Random Forest Prognostication of Survival and 6-Month Outcome In Pediatric Patients Following Decompressive Craniectomy For Traumatic Brain Injury.

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY

Accounts of Chemical Research Pub Date : 2024-10-28 DOI:10.1016/j.wneu.2024.10.075

Ryan D Morgan, Brandon W Youssi, Rafael Cacao, Cristian Hernandez, Laszlo Nagy

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引用次数: 0

Abstract

Introduction: There is a dearth of literature regarding prognostic and predictive factors for outcome following pediatric decompressive craniectomy (DC) following traumatic brain injury (TBI). The aim of this study was to develop a random forest machine learning algorithm to predict outcomes following DC in pediatrics.

Methods and materials: This is a multi-institutional retrospective study assessing the 6-month postoperative outcome in pediatric patients that underwent DC. We developed a machine learning model using classification and survival random forest algorithms (CRF and SRF respectively) for the prediction of outcomes. Data was collected on clinical signs, radiographic studies, and laboratory studies. The outcome measures for the CRF were mortality and good or bad outcome based on Glasgow Outcome Scale (GOS) at 6-months. A GOS score of 4 or higher indicated a good outcome. The outcomes for the SRF model assessed mortality at during the follow-up period.

Results: A total of 40 pediatric patients were included. There was a hospital mortality rate of 27.5%, and 75.8% of survivors had a good outcome at 6-month follow up. The CRF for 6-month mortality had a ROC AUC of 0.984; whereas, the 6-month good/bad outcome had a ROC AUC of 0.873. The SRF was trained at the 6-month timepoint with a ROC AUC of 0.921.

Conclusion: CRF and SRF models successfully predicted 6-month outcomes and mortality following DC in pediatric TBI patients. These results suggest random forest models may be efficacious for predicting outcome in this patient population.

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随机森林预测创伤性脑损伤减压颅骨切除术后小儿患者的存活率和 6 个月预后

导言：关于小儿创伤性脑损伤（TBI）后减压开颅术（DC）的预后和预测因素的文献十分匮乏。本研究旨在开发一种随机森林机器学习算法，用于预测小儿颅脑损伤减压术后的预后：这是一项多机构回顾性研究，评估了接受 DC 术的儿科患者术后 6 个月的预后情况。我们使用分类和生存随机森林算法（分别为 CRF 和 SRF）开发了一个机器学习模型，用于预测结果。我们收集了有关临床症状、放射学检查和实验室检查的数据。CRF的结果衡量指标是死亡率和6个月后基于格拉斯哥结果量表（GOS）的好坏结果。GOS 评分达到或超过 4 分表示预后良好。SRF模型的结果是评估随访期间的死亡率：结果：共纳入 40 名儿科患者。医院死亡率为 27.5%，75.8% 的幸存者在 6 个月的随访中结果良好。6个月死亡率CRF的ROC AUC为0.984；而6个月好/坏结果的ROC AUC为0.873。在 6 个月时间点训练的 SRF 的 ROC AUC 为 0.921：结论：CRF 和 SRF 模型成功预测了儿童创伤性脑损伤患者 DC 后 6 个月的预后和死亡率。这些结果表明，随机森林模型可以有效预测这类患者的预后。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.