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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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.	張蕊, 鄭黎強, 潘國偉. 疾病發病風險預測模型的應用與建立. 中國衛生統計, 2015, 32(4): 724-726.
3.	中國老年醫學中國卒中學會急救醫學分會中華醫學會急診醫學分會卒中學組, 中國醫學科學院海島學會急診醫學分會急救與創傷研究教育部重點實驗室, 急救創新單元. 卒中相關性肺炎診治中國專家共識(2019更新版). 中國卒中雜志, 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, Dr?ge 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.	陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 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. 基于機器學習算法的腦出血相關肺炎預測模型研究. 中國卒中雜志, 2020, 15(3): 243-249.
22.	吳倩, 路偉. 老年急性腦出血并發相關性肺炎的危險因素分析及風險預測模型的建立. 貴州醫科大學學報, 2020, 45(10): 1219-1224.
23.	王秋義, 尤丕聰. 腦卒中相關性肺炎病原菌特點及風險列線圖模型建立. 中華醫院感染學雜志, 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.	陳茹, 王勝鋒, 周家琛, 等. 預測模型研究的偏倚風險和適用性評估工具解讀. 中華流行病學雜志, 2020, 41(5): 776-781.
27.	梁天佳, 莫明玉, 周開斌, 等. 氣道廓清術在腦卒中相關性肺炎氣管切開患者康復中的應用. 中國呼吸與危重監護雜志, 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.	張銳, 黃榮, 譚登云, 等. 卒中相關性肺炎影響因素的模型構建及其與3種預測評分系統的比較. 中華醫院感染學雜志, 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.	谷強, 余孝君, 張津. 高齡與中低齡老年輕型缺血性腦卒中臨床特點及預后分析. 首都醫科大學學報, 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響因素. 中華醫院感染學雜志, 2022, 32(24): 3731-3735.
34.	孫玲莉, 董世杰, 楊貴軍. 常用多重插補法的插補重數選擇. 統計與決策, 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. 張蕊, 鄭黎強, 潘國偉. 疾病發病風險預測模型的應用與建立. 中國衛生統計, 2015, 32(4): 724-726.
3. 中國老年醫學中國卒中學會急救醫學分會中華醫學會急診醫學分會卒中學組, 中國醫學科學院海島學會急診醫學分會急救與創傷研究教育部重點實驗室, 急救創新單元. 卒中相關性肺炎診治中國專家共識(2019更新版). 中國卒中雜志, 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, Dr?ge 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. 陳香萍, 張奕, 莊一渝, 等. PROBAST: 診斷或預后多因素預測模型研究偏倚風險的評估工具. 中國循證醫學雜志, 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. 基于機器學習算法的腦出血相關肺炎預測模型研究. 中國卒中雜志, 2020, 15(3): 243-249.
22. 吳倩, 路偉. 老年急性腦出血并發相關性肺炎的危險因素分析及風險預測模型的建立. 貴州醫科大學學報, 2020, 45(10): 1219-1224.
23. 王秋義, 尤丕聰. 腦卒中相關性肺炎病原菌特點及風險列線圖模型建立. 中華醫院感染學雜志, 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. 陳茹, 王勝鋒, 周家琛, 等. 預測模型研究的偏倚風險和適用性評估工具解讀. 中華流行病學雜志, 2020, 41(5): 776-781.
27. 梁天佳, 莫明玉, 周開斌, 等. 氣道廓清術在腦卒中相關性肺炎氣管切開患者康復中的應用. 中國呼吸與危重監護雜志, 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. 張銳, 黃榮, 譚登云, 等. 卒中相關性肺炎影響因素的模型構建及其與3種預測評分系統的比較. 中華醫院感染學雜志, 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. 谷強, 余孝君, 張津. 高齡與中低齡老年輕型缺血性腦卒中臨床特點及預后分析. 首都醫科大學學報, 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響因素. 中華醫院感染學雜志, 2022, 32(24): 3731-3735.
34. 孫玲莉, 董世杰, 楊貴軍. 常用多重插補法的插補重數選擇. 統計與決策, 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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