Risk prediction models for stroke-associated pneumonia: a systematic review_Chinese Journal of Evidence-Based Medicine

Authors：

CAO Yi ^1,2 , ZENG Xi ² , ZHAO Xinfei ¹ ,  PENG Min ³

1. Department of Nursing, the Guizhou Medical University, Guiyang 550025, P. R. China;
2. Department of Neurosurgery, the Affiliated Hospital of Guizhou Medical University, Guiyang 550004, P. R. China;
3. Department of Nursing Quality Management, the Affiliated Hospital of Guizhou Medical University, Guiyang 550004, P. R. China;

Corresponding author：

PENG Min, Email: 469238409@qq.com

Keywords：

Stroke-related pneumonia; Predictive models; Risk assessment; Systematic reviews

DOI：

10.7507/1672-2531.202303014

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective To systematically review the predictive model of stroke-related pneumonia risk. Methods The CNKI, WanFang Data, CBM, PubMed, Web of Science, Embase, MEDLINE and Cochrane Library databases were electronically searched to collect studies on risk prediction models for stroke-associated pneumonia from inception to February 15, 2023. Two researchers independently screened the literature and extracted data. The risk of bias and applicability of the models were assessed using PROBAST. Results A total of 18 studies and 27 SAP risk prediction models were included. The AUC values for inclusion in the model ranged from 0.67 to 0.96, and the number of candidate predictors ranged from 4 to 25, with the most common predictors being age, NIHSS score, dysphagia, mRS score, and impaired consciousness (GCS score). Conclusion The overall predictive performance of SAP risk prediction models is good, but their predictive performance cannot be directly compared because of the differences in study type, study population, and SAP diagnostic criteria. Moreover, 72.3% of the models are not externally validated, and most of the studies have a high risk of bias.

Citation： CAO Yi, ZENG Xi, ZHAO Xinfei, PENG Min. Risk prediction models for stroke-associated pneumonia: a systematic review. Chinese Journal of Evidence-Based Medicine, 2023, 23(11): 1259-1268. doi: 10.7507/1672-2531.202303014 Copy

