Risk prediction model for acute exacerbation of chronic obstructive pulmonary disease: a systematic review_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

ZHANG Ziwei , ZHAN Lirui , LI Wen ,  YANG Jing ,  ZHANG Caihong

1. Hainan Medical College, Haikou, Hainan 570100, P. R. China;
2. Department of Respiratory Medicine, The First Affiliated Hospital of Hainan Medical College, Haikou, Hainan 570102, P. R. China;
3. Nursing Homes in Hainan Province, Haikou, Hainan 570100, P. R. China;

Corresponding?author：

YANG Jing, Email: 1510322535@qq.com; ZHANG Caihong, Email: 1770873893@qq.com

Keywords：

Chronic obstructive pulmonary disease; acute exacerbation; risk prediction; model

DOI：

10.7507/1671-6205.202205055

Video：

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Objective To systematically evaluate risk prediction models for acute exacerbation of chronic obstructive pulmonary disease (COPD), and provide a reference for early clinical identification. Methods The literature on the risk prediction models of acute exacerbation of COPD published by CNKI, VIP, Cochrane, Embase and Web of Science database was searched in Chinese and English from inception to April 2022, and relevant studies were collected on the development of risk prediction models for acute exacerbations of COPD. After independent screening of the literature and extraction of information by two independent researchers, the quality of the included literature was evaluated using the PROBASTA tool. Results Five prospective studies, one retrospective case-control study and seven retrospective cohort studies were included, totally 13 papers containing 24 models. Twelve studies (92.3%) reported the area under the receiver operator characteristic curve ranging 0.66 to 0.969. Only five studies reported calibrated statistics, and three studies were internally and externally validated. The overall applicability of 13 studies was good, but there was a high risk of bias, mainly in the area of analysis. Conclusions The existing predictive risk models for acute exacerbations of COPD are unsatisfactory, with wide variation in model performance, inappropriate and incomplete inclusion of predictors, and a need for better ways to develop and validate high-quality predictive models. Future research should refine the study design and study report, and continue to update and validate existing models. Secondly medical staff should develop and implement risk stratification strategies for acute exacerbations of COPD based on predicted risk classification results in order to reduce the frequency of acute exacerbations and to facilitate the rational allocation of medical resources.

Citation： ZHANG Ziwei, ZHAN Lirui, LI Wen, YANG Jing, ZHANG Caihong. Risk prediction model for acute exacerbation of chronic obstructive pulmonary disease: a systematic review. Chinese Journal of Respiratory and Critical Care Medicine, 2022, 21(9): 622-628. doi: 10.7507/1671-6205.202205055 Copy

