PDF(Pubmed)
Abstract：
BACKGROUND: Despite the increased use of mechanical thrombectomy (MT) in recent years, there remains a lack of research on in-hospital mortality rates following the procedure, the primary factors influencing these rates, and the potential for predicting them. This study aimed to utilize interpretable machine learning (ML) to help clarify these uncertainties.
METHODS: This retrospective study involved patients with anterior circulation large vessel occlusion (LVO)-related ischemic stroke who underwent MT. The patient division was made into two groups: (I) the in-hospital death group, referred to as miserable outcome, and (II) the in-hospital survival group, or favorable outcome. Python 3.10.9 was utilized to develop the machine learning models, which consisted of two types based on input features: (I) the Pre-MT model, incorporating baseline features, and (II) the Post-MT model, which included both baseline and MT-related features. After a feature selection process, the models were trained, internally evaluated, and tested, after which interpretation frameworks were employed to clarify the decision-making processes.
RESULTS: This study included 602 patients with a median age of 76 years (interquartile range (IQR) 65-83), out of which 54% (n = 328) were female, and 22% (n = 133) had miserable outcomes. Selected baseline features were age, baseline National Institutes of Health Stroke Scale (NIHSS) value, neutrophil-to-lymphocyte ratio (NLR), international normalized ratio (INR), the type of the affected vessel (\'Vessel type\'), peripheral arterial disease (PAD), baseline glycemia, and premorbid modified Rankin scale (pre-mRS). The highest odds ratio of 4.504 was observed with the presence of peripheral arterial disease (95% confidence interval (CI), 2.120-9.569). The Pre-MT model achieved an area under the curve (AUC) value of around 79% utilizing these features, and the interpretable framework discovered the baseline NIHSS value as the most influential factor. In the second data set, selected features were the same, excluding pre-mRS and including puncture-to-procedure-end time (PET) and onset-to-puncture time (OPT). The AUC value of the Post-MT model was around 84% with age being the highest-ranked feature.
CONCLUSIONS: This study demonstrates the moderate to strong effectiveness of interpretable machine learning models in predicting in-hospital mortality following mechanical thrombectomy for ischemic stroke, with AUCs of 0.792 for the Pre-MT model and 0.837 for the Post-MT model. Key predictors included patient age, baseline NIHSS, NLR, INR, occluded vessel type, PAD, baseline glycemia, pre-mRS, PET, and OPT. These findings provide valuable insights into risk factors and could improve post-procedural patient management.
METHODS: This retrospective study involved patients with anterior circulation large vessel occlusion (LVO)-related ischemic stroke who underwent MT. The patient division was made into two groups: (I) the in-hospital death group, referred to as miserable outcome, and (II) the in-hospital survival group, or favorable outcome. Python 3.10.9 was utilized to develop the machine learning models, which consisted of two types based on input features: (I) the Pre-MT model, incorporating baseline features, and (II) the Post-MT model, which included both baseline and MT-related features. After a feature selection process, the models were trained, internally evaluated, and tested, after which interpretation frameworks were employed to clarify the decision-making processes.
RESULTS: This study included 602 patients with a median age of 76 years (interquartile range (IQR) 65-83), out of which 54% (n = 328) were female, and 22% (n = 133) had miserable outcomes. Selected baseline features were age, baseline National Institutes of Health Stroke Scale (NIHSS) value, neutrophil-to-lymphocyte ratio (NLR), international normalized ratio (INR), the type of the affected vessel (\'Vessel type\'), peripheral arterial disease (PAD), baseline glycemia, and premorbid modified Rankin scale (pre-mRS). The highest odds ratio of 4.504 was observed with the presence of peripheral arterial disease (95% confidence interval (CI), 2.120-9.569). The Pre-MT model achieved an area under the curve (AUC) value of around 79% utilizing these features, and the interpretable framework discovered the baseline NIHSS value as the most influential factor. In the second data set, selected features were the same, excluding pre-mRS and including puncture-to-procedure-end time (PET) and onset-to-puncture time (OPT). The AUC value of the Post-MT model was around 84% with age being the highest-ranked feature.
CONCLUSIONS: This study demonstrates the moderate to strong effectiveness of interpretable machine learning models in predicting in-hospital mortality following mechanical thrombectomy for ischemic stroke, with AUCs of 0.792 for the Pre-MT model and 0.837 for the Post-MT model. Key predictors included patient age, baseline NIHSS, NLR, INR, occluded vessel type, PAD, baseline glycemia, pre-mRS, PET, and OPT. These findings provide valuable insights into risk factors and could improve post-procedural patient management.
