Construction and validation of a predictive model of acute exacerbation readmission risk within 30 days in elderly patients with chronic obstructive pulmonary disease_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

ZHANG Rui ¹ ^△ , WU Zhenzhen ¹ ^△ , CHANG Yan ¹ ^△ ,  LU Hongyan ²

1. School of Nursing, Ningxia Medical University, Yinchuan, Ningxia 750004, P. R. China;
2. Department of Nursing, General Hospital of Ningxia Medical University, Yinchuan, Ningxia 750004, P. R. China;

Corresponding author：

LU Hongyan, Email: hyalu@hotmail.com

Keywords：

Chronic obstructive pulmonary disease; Elderly; Readmission within 30 days; Risk prediction model

DOI：

10.7507/1671-6205.202011079

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Objective To analyze the influencing factors of acute exacerbation readmission in elderly patients with chronic obstructive pulmonary disease (COPD) within 30 days, construct and validate the risk prediction model.Methods A total of 1120 elderly patients with COPD in the respiratory department of 13 general hospitals in Ningxia from April 2019 to August 2020 were selected by convenience sampling method and followed up until 30 days after discharge. According to the time of filling in the questionnaire, 784 patients who entered the study first served as the modeling group, and 336 patients who entered the study later served as the validation group to verify the prediction effect of the model.Results Education level, smoking status, number of acute exacerbations of COPD hospitalizations in the past 1 year, regular use of medication, rehabilitation and exercise, nutritional status and seasonal factors were the influencing factors of patients’ readmission to hospital. The risk prediction model was constructed: Z=–8.225–0.310×assignment of education level+0.564×assignment of smoking status+0.873×assignment of number of acute exacerbations of COPD hospitalizations in the past 1 year+0.779×assignment of regular use of medication+0.617×assignment of rehabilitation and exercise +0.970×assignment of nutritional status+assignment of seasonal factors [1.170×spring (0, 1)+0.793×autumn (0, 1)+1.488×winter (0, 1)]. The area under ROC curve was 0.746, the sensitivity was 75.90%, and the specificity was 64.30%. Hosmer-Lemeshow test showed that P=0.278. Results of model validation showed that the sensitivity, the specificity and the accuracy were 69.44%, 85.71% and 81.56%, respectively.Conclusions Education level, smoking status, number of acute exacerbations of COPD hospitalizations in the past 1 year, regular use of medication, rehabilitation and exercise, nutritional status and seasonal factors are the influencing factors of patients’ readmission to hospital. The risk prediction model is constructed based on these factor. This model has good prediction effect, can provide reference for the medical staff to take preventive treatment and nursing measures for high-risk patients.

Citation： ZHANG Rui, WU Zhenzhen, CHANG Yan, LU Hongyan. Construction and validation of a predictive model of acute exacerbation readmission risk within 30 days in elderly patients with chronic obstructive pulmonary disease. Chinese Journal of Respiratory and Critical Care Medicine, 2021, 20(7): 457-464. doi: 10.7507/1671-6205.202011079 Copy