1.	Wedzicha JA, Miravitlles M, Hurst JR, et al. Management of COPD Exacerbations: a European Respiratory Society/American Thoracic Society Guideline. Eur Respir J, 2017, 49(3): 1600791.
2.	Halpin DMG, Criner GJ, Papi A, et al. Global Initiative for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease. The 2020 GOLD Science Committee Report on COVID-19 and Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med, 2021, 203(1): 24-36.
3.	Ko FW, Chan KP, Hui DS, et al. Acute exacerbation of COPD. Respirology, 2016, 21(7): 1152-1165.
4.	Erhabor GE, Adeniyi B, Arawomo AO, et al. Acute exacerbation of COPD: clinical perspectives and literature review. West Afr J Med, 2021, 38(11): 1129-1142.
5.	Hillas G, Perlikos F, Tzanakis N. Acute exacerbation of COPD: is it the "stroke of the lungs"?. Int J Chron Obstruct Pulmon Dis, 2016, 11: 1579-1586.
6.	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.
7.	Moons KGM, de Groot JAH, 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.
8.	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.
9.	王明航, 蔡坤坤, 施丁莉, 等. 基于回歸分析法建立慢性阻塞性肺疾病急性加重風險預測模型與驗證. 中華危重病急救醫學, 2021, 33(1): 64-68.
10.	陳雪影. 建立預測重度慢性阻塞性肺疾病急性加重風險列線圖. 華中科技大學, 2020. DOI: 10.27157/d.cnki.ghzku.2020.004276.
11.	張瑞, 吳珍珍, 常艷, 等. 老年慢性阻塞性肺疾病患者30天內急性加重再入院風險預測模型的構建與驗證. 中國呼吸與危重監護雜志, 2021, 20(7): 457-464.
12.	Wu CT, Li GH, Huang CT, et al. Acute exacerbation of a chronic obstructive pulmonary disease prediction system using wearable device data, machine learning, and deep learning: development and cohort study. JMIR Mhealth Uhealth, 2021, 9(5): e22591.
13.	Kor CT, Li YR, Lin PR, et al. Explainable machine learning model for predicting first-time acute exacerbation in patients with chronic obstructive pulmonary disease. J Pers Med, 2022, 12(2): 228.
14.	Bertens LC, Reitsma JB, Moons KG, et al. Development and validation of a model to predict the risk of exacerbations in chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis, 2013, 8: 493-499.
15.	Yii A, Loh CH, Tiew PY, et al. A clinical prediction model for hospitalized COPD exacerbations based on "treatable traits". Int J Chron Obstruct Pulmon Dis, 2019, 14: 719-728.
16.	Samp JC, Joo MJ, Schumock GT, et al. Predicting acute exacerbations in chronic obstructive pulmonary disease. J Manag Care Spec Pharm, 2018, 24(3): 265-279.
17.	Tavakoli H, Chen W, Sin DD, et al. Predicting severe chronic obstructive pulmonary disease exacerbations. Developing a population surveillance approach with administrative data. Ann Am Thorac Soc, 2020, 17(9): 1069-1076.
18.	Amalakuhan B, Kiljanek L, Parvathaneni A, et al. A prediction model for COPD readmissions: catching up, catching our breath, and improving a national problem. J Community Hosp Intern Med Perspect, 2012, 2(1): 9915.
19.	Shah SA, Velardo C, Farmer A, et al. Exacerbations in chronic obstructive pulmonary disease: identification and prediction using a digital health system. J Med Internet Res, 2017, 19(3): e69.
20.	Yoon HY, Park SY, Lee CH, et al. Prediction of first acute exacerbation using COPD subtypes identified by cluster analysis. Int J Chron Obstruct Pulmon Dis, 2019, 14: 1389-1397.
21.	Adibi A, Sin DD, Safari A, et al. The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study. Lancet Respir Med, 2020, 8(10): 1013-1021.
22.	Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 2015, 350: g7594.
23.	Retamales I, Elliott WM, Meshi B, et al. Amplification of inflammation in emphysema and its association with latent adenoviral infection. Am J Respir Crit Care Med, 2001, 164(3): 469-473.
24.	Hacievliyagil SS, Gunen H, Mutlu LC, et al. Association between cytokines in induced sputum and severity of chronic obstructive pulmonary disease. Respir Med, 2006, 100(5): 846-854.
25.	Takabatake N, Nakamura H, Abe S, et al. The relationship between chronic hypoxemia and activation of the tumor necrosis factor-α system in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med, 2000, 161(4 Pt 1): 1179-1184.
26.	Lee SJ, Lee HR, Lee TW, et al. Usefulness of neutrophil to lymphocyte ratio in patients with chronic obstructive pulmonary disease: a prospective observational study. Korean J Intern Med, 2016, 31(5): 891-898.
27.	Miravitlles M, Llor C. Non-eosinophilic severe exacerbations of COPD: what about antibiotics?. Lancet Respir Med, 2019, 7(11): e33.
28.	Avdeev SN, Nuralieva GS, Gainitdinova VV, et al. Clinical efficacy of mechanical bacterial lysate in the prevention of infectious exacerbations of chronic obstructive pulmonary disease. Ter Arkh, 2020, 92(4): 57-63.
29.	孫澤麗, 丁國正. 慢性阻塞性肺疾病頻繁急性加重的影響因素分析. 中國醫藥導報, 2021, 18(13): 85-88,197.
30.	陳幼麗, 陳偉文, 林天來. 慢性阻塞性肺疾病患者發生撤機相關肺水腫的研究進展. 中國呼吸與危重監護雜志, 2021, 20(6): 447-451.
31.	Zhang JX, Bai C. The significance of serum interleukin-8 in acute exacerbations of chronic obstructive pulmonary disease. Tanaffos, 2018, 17(1): 13-21.
32.	黃慧琴, 鄒燕, 李勇, 等. 福州地區慢性阻塞性肺病急性發作與氣象因素的相關性分析及其預警模型的初步探討. 福建醫藥雜志, 2020, 42(1): 23-26.

