ICH-LR2S2: A new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage

Abstract Purpose We develop a new risk score to predict patients with stroke-associated pneumonia (SAP) who have an acute intracranial hemorrhage (ICH). Method We applied logistic regression to develop a new risk score called ICH-LR2S2. It was derived from examining a dataset of 70,540 ICH patients between 2015 and 2018 from the Chinese Stroke Center Alliance (CSCA). During the training of ICH-LR2S2, patients were randomly divided into two groups – 80% for the training set and 20% for model validation. A prospective test set was developed using 12,523 patients recruited in 2019. To further verify its effectiveness, we tested ICH-LR2S2 on an external dataset of 24,860 patients from the China National Stroke Registration Management System II (CNSR II). The performance of ICH-LR2S2 was measured by the area under the receiver operating characteristic curve (AUROC). Results The incidence of SAP in the dataset was 25.52%. A 24-point ICH-LR2S2 was developed from independent predictors, including age, modified Rankin Scale, fasting blood glucose, National Institutes of Health Stroke Scale admission score, Glasgow Coma Scale score, C-reactive protein, dysphagia, Chronic Obstructive Pulmonary Disease, and current smoking. The results showed that ICH-LR2S2 achieved an AUC = 0.749 [95% CI 0.739–0.759], which outperforms the best baseline ICH-APS (AUC = 0.704) [95% CI 0.694–0.714]. Compared with the previous ICH risk scores, ICH-LR2S2 incorporates fasting blood glucose and C-reactive protein, improving its discriminative ability. Machine learning methods such as XGboost (AUC = 0.772) [95% CI 0.762–0.782] can further improve our prediction performance. It also performed well when further validated by the external independent cohort of patients (n = 24,860), ICH-LR2S2 AUC = 0.784 [95% CI 0.774–0.794]. Conclusion ICH-LR2S2 accurately distinguishes SAP patients based on easily available clinical features. It can help identify high-risk patients in the early stages of diseases.

摘要 研究目的：本研究开发一款全新的风险评分模型，用于预测合并急性颅内出血（intracranial hemorrhage, ICH）的卒中相关性肺炎（stroke-associated pneumonia, SAP）患者。
方法：本研究采用逻辑回归方法构建一款名为ICH-LR2S2的新型风险评分模型。该模型基于中国卒中中心联盟（Chinese Stroke Center Alliance, CSCA）2015至2018年间收录的70540例ICH患者数据集开发。在ICH-LR2S2的训练过程中，患者被随机分为两组：80%作为训练集，剩余20%作为模型验证集。本研究同时构建了前瞻性测试集，纳入2019年招募的12523例患者。为进一步验证模型有效性，我们利用中国国家卒中登记管理系统II（China National Stroke Registration Management System II, CNSR II）收录的24860例患者的外部数据集对ICH-LR2S2进行测试。模型性能采用受试者工作特征曲线下面积（area under the receiver operating characteristic curve, AUROC）进行评估。
结果：本研究数据集内SAP的发生率为25.52%。研究基于年龄、改良Rankin量表（modified Rankin Scale）评分、空腹血糖、入院时美国国立卫生研究院卒中量表（National Institutes of Health Stroke Scale, NIHSS）评分、格拉斯哥昏迷量表（Glasgow Coma Scale, GCS）评分、C反应蛋白、吞咽困难、慢性阻塞性肺疾病（Chronic Obstructive Pulmonary Disease）以及当前吸烟史等独立预测因素，构建了总分为24分的ICH-LR2S2风险评分模型。结果显示，ICH-LR2S2的曲线下面积为0.749 [95%置信区间（confidence interval, CI）：0.739~0.759]，优于表现最佳的基线模型ICH-APS（AUC=0.704，95%CI：0.694~0.714）。相较于既往ICH风险评分模型，ICH-LR2S2纳入了空腹血糖与C反应蛋白，提升了模型的判别能力。诸如XGBoost等机器学习方法可进一步优化预测性能，其AUC可达0.772（95%CI：0.762~0.782）。在针对24860例患者的外部独立队列验证中，ICH-LR2S2同样表现优异，AUC达0.784（95%CI：0.774~0.794）。
结论：ICH-LR2S2可基于易于获取的临床特征精准区分SAP患者，能够在疾病早期识别高危人群。