| 1 |
World Health Organization. Chronic obstructive pulmonary disease (COPD)[EB/OL].[2026-03-17].
URL
|
| 2 |
Global Initiative For Chronic Obstructive Lung Disease. Pocket guide to COPD diagnosis, management, and prevention: a guide for health care professionals: 2025 report[EB/OL]. (2024-12-13)[2026-03-17].
URL
|
| 3 |
Agustí A, Celli BR, Criner GJ, et al. Global initiative for chronic obstructive lung disease 2023 report: GOLD executive summary[J]. Am J Respir Crit Care Med, 2023, 207(7): 819-837.
|
| 4 |
罗宸婧，盛芳芳，张静，等. 以全球视角对中国慢性阻塞性肺疾病负担及未来趋势分析[J/OL]. 中华肺部疾病杂志(电子版), 2024, 17(6): 855-860.
|
| 5 |
Boers E, Barrett M, Su JG, et al. Global burden of chronic obstructive pulmonary disease through 2050[J]. JAMA Netw Open, 2023, 6(12): e2346598.
|
| 6 |
Macintyre NR. Acute hypercapnic respiratory failure in COPD[J]. Respir Care, 2023, 68(7): 973-982.
|
| 7 |
Rochwerg B, Brochard L, Elliott MW, et al. Official ERS/ATS clinical practice guidelines: noninvasive ventilation for acute respiratory failure[J]. Eur Respir J, 2017, 50(2): 1602426.
|
| 8 |
Wedzicha JA, Miravitlles M, Hurst JR, et al. Management of COPD exacerbations: a european respiratory society/american thoracic society guideline[J]. Eur Respir J, 2017, 49(3): 1600791.
|
| 9 |
Osadnik CR, Tee VS, Carson-chahhoud KV, et al. Non-invasive ventilation for the management of acute hypercapnic respiratory failure due to exacerbation of chronic obstructive pulmonary disease[J]. Cochrane Database Syst Rev, 2017, 7(7): CD004104.
|
| 10 |
Luo Z, Li Y, Li W, et al. Effect of high-intensity vs low-intensity noninvasive positive pressure ventilation on the need for endotracheal intubation in patients with an acute exacerbation of COPD: the HAPPEN randomized clinical trial[J]. JAMA, 2024, 332(20): 1709-1722.
|
| 11 |
Jiang R, Li X, He Y, et al. Clinical efficacy of high-intensity noninvasive positive-pressure ventilation in AECOPD combined with severe type Ⅱ respiratory failure[J]. Zhong Nan Da Xue Xue Bao Yi Xue Ban, 2024, 49(2): 266-272.
|
| 12 |
Shahid H, Ahmed M, Sami S Z, et al. High-intensity versus low-intensity non-invasive ventilation in acute exacerbations of COPD: a systematic review and meta-analysis[J]. Respir Med, 2025, 247: 108277.
|
| 13 |
Zhou Z, Wang Y, Wang Y, et al. A diagnostic nomogram for predicting hypercapnic respiratory failure in patients with acute exacerbation of COPD[J]. Int J Chron Obstruct Pulmon Dis, 2024, 19: 1079-1091.
|
| 14 |
Hu S, Zhang Y, Cui Z, et al. Development and validation of a model for predicting the early occurrence of respiratory failure in ICU-admitted AECOPD patients: a retrospective analysis based on the MIMIC-Ⅳ database[J]. BMC Pulm Med, 2024, 24(1): 302.
|
| 15 |
Dong F, Ren Y, Huang Y, et al. Development and validation of risk prediction model for in-hospital mortality among patients hospitalized with AECOPD[J]. Front Med (Lausanne), 2021, 8: 630870.
|
| 16 |
Zhang Y, Chen H, Hu S, et al. Construction and validation of a nomogram prediction model for death risk in COPD complicated by hypercapnic respiratory failure in the ICU[J]. Respir Med, 2025, 245: 108188.
|
| 17 |
Zhang Y, Zheng S, Wang D, et al. Development and validation of a machine learning-based model to predict the risk of hospitalization death in hospitalized patients with AECOPD[J]. Sci Rep, 2025, 15(1): 35918.
|
| 18 |
Jia Q, Chen Y, Zen Q, et al. Development and validation of machine learning-based models for prediction of intensive care unit admission and in-hospital mortality in patients with acute exacerbations of chronic obstructive pulmonary disease[J]. Chronic Obstr Pulm Dis, 2024, 11(5): 460-471.
