Exploration and establishment of individual applicability evaluation method on traditional Chinese medicine clinical trials_Chinese Journal of Evidence-Based Medicine

Authors：

WEI Xuxu ¹ , GUAN Manke ¹ , SHI Zhaofeng ¹ , YOU Liangzhen ¹ , JIANG Yin ¹ ,  ZHAO Chen ² ,  SHANG Hongcai ^1,3

1. Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing 100700, P.R.China;
2. Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, P.R.China;
3. International Evidence-Based Chinese Medicine Institute, Beijing University of Chinese Medicine, Beijing 100029, P.R.China;

Corresponding author：

ZHAO Chen, Email: zyyzc2007@163.com; SHANG Hongcai, Email: shanghongcai@126.com

Keywords：

Traditional Chinese medicine; Clinical trial; Evidence applicability; Individualization; Raw data

DOI：

10.7507/1672-2531.202102070

Video：

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In the process of evidence-based practice, the evaluation of evidence applicability relied on the subjective judgment of clinicians, while the systematic method of which was still in lack. The complex clinical information of traditional Chinese medicine (TCM) enhanced the uncertainty and risk of applying evidence. Based on the analysis of the process of evidence-based practice, this paper introduced the method of TCM evidence applicability evaluation and used the raw data of clinical trials to develop a clinical prediction model to enable the assessment of the evidence applicability on individual patients. The establishment of individual evidence applicability evaluation method could promote the rational application of TCM evidence in the long term.

Citation： WEI Xuxu, GUAN Manke, SHI Zhaofeng, YOU Liangzhen, JIANG Yin, ZHAO Chen, SHANG Hongcai. Exploration and establishment of individual applicability evaluation method on traditional Chinese medicine clinical trials. Chinese Journal of Evidence-Based Medicine, 2021, 21(7): 858-862. doi: 10.7507/1672-2531.202102070 Copy