1.	Perera PN, Armstrong EP, Sherrill DL, et al. Acute exacerbations of COPD in The United States: inpatient burden and predictors of costs and mortality. COPD, 2012, 9(2): 131-141.
2.	Lau CS, Siracuse BL, Chamberlain RS. Readmission after COPD exacerbation scale: determining 30-day readmission risk for COPD patients. Int J Chron Obstruct Pulmon Dis, 2017, 12: 1891-1902.
3.	Furumoto A, Ohkusa Y, Chen M, et al. Additive effect of pneumococcal vaccine and influenza vaccine on acute exacerbation in patients with chronic lung disease. Vaccine, 2008, 26(33): 4284-4289.
4.	Goto T, Faridi MK, Camargo CA, et al. Time-varying readmission diagnoses during 30 days after hospitalization for COPD exacerbation. Med Care, 2018, 56(8): 673-678.
5.	Goto T, Jo T, Matsui H, et al. Machine learning-based prediction models for 30-day readmission after hospitalization for chronic obstructive pulmonary disease. COPD, 2019, 16(5-6): 338-343.
6.	Hakim MA, Garden FL, Jennings MD, et al. Performance of the LACE index to predict 30-day hospital readmissions in patients with chronic obstructive pulmonary disease. Clin Epidemiol, 2018, 10: 51-59.
7.	Gershon AS, Thiruchelvam D, Aaron S, et al. Socioeconomic status (SES) and 30-day hospital readmissions for chronic obstructive pulmonary (COPD) disease: a population-based cohort study. PLoS One, 2019, 14(5): e216741.
8.	Bernabeu-Mora R, García-Guillamón G, Valera-Novella E, et al. Frailty is a predictive factor of readmission within 90 days of hospitalization for acute exacerbations of chronic obstructive pulmonary disease: a longitudinal study. Ther Adv Respir Dis, 2017, 11(10): 383-392.
9.	Iyer AS, Bhatt SP, Garner JJ, et al. Depression is associated with readmission for acute exacerbation of chronic obstructive pulmonary disease. Ann Am Thorac Soc, 2016, 13(2): 197-203.
10.	周寅川, 荣蓉, 黄祎丹, 等. 慢性阻塞性肺疾病急性加重期患者出院后 30 d 内再入院的影响因素分析及 Nomogram 模型构建. 实用心脑肺血管病杂志, 2020, 28(08): 57-63.
11.	李楠, 王雪莹, 郭佳桐, 等. 中青年人群非酒精性脂肪肝发生风险预测模型的建立. 中国慢性病预防与控制, 2021, 29(3): 167-171.
12.	张庆, 徐爱晖. CAT 评分与 mMRC 评分对慢性阻塞性肺疾病患者病情评估比较. 临床肺科杂志, 2018, 23(12): 2156-2159.
