利用机器学习对前循环大血管闭塞患者进行机械血栓切除术前无症状颅内出血的预测。

IF 1.9 4区 医学 Q3 CLINICAL NEUROLOGY
Haydn Hoffman, Joel Sequeiros Chirinos, Nickalus Khan, Christopher Nickele, Violiza Inoa, Lucas Elijovich, Cheran Elangovan, Balaji Krishnaiah, Daniel Hoit, Adam S Arthur, Nitin Goyal
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引用次数: 0

摘要

导言:机械取栓术(MT)后的无症状颅内出血(sICH)与较差的预后有关。我们试图开发并在内部验证一种机器学习(ML)模型,用于预测前循环大血管闭塞患者行机械取栓术前的 sICH:方法:我们对在一家医疗机构因 ICA/M1/M2 闭塞而接受 MT 的连续成人患者进行了回顾性研究。数据被分成 80% 的训练集和 20% 的暂停测试集。筛选出 9 个 ML 模型。将表现最好的 ML 模型与逻辑回归(LR)和之前描述的临床预测模型进行了比较。使用SHAPLE Additive exPlanations(SHAP)来确定ML模型中最具预测性的特征:共有 497 名患者符合纳入标准。ML模型中表现最好的是XGBoost。ML模型在测试集上的接收者操作特征曲线下面积(AUC)为0.79(95% CI 0.67 - 0.89),显著高于LR模型(0.54 [95% CI 0.33 - 0.76])(p < 0.001)。ML 模型的表现也明显优于 TAG 评分(0.69 [95% CI 0.55 - 0.85],p < 0.001)、STBA 评分(0.45 [95% CI 0.30 - 0.60],p < 0.001)和 ChatGPT 4.0(0.60 [95% CI 0.48 - 0.68],p < 0.001)。根据SHAP值,ML模型中最能预测sICH的特征是较低的ASPECTS评分、较低的并发症评分和较高的NIHSS:ML模型能准确预测MT前的sICH。结论:ML 模型能准确预测 MT 前的ICH,其表现优于标准统计模型和之前描述的临床预测模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Symptomatic Intracranial Hemorrhage Before Mechanical Thrombectomy Using Machine Learning in Patients with Anterior Circulation Large Vessel Occlusion.

Background: Symptomatic intracranial hemorrhage (sICH) after mechanical thrombectomy (MT) is associated with worse outcomes. We sought to develop and internally validate a machine learning (ML) model to predict sICH prior to MT in patients with anterior circulation large vessel occlusion.

Methods: Consecutive adults who underwent MT for internal carotid artery/M1/M2 occlusions at a single institution were reviewed. The data was split into 80% training and 20% hold-out test sets. 9 ML models were screened. The top performing ML model was compared to logistic regression and previously described clinical prediction models. SHapley Additive exPlanations were used to identify the most predictive features in the ML model.

Results: A total of 497 patients met inclusion criteria. The top performing ML model was extreme gradient boosting. The area under the receiver operating characteristics curve for the ML model on the test set was 0.79 (95% confidence interval [CI] 0.67-0.89), which was significantly higher (P < 0.001) than the logistic regression model (0.54 [95% CI 0.33-0.76]). The ML model also performed significantly better than the TAG = TICI-ASPECTS-glucose score (0.69 [95% CI 0.55-0.85], P < 0.001), Systolic blood pressure-Time-Blood glucose-ASPECTS score (0.45 [95% CI 0.30-0.60], P < 0.001), and ChatGPT 4.0 (0.60 [95% CI 0.48-0.68], P < 0.001). Based on SHapley Additive exPlanations values the most predictive features of sICH in the ML model were lower Alberta Stroke Program Early CT score, lower collateral score, and higher presenting National Institutes of Health Stroke Scale.

Conclusions: An ML model accurately predicted sICH prior to MT. It performed better than a standard statistical model and previously described clinical prediction models.

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来源期刊
World neurosurgery
World neurosurgery CLINICAL NEUROLOGY-SURGERY
CiteScore
3.90
自引率
15.00%
发文量
1765
审稿时长
47 days
期刊介绍: World Neurosurgery has an open access mirror journal World Neurosurgery: X, sharing the same aims and scope, editorial team, submission system and rigorous peer review. The journal''s mission is to: -To provide a first-class international forum and a 2-way conduit for dialogue that is relevant to neurosurgeons and providers who care for neurosurgery patients. The categories of the exchanged information include clinical and basic science, as well as global information that provide social, political, educational, economic, cultural or societal insights and knowledge that are of significance and relevance to worldwide neurosurgery patient care. -To act as a primary intellectual catalyst for the stimulation of creativity, the creation of new knowledge, and the enhancement of quality neurosurgical care worldwide. -To provide a forum for communication that enriches the lives of all neurosurgeons and their colleagues; and, in so doing, enriches the lives of their patients. Topics to be addressed in World Neurosurgery include: EDUCATION, ECONOMICS, RESEARCH, POLITICS, HISTORY, CULTURE, CLINICAL SCIENCE, LABORATORY SCIENCE, TECHNOLOGY, OPERATIVE TECHNIQUES, CLINICAL IMAGES, VIDEOS
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