1. Wedzicha JA, Miravitlles M, Hurst JR, et al. Management of COPD Exacerbations: a European Respiratory Society/American Thoracic Society Guideline. Eur Respir J, 2017, 49(3): 1600791.
2. Halpin DMG, Criner GJ, Papi A, et al. Global Initiative for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease. The 2020 GOLD Science Committee Report on COVID-19 and Chronic Obstructive Pulmonary Disease. Am J Respir Crit Care Med, 2021, 203(1): 24-36.
3. Ko FW, Chan KP, Hui DS, et al. Acute exacerbation of COPD. Respirology, 2016, 21(7): 1152-1165.
4. Erhabor GE, Adeniyi B, Arawomo AO, et al. Acute exacerbation of COPD: clinical perspectives and literature review. West Afr J Med, 2021, 38(11): 1129-1142.
5. Hillas G, Perlikos F, Tzanakis N. Acute exacerbation of COPD: is it the "stroke of the lungs"?. Int J Chron Obstruct Pulmon Dis, 2016, 11: 1579-1586.
6. 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.
7. Moons KGM, de Groot JAH, 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.
8. 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.
9. 王明航, 蔡坤坤, 施丁莉, 等. 基于回歸分析法建立慢性阻塞性肺疾病急性加重風險預測模型與驗證. 中華危重病急救醫學, 2021, 33(1): 64-68.
10. 陳雪影. 建立預測重度慢性阻塞性肺疾病急性加重風險列線圖. 華中科技大學, 2020. DOI: 10.27157/d.cnki.ghzku.2020.004276.
11. 張瑞, 吳珍珍, 常艷, 等. 老年慢性阻塞性肺疾病患者30天內急性加重再入院風險預測模型的構建與驗證. 中國呼吸與危重監護雜志, 2021, 20(7): 457-464.
12. Wu CT, Li GH, Huang CT, et al. Acute exacerbation of a chronic obstructive pulmonary disease prediction system using wearable device data, machine learning, and deep learning: development and cohort study. JMIR Mhealth Uhealth, 2021, 9(5): e22591.
13. Kor CT, Li YR, Lin PR, et al. Explainable machine learning model for predicting first-time acute exacerbation in patients with chronic obstructive pulmonary disease. J Pers Med, 2022, 12(2): 228.
14. Bertens LC, Reitsma JB, Moons KG, et al. Development and validation of a model to predict the risk of exacerbations in chronic obstructive pulmonary disease. Int J Chron Obstruct Pulmon Dis, 2013, 8: 493-499.
15. Yii A, Loh CH, Tiew PY, et al. A clinical prediction model for hospitalized COPD exacerbations based on "treatable traits". Int J Chron Obstruct Pulmon Dis, 2019, 14: 719-728.
16. Samp JC, Joo MJ, Schumock GT, et al. Predicting acute exacerbations in chronic obstructive pulmonary disease. J Manag Care Spec Pharm, 2018, 24(3): 265-279.
17. Tavakoli H, Chen W, Sin DD, et al. Predicting severe chronic obstructive pulmonary disease exacerbations. Developing a population surveillance approach with administrative data. Ann Am Thorac Soc, 2020, 17(9): 1069-1076.
18. Amalakuhan B, Kiljanek L, Parvathaneni A, et al. A prediction model for COPD readmissions: catching up, catching our breath, and improving a national problem. J Community Hosp Intern Med Perspect, 2012, 2(1): 9915.
19. Shah SA, Velardo C, Farmer A, et al. Exacerbations in chronic obstructive pulmonary disease: identification and prediction using a digital health system. J Med Internet Res, 2017, 19(3): e69.
20. Yoon HY, Park SY, Lee CH, et al. Prediction of first acute exacerbation using COPD subtypes identified by cluster analysis. Int J Chron Obstruct Pulmon Dis, 2019, 14: 1389-1397.
21. Adibi A, Sin DD, Safari A, et al. The Acute COPD Exacerbation Prediction Tool (ACCEPT): a modelling study. Lancet Respir Med, 2020, 8(10): 1013-1021.
22. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 2015, 350: g7594.
23. Retamales I, Elliott WM, Meshi B, et al. Amplification of inflammation in emphysema and its association with latent adenoviral infection. Am J Respir Crit Care Med, 2001, 164(3): 469-473.
24. Hacievliyagil SS, Gunen H, Mutlu LC, et al. Association between cytokines in induced sputum and severity of chronic obstructive pulmonary disease. Respir Med, 2006, 100(5): 846-854.
25. Takabatake N, Nakamura H, Abe S, et al. The relationship between chronic hypoxemia and activation of the tumor necrosis factor-α system in patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med, 2000, 161(4 Pt 1): 1179-1184.
26. Lee SJ, Lee HR, Lee TW, et al. Usefulness of neutrophil to lymphocyte ratio in patients with chronic obstructive pulmonary disease: a prospective observational study. Korean J Intern Med, 2016, 31(5): 891-898.
27. Miravitlles M, Llor C. Non-eosinophilic severe exacerbations of COPD: what about antibiotics?. Lancet Respir Med, 2019, 7(11): e33.
28. Avdeev SN, Nuralieva GS, Gainitdinova VV, et al. Clinical efficacy of mechanical bacterial lysate in the prevention of infectious exacerbations of chronic obstructive pulmonary disease. Ter Arkh, 2020, 92(4): 57-63.
29. 孫澤麗, 丁國正. 慢性阻塞性肺疾病頻繁急性加重的影響因素分析. 中國醫藥導報, 2021, 18(13): 85-88,197.
30. 陳幼麗, 陳偉文, 林天來. 慢性阻塞性肺疾病患者發生撤機相關肺水腫的研究進展. 中國呼吸與危重監護雜志, 2021, 20(6): 447-451.
31. Zhang JX, Bai C. The significance of serum interleukin-8 in acute exacerbations of chronic obstructive pulmonary disease. Tanaffos, 2018, 17(1): 13-21.
32. 黃慧琴, 鄒燕, 李勇, 等. 福州地區慢性阻塞性肺病急性發作與氣象因素的相關性分析及其預警模型的初步探討. 福建醫藥雜志, 2020, 42(1): 23-26.

Chinese Journal of Respiratory and Critical Care Medicine

Risk prediction model for acute exacerbation of chronic obstructive pulmonary disease: a systematic review

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