1.	Grant SW, Collins GS, Nashef SAM. Statistical primer: developing and validating a risk prediction model. Eur J Cardiothorac Surg, 2018, 54(2): 203-208.
2.	张蕊, 郑黎强, 潘国伟. 疾病发病风险预测模型的应用与建立. 中国卫生统计, 2015, 32(4): 724-726.
3.	中国老年医学中国卒中学会急救医学分会中华医学会急诊医学分会卒中学组, 中国医学科学院海岛学会急诊医学分会急救与创伤研究教育部重点实验室, 急救创新单元. 卒中相关性肺炎诊治中国专家共识(2019更新版). 中国卒中杂志, 2019, 14(12): 1251-1262.
4.	Hilker R, Poetter C, Findeisen N, et al. Nosocomial pneumonia after acute stroke: implications for neurological intensive care medicine. Stroke, 2003, 34(4): 975-981.
5.	Smith CJ, Bray BD, Hoffman A, et al. Can a novel clinical risk score improve pneumonia prediction in acute stroke care? A UK multicenter cohort study. J Am Heart Assoc, 2015, 4(1): e001307.
6.	Harms H, Grittner U, Dröge H, et al. Predicting post-stroke pneumonia: the PANTHERIS score. Acta Neurol Scand, 2013, 128(3): 178-184.
7.	Yan J, Zhai W, Li Z, et al. ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage. J Transl Med, 2022, 20(1): 193.
8.	Li YM, Zhao L, Liu YG, et al. Novel predictors of stroke-associated pneumonia: a single center analysis. Front Neurol, 2022, 13: 857420.
9.	Zheng Y, Lin YX, He Q, et al. Novel machine learning models to predict pneumonia events in supratentorial intracerebral hemorrhage populations: an analysis of the Risa-MIS-ICH study. Front Neurol, 2022, 13: 955271.
10.	Zhang WB, Tang TC, Zhang AK, et al. A clinical prediction model based on post large artery atherosclerosis infarction pneumonia. Neurologist, 2023, 28(1): 19-24.
11.	Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
12.	Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
13.	陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
14.	Hoffmann S, Malzahn U, Harms H, et al. Development of a clinical score (A2DS2) to predict pneumonia in acute ischemic stroke. Stroke, 2012, 43(10): 2617-2623.
15.	Ji R, Shen H, Pan Y, et al. Novel risk score to predict pneumonia after acute ischemic stroke. Stroke, 2013, 44(5): 1303-1309.
16.	Ji R, Shen H, Pan Y, et al. Risk score to predict hospital-acquired pneumonia after spontaneous intracerebral hemorrhage. Stroke, 2014, 45(9): 2620-2628.
17.	Kumar S, Marchina S, Massaro J, et al. ACDD4 score: a simple tool for assessing risk of pneumonia after stroke. J Neurol Sci, 2017, 372: 399-402.
18.	Huang GQ, Lin YT, Wu YM, et al. Individualized prediction of stroke-associated pneumonia for patients with acute ischemic stroke. Clin Interv Aging, 2019, 14: 1951-1962.
19.	Ge Y, Wang Q, Wang L, et al. Predicting post-stroke pneumonia using deep neural network approaches. Int J Med Inform, 2019, 132: 103986.
20.	Li X, Wu M, Sun C, et al. Using machine learning to predict stroke-associated pneumonia in Chinese acute ischaemic stroke patients. Eur J Neurol, 2020, 27(8): 1656-1663.
21.	王孟, 覃露, 王春娟, 等. 基于机器学习算法的脑出血相关肺炎预测模型研究. 中国卒中杂志, 2020, 15(3): 243-249.
22.	吴倩, 路伟. 老年急性脑出血并发相关性肺炎的危险因素分析及风险预测模型的建立. 贵州医科大学学报, 2020, 45(10): 1219-1224.
23.	王秋义, 尤丕聪. 脑卒中相关性肺炎病原菌特点及风险列线图模型建立. 中华医院感染学杂志, 2021, 31(7): 967-971.
24.	Kwon HM, Jeong SW, Lee SH, et al. The pneumonia score: a simple grading scale for prediction of pneumonia after acute stroke. Am J Infect Control, 2006, 34(2): 64-68.
25.	Wang Y, Wu Z. The establishment of a stroke-associated pneumonia predictive scoring system. Neurology Asia, 2021, 26(3): 485-490.
26.	陈茹, 王胜锋, 周家琛, 等. 预测模型研究的偏倚风险和适用性评估工具解读. 中华流行病学杂志, 2020, 41(5): 776-781.
27.	梁天佳, 莫明玉, 周开斌, 等. 气道廓清术在脑卒中相关性肺炎气管切开患者康复中的应用. 中国呼吸与危重监护杂志, 2021, 20(4): 270-273.
28.	Liu DD, Chu SF, Chen C, et al. Research progress in stroke-induced immunodepression syndrome (SIDS) and stroke-associated pneumonia (SAP). Neurochem Int, 2018, 114: 42-54.
29.	Yan D, Dai C, Xu R, et al. Predictive ability of systemic inflammation response index for the risk of pneumonia in patients with acute ischemic stroke. Gerontology, 2023, 69(2): 181-188.
30.	张锐, 黄荣, 谭登云, 等. 卒中相关性肺炎影响因素的模型构建及其与3种预测评分系统的比较. 中华医院感染学杂志, 2022, 32(18): 2767-2771.
31.	Erfani Z, Jelodari Mamaghani H, Rawling JA, et al. Pneumonia in nervous system injuries: an analytic review of literature and recommendations. Cureus, 2022, 14(6): e25616.
32.	谷强, 余孝君, 张津. 高龄与中低龄老年轻型缺血性脑卒中临床特点及预后分析. 首都医科大学学报, 2022, 43(3): 474-479.
33.	戴靖榕, 李婕, 李燕, 等. 老年多病共存住院患者肺部感染影响因素. 中华医院感染学杂志, 2022, 32(24): 3731-3735.
34.	孙玲莉, 董世杰, 杨贵军. 常用多重插补法的插补重数选择. 统计与决策, 2019, 35(23): 5-10.

