Risk prediction model construction of one year unplanned readmission in patients with chronic obstructive pulmonary disease_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

ZHU Jieyun ¹ , GAO Min ¹ , YE Changguang ² , PAN Dongzan ¹ , ZHOU Juan ¹ , MENG Xiaoning ¹ , HE Lei ² ,  LU Zhao ¹ , CAI Zhaoqiang ²

1. International Medical Department, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi 530021, P. R. China;
2. Department of Respiratory and Critical Care Medicine, HePu People's Hospital, Hepu, Guangxi 536100, P. R. China;

Corresponding author：

LU Zhao, Email: 170132909@qq.com

Keywords：

Chronic obstructive pulmonary disease; nomogram model; readmission within one year

DOI：

10.7507/1671-6205.202405118

Video：

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Objective To investigate the influencing factors of unplanned readmission in patients with chronic obstructive pulmonary disease (COPD) within 1 year, construct a risk prediction model and evaluate its effect. Methods Clinical data of 403 inpatients with COPD were continuously collected from January 2023 to May 2023, including 170 cases in the readmission group and 233 cases in the non readmission group. LASSO regression was applied to screen the optimized variables and multivariate logistic regression analyses were applied to explore the risk factors of unplanned readmission in patients with COPD within 1 year. After that a nomogram prediction model was constructed and evaluated its discrimination, calibration, and clinical applicability. Results The incidence of unplanned readmission in patients with COPD within 1 year was 42.2%. Respiratory failure, number of acute exacerbation in the last year, creatinine and white blood cell count were risk factors for unplanned admission of patients with COPD within one year (P<0.05). Creatinine, white blood cell count, the number of acute exacerbation in the last year, the course of disease, concomitant respiratory failure and high uric acid were included in the nomogram model, the area under curve (AUC) and its 95% confidential interval (CI) of the nomogram model was 0.687 (0.636 - 0.739), with the sensitivity, specificity, and accuracy were 0.824, 0.742 and 0.603, respectively. The AUC of the nomogram after re-sampling 1 000 times was 0.687 (0.634 - 0.739). The calibration curve showed a high degree of three line overlap and the clinical decision curve showed that the nomogram model provided better net benefits than the treat-all tactics or the treat-none tactics with threshold probabilities of 15.0% - 55.0%. Conclusion The nomogram model constructed based on creatinine, white blood cell count, the number of acute exacerbation in the last year, the course of disease, concomitant respiratory failure and high uric acid has good predictive value for unplanned readmission in patients with COPD within 1 year.

Citation： ZHU Jieyun, GAO Min, YE Changguang, PAN Dongzan, ZHOU Juan, MENG Xiaoning, HE Lei, LU Zhao, CAI Zhaoqiang. Risk prediction model construction of one year unplanned readmission in patients with chronic obstructive pulmonary disease. Chinese Journal of Respiratory and Critical Care Medicine, 2025, 24(1): 1-8. doi: 10.7507/1671-6205.202405118 Copy