13.	Fuino M, Rudnytskyi I, Wagner J. On the characteristics of reporting ADL limitations and formal LTC usage across Europe. Eur Actuar J, 2020, 10(2): 557-597.
14.	阮顺莉, 郭菊红, 陈茜蒙, 等. 1025 名居家 60 岁以上老年人吞咽障碍现状及其影响因素分析. 护理学报, 2017, 24(20): 41-44.
15.	刘锐, 陈伟伟, 黄坤, 等. MNA-SF 和 NRS2002 评估老年恶性梗阻性黄疸病人营养状况及其影响因素的应用研究. 肠外与肠内营养, 2019, 26(2): 74-78.
16.	王晓娟, 方向阳. 慢性阻塞性肺疾病全球倡议 2019: 慢性阻塞性肺疾病诊断、治疗与预防全球策略解读. 中国全科医学, 2019, 22(18): 2141-2149.
17.	刘丝雨. AECOPD 复发再入院的危险因素分析研究进展. 临床肺科杂志, 2017, 6(22): 1126-1130.
18.	陈虎, 焦雅辉, 梁铭会, 等. 北京市五家大型综合医院“重返类”指标的比较分析. 中国卫生质量管理, 2010, 17(1): 4-7.
19.	Liu HL, Wang N, Chen W, et al. Hospitalization trends in adult patients with COPD and other respiratory diseases in northeast China from 2005 to 2015. Biomed Res Int, 2018, 2018: 1060497.
20.	蔡柏蔷, 陈荣昌. 慢性阻塞性肺疾病急性加重(AECOPD)诊治中国专家共识(2017 年更新版). 国际呼吸杂志, 2017, 37(14): 1041-1057.
21.	李燕, 刘志燕, 黄美珍. 老年 COPD 患者营养风险现状及其影响因素. 国际护理学杂志, 2016, 35(12): 1630-1633.
22.	Sneh A, Karan M, Anant M, et al. Serum inflammatory markers and nutritional status in patients with stable chronic obstructive pulmonary disease. Lung India, 2019, 36(5): 393-398.
23.	Akner G, Larsson K. Undernutrition state in patients with chronic obstructive pulmonary disease. A critical appraisal on diagnostics and treatment. Respir Med, 2016, 117: 81-91.
24.	伍建光, 陈正贤, 甘兵. 慢性阻塞性肺疾病患者 30 日再入院率与体重指数的关系分析. 中国医学创新, 2018, 15(9): 83-85.
25.	Kume H. Role of bronchodilators in therapy for COPD-mechanisms of LABA and LAMA on airway smooth muscle. Nihon Rinsho, 2016, 74(5): 813-819.
26.	罗倩, 王连红, 韩碟, 等. 老年慢性阻塞性肺疾病患者自我管理知识–态度–行为现状及影响因素的研究. 中国呼吸与危重监护杂志, 2018, 17(6): 552-556.
27.	Sethi S, Maloney J, Grove L, et al. Airway inflammation and bronchial bacterial colonization in chronic obstructive pulmonary disease. Am J Respir Crit Care Med, 2006, 173(9): 991-998.
28.	Hansel NN, Mccormack MK, Kim V. The effects of air pollution and temperature on COPD. COPD, 2016, 13(3): 372-379.
29.	方佳英, 梁彩韵, 丁赞. 慢性阻塞性肺疾病急性加重与季节变化及气象因素的相关性研究. 汕头大学医学院学报, 2019, 32(4): 231-233.
30.	Collinsworth AW, Brown RM, James CS, et al. The impact of patient education and shared decision making on hospital readmissions for COPD. Int J Obstruct Pulmon Dis, 2018, 13: 1325-1332.

