Interpretation of the TRIPOD statement: a reporting guideline for multivariable prediction model for individual prognosis or diagnosis_Chinese Journal of Evidence-Based Medicine

Authors：

CAO Yulong ¹ , SHAN Jiao ² , GONG Zhizhong ³ , KUANG Jiqiu ¹ ,  GAO Yan ¹

1. Department of Hospital-Acquired Infection Control, Peking University People’s Hospital, Beijing 100044, P.R.China;
2. Department of Hospital-Acquired Infection Control, Beijing Jishuitan Hospital, Beijing 100035, P.R.China;
3. School of Public Policy & Management, Tsinghua University, Beijing 100191, P.R.China;

Corresponding author：

GAO Yan, Email: gaoyan6384@163.com

Keywords：

Clinical prediction model; Reporting guideline; Interpretation

DOI：

10.7507/1672-2531.201912032

Video：

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In recent years, the potential value of clinical big data have been gradually realized, and disease prediction models have begun to become a hot spot in clinical research. Predictive models of different types of diseases play an increasingly important role in individual risk assessment. However, due to the lack of reporting specifications for studies on disease prediction model, the structure and quality of reports are mostly mixed. In 2015, BMJ published a paper entitled "Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement" stated that there should be a uniform study of predictive models for disease diagnosis and prognosis. This article interprets key contents of the statement to promote research and understanding of the report specification.

Citation： CAO Yulong, SHAN Jiao, GONG Zhizhong, KUANG Jiqiu, GAO Yan. Interpretation of the TRIPOD statement: a reporting guideline for multivariable prediction model for individual prognosis or diagnosis. Chinese Journal of Evidence-Based Medicine, 2020, 20(4): 492-496. doi: 10.7507/1672-2531.201912032 Copy

1.	范美玉, 陈敏. 基于大数据的精准医疗服务体系研究. 中国医院管理, 2016, 36(1): 10-11.
2.	谷鸿秋, 周支瑞, 章仲恒, 等. 临床预测模型: 基本概念, 应用场景及研究思路. 中国循证心血管医学杂志, 2018, 10(12): 1454-1456+1462.
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4.	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-W73.
5.	van Walraven C, Jackson TD, Daneman N. Derivation and validation of the surgical site infections risk model using health administrative data. Infect Control Hosp Epidemiol, 2016, 37(4): 455-465.
6.	Imaizumi T, Nakatochi M, Akiyama S, et al. Urinary podocalyxin as a biomarker to diagnose membranous nephropathy. PLoS One, 2016, 11(9): e0163507.
7.	Eom BW, Ryu KW, Nam BH, et al. Survival nomogram for curatively resected Korean gastric cancer patients: multicenter retrospective analysis with external validation. PLoS One, 2015, 10(2): e0119671.
8.	Greving JP, Wermer MJ, Brown RD Jr, et al. Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: a pooled analysis of six prospective cohort studies. Lancet Neurol, 2014, 13(1): 59-66.
9.	Debray TP, Vergouwe Y, Koffijberg H, et al. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol, 2015, 68(3): 279-289.
10.	Schuit E, Groenwold RH, Harrell FE Jr, et al. Unexpected predictor-outcome associations in clinical prediction research: causes and solutions. CMAJ, 2013, 185(10): E499-E505.
11.	Reese SM, Knepper B, Young HL, et al. Development of a surgical site infection prediction model in orthopaedic trauma: The Denver Health Model. Injury, 2017, 48(12): 2699-2704.
12.	Wong J, Taljaard M, Forster AJ, et al. Addition of time-dependent covariates to a survival model significantly improved predictions for daily risk of hospital death. J Eval Clin Pract, 2013, 19(2): 351-357.
13.	Lubetzky-Vilnai A, Ciol M, McCoy SW. Statistical analysis of clinical prediction rules for rehabilitation interventions: current state of the literature. Arch Phys Med Rehabil, 2014, 95(1): 188-196.
14.	Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J, 2014, 35(29): 1925-1931.
15.	Meffert PJ, Baumeister SE, Lerch MM, et al. Development, external validation, and comparative assessment of a new diagnostic score for hepatic steatosis. Am J Gastroenterol, 2014, 109(9): 1404-1414.
16.	Järvinen TL, Jokihaara J, Guy P, et al. Conflicts at the heart of the FRAX tool. CMAJ, 2014, 186(3): 165-167.
17.	Binder H, Sauerbrei W, Royston P. Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: a simulation study with continuous response. Stat Med, 2013, 32(13): 2262-2277.
18.	Nijman RG, Vergouwe Y, Thompson M, et al. Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: diagnostic study. BMJ, 2013, 346: f1706.
19.	Siontis GC, Tzoulaki I, Castaldi PJ, et al. External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination. J Clin Epidemiol, 2015, 68(1): 25-34.