1.	《慢性阻塞性肺疾病诊治指南(2021年修订版)》诊断要点. 实用心脑肺血管病杂志, 2021, 29(6): 134.
2.	胡国平, 李玉群, 吴泽龙, 等. 慢性阻塞性肺疾病急性加重与慢性阻塞性肺疾病合并社区获得性肺炎的差异性研究. 中国临床新医学, 2018, 11(7): 629-632.
3.	张紫薇, 詹立睿, 李文, 等. 慢性阻塞性肺疾病急性加重风险预测模型的系统评价. 中国呼吸与危重监护杂志, 2022, 21(9): 622-628.
4.	Ko FW, Chan KP, Hui DS, et al. Acute exacerbation of COPD. Respirology, 2016, 21(7): 1152-1165.
5.	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.
6.	周丹, 陈灵敏, 陈钢强, 等. 构建Nomogram预测模型探讨慢性阻塞性肺疾病患者并发呼吸衰竭的危险因素. 中国急救医学, 2023, 43(5): 383-387.
7.	Balachandran VP, Gonen M, Smith JJ, et al. Nomograms in oncology: more than meets the eye. Lancet Oncol, 2015, 16(4): e173-e180.
8.	林泽辉. 慢性阻塞性肺疾病急性加重患者院内死亡临床预测模型的构建与验证. 广州中医药大学, 2020.
9.	张艺旋, 冯天元, 高一飞. Ⅱ型糖尿病患者伴发抑郁危险预测模型的构建及验证. 中国循证医学杂志, 2023, 23(8): 874-879.
10.	杨舒雯. 慢性阻塞性肺疾病急性加重患者再入院的相关危险因素分析. 福建医科大学, 2021.
11.	喻莹. 慢性阻塞性肺疾病患者再入院危险因素分析[D]. 南昌大学, 2023.
12.	中华医学会呼吸病学分会慢性阻塞性肺疾病学组, 中国医师协会呼吸医师分会慢性阻塞性肺疾病工作委员会. 慢性阻塞性肺疾病诊治指南(2021年修订版). 中华结核和呼吸杂志, 2021, 44(3): 170-205.
13.	Wang C, Xu JY, Yang L, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] Study) : a national cross-sectional study. Lancet, 2018, 391(10131): 1706-1717.
14.	GBD Chronic Respiratory Disease Collaborators. Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med, 2020, 8(6): 585-596.
15.	Fang X, Wang X, Bai C. COPD in China: the burden and importance of proper management. Chest, 2011, 139(4): 920-929.
16.	Vogelmeier CF, Criner GJ, Martinez FJ, et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. GOLD Executive Summary. Am J Respir Crit Care Med, 2017, 195(5): 557-582.
17.	Xu T, Sun W, Zhao H, et al. Characteristics of 12-Month Readmission for Hospitalized Patients with COPD: A Propensity Score Matched Analysis of Prospective Multicenter Study. Int J Chron Obstruct Pulmon Dis, 2022, 17: 2329-2341.
18.	Suissa S, Dell'Aniello S, Ernst P. Long-term natural history of chronic obstructive pulmonary disease: severe exacerbations and mortality. Thorax, 2012, 67(11): 957-963.
19.	Bandini M, Fossati N, Briganti A. Nomograms in urologic oncology, advantages and disadvantages. Curr Opin Urol, 2019, 29(1): 42-51.
20.	张瑞, 吴珍珍, 常艳, 等. 老年慢性阻塞性肺疾病患者30天内急性加重再入院风险预测模型的构建与验证. 中国呼吸与危重监护杂志, 2021, 20(7): 457-464.
21.	康林, 王焱莘, 祝莹, 等. 慢性阻塞性肺疾病并发社区获得性肺炎患者病原菌分布影像学特点及并发呼吸衰竭的影响因素分析. 河北医学, 2023, 29(7): 1196-1201.
22.	李方方, 袁晓雨, 张铁栓. 慢性阻塞性肺疾病急性加重期患者血脂水平与PCTIL-6和CRP含量的相关性研究. 中国临床新医学, 2020, 13(3): 280-284.
23.	陈康勰, 黄耀光, 李辉, 等. 常用血常规指标对慢性阻塞性肺疾病急性加重的诊断价值. 中国全科医学, 2020, 23(13): 1663-1665,1671.
24.	黄若新. 中性粒细胞/淋巴细胞比值、降钙素原、D-二聚体在慢性阻塞性肺疾病急性加重合并呼吸衰竭病情评估及疾病转归的预测价值. 中国医药科学, 2022, 12(24): 156-160.
25.	顾馨雨, 於江泉, 杨艳霞, 等. 慢性阻塞性肺疾病急性加重期并发呼吸衰竭危险因素的Meta分析. 实用心脑肺血管病杂志, 2023, 31(5): 82-88,95.
26.	喻婷, 徐子文, 邵爱丹, 等. 肾功能、血脂与慢性阻塞性肺疾病预后相关性的研究进展. 医学信息, 2023, 36(23): 184-188.
27.	曾环峰, 孙杰. 慢性阻塞性肺疾病合并肾功能不全的关系研究. 医学信息, 2022, 35(13): 46-48.
28.	Mapel DW, Marton JP. Prevalence of renal and hepatobiliary disease, laboratory abnormalities, and potentially toxic medication exposures among persons with COPD. Int J Chron Obstruct Pulmon Dis, 2013, 8: 127-134.
29.	Durmus Kocak N, Sasak G, Aka Akturk U, et al. Serum uric acid levels and uric acid/creatinine ratios in stable chronic obstructive pulmonary disease (COPD) patients: are these parameters efficient predictors of patients at risk for exacerbation and/or severity of disease?. Med Sci Monit, 2016, 22: 4169-4176.
30.	Rumora L, Hlapčić I, Popović-Grle S, et al. Uric acid and uric acid to creatinine ratio in the assessment of chronic obstructive pulmonary disease: Potential biomarkers in multicomponent models comprising IL-1beta. PLoS One, 2020, 15(6): e0234363.