1.	赵晨, 刘智, 商洪才. 中医临床疗效评价差异化策略的提出—个体化研究方法学元素初探. 世界中医药, 2017, 12(6): 1221-1225.
2.	关曼柯, 魏旭煦, 赵晨, 等. 循证决策模式下中医临床个体化疗效评价研究的定位与未来发展路径. 中国循证医学杂志, 2020, 20(10): 1124-1129.
3.	Schünemann HJ, Wiercioch W, Brozek J, et al. GRADE Evidence to Decision (EtD) frameworks for adoption, adaptation, and de novo development of trustworthy recommendations: GRADE-ADOLOPMENT. J Clin Epidemiol, 2017, 81: 101-110.
4.	国家卫生计生委, 国家中医药管理局. 关于印发医疗机构临床路径管理指导原则的通知, 2017-08-30. Available at: http://www.nhc.gov.cn/cms-search/xxgk/getManuscriptXxgk.htm?id=fd506f531bd14756acffa441ea8a06b9.
5.	吴昕霞, 庄昱, 董书, 等. 临床路径管理本土化改进的策略与实践. 中国医院管理, 2019, 39(10): 34-35.
6.	李幼平, 李静, 孙鑫, 等. 循证医学在中国的起源与发展: 献给中国循证医学 2 0周年. 中国循证医学杂志, 2016, 16(1): 2-6.
7.	Tian G, Zhao C, Zhang X, et al. Evidence-based traditional Chinese medicine research: Two decades of development, its impact, and breakthrough. J Evid Based Med, 2021, 14(1): 65-74.
8.	Wang C, Schmid CH, Fielding RA, et al. Effect of tai chi versus aerobic exercise for fibromyalgia: comparative effectiveness randomized controlled trial. BMJ, 2018, 360: k851.
9.	Wang C, Cao B, Liu QQ, et al. Oseltamivir compared with the Chinese traditional therapy maxingshigan-yinqiaosan in the treatment of H1N1 influenza: a randomized trial. Ann Intern Med, 2011, 155(4): 217-225.
10.	Liu Z, Liu Y, Xu H, et al. Effect of Electroacupuncture on urinary leakage among women with stress urinary incontinence: a randomized clinical trial. JAMA, 2017, 317(24): 2493-2501.
11.	Liu Z, Yan S, Wu J, et al. Acupuncture for chronic severe functional constipation: a randomized trial. Ann Intern Med, 2016, 165(11): 761-769.
12.	Wang C, Schmid CH, Rones R, et al. A randomized trial of tai chi for fibromyalgia. N Engl J Med, 2010, 363(8): 743-754.
13.	Sackett DL, Rosenberg WM, Gray JA, et al. Evidence based medicine: what it is and what it isn't. BMJ, 1996, 312(7023): 71-72.
14.	何俐, 屈云, 李幼平. 循证医学的定义,发展,基础及实践. 中国临床康复, 2003, (4): 540-541.
15.	Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ, 2009, 339: b2700.
16.	Schulz KF, Altman DG, Moher D, et al. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ, 2010, 340: c332.
17.	Shea BJ, Reeves BC, Wells G, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ, 2017, 358: j4008.
18.	Higgins JP, Altman DG, Gøtzsche PC, et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ, 2011, 343: d5928.
19.	杜武勋, 朱明丹, 袁宏伟, 等. 病证结合, 方证相应的系统—系统的方证相应动态研究. 中国中西医结合杂志, 2012, 32(6): 839-842.
20.	郑好飞, 杨巧丽, 刘颖. 浅谈中医学思想与“精准医学”理念的相通之处. 中华中医药杂志, 2017, 32(6): 2369-2371.
21.	Rothwell PM, Warlow CP. Prediction of benefit from carotid endarterectomy in individual patients: a risk-modelling study. European Carotid Surgery Trialists' Collaborative Group. Lancet, 1999, 353(9170): 2105-2110.
22.	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.
23.	Ranstam J, Cook JA, Collins GS. Clinical prediction models. Br J Surg, 2016, 103(13): 1886.
24.	Alba AC, Agoritsas T, Walsh M, et al. Discrimination and calibration of clinical prediction models: users' guides to the medical literature. JAMA, 2017, 318(14): 1377-1384.
25.	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.
26.	Farooq V, van Klaveren D, Steyerberg EW, et al. Anatomical and clinical characteristics to guide decision making between coronary artery bypass surgery and percutaneous coronary intervention for individual patients: development and validation of SYNTAX score II. Lancet, 2013, 381(9867): 639-650.
27.	Chieffo A, Meliga E, Latib A, et al. Drug-eluting stent for left main coronary artery disease. The DELTA registry: a multicenter registry evaluating percutaneous coronary intervention versus coronary artery bypass grafting for left main treatment. JACC Cardiovasc Interv, 2012, 5(7): 718-727.
28.	刘为民, 崔学军, 何丽云, 等. 中医临床患者报告结局及其评估. 世界科学技术(中医药现代化), 2012, 14(3): 1688-1693.
29.	胡学军, 商洪才, 张伯礼, 等. 生存质量及其量表在中医药疗效评价中的应用. 天津中医药, 2004, (3): 191-193.
30.	Shi Z, Zhao C, Guan M, et al. Individualized efficiency of traditional chinese medicine for non-st segment elevation acute coronary syndrome: study protocol for observational research by the evidence-based goal attainment scale. Evid Based Complement Alternat Med, 2020, 2020: 7653040.
31.	商洪才, 李幼平, 张伯礼, 等. 中医药临床疗效个体化评价方法初探—循证目标成就量表法的提出. 中国循证医学杂志, 2007, 7(7): 537-541.
32.	McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature, 2020, 577(7788): 89-94.
33.	Fleuren LM, Klausch TLT, Zwager CL, et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med, 2020, 46(3): 383-400.
34.	Tyler NS, Mosquera-Lopez CM, Wilson LM, et al. An artificial intelligence decision support system for the management of type 1 diabetes. Nat Metab, 2020, 2(7): 612-619.
35.	Zhang X, Tian G, Shi Z, et al. Initiate Danhong Injection before or after percutaneous coronary intervention for microvascular obstruction in ST-elevation myocardial infarction (DIRECTION): study protocol for a randomized controlled trial. Trials, 2020, 21(1): 48.

