Background: Abusive head trauma is the leading cause of death from physical abuse. Misdiagnosis of abusive head trauma as well as other types of brain abnormalities in infants is common and contributes to increased morbidity and mortality. We previously derived the Pittsburgh Infant Brain Injury Score (PIBIS), a clinical prediction rule to assist physicians deciding which high-risk infants should undergo computed tomography of the head.
Methods: Well-appearing infants 30 to 364 days of age with temperature <38.3°C, no history of trauma, and a symptom associated with an increased risk of having a brain abnormality were eligible for enrollment in this prospective, multicenter clinical prediction rule validation. By using a predefined neuroimaging paradigm, subjects were classified as cases or controls. The sensitivity, specificity, and negative and positive predictive values of the rule for prediction of brain injury were calculated.
Results: A total of 1040 infants were enrolled: 214 cases and 826 controls. The 5-point PIBIS included abnormality on dermatologic examination (2 points), age ≥3.0 months (1 point), head circumference >85th percentile (1 point), and serum hemoglobin <11.2g/dL (1 point). At a score of 2, the sensitivity and specificity for abnormal neuroimaging was 93.3% (95% confidence interval 89.0%-96.3%) and 53% (95% confidence interval 49.3%-57.1%), respectively.
Conclusions: Our data suggest that the PIBIS accurately identifies infants who would benefit from neuroimaging to evaluate for brain injury. An implementation analysis is needed before the PIBIS can be integrated into clinical practice.
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http://dx.doi.org/10.1542/peds.2015-3756 | DOI Listing |
Biomech Model Mechanobiol
January 2025
Department of Mechanical Engineering, University of Utah, Salt Lake City, UT, 84112, USA.
When infants are admitted to the hospital with skull fractures, providers must distinguish between cases of accidental and abusive head trauma. Limited information about the incident is available in such cases, and witness statements are not always reliable. In this study, we introduce a novel, data-driven approach to predict fall parameters that lead to skull fractures in infants in order to aid in determinations of abusive head trauma.
View Article and Find Full Text PDFPrehosp Emerg Care
January 2025
Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado.
Objectives: Abusive head trauma (AHT) is a leading cause of death in young children. Analyses of patient characteristics presenting to Emergency Medical Services (EMS) are often limited to structured data fields. Artificial Intelligence (AI) and Large Language Models (LLM) may identify rare presentations like AHT through factors not found in structured data.
View Article and Find Full Text PDFChild Abuse Negl
January 2025
Johns Hopkins School of Medicine, United States of America. Electronic address:
Background: Identifying non-accidental trauma (NAT) in pediatric trauma patients is challenging. We developed a machine learning model that uses demographic characteristics and ICD10 codes to detect the first diagnosis of NAT.
Methods: We analyzed data from the Maryland Health Services Cost Review Commission (2015-2020) for patients aged 0-19 years.
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