Elsaid Ahmed F, Fahmi Rasha M, Shehta Nahed, Ramadan Bothina M
Department of Public Health and Community Medicine, Zagazig University, Zagazig, Egypt.
Neurology Department, Faculty of Medicine, Zagazig University, Zagazig, Egypt.
Front Neurol. 2022 Nov 24;13:951401. doi: 10.3389/fneur.2022.951401. eCollection 2022.
Patients with ischemic stroke frequently develop hemorrhagic transformation (HT), which could potentially worsen the prognosis. The objectives of the current study were to determine the incidence and predictors of HT, to evaluate predictor interaction, and to identify the optimal predicting models.
A prospective study included 360 patients with ischemic stroke, of whom 354 successfully continued the study. Patients were subjected to thorough general and neurological examination and T2 diffusion-weighted MRI, at admission and 1 week later to determine the incidence of HT. HT predictors were selected by a filter-based minimum redundancy maximum relevance (mRMR) algorithm independent of model performance. Several machine learning algorithms including multivariable logistic regression classifier (LRC), support vector classifier (SVC), random forest classifier (RFC), gradient boosting classifier (GBC), and multilayer perceptron classifier (MLPC) were optimized for HT prediction in a randomly selected half of the sample (training set) and tested in the other half of the sample (testing set). The model predictive performance was evaluated using receiver operator characteristic (ROC) and visualized by observing case distribution relative to the models' predicted three-dimensional (3D) hypothesis spaces within the testing dataset true feature space. The interaction between predictors was investigated using generalized additive modeling (GAM).
The incidence of HT in patients with ischemic stroke was 19.8%. Infarction size, cerebral microbleeds (CMB), and the National Institute of Health stroke scale (NIHSS) were identified as the best HT predictors. RFC (AUC: 0.91, 95% CI: 0.85-0.95) and GBC (AUC: 0.91, 95% CI: 0.86-0.95) demonstrated significantly superior performance compared to LRC (AUC: 0.85, 95% CI: 0.79-0.91) and MLPC (AUC: 0.85, 95% CI: 0.78-0.92). SVC (AUC: 0.90, 95% CI: 0.85-0.94) outperformed LRC and MLPC but did not reach statistical significance. LRC and MLPC did not show significant differences. The best models' 3D hypothesis spaces demonstrated non-linear decision boundaries suggesting an interaction between predictor variables. GAM analysis demonstrated a linear and non-linear significant interaction between NIHSS and CMB and between NIHSS and infarction size, respectively.
Cerebral microbleeds, NIHSS, and infarction size were identified as HT predictors. The best predicting models were RFC and GBC capable of capturing nonlinear interaction between predictors. Predictor interaction suggests a dynamic, rather than, fixed cutoff risk value for any of these predictors.
缺血性中风患者常发生出血性转化（HT），这可能会使预后恶化。本研究的目的是确定HT的发生率和预测因素，评估预测因素之间的相互作用，并确定最佳预测模型。
一项前瞻性研究纳入了360例缺血性中风患者，其中354例成功完成研究。患者在入院时和1周后接受全面的体格和神经学检查以及T2加权扩散加权磁共振成像（MRI），以确定HT的发生率。通过基于过滤器的最小冗余最大相关性（mRMR）算法独立于模型性能来选择HT预测因素。几种机器学习算法，包括多变量逻辑回归分类器（LRC）、支持向量分类器（SVC）、随机森林分类器（RFC）、梯度提升分类器（GBC）和多层感知器分类器（MLPC），在随机选择的一半样本（训练集）中针对HT预测进行优化，并在另一半样本（测试集）中进行测试。使用受试者工作特征（ROC）评估模型预测性能，并通过观察测试数据集中真实特征空间内相对于模型预测的三维（3D）假设空间的病例分布进行可视化。使用广义相加模型（GAM）研究预测因素之间的相互作用。
缺血性中风患者中HT的发生率为19.8%。梗死面积、脑微出血（CMB）和美国国立卫生研究院卒中量表（NIHSS）被确定为最佳的HT预测因素。与LRC（曲线下面积[AUC]：0.85，95%置信区间[CI]：0.79 - 0.91）和MLPC（AUC：0.85，95% CI：0.78 - 0.92）相比，RFC（AUC：0.91，95% CI：0.85 - 0.95）和GBC（AUC：0.91，95% CI：0.86 - 0.95）表现出显著更优的性能。SVC（AUC：0.90，95% CI：0.85 - 0.94）优于LRC和MLPC，但未达到统计学显著性。LRC和MLPC之间未显示出显著差异。最佳模型的3D假设空间显示出非线性决策边界，表明预测变量之间存在相互作用。GAM分析分别显示NIHSS与CMB之间以及NIHSS与梗死面积之间存在线性和非线性显著相互作用。
脑微出血、NIHSS和梗死面积被确定为HT的预测因素。最佳预测模型是能够捕捉预测因素之间非线性相互作用的RFC和GBC。预测因素之间的相互作用表明，这些预测因素中的任何一个都具有动态而非固定的截断风险值。
