Taira Haruka, Yasuda Hideto, Katsura Morihiro, Oishi Takatoshi, Shinzato Yutaro, Kishihara Yuki, Amagasa Shunsuke, Kashiura Masahiro, Kondo Yutaka, Kushimoto Shigeki, Moriya Takashi
Department of Emergency and Critical Care Medicine Jichi Medical University Saitama Medical Center Saitama Japan.
Department of Clinical Research Education and Training Unit Keio University Hospital Clinical and Translational Research Center (CTR) Tokyo Japan.
Acute Med Surg. 2025 Jul 11;12(1):e70073. doi: 10.1002/ams2.70073. eCollection 2025 Jan-Dec.
To develop and evaluate a predictive model for delayed pseudoaneurysm formation after non-operative management (NOM) in children with blunt splenic injuries.
A post hoc analysis of a multicenter cohort study in Japan included patients aged ≤16 years who underwent NOM for isolated blunt splenic injuries. The outcome was the formation of a pseudoaneurysm, which was not identified on admission and confirmed at least 24 h after admission. Predictors were determined from data available within 24 h of hospital arrival. Five predictive models were developed using logistic regression analysis and evaluated using discrimination (receiver operating characteristic [ROC] and precision-recall curve [PRC]), calibration (calibration plot and Brier score) and decision curve analysis (DCA) with bootstrap resampling data.
Pseudoaneurysms developed in 41 (9.4%) of 434 cases of isolated splenic injury in our cohort. Model 1 (19 predictors) had the highest ROC (0.828) and PRC (0.358), followed by model 5 (8 predictors; ROC 0.805, PRC 0.295). Calibration was similar across models, indicating good calibration. Models 1 and 5 outperformed the other DCAs. Overall, model 5, incorporating factors such as age, sex, Injury Severity Score, American Association for the Surgery of Trauma-Organ Injury Scale, contrast extravasation on computed tomography, concomitant injuries, cryoprecipitate dose and NOM details, was simpler and showed better predictive ability than the other models.
A predictive model for delayed pseudoaneurysm formation was developed with moderate discrimination and calibration. Further improvement using different modelling methods, such as machine learning, may be necessary.
建立并评估钝性脾损伤患儿非手术治疗（NOM）后延迟性假性动脉瘤形成的预测模型。
对日本一项多中心队列研究进行事后分析，纳入年龄≤16岁、因单纯钝性脾损伤接受NOM的患者。观察指标为假性动脉瘤形成，即入院时未发现且入院后至少24小时确诊。预测因素根据入院后24小时内可得数据确定。采用逻辑回归分析建立5个预测模型，并使用判别分析（受试者工作特征曲线[ROC]和精确召回率曲线[PRC]）、校准分析（校准图和Brier评分）以及决策曲线分析（DCA）和自助重采样数据进行评估。
在我们的队列中，434例单纯脾损伤患者中有41例（9.4%）发生了假性动脉瘤。模型1（19个预测因素）的ROC（0.828）和PRC（0.358）最高，其次是模型5（8个预测因素；ROC 0.805，PRC 0.295）。各模型校准情况相似，表明校准良好。模型1和模型5的DCA表现优于其他模型。总体而言，模型5纳入了年龄、性别、损伤严重程度评分、美国创伤外科协会器官损伤分级、计算机断层扫描对比剂外渗、合并伤、冷沉淀剂量和NOM细节等因素，比其他模型更简单且预测能力更好。
建立了一个延迟性假性动脉瘤形成的预测模型，具有中等的判别能力和校准度。可能需要使用不同的建模方法（如机器学习）进行进一步改进。