1. Grant SW, Collins GS, Nashef SAM. Statistical primer: developing and validating a risk prediction model. Eur J Cardiothorac Surg, 2018, 54(2): 203-208.
2. 张蕊, 郑黎强, 潘国伟. 疾病发病风险预测模型的应用与建立. 中国卫生统计, 2015, 32(4): 724-726.
3. 中国老年医学中国卒中学会急救医学分会中华医学会急诊医学分会卒中学组, 中国医学科学院海岛学会急诊医学分会急救与创伤研究教育部重点实验室, 急救创新单元. 卒中相关性肺炎诊治中国专家共识(2019更新版). 中国卒中杂志, 2019, 14(12): 1251-1262.
4. Hilker R, Poetter C, Findeisen N, et al. Nosocomial pneumonia after acute stroke: implications for neurological intensive care medicine. Stroke, 2003, 34(4): 975-981.
5. Smith CJ, Bray BD, Hoffman A, et al. Can a novel clinical risk score improve pneumonia prediction in acute stroke care? A UK multicenter cohort study. J Am Heart Assoc, 2015, 4(1): e001307.
6. Harms H, Grittner U, Dröge H, et al. Predicting post-stroke pneumonia: the PANTHERIS score. Acta Neurol Scand, 2013, 128(3): 178-184.
7. Yan J, Zhai W, Li Z, et al. ICH-LR2S2: a new risk score for predicting stroke-associated pneumonia from spontaneous intracerebral hemorrhage. J Transl Med, 2022, 20(1): 193.
8. Li YM, Zhao L, Liu YG, et al. Novel predictors of stroke-associated pneumonia: a single center analysis. Front Neurol, 2022, 13: 857420.
9. Zheng Y, Lin YX, He Q, et al. Novel machine learning models to predict pneumonia events in supratentorial intracerebral hemorrhage populations: an analysis of the Risa-MIS-ICH study. Front Neurol, 2022, 13: 955271.
10. Zhang WB, Tang TC, Zhang AK, et al. A clinical prediction model based on post large artery atherosclerosis infarction pneumonia. Neurologist, 2023, 28(1): 19-24.
11. Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
12. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
13. 陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
14. Hoffmann S, Malzahn U, Harms H, et al. Development of a clinical score (A2DS2) to predict pneumonia in acute ischemic stroke. Stroke, 2012, 43(10): 2617-2623.
15. Ji R, Shen H, Pan Y, et al. Novel risk score to predict pneumonia after acute ischemic stroke. Stroke, 2013, 44(5): 1303-1309.
16. Ji R, Shen H, Pan Y, et al. Risk score to predict hospital-acquired pneumonia after spontaneous intracerebral hemorrhage. Stroke, 2014, 45(9): 2620-2628.
17. Kumar S, Marchina S, Massaro J, et al. ACDD4 score: a simple tool for assessing risk of pneumonia after stroke. J Neurol Sci, 2017, 372: 399-402.
18. Huang GQ, Lin YT, Wu YM, et al. Individualized prediction of stroke-associated pneumonia for patients with acute ischemic stroke. Clin Interv Aging, 2019, 14: 1951-1962.
19. Ge Y, Wang Q, Wang L, et al. Predicting post-stroke pneumonia using deep neural network approaches. Int J Med Inform, 2019, 132: 103986.
20. Li X, Wu M, Sun C, et al. Using machine learning to predict stroke-associated pneumonia in Chinese acute ischaemic stroke patients. Eur J Neurol, 2020, 27(8): 1656-1663.
21. 王孟, 覃露, 王春娟, 等. 基于机器学习算法的脑出血相关肺炎预测模型研究. 中国卒中杂志, 2020, 15(3): 243-249.
22. 吴倩, 路伟. 老年急性脑出血并发相关性肺炎的危险因素分析及风险预测模型的建立. 贵州医科大学学报, 2020, 45(10): 1219-1224.
23. 王秋义, 尤丕聪. 脑卒中相关性肺炎病原菌特点及风险列线图模型建立. 中华医院感染学杂志, 2021, 31(7): 967-971.
24. Kwon HM, Jeong SW, Lee SH, et al. The pneumonia score: a simple grading scale for prediction of pneumonia after acute stroke. Am J Infect Control, 2006, 34(2): 64-68.
25. Wang Y, Wu Z. The establishment of a stroke-associated pneumonia predictive scoring system. Neurology Asia, 2021, 26(3): 485-490.
26. 陈茹, 王胜锋, 周家琛, 等. 预测模型研究的偏倚风险和适用性评估工具解读. 中华流行病学杂志, 2020, 41(5): 776-781.
27. 梁天佳, 莫明玉, 周开斌, 等. 气道廓清术在脑卒中相关性肺炎气管切开患者康复中的应用. 中国呼吸与危重监护杂志, 2021, 20(4): 270-273.
28. Liu DD, Chu SF, Chen C, et al. Research progress in stroke-induced immunodepression syndrome (SIDS) and stroke-associated pneumonia (SAP). Neurochem Int, 2018, 114: 42-54.
29. Yan D, Dai C, Xu R, et al. Predictive ability of systemic inflammation response index for the risk of pneumonia in patients with acute ischemic stroke. Gerontology, 2023, 69(2): 181-188.
30. 张锐, 黄荣, 谭登云, 等. 卒中相关性肺炎影响因素的模型构建及其与3种预测评分系统的比较. 中华医院感染学杂志, 2022, 32(18): 2767-2771.
31. Erfani Z, Jelodari Mamaghani H, Rawling JA, et al. Pneumonia in nervous system injuries: an analytic review of literature and recommendations. Cureus, 2022, 14(6): e25616.
32. 谷强, 余孝君, 张津. 高龄与中低龄老年轻型缺血性脑卒中临床特点及预后分析. 首都医科大学学报, 2022, 43(3): 474-479.
33. 戴靖榕, 李婕, 李燕, 等. 老年多病共存住院患者肺部感染影响因素. 中华医院感染学杂志, 2022, 32(24): 3731-3735.
34. 孙玲莉, 董世杰, 杨贵军. 常用多重插补法的插补重数选择. 统计与决策, 2019, 35(23): 5-10.

Chinese Journal of Evidence-Based Medicine

Risk prediction models for stroke-associated pneumonia: a systematic review

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