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.	《慢性阻塞性肺疾病診治指南(2021年修訂版)》診斷要點. 實用心腦肺血管病雜志, 2021, 29(6): 134.
2.	胡國平, 李玉群, 吳澤龍, 等. 慢性阻塞性肺疾病急性加重與慢性阻塞性肺疾病合并社區獲得性肺炎的差異性研究. 中國臨床新醫學, 2018, 11(7): 629-632.
3.	張紫薇, 詹立睿, 李文, 等. 慢性阻塞性肺疾病急性加重風險預測模型的系統評價. 中國呼吸與危重監護雜志, 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, 陳靈敏, 陳鋼強, 等. 構建Nomogram預測模型探討慢性阻塞性肺疾病患者并發呼吸衰竭的危險因素. 中國急救醫學, 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.	林澤輝. 慢性阻塞性肺疾病急性加重患者院內死亡臨床預測模型的構建與驗證. 廣州中醫藥大學, 2020.
9.	張藝旋, 馮天元, 高一飛. Ⅱ型糖尿病患者伴發抑郁危險預測模型的構建及驗證. 中國循證醫學雜志, 2023, 23(8): 874-879.
10.	楊舒雯. 慢性阻塞性肺疾病急性加重患者再入院的相關危險因素分析. 福建醫科大學, 2021.
11.	喻瑩. 慢性阻塞性肺疾病患者再入院危險因素分析[D]. 南昌大學, 2023.
12.	中華醫學會呼吸病學分會慢性阻塞性肺疾病學組, 中國醫師協會呼吸醫師分會慢性阻塞性肺疾病工作委員會. 慢性阻塞性肺疾病診治指南(2021年修訂版). 中華結核和呼吸雜志, 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.	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.
21.	康林, 王焱莘, 祝瑩, 等. 慢性阻塞性肺疾病并發社區獲得性肺炎患者病原菌分布影像學特點及并發呼吸衰竭的影響因素分析. 河北醫學, 2023, 29(7): 1196-1201.
22.	李方方, 袁曉雨, 張鐵栓. 慢性阻塞性肺疾病急性加重期患者血脂水平與PCTIL-6和CRP含量的相關性研究. 中國臨床新醫學, 2020, 13(3): 280-284.
23.	陳康勰, 黃耀光, 李輝, 等. 常用血常規指標對慢性阻塞性肺疾病急性加重的診斷價值. 中國全科醫學, 2020, 23(13): 1663-1665,1671.
24.	黃若新. 中性粒細胞/淋巴細胞比值、降鈣素原、D-二聚體在慢性阻塞性肺疾病急性加重合并呼吸衰竭病情評估及疾病轉歸的預測價值. 中國醫藥科學, 2022, 12(24): 156-160.
25.	顧馨雨, 於江泉, 楊艷霞, 等. 慢性阻塞性肺疾病急性加重期并發呼吸衰竭危險因素的Meta分析. 實用心腦肺血管病雜志, 2023, 31(5): 82-88,95.
26.	喻婷, 徐子文, 邵愛丹, 等. 腎功能、血脂與慢性阻塞性肺疾病預后相關性的研究進展. 醫學信息, 2023, 36(23): 184-188.
27.	曾環峰, 孫杰. 慢性阻塞性肺疾病合并腎功能不全的關系研究. 醫學信息, 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, Hlap?i? I, Popovi?-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. 《慢性阻塞性肺疾病診治指南(2021年修訂版)》診斷要點. 實用心腦肺血管病雜志, 2021, 29(6): 134.
2. 胡國平, 李玉群, 吳澤龍, 等. 慢性阻塞性肺疾病急性加重與慢性阻塞性肺疾病合并社區獲得性肺炎的差異性研究. 中國臨床新醫學, 2018, 11(7): 629-632.
3. 張紫薇, 詹立睿, 李文, 等. 慢性阻塞性肺疾病急性加重風險預測模型的系統評價. 中國呼吸與危重監護雜志, 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, 陳靈敏, 陳鋼強, 等. 構建Nomogram預測模型探討慢性阻塞性肺疾病患者并發呼吸衰竭的危險因素. 中國急救醫學, 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. 林澤輝. 慢性阻塞性肺疾病急性加重患者院內死亡臨床預測模型的構建與驗證. 廣州中醫藥大學, 2020.
9. 張藝旋, 馮天元, 高一飛. Ⅱ型糖尿病患者伴發抑郁危險預測模型的構建及驗證. 中國循證醫學雜志, 2023, 23(8): 874-879.
10. 楊舒雯. 慢性阻塞性肺疾病急性加重患者再入院的相關危險因素分析. 福建醫科大學, 2021.
11. 喻瑩. 慢性阻塞性肺疾病患者再入院危險因素分析[D]. 南昌大學, 2023.
12. 中華醫學會呼吸病學分會慢性阻塞性肺疾病學組, 中國醫師協會呼吸醫師分會慢性阻塞性肺疾病工作委員會. 慢性阻塞性肺疾病診治指南(2021年修訂版). 中華結核和呼吸雜志, 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. 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.
21. 康林, 王焱莘, 祝瑩, 等. 慢性阻塞性肺疾病并發社區獲得性肺炎患者病原菌分布影像學特點及并發呼吸衰竭的影響因素分析. 河北醫學, 2023, 29(7): 1196-1201.
22. 李方方, 袁曉雨, 張鐵栓. 慢性阻塞性肺疾病急性加重期患者血脂水平與PCTIL-6和CRP含量的相關性研究. 中國臨床新醫學, 2020, 13(3): 280-284.
23. 陳康勰, 黃耀光, 李輝, 等. 常用血常規指標對慢性阻塞性肺疾病急性加重的診斷價值. 中國全科醫學, 2020, 23(13): 1663-1665,1671.
24. 黃若新. 中性粒細胞/淋巴細胞比值、降鈣素原、D-二聚體在慢性阻塞性肺疾病急性加重合并呼吸衰竭病情評估及疾病轉歸的預測價值. 中國醫藥科學, 2022, 12(24): 156-160.
25. 顧馨雨, 於江泉, 楊艷霞, 等. 慢性阻塞性肺疾病急性加重期并發呼吸衰竭危險因素的Meta分析. 實用心腦肺血管病雜志, 2023, 31(5): 82-88,95.
26. 喻婷, 徐子文, 邵愛丹, 等. 腎功能、血脂與慢性阻塞性肺疾病預后相關性的研究進展. 醫學信息, 2023, 36(23): 184-188.
27. 曾環峰, 孫杰. 慢性阻塞性肺疾病合并腎功能不全的關系研究. 醫學信息, 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, Hlap?i? I, Popovi?-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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