Decision-making in pediatric blunt solid organ injury: a deep learning approach to predict massive transfusion, need for operative management, and mortality risk
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In brief
In brief
Machine learning models using clinical, laboratory, and imaging data from the first 4 hours can predict which pediatric blunt solid organ injury patients will require massive transfusion, operative intervention, or face mortality risk with over 90% accuracy. This approach may help clinicians identify high-risk patients earlier than traditional vital signs and hemoglobin monitoring alone.
Written by the GCMD Library team from the article.
Abstract
Background
The principal triggers for intervention in the setting of pediatric blunt solid organ injury (BSOI) are declining hemoglobin values and hemodynamic instability. The clinical management of BSOI is, however, complex. We therefore hypothesized that state-of-art machine learning (computer-based) algorithms could be leveraged to discover new combinations of clinical variables that might herald the need for an escalation in care. We developed algorithms to predict the need for massive transfusion (MT), failure of non-operative management (NOM), mortality, and successful non-operative management without intervention, all within 4 hours of emergency department (ED) presentation.
Methods
Children (≤18 years) who sustained a BSOI (liver, spleen, and/or kidney) between 2009 and 2018 were identified in the trauma registry at a pediatric level 1 trauma center. Deep learning models were developed using clinical values [vital signs, shock index-pediatric adjusted (SIPA), organ injured, and blood products received], laboratory results [hemoglobin, base deficit, INR, lactate, thromboelastography (TEG)], and imaging findings [focused assessment with sonography in trauma (FAST) and grade of injury on computed tomography scan] from pre-hospital to ED settings for prediction of MT, failure of NOM, mortality, and successful NOM without intervention. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC) were used to evaluate each model's performance.
Results
A total of 477 patients were included, of which 5.7% required MT (27/477), 7.2% failed NOM (34/477), 4.4% died (21/477), and 89.1% had successful NOM (425/477). The accuracy of the models in the validation set was as follows: MT (90.5%), failure of NOM (83.8%), mortality (91.9%), and successful NOM without intervention (90.3%). Serial vital signs, the grade of organ injury, hemoglobin, and positive FAST had low correlations with outcomes.
Conclusion
Deep learning-based models using a combination of clinical, laboratory and radiographic features can predict the need for emergent intervention (MT, angioembolization, or operative management) and mortality with high accuracy and sensitivity using data available in the first 4 hours of admission. Further research is needed to externally validate and determine the feasibility of prospectively applying this framework to improve care and outcomes.
Level of Evidence
III
Study Type
Retrospective comparative study (Prognosis/Care Management).