1. 《慢性阻塞性肺疾病诊治指南(2021年修订版)》诊断要点. 实用心脑肺血管病杂志, 2021, 29(6): 134.
2. 胡国平, 李玉群, 吴泽龙, 等. 慢性阻塞性肺疾病急性加重与慢性阻塞性肺疾病合并社区获得性肺炎的差异性研究. 中国临床新医学, 2018, 11(7): 629-632.
3. 张紫薇, 詹立睿, 李文, 等. 慢性阻塞性肺疾病急性加重风险预测模型的系统评价. 中国呼吸与危重监护杂志, 2022, 21(9): 622-628.
4. Ko FW, Chan KP, Hui DS, et al. Acute exacerbation of COPD. Respirology, 2016, 21(7): 1152-1165.
5. 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.
6. 周丹, 陈灵敏, 陈钢强, 等. 构建Nomogram预测模型探讨慢性阻塞性肺疾病患者并发呼吸衰竭的危险因素. 中国急救医学, 2023, 43(5): 383-387.
7. Balachandran VP, Gonen M, Smith JJ, et al. Nomograms in oncology: more than meets the eye. Lancet Oncol, 2015, 16(4): e173-e180.
8. 林泽辉. 慢性阻塞性肺疾病急性加重患者院内死亡临床预测模型的构建与验证. 广州中医药大学, 2020.
9. 张艺旋, 冯天元, 高一飞. Ⅱ型糖尿病患者伴发抑郁危险预测模型的构建及验证. 中国循证医学杂志, 2023, 23(8): 874-879.
10. 杨舒雯. 慢性阻塞性肺疾病急性加重患者再入院的相关危险因素分析. 福建医科大学, 2021.
11. 喻莹. 慢性阻塞性肺疾病患者再入院危险因素分析[D]. 南昌大学, 2023.
12. 中华医学会呼吸病学分会慢性阻塞性肺疾病学组, 中国医师协会呼吸医师分会慢性阻塞性肺疾病工作委员会. 慢性阻塞性肺疾病诊治指南(2021年修订版). 中华结核和呼吸杂志, 2021, 44(3): 170-205.
13. Wang C, Xu JY, Yang L, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] Study) : a national cross-sectional study. Lancet, 2018, 391(10131): 1706-1717.
14. GBD Chronic Respiratory Disease Collaborators. Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med, 2020, 8(6): 585-596.
15. Fang X, Wang X, Bai C. COPD in China: the burden and importance of proper management. Chest, 2011, 139(4): 920-929.
16. Vogelmeier CF, Criner GJ, Martinez FJ, et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. GOLD Executive Summary. Am J Respir Crit Care Med, 2017, 195(5): 557-582.
17. Xu T, Sun W, Zhao H, et al. Characteristics of 12-Month Readmission for Hospitalized Patients with COPD: A Propensity Score Matched Analysis of Prospective Multicenter Study. Int J Chron Obstruct Pulmon Dis, 2022, 17: 2329-2341.
18. Suissa S, Dell'Aniello S, Ernst P. Long-term natural history of chronic obstructive pulmonary disease: severe exacerbations and mortality. Thorax, 2012, 67(11): 957-963.
19. Bandini M, Fossati N, Briganti A. Nomograms in urologic oncology, advantages and disadvantages. Curr Opin Urol, 2019, 29(1): 42-51.
20. 张瑞, 吴珍珍, 常艳, 等. 老年慢性阻塞性肺疾病患者30天内急性加重再入院风险预测模型的构建与验证. 中国呼吸与危重监护杂志, 2021, 20(7): 457-464.
21. 康林, 王焱莘, 祝莹, 等. 慢性阻塞性肺疾病并发社区获得性肺炎患者病原菌分布影像学特点及并发呼吸衰竭的影响因素分析. 河北医学, 2023, 29(7): 1196-1201.
22. 李方方, 袁晓雨, 张铁栓. 慢性阻塞性肺疾病急性加重期患者血脂水平与PCTIL-6和CRP含量的相关性研究. 中国临床新医学, 2020, 13(3): 280-284.
23. 陈康勰, 黄耀光, 李辉, 等. 常用血常规指标对慢性阻塞性肺疾病急性加重的诊断价值. 中国全科医学, 2020, 23(13): 1663-1665,1671.
24. 黄若新. 中性粒细胞/淋巴细胞比值、降钙素原、D-二聚体在慢性阻塞性肺疾病急性加重合并呼吸衰竭病情评估及疾病转归的预测价值. 中国医药科学, 2022, 12(24): 156-160.
25. 顾馨雨, 於江泉, 杨艳霞, 等. 慢性阻塞性肺疾病急性加重期并发呼吸衰竭危险因素的Meta分析. 实用心脑肺血管病杂志, 2023, 31(5): 82-88,95.
26. 喻婷, 徐子文, 邵爱丹, 等. 肾功能、血脂与慢性阻塞性肺疾病预后相关性的研究进展. 医学信息, 2023, 36(23): 184-188.
27. 曾环峰, 孙杰. 慢性阻塞性肺疾病合并肾功能不全的关系研究. 医学信息, 2022, 35(13): 46-48.
28. Mapel DW, Marton JP. Prevalence of renal and hepatobiliary disease, laboratory abnormalities, and potentially toxic medication exposures among persons with COPD. Int J Chron Obstruct Pulmon Dis, 2013, 8: 127-134.
29. Durmus Kocak N, Sasak G, Aka Akturk U, et al. Serum uric acid levels and uric acid/creatinine ratios in stable chronic obstructive pulmonary disease (COPD) patients: are these parameters efficient predictors of patients at risk for exacerbation and/or severity of disease?. Med Sci Monit, 2016, 22: 4169-4176.
30. Rumora L, Hlapčić I, Popović-Grle S, et al. Uric acid and uric acid to creatinine ratio in the assessment of chronic obstructive pulmonary disease: Potential biomarkers in multicomponent models comprising IL-1beta. PLoS One, 2020, 15(6): e0234363.

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Risk prediction model construction of one year unplanned readmission in patients with chronic obstructive pulmonary disease

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