From the Division of Trauma and Burn Surgery Children's National Hospital, Washington, DC.
J Trauma Acute Care Surg. 2023 Feb 1;94(2):304-311. doi: 10.1097/TA.0000000000003709. Epub 2022 Jun 14.
Early recognition and intervention of hemorrhage are associated with decreased morbidity in children. Triage models have been developed to aid in the recognition of hemorrhagic shock after injury but require complete data and have limited accuracy. To address these limitations, we developed a Bayesian belief network, a machine learning model that represents the joint probability distribution for a set of observed or unobserved independent variables, to predict blood transfusion after injury in children and adolescents.
We abstracted patient, injury, and resuscitation characteristics of injured children and adolescents (age 1 to 18 years) from the 2017 to 2019 Trauma Quality Improvement Project database. We trained a Bayesian belief network to predict blood transfusion within 4 hours after arrival to the hospital following injury using data from 2017 and recalibrated the model using data from 2018. We validated our model on a subset of patients from the 2019 Trauma Quality Improvement Project. We evaluated model performance using the area under the receiver operating characteristic curve and calibration curves and compared performance with pediatric age-adjusted shock index (SIPA) and reverse shock index with Glasgow Coma Scale (rSIG) using sensitivity, specificity, accuracy, and Matthew's correlation coefficient (MCC).
The final model included 14 predictor variables and had excellent discrimination and calibration. The model achieved an area under the receiver operating characteristic curve of 0.92 using emergency department data. When used as a binary predictor at an optimal threshold probability, the model had similar sensitivity, specificity, accuracy, and MCC compared with SIPA when only age, systolic blood pressure, and heart rate were observed. With the addition of the Glasgow Coma Scale score, the model has a higher accuracy and MCC than SIPA and rSIG.
A Bayesian belief network predicted blood transfusion after injury in children and adolescents better than SIPA and rSIG. This probabilistic model may allow clinicians to stratify hemorrhagic control interventions based upon risk.
Prognostic and Epidemiologic; Level III.
早期识别和干预出血与降低儿童发病率有关。已经开发了分诊模型来帮助识别损伤后的出血性休克，但需要完整的数据，并且准确性有限。为了解决这些限制，我们开发了一个贝叶斯信念网络，这是一种机器学习模型，它表示一组观察到的或未观察到的独立变量的联合概率分布，以预测儿童和青少年受伤后的输血。
我们从 2017 年至 2019 年创伤质量改进项目数据库中提取了受伤儿童和青少年（1 至 18 岁）的患者、损伤和复苏特征。我们使用 2017 年的数据训练了一个贝叶斯信念网络，以预测受伤后 4 小时内到达医院的输血，并使用 2018 年的数据重新校准模型。我们在 2019 年创伤质量改进项目的患者子集中验证了我们的模型。我们使用接收者操作特征曲线下的面积和校准曲线评估模型性能，并使用敏感性、特异性、准确性和马修相关系数（MCC）比较了模型与儿科年龄调整休克指数（SIPA）和格拉斯哥昏迷量表的反向休克指数（rSIG）的性能。
最终模型包括 14 个预测变量，具有出色的区分度和校准度。该模型在使用急诊科数据时，获得了 0.92 的接收者操作特征曲线下的面积。当作为二进制预测器在最佳阈值概率下使用时，与仅观察年龄、收缩压和心率时的 SIPA 相比，该模型具有相似的敏感性、特异性、准确性和 MCC。随着格拉斯哥昏迷量表评分的增加，该模型的准确性和 MCC 高于 SIPA 和 rSIG。
贝叶斯信念网络预测儿童和青少年受伤后的输血比 SIPA 和 rSIG 更好。这种概率模型可以使临床医生根据风险对出血控制干预进行分层。
预后和流行病学；三级。