1. 赵晨, 刘智, 商洪才. 中医临床疗效评价差异化策略的提出—个体化研究方法学元素初探. 世界中医药, 2017, 12(6): 1221-1225.
2. 关曼柯, 魏旭煦, 赵晨, 等. 循证决策模式下中医临床个体化疗效评价研究的定位与未来发展路径. 中国循证医学杂志, 2020, 20(10): 1124-1129.
3. Schünemann HJ, Wiercioch W, Brozek J, et al. GRADE Evidence to Decision (EtD) frameworks for adoption, adaptation, and de novo development of trustworthy recommendations: GRADE-ADOLOPMENT. J Clin Epidemiol, 2017, 81: 101-110.
4. 国家卫生计生委, 国家中医药管理局. 关于印发医疗机构临床路径管理指导原则的通知, 2017-08-30. Available at: http://www.nhc.gov.cn/cms-search/xxgk/getManuscriptXxgk.htm?id=fd506f531bd14756acffa441ea8a06b9.
5. 吴昕霞, 庄昱, 董书, 等. 临床路径管理本土化改进的策略与实践. 中国医院管理, 2019, 39(10): 34-35.
6. 李幼平, 李静, 孙鑫, 等. 循证医学在中国的起源与发展: 献给中国循证医学 2 0周年. 中国循证医学杂志, 2016, 16(1): 2-6.
7. Tian G, Zhao C, Zhang X, et al. Evidence-based traditional Chinese medicine research: Two decades of development, its impact, and breakthrough. J Evid Based Med, 2021, 14(1): 65-74.
8. Wang C, Schmid CH, Fielding RA, et al. Effect of tai chi versus aerobic exercise for fibromyalgia: comparative effectiveness randomized controlled trial. BMJ, 2018, 360: k851.
9. Wang C, Cao B, Liu QQ, et al. Oseltamivir compared with the Chinese traditional therapy maxingshigan-yinqiaosan in the treatment of H1N1 influenza: a randomized trial. Ann Intern Med, 2011, 155(4): 217-225.
10. Liu Z, Liu Y, Xu H, et al. Effect of Electroacupuncture on urinary leakage among women with stress urinary incontinence: a randomized clinical trial. JAMA, 2017, 317(24): 2493-2501.
11. Liu Z, Yan S, Wu J, et al. Acupuncture for chronic severe functional constipation: a randomized trial. Ann Intern Med, 2016, 165(11): 761-769.
12. Wang C, Schmid CH, Rones R, et al. A randomized trial of tai chi for fibromyalgia. N Engl J Med, 2010, 363(8): 743-754.
13. Sackett DL, Rosenberg WM, Gray JA, et al. Evidence based medicine: what it is and what it isn't. BMJ, 1996, 312(7023): 71-72.
14. 何俐, 屈云, 李幼平. 循证医学的定义,发展,基础及实践. 中国临床康复, 2003, (4): 540-541.
15. Liberati A, Altman DG, Tetzlaff J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ, 2009, 339: b2700.
16. Schulz KF, Altman DG, Moher D, et al. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMJ, 2010, 340: c332.
17. Shea BJ, Reeves BC, Wells G, et al. AMSTAR 2: a critical appraisal tool for systematic reviews that include randomised or non-randomised studies of healthcare interventions, or both. BMJ, 2017, 358: j4008.
18. Higgins JP, Altman DG, Gøtzsche PC, et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ, 2011, 343: d5928.
19. 杜武勋, 朱明丹, 袁宏伟, 等. 病证结合, 方证相应的系统—系统的方证相应动态研究. 中国中西医结合杂志, 2012, 32(6): 839-842.
20. 郑好飞, 杨巧丽, 刘颖. 浅谈中医学思想与“精准医学”理念的相通之处. 中华中医药杂志, 2017, 32(6): 2369-2371.
21. Rothwell PM, Warlow CP. Prediction of benefit from carotid endarterectomy in individual patients: a risk-modelling study. European Carotid Surgery Trialists' Collaborative Group. Lancet, 1999, 353(9170): 2105-2110.
22. 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.
23. Ranstam J, Cook JA, Collins GS. Clinical prediction models. Br J Surg, 2016, 103(13): 1886.
24. Alba AC, Agoritsas T, Walsh M, et al. Discrimination and calibration of clinical prediction models: users' guides to the medical literature. JAMA, 2017, 318(14): 1377-1384.
25. 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.
26. Farooq V, van Klaveren D, Steyerberg EW, et al. Anatomical and clinical characteristics to guide decision making between coronary artery bypass surgery and percutaneous coronary intervention for individual patients: development and validation of SYNTAX score II. Lancet, 2013, 381(9867): 639-650.
27. Chieffo A, Meliga E, Latib A, et al. Drug-eluting stent for left main coronary artery disease. The DELTA registry: a multicenter registry evaluating percutaneous coronary intervention versus coronary artery bypass grafting for left main treatment. JACC Cardiovasc Interv, 2012, 5(7): 718-727.
28. 刘为民, 崔学军, 何丽云, 等. 中医临床患者报告结局及其评估. 世界科学技术(中医药现代化), 2012, 14(3): 1688-1693.
29. 胡学军, 商洪才, 张伯礼, 等. 生存质量及其量表在中医药疗效评价中的应用. 天津中医药, 2004, (3): 191-193.
30. Shi Z, Zhao C, Guan M, et al. Individualized efficiency of traditional chinese medicine for non-st segment elevation acute coronary syndrome: study protocol for observational research by the evidence-based goal attainment scale. Evid Based Complement Alternat Med, 2020, 2020: 7653040.
31. 商洪才, 李幼平, 张伯礼, 等. 中医药临床疗效个体化评价方法初探—循证目标成就量表法的提出. 中国循证医学杂志, 2007, 7(7): 537-541.
32. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature, 2020, 577(7788): 89-94.
33. Fleuren LM, Klausch TLT, Zwager CL, et al. Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy. Intensive Care Med, 2020, 46(3): 383-400.
34. Tyler NS, Mosquera-Lopez CM, Wilson LM, et al. An artificial intelligence decision support system for the management of type 1 diabetes. Nat Metab, 2020, 2(7): 612-619.
35. Zhang X, Tian G, Shi Z, et al. Initiate Danhong Injection before or after percutaneous coronary intervention for microvascular obstruction in ST-elevation myocardial infarction (DIRECTION): study protocol for a randomized controlled trial. Trials, 2020, 21(1): 48.

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