School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
Department of Clinical Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Eur J Neurol. 2020 Aug;27(8):1656-1663. doi: 10.1111/ene.14295. Epub 2020 May 31.
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.
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.
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.
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.
卒中相关性肺炎（SAP）是急性缺血性卒中（AIS）后一种常见、严重但可预防的并发症。特别需要早期识别 SAP 高危患者。然而，以前的预测模型尚未在临床实践中广泛应用。因此，我们旨在使用机器学习（ML）方法为中国 AIS 患者开发一种预测 SAP 的模型。
2016 年 9 月至 2019 年 11 月，前瞻性地在南京第一医院国家高级卒中中心（中国）收集急性缺血性卒中患者，数据随机分为训练集和测试集。使用训练集，开发了五种 ML 模型（正则化逻辑回归、支持向量机、随机森林分类器、极端梯度提升（XGBoost）和全连接深度神经网络）。通过测试集的受试者工作特征曲线下面积来评估这些模型。我们的模型还与卒中前独立性（改良 Rankin 量表）、性别、年龄、国立卫生研究院卒中量表（ISAN）和肺炎预测（PNA）评分进行了比较。
共有 3160 例 AIS 患者最终纳入本回顾性研究。在五种 ML 模型中，XGBoost 模型表现最佳。XGBoost 模型在测试集上的曲线下面积为 0.841（灵敏度为 81.0%，特异性为 73.3%）。它的性能也明显优于 ISAN 和 PNA 评分。
我们的研究表明，XGBoost 模型结合六个常见变量可以更优化地预测中国 AIS 患者的 SAP，优于 ISAN 和 PNA 评分。