1. 范美玉, 陈敏. 基于大数据的精准医疗服务体系研究. 中国医院管理, 2016, 36(1): 10-11.
2. 谷鸿秋, 周支瑞, 章仲恒, 等. 临床预测模型: 基本概念, 应用场景及研究思路. 中国循证心血管医学杂志, 2018, 10(12): 1454-1456+1462.
3. 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.
4. 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-W73.
5. van Walraven C, Jackson TD, Daneman N. Derivation and validation of the surgical site infections risk model using health administrative data. Infect Control Hosp Epidemiol, 2016, 37(4): 455-465.
6. Imaizumi T, Nakatochi M, Akiyama S, et al. Urinary podocalyxin as a biomarker to diagnose membranous nephropathy. PLoS One, 2016, 11(9): e0163507.
7. Eom BW, Ryu KW, Nam BH, et al. Survival nomogram for curatively resected Korean gastric cancer patients: multicenter retrospective analysis with external validation. PLoS One, 2015, 10(2): e0119671.
8. Greving JP, Wermer MJ, Brown RD Jr, et al. Development of the PHASES score for prediction of risk of rupture of intracranial aneurysms: a pooled analysis of six prospective cohort studies. Lancet Neurol, 2014, 13(1): 59-66.
9. Debray TP, Vergouwe Y, Koffijberg H, et al. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J Clin Epidemiol, 2015, 68(3): 279-289.
10. Schuit E, Groenwold RH, Harrell FE Jr, et al. Unexpected predictor-outcome associations in clinical prediction research: causes and solutions. CMAJ, 2013, 185(10): E499-E505.
11. Reese SM, Knepper B, Young HL, et al. Development of a surgical site infection prediction model in orthopaedic trauma: The Denver Health Model. Injury, 2017, 48(12): 2699-2704.
12. Wong J, Taljaard M, Forster AJ, et al. Addition of time-dependent covariates to a survival model significantly improved predictions for daily risk of hospital death. J Eval Clin Pract, 2013, 19(2): 351-357.
13. Lubetzky-Vilnai A, Ciol M, McCoy SW. Statistical analysis of clinical prediction rules for rehabilitation interventions: current state of the literature. Arch Phys Med Rehabil, 2014, 95(1): 188-196.
14. Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. Eur Heart J, 2014, 35(29): 1925-1931.
15. Meffert PJ, Baumeister SE, Lerch MM, et al. Development, external validation, and comparative assessment of a new diagnostic score for hepatic steatosis. Am J Gastroenterol, 2014, 109(9): 1404-1414.
16. Järvinen TL, Jokihaara J, Guy P, et al. Conflicts at the heart of the FRAX tool. CMAJ, 2014, 186(3): 165-167.
17. Binder H, Sauerbrei W, Royston P. Comparison between splines and fractional polynomials for multivariable model building with continuous covariates: a simulation study with continuous response. Stat Med, 2013, 32(13): 2262-2277.
18. Nijman RG, Vergouwe Y, Thompson M, et al. Clinical prediction model to aid emergency doctors managing febrile children at risk of serious bacterial infections: diagnostic study. BMJ, 2013, 346: f1706.
19. Siontis GC, Tzoulaki I, Castaldi PJ, et al. External validation of new risk prediction models is infrequent and reveals worse prognostic discrimination. J Clin Epidemiol, 2015, 68(1): 25-34.

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