PROBAST: a tool for assessing risk of bias in the study of diagnostic or prognostic multi-factorial predictive models_Chinese Journal of Evidence-Based Medicine

Authors：

CHEN Xiangping ¹ , ZHANG Yi ¹ ,  ZHUANG Yiyu ² , ZHANG Zhongheng ³

1. Department of Intensive Care Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, P.R.China;
2. Department of Nursing, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, P.R.China;
3. Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, P.R.China;

Corresponding?author：

ZHUANG Yiyu, Email: zhuangyy@zju.edu.cn

Keywords：

PROBAST; Predictive model; Systematic review; Risk of bias

DOI：

10.7507/1672-2531.201910087

Video：

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This study aims to introduce how to use the PROBAST (prediction model risk of bias assessment tool) to evaluate risk of bias and applicability of the study of diagnostic or prognostic predictive models, including the introduction of the background, the scope of application and use of the tool. This tool mainly involves the four areas of participants, predictors, outcomes and analyses. The risk of bias in the research is evaluated through the four areas, while the applicability is evaluated in the first three. PROBAST provides a standardized approach to evaluate the critical appraisal of the study of diagnostic or prognostic predictive models, which screens qualified literature for data analysis and helps to establish a scientific basis for clinical decision-making.

Citation： CHEN Xiangping, ZHANG Yi, ZHUANG Yiyu, ZHANG Zhongheng. PROBAST: a tool for assessing risk of bias in the study of diagnostic or prognostic multi-factorial predictive models. Chinese Journal of Evidence-Based Medicine, 2020, 20(6): 737-744. doi: 10.7507/1672-2531.201910087 Copy

1.	張蕊, 鄭黎強, 潘國偉. 疾病發病風險預測模型的應用與建立. 中國衛生統計, 2015, 32(4): 724-726.
2.	Grant SW, Collins GS, Nashef SAM. Statistical Primer: developing and validating a risk prediction model. Eur J Cardiothorac Surg, 2018, 54(2): 203-208.
3.	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.
4.	Haider S, Sadiq SN, Moore D, et al. Prognostic prediction models for diabetic retinopathy progression: a systematic review. Eye (Lond), 2019, 33(5): 702-713.
5.	陳金宏, 何耀, 秦銀河. 心血管病預測模型研究進展. 中華流行病學雜志, 2010, 31(11): 1311-1314.
6.	張佳妮, 于詩嘉, 史鐵梅. 風險預測模型診斷子宮內膜癌的研究進展. 中國臨床醫學影像雜志, 2009, 20(2): 115-116.
7.	廖文圣, 邵彥翔, 孫光曦, 等. 預后預測模型在臨床非轉移腎細胞癌中的應用價值. 四川大學學報(醫學版), 2019, 50(5): 720-724.
8.	徐梟喻, 王梅新, 汪惠才. 糖尿病前期人群篩查方法與風險預測模型的研究進展. 護理研究, 2017, 31(35): 4468-4471.
9.	Rabar S, Lau R, O'Flynn N, et al. Risk assessment of fragility fractures: summary of NICE guidance. BMJ, 2012, 345: e3698.
10.	Jr GD, Lloydjones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol, 2014, 63(25): 2935-2959.
11.	Grady D, Berkowitz SA. Why is a good clinical prediction rule so hard to find? Arch Intern Med, 2011, 171(19): 1701-1702.
12.	Meier K, Parrish J, D'Souza R. Prediction models for determining the success of labor induction: a systematic review. Acta Obstet Gynecol Scand, 2019, 98(9): 1100-1112.
13.	Debray TP, Damen JA, Snell KI, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ, 2017, 356: i6460.
14.	Collins GS, Reitsma JB, Altman DG, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Br J Surg, 2015, 102(3): 148-158.
15.	陶立元, 劉玨, 曾琳, 等. 針對個體的預后或診斷多因素預測模型報告規范(TRIPOD)解讀. 中華醫學雜志, 2018, 98(44): 3556-3560.
16.	鄭黎強, 張蕊. 疾病發病風險預測模型擬合度評價方法的研究進展. 中國衛生統計, 2015, 32(3): 544-546.
17.	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.
18.	王俊峰, 章仲恒, 周支瑞, 等. 臨床預測模型: 模型的驗證. 中國循證心血管醫學雜志, 2019, 11(2): 141-144.
19.	安瑩, 王艷玲. 慢性阻塞性肺疾病急性加重期患者短期預后預測模型的建立. 中華護理雜志, 2019, 54(1): 42-46.
20.	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.

1. 張蕊, 鄭黎強, 潘國偉. 疾病發病風險預測模型的應用與建立. 中國衛生統計, 2015, 32(4): 724-726.
2. Grant SW, Collins GS, Nashef SAM. Statistical Primer: developing and validating a risk prediction model. Eur J Cardiothorac Surg, 2018, 54(2): 203-208.
3. 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.
4. Haider S, Sadiq SN, Moore D, et al. Prognostic prediction models for diabetic retinopathy progression: a systematic review. Eye (Lond), 2019, 33(5): 702-713.
5. 陳金宏, 何耀, 秦銀河. 心血管病預測模型研究進展. 中華流行病學雜志, 2010, 31(11): 1311-1314.
6. 張佳妮, 于詩嘉, 史鐵梅. 風險預測模型診斷子宮內膜癌的研究進展. 中國臨床醫學影像雜志, 2009, 20(2): 115-116.
7. 廖文圣, 邵彥翔, 孫光曦, 等. 預后預測模型在臨床非轉移腎細胞癌中的應用價值. 四川大學學報(醫學版), 2019, 50(5): 720-724.
8. 徐梟喻, 王梅新, 汪惠才. 糖尿病前期人群篩查方法與風險預測模型的研究進展. 護理研究, 2017, 31(35): 4468-4471.
9. Rabar S, Lau R, O'Flynn N, et al. Risk assessment of fragility fractures: summary of NICE guidance. BMJ, 2012, 345: e3698.
10. Jr GD, Lloydjones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol, 2014, 63(25): 2935-2959.
11. Grady D, Berkowitz SA. Why is a good clinical prediction rule so hard to find? Arch Intern Med, 2011, 171(19): 1701-1702.
12. Meier K, Parrish J, D'Souza R. Prediction models for determining the success of labor induction: a systematic review. Acta Obstet Gynecol Scand, 2019, 98(9): 1100-1112.
13. Debray TP, Damen JA, Snell KI, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ, 2017, 356: i6460.
14. Collins GS, Reitsma JB, Altman DG, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Br J Surg, 2015, 102(3): 148-158.
15. 陶立元, 劉玨, 曾琳, 等. 針對個體的預后或診斷多因素預測模型報告規范(TRIPOD)解讀. 中華醫學雜志, 2018, 98(44): 3556-3560.
16. 鄭黎強, 張蕊. 疾病發病風險預測模型擬合度評價方法的研究進展. 中國衛生統計, 2015, 32(3): 544-546.
17. 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.
18. 王俊峰, 章仲恒, 周支瑞, 等. 臨床預測模型: 模型的驗證. 中國循證心血管醫學雜志, 2019, 11(2): 141-144.
19. 安瑩, 王艷玲. 慢性阻塞性肺疾病急性加重期患者短期預后預測模型的建立. 中華護理雜志, 2019, 54(1): 42-46.
20. 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.

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