Risk prediction models for prognosis in patients with idiopathic pulmonary fibrosis: a systematic review_Chinese Journal of Evidence-Based Medicine

Authors：

ZHAI Keke ^1,2 , ZANG Danyang ^1,2 , YANG Shuguang ^1,2 , XU Lili ^1,2 , YAN Ruonan ¹ ,  YU Xueqing ^1,2

1. The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, P. R. China;
2. Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases co-constructed by Henan province & Education Ministry of P. R. China, Zhengzhou 450046, P. R. China;

Corresponding author：

YU Xueqing, Email: yxqshi@163.com

Keywords：

Idiopathic pulmonary fibrosis; Prognosis; Risk prediction; Model; Systematic review

DOI：

10.7507/1672-2531.202505008

Video：

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Objective To systematically evaluate the prognostic prediction models for Idiopathic Pulmonary Fibrosis (IPF). Methods A computer-based search was conducted in the PubMed, Embase, Web of Science, and Cochrane Library databases for literature relevant to the research objective, with the search period ranging from database inception to Jun 2025. Two researchers independently screened the articles. Data were extracted according to the key assessment and data extraction checklist for systematic reviews of prediction models (CHARMS). The risk of bias and applicability of the models were assessed using the PROBAST (Prediction model Risk of Bias Assessment Tool). The quality of model reporting was evaluated using the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) checklist. Results A total of 49 studies were included, of which 26 (53.06%) reported both model development and validation. The most common predictors included gender, age, diffusing capacity for carbon monoxide, forced vital capacity (FVC), and FVC percentage of predicted value. In terms of bias risk, 32 studies (65.31%) were classified as high risk of bias, mainly due to factors related to study subjects and predictors. Regarding applicability, 26 studies (53.06%) were rated as high risk, 11 studies (22.45%) were rated as unclear, and only 12 studies (24.49%) were rated as low risk, suggesting limited clinical applicability of the models. As for reporting quality, existing models showed generally insufficient adherence to the TRIPOD statement, especially in key areas such as research methods and result reporting, where normative issues were prominent. Of the 22 signaling questions in the TRIPOD checklist, most studies achieved only moderate reporting quality, with 8 signaling questions (1, 5c, 6b, 7b, 8, 11e, 13a, 14a) showing key information omissions or vague descriptions. Conclusion Existing prognostic prediction models for IPF generally exhibit high methodological bias risk and reporting deficiencies. Future studies should control for modeling biases based on the PROBAST framework, adhere to the TRIPOD guidelines for transparent reporting, and optimize clinical applicability through external validation.

1.	Raghu G, Remy-Jardin M, Richeldi L, et al. Idiopathic pulmonary fibrosis (an update) and progressive pulmonary fibrosis in adults: an official ATS/ERS/JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med, 2022, 205(9): e18-e47.
2.	Maher TM, Bendstrup E, Dron L, et al. Global incidence and prevalence of idiopathic pulmonary fibrosis. Respir Res, 2021, 22(1): 197.
3.	Lederer DJ, Martinez FJ. Idiopathic pulmonary fibrosis. N Engl J Med, 2018, 378(19): 1811-1823.
4.	Cottin V, Schmidt A, Catella L, et al. Burden of idiopathic pulmonary fibrosis progression: a 5-year longitudinal follow-up study. PLoS One, 2017, 12(1): e0166462.
5.	Cox IA, de Graaff B, Ahmed H, et al. The economic burden of idiopathic pulmonary fibrosis in Australia: a cost of illness study. Eur J Health Econ, 2023, 24(7): 1121-1139.
6.	Steyerberg EW, Moons KG, van der Windt DA, et al. Prognosis research strategy (PROGRESS) 3: prognostic model research. PLoS Med, 2013, 10(2): e1001381.
7.	Di J, Li X, Yang J, et al. Bias and reporting quality of clinical prognostic models for idiopathic pulmonary fibrosis: a cross-sectional study. Risk Manag Healthc Policy, 2022, 15: 1189-1201.
8.	Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
9.	Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 2015, 350: g7594.
10.	Snell KIE, Levis B, Damen JAA, et al. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ, 2023, 381: e073538.
11.	Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
12.	Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
13.	Moons KG, Altman DG, Reitsma JB, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med, 2015, 162(1): W1-73.
14.	Kaul T, Damen JAA, Wynants L, et al. Assessing the quality of prediction models in health care using the prediction model risk of bias assessment tool (PROBAST): an evaluation of its use and practical application. J Clin Epidemiol, 2025, 181: 111732.
15.	Cross JL, Choma MA, Onofrey JA. Bias in medical AI: implications for clinical decision-making. PLOS Digit Health, 2024, 3(11): e0000651.
16.	Adam H, Balagopalan A, Alsentzer E, et al. Mitigating the impact of biased artificial intelligence in emergency decision-making. Commun Med (Lond), 2022, 2(1): 149.
17.	Guo Q, Li W, Wang J, et al. Construction and validation of a clinical prediction model for sepsis using peripheral perfusion index to predict in-hospital and 28-day mortality risk. Sci Rep, 2024, 14(1): 26827.
18.	. Chen Z, Lin Z, Lin Z, et al. The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis. Ther Adv Respir Dis, 2024, 18: 17534666241282538.
19.	Sakamoto S, Shimizu H, Isshiki T, et al. New risk scoring system for predicting 3-month mortality after acute exacerbation of idiopathic pulmonary fibrosis. Sci Rep, 2022, 12(1): 1134.
20.	Lee JH, Jang JH, Jang HJ, et al. New prognostic scoring system for mortality in idiopathic pulmonary fibrosis by modifying the gender, age, and physiology model with desaturation during the six-minute walk test. Front Med (Lausanne), 2023, 10: 1052129.
21.	Sun H, Yang X, Sun X, et al. Lung shrinking assessment on HRCT with elastic registration technique for monitoring idiopathic pulmonary fibrosis. Eur Radiol, 2023, 33(4): 2279-2288.
22.	Sadr H, Nazari M, Khodaverdian Z, et al. Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches. Eur J Med Res, 2025, 30(1): 418.
23.	Wu Y, Li P, Wang M, et al. A systematic review of mortality risk prediction models for idiopathic pulmonary fibrosis. Br J Hosp Med (Lond), 2025, 86(4): 1-22.

