Morgan Ryan D, Youssi Brandon W, Cacao Rafael, Hernandez Cristian, Nagy Laszlo
School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, USA.
School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, USA.
World Neurosurg. 2025 Jan;193:861-867. doi: 10.1016/j.wneu.2024.10.075. Epub 2024 Nov 15.
There is a dearth of literature regarding prognostic and predictive factors for outcome following pediatric decompressive craniectomy (DC) performed after traumatic brain injury (TBI). The aim of this study was to develop a random forest machine learning algorithm to predict outcomes following DC in pediatric patients.
This multi-institutional retrospective study assessed the 6-month postoperative outcome in pediatric patients who underwent DC. We developed a machine learning model using classification random forest (CRF) and survival random forest (SRF) algorithms for prediction of outcomes. Data on clinical signs, radiographic studies, and laboratory studies were collected. Outcome measures for the CRF model were mortality and good or bad outcome based on Glasgow Outcome Scale at 6 months. A Glasgow Outcome Scale score of ≥4 indicated a good outcome. Outcome for the SRF model was mortality during the follow-up period.
The study included 40 pediatric patients. Hospital mortality rate was 27.5%, and 75.8% of survivors had a good outcome at 6-month follow up. The CRF model for 6-month mortality had a receiver operating characteristic area under the curve of 0.984, whereas, 6-month good and bad outcomes had a receiver operating characteristic area under the curve of 0.873. The SRF model was trained at the 6-month time point with a receiver operating characteristic area under the curve of 0.921.
CRF and SRF models successfully predicted 6-month outcomes and mortality following DC in pediatric patients with TBI. These results suggest that random forest models may be efficacious for predicting outcome in this patient population.
关于创伤性脑损伤（TBI）后小儿减压颅骨切除术（DC）预后和预测因素的文献匮乏。本研究的目的是开发一种随机森林机器学习算法，以预测小儿患者DC后的预后。
这项多机构回顾性研究评估了接受DC的小儿患者术后6个月的预后。我们使用分类随机森林（CRF）和生存随机森林（SRF）算法开发了一种机器学习模型，用于预测预后。收集了临床体征、影像学研究和实验室研究的数据。CRF模型的预后指标为死亡率以及基于6个月时格拉斯哥预后量表的预后良好或不良。格拉斯哥预后量表得分≥4表明预后良好。SRF模型的预后指标为随访期间的死亡率。
该研究纳入了40名小儿患者。医院死亡率为27.5%，75.8%的幸存者在6个月随访时预后良好。6个月死亡率的CRF模型曲线下面积为0.984，而6个月预后良好和不良的曲线下面积为0.873。SRF模型在6个月时间点进行训练，曲线下面积为0.921。
CRF和SRF模型成功预测了TBI小儿患者DC后的6个月预后和死亡率。这些结果表明，随机森林模型可能对预测该患者群体的预后有效。
