| 曾小科,徐亚丽,刘原,左浩,李春.经胸右心声学造影及经食管超声心动图联合临床及实验室指标机器学习模型预测卵圆孔未闭相关卒中[J].中国医学影像技术,2025,41(9):1517~1521 |
| 经胸右心声学造影及经食管超声心动图联合临床及实验室指标机器学习模型预测卵圆孔未闭相关卒中 |
| Machine learning models based on contrast-transthoracic echocardiography and transesophageal echocardiography combined with clinical and laboratory indicators for predicting patent foramen ovale-associated stroke |
| 投稿时间：2024-12-02 修订日期：2025-06-05 |
| DOI：10.13929/j.issn.1003-3289.2025.09.013 |
| 中文关键词: 脑卒中|卵圆孔,未闭|机器学习 |
| 英文关键词:stroke|foramen ovale, patent|machine learning |
| 基金项目:陆军军医大学优秀人才项目(2019R038)。 |
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| 中文摘要: |
| 目的 观察基于经胸右心声学造影(cTTE)及经食管超声心动图(TEE)联合临床及实验室指标机器学习(ML)预测卵圆孔未闭相关卒中(PFO-AS)的价值。方法 回顾性纳入313例经cTTE及TEE诊断的PFO患者,其中65例合并缺血性脑卒中且明确为PFO-AS(PFO-AS组),以其余248为非PFO-AS组。按7∶3比例划分为训练集(n=219,含48例PFO-AS及171例非PFO-AS)与测试集(n=94,含17例PFO-AS及77例非PFO-AS)。基于训练集数据采用单因素及多因素logistic回归(LR)分析临床、实验室指标及cTTE、TEE参数,筛选PFO-AS的独立预测因素,并以之构建LR、K近邻法(KNN)、支持向量机(SVM)、随机森林(RF)、决策树(DT)、反向传播神经网络(BPNN)和梯度提升机(GBM)共7种ML模型;评估各模型预测PFO-AS效能并筛选最优模型。结果 患者年龄＞49~69岁、既往吸烟史、血浆白蛋白≥43.8 g/L,以及cTTE显示静息态下存在大量右向左分流和TEE示合并房间隔膨出瘤均为PFO-AS的独立预测因素;以之构建的LR、KNN、SVM、RF、DT、BPNN及GBM模型在训练集的曲线下面积(AUC)为0.779~0.853,在测试集为0.730~0.877。RF模型在训练集与测试集的敏感度、特异度及AUC均较高且相当,在测试集的精确率较高而Brier分数较小,为最优ML模型。结论 基于cTTE及TEE联合临床及实验室指标的RF模型能有效预测PFO-AS。 |
| 英文摘要: |
| Objective To develop the value of machine learning (ML) models based on contrast-transthoracic echocardiography (cTTE) and transesophageal echocardiography (TEE) combined with clinical and laboratory indicators for predicting patent foramen ovale-associated stroke (PFO-AS). Methods Totally 313 patients with PFO diagnosed with cTTE and TEE were retrospectively enrolled. Among them, 65 cases were found complicated with ischemic stroke and confirmed as PFO-AS (PFO-AS group), and the rest 248 cases without ischemic stroke were classified as non-PFO-AS group. The patients were divided into training set (n=219, including 48 cases of PFO-AS and 171 cases of non-PFO-AS) and test set (n=94, including 17 cases of PFO-AS and 77 cases of non-PFO-AS) at the ratio of 7∶3. Univariable and multivariable logistic regression (LR) were used to analyze clinical and laboratory indicators as well as cTTE and TEE parameters in training set to screen independent predictive factors of PFO-AS. ML models, including LR, K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), decision tree (DT), back propagation neural network (BPNN) and gradient boosting machine (GBM) were constructed, and the predictive efficacy of the models for predicting PFO-AS was evaluated, then the optimal model was selected. Results Patient's age＞49—69 years, with smoking history, plasma albumin≥43.8 g/L, significant right-to-left shunt at rest shown on cTTE and complicated atrial septal aneurysm shown on TEE were all independent predictors of PFO-AS, which were used to construct ML models. The area under the curve (AUC) of LR, KNN, SVM, RF, DT, BPNN and GBM models in training set was 0.779—0.853, while in test set was 0.730—0.877. RF model had relatively high and comparable sensitivity, specificity and AUC in both training and test sets, also higher precision and smaller Brier score in test set, hence was regarded as the optimal ML model. Conclusion RF model based on cTTE and TEE combined with clinical and laboratory indicators could be used to effectively predict PFO-AS. |
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