1. Raghu G, Remy-Jardin M, Richeldi L, et al. Idiopathic pulmonary fibrosis (an update) and progressive pulmonary fibrosis in adults: an official ATS/ERS/JRS/ALAT clinical practice guideline. Am J Respir Crit Care Med, 2022, 205(9): e18-e47.
2. Maher TM, Bendstrup E, Dron L, et al. Global incidence and prevalence of idiopathic pulmonary fibrosis. Respir Res, 2021, 22(1): 197.
3. Lederer DJ, Martinez FJ. Idiopathic pulmonary fibrosis. N Engl J Med, 2018, 378(19): 1811-1823.
4. Cottin V, Schmidt A, Catella L, et al. Burden of idiopathic pulmonary fibrosis progression: a 5-year longitudinal follow-up study. PLoS One, 2017, 12(1): e0166462.
5. Cox IA, de Graaff B, Ahmed H, et al. The economic burden of idiopathic pulmonary fibrosis in Australia: a cost of illness study. Eur J Health Econ, 2023, 24(7): 1121-1139.
6. Steyerberg EW, Moons KG, van der Windt DA, et al. Prognosis research strategy (PROGRESS) 3: prognostic model research. PLoS Med, 2013, 10(2): e1001381.
7. Di J, Li X, Yang J, et al. Bias and reporting quality of clinical prognostic models for idiopathic pulmonary fibrosis: a cross-sectional study. Risk Manag Healthc Policy, 2022, 15: 1189-1201.
8. Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.
9. Collins GS, Reitsma JB, Altman DG, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ, 2015, 350: g7594.
10. Snell KIE, Levis B, Damen JAA, et al. Transparent reporting of multivariable prediction models for individual prognosis or diagnosis: checklist for systematic reviews and meta-analyses (TRIPOD-SRMA). BMJ, 2023, 381: e073538.
11. Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10): e1001744.
12. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
13. Moons KG, Altman DG, Reitsma JB, et al. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med, 2015, 162(1): W1-73.
14. Kaul T, Damen JAA, Wynants L, et al. Assessing the quality of prediction models in health care using the prediction model risk of bias assessment tool (PROBAST): an evaluation of its use and practical application. J Clin Epidemiol, 2025, 181: 111732.
15. Cross JL, Choma MA, Onofrey JA. Bias in medical AI: implications for clinical decision-making. PLOS Digit Health, 2024, 3(11): e0000651.
16. Adam H, Balagopalan A, Alsentzer E, et al. Mitigating the impact of biased artificial intelligence in emergency decision-making. Commun Med (Lond), 2022, 2(1): 149.
17. Guo Q, Li W, Wang J, et al. Construction and validation of a clinical prediction model for sepsis using peripheral perfusion index to predict in-hospital and 28-day mortality risk. Sci Rep, 2024, 14(1): 26827.
18. . Chen Z, Lin Z, Lin Z, et al. The applications of CT with artificial intelligence in the prognostic model of idiopathic pulmonary fibrosis. Ther Adv Respir Dis, 2024, 18: 17534666241282538.
19. Sakamoto S, Shimizu H, Isshiki T, et al. New risk scoring system for predicting 3-month mortality after acute exacerbation of idiopathic pulmonary fibrosis. Sci Rep, 2022, 12(1): 1134.
20. Lee JH, Jang JH, Jang HJ, et al. New prognostic scoring system for mortality in idiopathic pulmonary fibrosis by modifying the gender, age, and physiology model with desaturation during the six-minute walk test. Front Med (Lausanne), 2023, 10: 1052129.
21. Sun H, Yang X, Sun X, et al. Lung shrinking assessment on HRCT with elastic registration technique for monitoring idiopathic pulmonary fibrosis. Eur Radiol, 2023, 33(4): 2279-2288.
22. Sadr H, Nazari M, Khodaverdian Z, et al. Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches. Eur J Med Res, 2025, 30(1): 418.
23. Wu Y, Li P, Wang M, et al. A systematic review of mortality risk prediction models for idiopathic pulmonary fibrosis. Br J Hosp Med (Lond), 2025, 86(4): 1-22.

Chinese Journal of Evidence-Based Medicine

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