摘要：
背景：尽管近年来机械血栓切除术（MT）的使用有所增加，仍然缺乏对手术后住院死亡率的研究，影响这些比率的主要因素，以及预测它们的潜力。本研究旨在利用可解释的机器学习（ML）来帮助澄清这些不确定性。
方法：这项回顾性研究涉及前循环大血管闭塞（LVO）相关的缺血性卒中患者。将患者分为两组：（I）住院死亡组，被称为悲惨的结果，和(II)住院生存组，或有利的结果。Python3.10.9用于开发机器学习模型，它由基于输入特征的两种类型组成：(I)Pre-MT模型，合并基线特征，(二)后MT模式，其中包括基线和MT相关特征。在特征选择过程之后，模型经过训练，内部评估，并经过测试，之后，采用解释框架来澄清决策过程。
结果：这项研究包括602例患者，中位年龄为76岁（四分位距（IQR）65-83），其中54%(n=328)是女性，和22％（n=133）有悲惨的结果。选定的基线特征是年龄，基线美国国立卫生研究院卒中量表（NIHSS）值，中性粒细胞与淋巴细胞比率（NLR），国际标准化比率(INR)，受影响船只的类型(“船只类型”)，外周动脉疾病（PAD），基线血糖,和病前改良的Rankin量表(pre-mRS)。在存在外周动脉疾病的情况下观察到最高比值比4.504(95%置信区间(CI),2.120-9.569）。Pre-MT模型利用这些特征实现了约79%的曲线下面积(AUC)值,可解释框架发现基线NIHSS值是最有影响的因素。在第二个数据集中，选择的特征是相同的，不包括mRS前，包括穿刺至手术结束时间（PET）和开始至穿刺时间（OPT）。Post-MT模型的AUC值约为84%，年龄是排名最高的特征。
结论：本研究表明，可解释的机器学习模型在预测缺血性卒中机械取栓后院内死亡率方面具有中等到强的有效性，前MT模型的AUC为0.792，后MT模型的AUC为0.837。主要预测因素包括患者年龄，基线NIHSS,NLR,INR,闭塞血管类型,PAD,基线血糖,pre-mRS,PET,OPT。这些发现为风险因素提供了有价值的见解，并可以改善术后患者管理。
方法：这项回顾性研究涉及前循环大血管闭塞（LVO）相关的缺血性卒中患者。将患者分为两组：（I）住院死亡组，被称为悲惨的结果，和(II)住院生存组，或有利的结果。Python3.10.9用于开发机器学习模型，它由基于输入特征的两种类型组成：(I)Pre-MT模型，合并基线特征，(二)后MT模式，其中包括基线和MT相关特征。在特征选择过程之后，模型经过训练，内部评估，并经过测试，之后，采用解释框架来澄清决策过程。
结果：这项研究包括602例患者，中位年龄为76岁（四分位距（IQR）65-83），其中54%(n=328)是女性，和22％（n=133）有悲惨的结果。选定的基线特征是年龄，基线美国国立卫生研究院卒中量表（NIHSS）值，中性粒细胞与淋巴细胞比率（NLR），国际标准化比率(INR)，受影响船只的类型(“船只类型”)，外周动脉疾病（PAD），基线血糖,和病前改良的Rankin量表(pre-mRS)。在存在外周动脉疾病的情况下观察到最高比值比4.504(95%置信区间(CI),2.120-9.569）。Pre-MT模型利用这些特征实现了约79%的曲线下面积(AUC)值,可解释框架发现基线NIHSS值是最有影响的因素。在第二个数据集中，选择的特征是相同的，不包括mRS前，包括穿刺至手术结束时间（PET）和开始至穿刺时间（OPT）。Post-MT模型的AUC值约为84%，年龄是排名最高的特征。
结论：本研究表明，可解释的机器学习模型在预测缺血性卒中机械取栓后院内死亡率方面具有中等到强的有效性，前MT模型的AUC为0.792，后MT模型的AUC为0.837。主要预测因素包括患者年龄，基线NIHSS,NLR,INR,闭塞血管类型,PAD,基线血糖,pre-mRS,PET,OPT。这些发现为风险因素提供了有价值的见解，并可以改善术后患者管理。
