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

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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.

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External Organisations
Beijing Tiantan Hospital, Capital Medical University; China National Clinical Research Center for Neurological Diseases; Fudan University; Beijing Tian Tan Hospital; Chinese Institute for Brain Research; Chinese Academy of Medical Sciences & Peking Union Medical College

Associated Persons
Jing Yan (Creator); Weiqi Zhai (Creator); Zhaoxia Li (Creator); LingLing Ding (Creator); Jia You (Creator); Jiayi Zeng (Creator); Xin Yang (Creator); Chunjuan Wang (Creator); Xia Meng (Creator); Yong Jiang (Creator); Shouyan Wang (Creator); Yilong Wang (Creator); Zixiao Li (Creator); Shanfeng Zhu (Creator); Yongjun Wang (Creator); Xingquan Zhao (Creator); Jianfeng Feng (Creator)