作者：李春标1 ，王婷2 ，刘艺1 ，袁娟1 ，袁琳丽1

单位：

1.230012安徽省合肥市，安徽中医药大学护理学院　2.230001安徽省合肥市，安徽中医药大学第二附属医 院护理部

Units：

1.School of Nursing, Anhui University of Chinese Medicine, Hefei 230012, China 2.Department of Nursing, the Second Affiliated Hospital of Anhui University of Chinese Medicine, Hefei 230001, China

关键词：

缺血性卒中；卒中相关性肺炎；预测；模型；系统评价（主题）

Keywords：

　Ischemic stroke; Stroke-associated pneumonia; Forecasting; Model; Systematic reviews as topic

CLC：

DOI：

10.12114/j.issn.1008-5971.2024.00.076

Funds：

安徽省高校科研项目（2023AH050784）；安徽省科研编制计划项目（2022AH050463）

摘要：

目的　系统评价急性缺血性卒中（AIS）患者卒中相关性肺炎（SAP）风险预测模型。方法　检索Web of Science、Cochrane Library、PubMed、Embase、中国知网、中国生物医学文献数据库、维普网、万方数据知识服务平 台发表的有关AIS患者SAP风险预测模型的开发研究，检索时限为各数据库建库至2023-08-30。由两名研究人员进行 文献筛选及数据提取，并采用预测模型偏倚风险评估工具（PROBAST）评价纳入文献的质量。结果　最终纳入文献15 篇，共构建了24个模型，本研究仅选择各文献中性能表现最佳的模型。在模型性能表现方面，所有模型预测AIS患者 发生SAP的AUC为0.739～0.966；7篇文献报道了模型的校准方法；在模型验证方法方面，5篇文献仅进行了内部验证， 2篇文献仅进行了外部验证，2篇文献同时进行了内部验证和外部验证。文献质量评价结果显示，15篇文献均为高偏倚 风险；6篇文献为高适用性风险，9篇文献为低适用性风险。结论　现有AIS患者SAP风险预测模型具有良好的区分度， 但其校准度尚不明确，偏倚风险较高，适用性一般，未来研究人员应参照PROBAST构建性能更好的AIS患者SAP风险 预测模型。

Abstract：

　Objective To systematically evaluate the risk prediction models for stroke-associated pneumonia (SAP) in patients with acute ischemic stroke (AIS) . Methods The development of risk prediction models for SAP in AIS patients published in Web of Science, Cochrane Library, PubMed, Embase, CNKI, Chinese Biomedical Database, VIP, and Wanfang Data were searched. The search period was from the establishment of each database until August 30, 2023. Literature screening and data extraction were conducted by two researchers, and the quality of the included literature was evaluated using the prediction model risk of bias assessment tool (PROBAST) . Results In the end, 15 articles were included, and a total of 24 models were constructed. In this study, only the models with the best performance in each article were selected. In terms of model performance, the AUC of all models in predicting SAP in patients with AIS was 0.739 to 0.966; the calibration method of the model was reported in 7 articles; in terms of model validation methods, 5 papers only conducted internal validation, 2 papers only conducted external validation, and 2 papers conducted both internal and external validation simultaneously. The results of literature quality evaluation showed that all 15 literatures had high risk of bias; there were 6 literatures with high applicability risk and 9 literatures with low applicability risk. Conclusion The existing SAP risk prediction models for AIS patients have good discrimination, but their calibration degree is not clear, the risk of bias is high, and the applicability is general. Future researchers should refer to PROBAST to build better risk prediction models for SAP in AIS patients.

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