|
| 19 |
Zhu Y, Wang M, Gu XN, et al. Development and validation of the machine learning model for acute exacerbation of chronic obstructive pulmonary disease prediction based on inflammatory biomarkers[J]. Front Med (Lausanne), 2025, 12: 1616712.
|
| 20 |
Jiang X, Li J, Ju J, et al. Construction and validation of a machine learning-based risk prediction model for invasive mechanical ventilation in AECOPD patients complicated with respiratory failure[J]. Int J Med Inform, 2026, 208: 106244.
|
| 21 |
Christodoulou E, Ma J, Collins GS, et al. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models[J]. J Clin Epidemiol, 2019, 110: 12-22.
|
| 22 |
Collins GS, Moons KGM, Dhiman P, et al. TRIPOD＋AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods[J]. BMJ, 2024, 385: e078378.
|
| 23 |
Moons KGM, Damen JAA, Kaul T, et al. PROBAST＋AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods[J]. BMJ, 2025, 388: e082505.
|
| 24 |
Efthimiou O, Seo M, Chalkou K, et al. Developing clinical prediction models: a step-by-step guide[J]. BMJ, 2024, 386: e078276.
|
| 25 |
Zhang Y, Tan X, Hu S, et al. Relationship between systemic immune-inflammation index and risk of respiratory failure and death in COPD: a retrospective cohort study based on the MIMIC-Ⅳ database[J]. Int J Chron Obstruct Pulmon Dis, 2024, 19: 459-473.
|
| 26 |
Neder JA, Kirby M, Santyr G, et al. V/Q mismatch: a novel target for COPD treatment[J]. Chest, 2022, 162(5): 1030-1047.
|
| 27 |
Csoma B, Vulpi MR, Dragonieri S, et al. Hypercapnia in COPD: causes, consequences, and therapy[J]. J Clin Med, 2022, 11(11): 3180.
|
| 28 |
Koopman M, Janssen DJA, Franssen FME, et al. Lung hyperinflation as treatable trait in chronic obstructive pulmonary disease: a narrative review[J]. Int J Chron Obstruct Pulmon Dis, 2024, 19: 1561-1578.
|
| 29 |
Wang X, Zhang X, Li Y, et al. Elevated triglyceride-glucose index predicts poor clinical outcomes in critically ill AECOPD patients[J]. Int J Chron Obstruct Pulmon Dis, 2024, 19: 2217-2228.
|
| 30 |
Sievering AW, Wohlmuth P, Gessler N, et al. Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission[J]. BMC Med Inform Decis Mak, 2022, 22(1): 309.
|
| 31 |
Andaur Navarro CL, Damen JAA, Takada T, et al. Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review[J]. BMJ, 2021, 375: n2281.
|
| 32 |
Li W, Zhu Y, Li L, et al. Development and validation of a risk prediction model for respiratory failure in AECOPD patients[J]. 徐州医科大学学报，2025, 45(9): 666-672.
|
| 33 |
Chen L, Chen L, Zheng H, et al. Emergency admission parameters for predicting in-hospital mortality in patients with acute exacerbations of chronic obstructive pulmonary disease with hypercapnic respiratory failure[J]. BMC Pulm Med, 2021, 21(1): 258.
|
| 34 |
陈晓亮，张庆隆. 个体化预测COPD急性加重期并发呼吸衰竭的风险列线图模型的建立[J]. 中华现代护理杂志，2021, 27(25): 3477-3482.
|
| 35 |
Wang H, Zhou Y, Gong J, et al. Data-driven prediction for acute exacerbations of chronic obstructive pulmonary disease complicated with type 2 respiratory failure (Preprint)[EB/OL]. [2026-03-17]. DOI: 10.2196/preprints.33737.
URL
|
| 36 |
Zhang XH, Han MF, Teng XB, et al. Combined inflammatory markers for predicting acute exacerbations in chronic obstructive pulmonary disease with respiratory failure[J]. J Inflamm Res, 2025, 18: 2513-2520.
|
| 37 |
Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies[J]. Ann Intern Med, 2019, 170(1): 51-58.
|
| 38 |
Harrell FE. Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis[M]. 2nd ed, Cham: Springer, 2015: 66-121.
|
| 39 |
Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating[M]. 2nd ed, Cham: Springer, 2019: 78-102.
|
| 40 |
Zhu J, Song Q, Shen Y, et al. Construction of a risk prediction model for in-hospital mortality in patients with acute exacerbation of chronic obstructive pulmonary disease[J]. Eur J Med Res, 2026, 31(1): 298.
|
| 41 |
Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models[J]. Med Decis Making, 2006, 26(6): 565-574.
|