Using machine learning to predict stroke-associated pneumonia in Chinese acute ischaemic stroke patients
Using machine learning to predict stroke-associated pneumonia in Chinese acute ischaemic stroke patients
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DOI：
10.1111/ene.14295
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发表时间：
2020-05-31
影响因子：
5.1
通讯作者：
Zou, J.
中科院分区：
文献类型：
--
作者：
Li, X.;Wu, M.;Zou, J.
背景和目的脑卒中相关性肺炎（SAP）是急性缺血性脑卒中（AIS）后常见、严重但可预防的并发症。早期识别 SAP 高危患者尤为必要。然而，先前的预测模型尚未广泛应用于临床实践。因此，我们的目的是利用机器学习（ML）方法开发一种模型来预测中国AIS患者的SAP。方法2016年9月至2019年11月期间前瞻性收集南京市第一医院国家高级脑卒中中心的急性缺血性脑卒中患者，并将数据随机分为训练集和测试集。利用训练集，开发了五个 ML 模型（带调节的逻辑回归、支持向量机、随机森林分类器、极限梯度提升 (XGBoost) 和全连接深度神经网络）。这些模型通过测试集上接收者操作特征曲线下的面积进行评估。我们的模型还与卒中前独立性（改良Rankin量表）、性别、年龄、美国国立卫生研究院卒中量表（ISAN）和肺炎预测（PNA）评分进行比较。 结果 共有3160名AIS患者最终纳入这项回顾性研究。在五个 ML 模型中，XGBoost 模型表现最好。 XGBoost 模型在测试集上的曲线下面积为 0.841（敏感性，81.0%；特异性，73.3%）。它还取得了明显优于 ISAN 和 PNA 评分的性能。 结论 我们的研究表明，具有六个公共变量的 XGBoost 模型可以比 ISAN 和 PNA 评分更好地预测中国 AIS 患者的 SAP。
Background and purpose Stroke-associated pneumonia (SAP) is a common, severe but preventable complication after acute ischaemic stroke (AIS). Early identification of patients at high risk of SAP is especially necessary. However, previous prediction models have not been widely used in clinical practice. Thus, we aimed to develop a model to predict SAP in Chinese AIS patients using machine learning (ML) methods.Methods Acute ischaemic stroke patients were prospectively collected at the National Advanced Stroke Center of Nanjing First Hospital (China) between September 2016 and November 2019, and the data were randomly subdivided into a training set and a testing set. With the training set, five ML models (logistic regression with regulation, support vector machine, random forest classifier, extreme gradient boosting (XGBoost) and fully connected deep neural network) were developed. These models were assessed by the area under the curve of receiver operating characteristic on the testing set. Our models were also compared with pre-stroke Independence (modified Rankin Scale), Sex, Age, National Institutes of Health Stroke Scale (ISAN) and Pneumonia Prediction (PNA) scores.Results A total of 3160 AIS patients were eventually included in this retrospective study. Among the five ML models, the XGBoost model performed best. The area under the curve of the XGBoost model on the testing set was 0.841 (sensitivity, 81.0%; specificity, 73.3%). It also achieved significantly better performance than ISAN and PNA scores.Conclusions Our study demonstrated that the XGBoost model with six common variables can predict SAP in Chinese AIS patients more optimally than ISAN and PNA scores.