1. Perera PN, Armstrong EP, Sherrill DL, et al. Acute exacerbations of COPD in The United States: inpatient burden and predictors of costs and mortality. COPD, 2012, 9(2): 131-141.
2. Lau CS, Siracuse BL, Chamberlain RS. Readmission after COPD exacerbation scale: determining 30-day readmission risk for COPD patients. Int J Chron Obstruct Pulmon Dis, 2017, 12: 1891-1902.
3. Furumoto A, Ohkusa Y, Chen M, et al. Additive effect of pneumococcal vaccine and influenza vaccine on acute exacerbation in patients with chronic lung disease. Vaccine, 2008, 26(33): 4284-4289.
4. Goto T, Faridi MK, Camargo CA, et al. Time-varying readmission diagnoses during 30 days after hospitalization for COPD exacerbation. Med Care, 2018, 56(8): 673-678.
5. Goto T, Jo T, Matsui H, et al. Machine learning-based prediction models for 30-day readmission after hospitalization for chronic obstructive pulmonary disease. COPD, 2019, 16(5-6): 338-343.
6. Hakim MA, Garden FL, Jennings MD, et al. Performance of the LACE index to predict 30-day hospital readmissions in patients with chronic obstructive pulmonary disease. Clin Epidemiol, 2018, 10: 51-59.
7. Gershon AS, Thiruchelvam D, Aaron S, et al. Socioeconomic status (SES) and 30-day hospital readmissions for chronic obstructive pulmonary (COPD) disease: a population-based cohort study. PLoS One, 2019, 14(5): e216741.
8. Bernabeu-Mora R, García-Guillamón G, Valera-Novella E, et al. Frailty is a predictive factor of readmission within 90 days of hospitalization for acute exacerbations of chronic obstructive pulmonary disease: a longitudinal study. Ther Adv Respir Dis, 2017, 11(10): 383-392.
9. Iyer AS, Bhatt SP, Garner JJ, et al. Depression is associated with readmission for acute exacerbation of chronic obstructive pulmonary disease. Ann Am Thorac Soc, 2016, 13(2): 197-203.
10. 周寅川, 荣蓉, 黄祎丹, 等. 慢性阻塞性肺疾病急性加重期患者出院后 30 d 内再入院的影响因素分析及 Nomogram 模型构建. 实用心脑肺血管病杂志, 2020, 28(08): 57-63.
11. 李楠, 王雪莹, 郭佳桐, 等. 中青年人群非酒精性脂肪肝发生风险预测模型的建立. 中国慢性病预防与控制, 2021, 29(3): 167-171.
12. 张庆, 徐爱晖. CAT 评分与 mMRC 评分对慢性阻塞性肺疾病患者病情评估比较. 临床肺科杂志, 2018, 23(12): 2156-2159.
13. Fuino M, Rudnytskyi I, Wagner J. On the characteristics of reporting ADL limitations and formal LTC usage across Europe. Eur Actuar J, 2020, 10(2): 557-597.
14. 阮顺莉, 郭菊红, 陈茜蒙, 等. 1025 名居家 60 岁以上老年人吞咽障碍现状及其影响因素分析. 护理学报, 2017, 24(20): 41-44.
15. 刘锐, 陈伟伟, 黄坤, 等. MNA-SF 和 NRS2002 评估老年恶性梗阻性黄疸病人营养状况及其影响因素的应用研究. 肠外与肠内营养, 2019, 26(2): 74-78.
16. 王晓娟, 方向阳. 慢性阻塞性肺疾病全球倡议 2019: 慢性阻塞性肺疾病诊断、治疗与预防全球策略解读. 中国全科医学, 2019, 22(18): 2141-2149.
17. 刘丝雨. AECOPD 复发再入院的危险因素分析研究进展. 临床肺科杂志, 2017, 6(22): 1126-1130.
18. 陈虎, 焦雅辉, 梁铭会, 等. 北京市五家大型综合医院“重返类”指标的比较分析. 中国卫生质量管理, 2010, 17(1): 4-7.
19. Liu HL, Wang N, Chen W, et al. Hospitalization trends in adult patients with COPD and other respiratory diseases in northeast China from 2005 to 2015. Biomed Res Int, 2018, 2018: 1060497.
20. 蔡柏蔷, 陈荣昌. 慢性阻塞性肺疾病急性加重(AECOPD)诊治中国专家共识(2017 年更新版). 国际呼吸杂志, 2017, 37(14): 1041-1057.
21. 李燕, 刘志燕, 黄美珍. 老年 COPD 患者营养风险现状及其影响因素. 国际护理学杂志, 2016, 35(12): 1630-1633.
22. Sneh A, Karan M, Anant M, et al. Serum inflammatory markers and nutritional status in patients with stable chronic obstructive pulmonary disease. Lung India, 2019, 36(5): 393-398.
23. Akner G, Larsson K. Undernutrition state in patients with chronic obstructive pulmonary disease. A critical appraisal on diagnostics and treatment. Respir Med, 2016, 117: 81-91.
24. 伍建光, 陈正贤, 甘兵. 慢性阻塞性肺疾病患者 30 日再入院率与体重指数的关系分析. 中国医学创新, 2018, 15(9): 83-85.
25. Kume H. Role of bronchodilators in therapy for COPD-mechanisms of LABA and LAMA on airway smooth muscle. Nihon Rinsho, 2016, 74(5): 813-819.
26. 罗倩, 王连红, 韩碟, 等. 老年慢性阻塞性肺疾病患者自我管理知识–态度–行为现状及影响因素的研究. 中国呼吸与危重监护杂志, 2018, 17(6): 552-556.
27. Sethi S, Maloney J, Grove L, et al. Airway inflammation and bronchial bacterial colonization in chronic obstructive pulmonary disease. Am J Respir Crit Care Med, 2006, 173(9): 991-998.
28. Hansel NN, Mccormack MK, Kim V. The effects of air pollution and temperature on COPD. COPD, 2016, 13(3): 372-379.
29. 方佳英, 梁彩韵, 丁赞. 慢性阻塞性肺疾病急性加重与季节变化及气象因素的相关性研究. 汕头大学医学院学报, 2019, 32(4): 231-233.
30. Collinsworth AW, Brown RM, James CS, et al. The impact of patient education and shared decision making on hospital readmissions for COPD. Int J Obstruct Pulmon Dis, 2018, 13: 1325-1332.

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