgow Coma Scale, mechanism of injury, injury type (e.g., penetrating, blunt), systolic blood pressure, and age. The model achieved an area under the receiver operating characteristic curve of 0.83 (95% CI 0.80, 0.85) and had a calibration curve slope of 0.98 (95% CI 0.67, 1.29). TITAN had high specificity (98%), negative predictive value (97%), and accuracy (96%) at a binary probability threshold of 22.6%. Conclusion: The TITAN Bayesian network predicts the risk of intubation in pediatric trauma patients using five factors that are observable early in trauma resuscitation. Prospective validation of the model performance with patient outcomes is needed to assess real-life application benefits and risks. Level of Evidence: Prognostic and Epidemiological, Level III. Competing Interests: Conflicts of interest The authors have no conflict of interest to report. (Copyright © 2024 Elsevier Inc. All rights reserved.) |
| فهرسة مساهمة: | Keywords: Airway; Bayesian prediction; Injury; Intubation; Pediatrics; Trauma |
| تواريخ الأحداث: | Date Created: 20240920 Latest Revision: 20240920 |
| رمز التحديث: | 20240922 |
| DOI: | 10.1016/j.jpedsurg.2024.161888 |
| PMID: | 39304486 |
| قاعدة البيانات: | MEDLINE |
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Full Text Finder | تدمد: | 1531-5037 |
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| DOI: | 10.1016/j.jpedsurg.2024.161888 |