Mazza Orit, Shehory Onn, Lev Nirit
Graduate School of Business Administration, Bar Ilan University, Ramat Gan, Israel.
Lowenstein Rehabilitation Medical Center, Ra'anana, Israel.
Front Neurol. 2022 Feb 14;12:743728. doi: 10.3389/fneur.2021.743728. eCollection 2021.
Elevated blood pressure (BP) in acute ischemic stroke is common. A raised BP is related to mortality and disability, yet excessive BP lowering can be detrimental. The optimal BP management in acute ischemic stroke remains insufficient and relies on expert consensus statements. Permissive hypertension is recommended during the first 24-h after stroke onset, yet there is ongoing uncertainty regarding the most appropriate blood BP management in the acute phase of ischemic stroke. This study aims to develop a decision support tool for improving the management of extremely high BP during the first 24 h after acute ischemic stroke by using machine learning (ML) tools.
This diagnostic accuracy study used retrospective data from MIMIC-III and eICU databases. Decision trees were constructed by a hierarchical binary recursive partitioning algorithm to predict the BP-lowering of 10-30% off the maximal value when antihypertensive treatment was given in patients with an extremely high BP (above 220/110 or 180/105 mmHg for patients receiving thrombolysis), according to the American Heart Association/American Stroke Association (AHA/ASA), the European Society of Cardiology, and the European Society of Hypertension (ESC/ESH) guidelines. Regression trees were used to predict the time-weighted average BP. Implementation of synthetic minority oversampling technique was used to balance the dataset according to different antihypertensive treatments. The model performance of the decision tree was compared to the performance of neural networks, random forest, and logistic regression models.
In total, 7,265 acute ischemic stroke patients were identified. Diastolic BP (DBP) is the main variable for predicting BP reduction in the first 24 h after a stroke. For patients receiving thrombolysis with DBP <120 mmHg, Labetalol and Amlodipine are effective treatments. Above DBP of 120 mmHg, Amlodipine, Lisinopril, and Nicardipine are the most effective treatments. However, successful treatment depends on avoiding hyponatremia and on kidney functions.
This is the first study to address BP management in the acute phase of ischemic stroke using ML techniques. The results indicate that the treatment choice should be adjusted to different clinical and BP parameters, thus, providing a better decision-making approach.
急性缺血性卒中患者血压升高很常见。血压升高与死亡率和残疾相关，但过度降低血压可能有害。急性缺血性卒中的最佳血压管理仍不完善，且依赖专家共识声明。建议在卒中发作后的最初24小时内采用允许性高血压策略，但对于缺血性卒中急性期最合适的血压管理仍存在不确定性。本研究旨在通过使用机器学习（ML）工具开发一种决策支持工具，以改善急性缺血性卒中后最初24小时内极高血压的管理。
这项诊断准确性研究使用了来自MIMIC-III和eICU数据库的回顾性数据。根据美国心脏协会/美国卒中协会（AHA/ASA）、欧洲心脏病学会和欧洲高血压学会（ESC/ESH）指南，采用分层二元递归划分算法构建决策树，以预测血压极高（接受溶栓治疗的患者血压高于220/110或180/105 mmHg）的患者接受降压治疗时血压降低10%-30%的情况。使用回归树预测时间加权平均血压。采用合成少数过采样技术根据不同的降压治疗方法平衡数据集。将决策树的模型性能与神经网络、随机森林和逻辑回归模型的性能进行比较。
总共识别出7265例急性缺血性卒中患者。舒张压（DBP）是预测卒中后最初24小时内血压降低的主要变量。对于舒张压<120 mmHg的接受溶栓治疗的患者，拉贝洛尔和氨氯地平是有效的治疗方法。舒张压高于120 mmHg时，氨氯地平、赖诺普利和尼卡地平是最有效的治疗方法。然而，成功的治疗取决于避免低钠血症和肾功能情况。
这是第一项使用ML技术解决缺血性卒中急性期血压管理问题的研究。结果表明，治疗选择应根据不同的临床和血压参数进行调整，从而提供更好的决策方法。
