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

Video：

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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, 榮蓉, 黃祎丹, 等. 慢性阻塞性肺疾病急性加重期患者出院后 30 d 內再入院的影響因素分析及 Nomogram 模型構建. 實用心腦肺血管病雜志, 2020, 28(08): 57-63.
11.	李楠, 王雪瑩, 郭佳桐, 等. 中青年人群非酒精性脂肪肝發生風險預測模型的建立. 中國慢性病預防與控制, 2021, 29(3): 167-171.
12.	張慶, 徐愛暉. CAT 評分與 mMRC 評分對慢性阻塞性肺疾病患者病情評估比較. 臨床肺科雜志, 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.	阮順莉, 郭菊紅, 陳茜蒙, 等. 1025 名居家 60 歲以上老年人吞咽障礙現狀及其影響因素分析. 護理學報, 2017, 24(20): 41-44.
15.	劉銳, 陳偉偉, 黃坤, 等. MNA-SF 和 NRS2002 評估老年惡性梗阻性黃疸病人營養狀況及其影響因素的應用研究. 腸外與腸內營養, 2019, 26(2): 74-78.
16.	王曉娟, 方向陽. 慢性阻塞性肺疾病全球倡議 2019: 慢性阻塞性肺疾病診斷、治療與預防全球策略解讀. 中國全科醫學, 2019, 22(18): 2141-2149.
17.	劉絲雨. AECOPD 復發再入院的危險因素分析研究進展. 臨床肺科雜志, 2017, 6(22): 1126-1130.
18.	陳虎, 焦雅輝, 梁銘會, 等. 北京市五家大型綜合醫院“重返類”指標的比較分析. 中國衛生質量管理, 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.	蔡柏薔, 陳榮昌. 慢性阻塞性肺疾病急性加重(AECOPD)診治中國專家共識(2017 年更新版). 國際呼吸雜志, 2017, 37(14): 1041-1057.
21.	李燕, 劉志燕, 黃美珍. 老年 COPD 患者營養風險現狀及其影響因素. 國際護理學雜志, 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, 陳正賢, 甘兵. 慢性阻塞性肺疾病患者 30 日再入院率與體重指數的關系分析. 中國醫學創新, 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.	羅倩, 王連紅, 韓碟, 等. 老年慢性阻塞性肺疾病患者自我管理知識–態度–行為現狀及影響因素的研究. 中國呼吸與危重監護雜志, 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, 梁彩韻, 丁贊. 慢性阻塞性肺疾病急性加重與季節變化及氣象因素的相關性研究. 汕頭大學醫學院學報, 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, 榮蓉, 黃祎丹, 等. 慢性阻塞性肺疾病急性加重期患者出院后 30 d 內再入院的影響因素分析及 Nomogram 模型構建. 實用心腦肺血管病雜志, 2020, 28(08): 57-63.
11. 李楠, 王雪瑩, 郭佳桐, 等. 中青年人群非酒精性脂肪肝發生風險預測模型的建立. 中國慢性病預防與控制, 2021, 29(3): 167-171.
12. 張慶, 徐愛暉. CAT 評分與 mMRC 評分對慢性阻塞性肺疾病患者病情評估比較. 臨床肺科雜志, 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. 阮順莉, 郭菊紅, 陳茜蒙, 等. 1025 名居家 60 歲以上老年人吞咽障礙現狀及其影響因素分析. 護理學報, 2017, 24(20): 41-44.
15. 劉銳, 陳偉偉, 黃坤, 等. MNA-SF 和 NRS2002 評估老年惡性梗阻性黃疸病人營養狀況及其影響因素的應用研究. 腸外與腸內營養, 2019, 26(2): 74-78.
16. 王曉娟, 方向陽. 慢性阻塞性肺疾病全球倡議 2019: 慢性阻塞性肺疾病診斷、治療與預防全球策略解讀. 中國全科醫學, 2019, 22(18): 2141-2149.
17. 劉絲雨. AECOPD 復發再入院的危險因素分析研究進展. 臨床肺科雜志, 2017, 6(22): 1126-1130.
18. 陳虎, 焦雅輝, 梁銘會, 等. 北京市五家大型綜合醫院“重返類”指標的比較分析. 中國衛生質量管理, 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. 蔡柏薔, 陳榮昌. 慢性阻塞性肺疾病急性加重(AECOPD)診治中國專家共識(2017 年更新版). 國際呼吸雜志, 2017, 37(14): 1041-1057.
21. 李燕, 劉志燕, 黃美珍. 老年 COPD 患者營養風險現狀及其影響因素. 國際護理學雜志, 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, 陳正賢, 甘兵. 慢性阻塞性肺疾病患者 30 日再入院率與體重指數的關系分析. 中國醫學創新, 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. 羅倩, 王連紅, 韓碟, 等. 老年慢性阻塞性肺疾病患者自我管理知識–態度–行為現狀及影響因素的研究. 中國呼吸與危重監護雜志, 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, 梁彩韻, 丁贊. 慢性阻塞性肺疾病急性加重與季節變化及氣象因素的相關性研究. 汕頭大學醫學院學報, 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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