Clinic of Neurosurgery, Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava and University Hospital in Martin, Martin, Slovak Republic.
Bioinformatic Center, Biomedical Center Martin (BioMed), Jessenius Faculty of Medicine in Martin, Comenius University in Bratislava, Martin, Slovak Republic.
World Neurosurg. 2021 Apr;148:e450-e458. doi: 10.1016/j.wneu.2021.01.002. Epub 2021 Jan 12.
Various prognostic models are used to predict mortality and functional outcome in patients after traumatic brain injury with a trend to incorporate machine learning protocols. None of these models is focused exactly on the subgroup of patients indicated for decompressive craniectomy. Evidence regarding efficiency of this surgery is still incomplete, especially in patients undergoing primary decompressive craniectomy with evacuation of traumatic mass lesions.
In a prospective study with a 6-month follow-up period, we assessed postoperative outcome and mortality of 40 patients who underwent primary decompressive craniectomy for traumatic brain injuries during 2018-2019. The results were analyzed in relation to a wide spectrum of preoperatively available demographic, clinical, radiographic, and laboratory data. Random forest algorithms were trained for prediction of both mortality and unfavorable outcome, with their accuracy quantified by area under the receiver operating curves (AUCs) for out-of-bag samples.
At the end of the follow-up period, we observed mortality of 57.5%. Favorable outcome (Glasgow Outcome Scale [GOS] score 4-5) was achieved by 30% of our patients. Random forest-based prediction models constructed for 6-month mortality and outcome reached a moderate predictive ability, with AUC = 0.811 and AUC = 0.873, respectively. Random forest models trained on handpicked variables showed slightly decreased AUC = 0.787 for 6-month mortality and AUC = 0.846 for 6-month outcome and increased out-of-bag error rates.
Random forest algorithms show promising results in prediction of postoperative outcome and mortality in patients undergoing primary decompressive craniectomy. The best performance was achieved by Classification Random forest for 6-month outcome.
目前有多种预后模型被用于预测创伤性脑损伤患者的死亡率和功能结局，且这些模型有向机器学习协议整合的趋势。然而，这些模型中没有一个是专门针对接受去骨瓣减压术的患者亚组的。关于这种手术的有效性的证据仍然不完整，特别是在接受原发性去骨瓣减压术并清除创伤性肿块病变的患者中。
在一项前瞻性研究中，我们对 2018 年至 2019 年期间接受原发性去骨瓣减压术治疗创伤性脑损伤的 40 例患者进行了术后结局和死亡率评估。研究结果与术前可获得的广泛的人口统计学、临床、影像学和实验室数据进行了分析。随机森林算法被用于预测死亡率和不良结局，通过对袋外样本的接收者操作特征曲线（AUC）来量化其准确性。
在随访期末，我们观察到死亡率为 57.5%。我们的患者中有 30%实现了良好的结局（格拉斯哥结局量表[GOS]评分 4-5）。基于随机森林的 6 个月死亡率和结局预测模型具有中等的预测能力，AUC 分别为 0.811 和 0.873。基于手工挑选变量训练的随机森林模型的 AUC 略有下降，6 个月死亡率的 AUC 为 0.787，6 个月结局的 AUC 为 0.846，且袋外错误率增加。
随机森林算法在预测接受原发性去骨瓣减压术的患者的术后结局和死亡率方面显示出有前景的结果。分类随机森林在预测 6 个月结局方面表现最佳。
