Systematic Review and Meta-analysis: Techniques and a Guide for the Academic Surgeon_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

KevinPhan , DavidH. Tian , ChristopherCao , DeborahBlack ,  TristanD. Yan

Te Collaborative Research(CORE) Group, Macquarie University, Sydney, Australia;

Corresponding author：

TristanD. Yan, Email: tristanyan@annalscts.com

Keywords：

Systematic review; Meta-analysis; Surgery; Outcomes; Forest plot; Network meta-analysis; Meta-regression

DOI：

10.7507/1007-4848.20150110

Video：

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With the rapidly growing literature across the surgical disciplines, there is a corresponding need to critically appraise and summarize the currently available evidence so they can be applied appropriately to patient care. The interpretation of systematic reviews is particularly challenging in cases where few robust clinical trials have been performed to address a particular question. However, risk of bias can be minimized and potentially useful conclusions can be drawn if strict review methodology is adhered to, including an exhaustive literature search, quality appraisal of primary studies, appropriate statistical methodology, assessment of confidence in estimates and risk of bias. Therefore, the following article aims to: (Ⅰ) summarize to the important features of a thorough and rigorous systematic review or meta-analysis for the surgical literature; (Ⅱ) highlight several underused statistical approaches which may yield further interesting insights compared to conventional pair-wise data synthesis techniques; and (Ⅲ) propose a guide for thorough analysis and presentation of results.

Citation： KevinPhan, DavidH. Tian, ChristopherCao, DeborahBlack, TristanD. Yan. Systematic Review and Meta-analysis: Techniques and a Guide for the Academic Surgeon. Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, 2015, 22(5): 409-417. doi: 10.7507/1007-4848.20150110 Copy

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3.	Murad MH, Montori VM. Synthesizing evidence:shifing the focus from individual studies to the body of evidence. JAMA, 2013, 309:2217-2218.
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5.	Shea BJ, Grimshaw JM, Wells GA, et al. Development of AMSTAR:a measurement tool to assess the methodological quality of systematic reviews. BMC Med Res Methodol, 2007, 7:10.
6.	Wilson P, Petticrew M. Why promote the fndings of single research studies? BMJ, 2008, 336:722.
7.	Petticrew M. Why certain systematic reviews reach uncertain conclusions. BMJ, 2003, 326:756-758.
8.	Phan K, Tsai YC, Niranjan N, et al. Sutureless aortic valve replacement:a systematic review and meta-analysis. Ann Cardiothorac Surg, 2015, 4:100-111.
9.	Armstrong R, Hall BJ, Doyle J, et al. Cochrane Update. 'Scoping the scope' of a cochrane review. J Public Health (Oxf), 2011, 33:147-150.
10.	Wright CC, Sim J. Intention-to-treat approach to data from randomized controlled trials:a sensitivity analysis. J Clin Epidemiol, 2003, 56:833-842.
11.	Oxman AD, Guyatt GH. A consumer's guide to subgroup analyses. Ann Intern Med, 1992, 116:78-84.
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13.	Eysenck HJ. Meta-analysis and its problems. BMJ, 1994, 309:789-792.
14.	Cao C, Ang SC, Indraratna P, et al. Systematic review and meta-analysis of transcatheter aortic valve implantation versus surgical aortic valve replacement for severe aortic stenosis. Ann Cardiothorac Surg, 2013, 2:10-23.
15.	Van der Wees P, Qaseem A, Kaila M, et al. Prospective systematic review registration:perspective from the Guidelines International Network (G-I-N). Syst Rev, 2012, 1:3.
16.	Booth A, Clarke M, Dooley G, et al. PROSPERO at one year:an evaluation of its utility. Syst Rev, 2013, 2:4.
17.	Smith BJ, Darzins PJ, Quinn M, et al. Modern methods of searching the medical literature. Med J Aust, 1992, 157:603-611.
18.	Dickersin K, Scherer R, Lefebvre C. Identifying relevant studies for systematic reviews. BMJ, 1994, 309:1286-1291.
19.	Dickersin K, Hewitt P, Mutch L, et al. Perusing the literature:comparison of MEDLINE searching with a perinatal trials database. Control Clin Trials, 1985, 6:306-317.
20.	Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses:the PRISMA statement. J Clin Epidemiol, 2009, 62:1006-1012.
21.	Dickersin K, Chan S, Chalmers TC, et al. Publication bias and clinical trials. Control Clin Trials, 1987, 8:343-353.
22.	McAuley L, Pham B, Tugwell P, et al. Does the inclusion of grey literature influence estimates of intervention effectiveness reported in meta-analyses? Lancet, 2000, 356:1228-1231.
23.	Cook DJ, Guyatt GH, Ryan G, et al. Should unpublished data be included in meta-analyses? Current convictions and controversies. JAMA, 1993, 269:2749-2753.
24.	Moher D, Jadad AR, Nichol G, et al. Assessing the quality of randomized controlled trials:an annotated bibliography of scales and checklists. Control Clin Trials, 1995, 16:62-73.
25.	Jadad AR, Moore RA, Carroll D, et al. Assessing the quality of reports of randomized clinical trials:is blinding necessary? Control Clin Trials, 1996, 17:1-12.
26.	Higgins JP, Altman DG, Gøtzsche PC, et al. Te Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ, 2011, 343:d5928.
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29.	von Elm E, Altman DG, Egger M, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement:guidelines for reporting observational studies. BMJ, 2007, 335:806-808.
30.	von Elm E, Egger M. Te scandal of poor epidemiological research. BMJ, 2004, 329:868-869.
31.	Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology:a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA, 2000, 283:2008-2012.
32.	Phan K, Ha H, Phan S, et al. Early hemodynamic performance of the third generation St Jude Trifecta aortic prosthesis:systematic review and meta-analysis. J Torac Cardiovasc Surg, 2015, [Epub ahead of print].
33.	Lau J, Schmid CH, Chalmers TC. Cumulative meta-analysis of clinical trials builds evidence for exemplary medical care. J Clin Epidemiol, 1995, 48:45-60.
34.	Lau J, Antman EM, Jimenez-Silva J, et al. Cumulative meta-analysis of therapeutic trials for myocardial infarction. N Engl J Med, 1992, 327:248-254.
35.	Villanueva EV, Zavarsek S. Evaluating heterogeneity in cumulative meta-analyses. BMC Med Res Methodol, 2004, 4:18.
36.	Phan K, Xie A, Di Eusanio M, et al. A meta-analysis of minimally invasive versus conventional sternotomy for aortic valve replacement. Ann Torac Surg, 2014, 98:1499-1511.
37.	Phan K, Xie A, La Meir M, et al. Surgical ablation for treatment of atrial fbrillation in cardiac surgery:a cumulative meta-analysis of randomised controlled trials. Heart, 2014, 100:722-730.
38.	Lewis S, Clarke M. Forest plots:trying to see the wood and the trees. BMJ, 2001, 322:1479-1480.
39.	Phan K, Xie A, Tsai YC, et al. Ministernotomy or minithoracotomy for minimally invasive aortic valve replacement:a Bayesian network meta-analysis. Ann Cardiothorac Surg, 2015, 4:3-14.
40.	Thompson SG. Why sources of heterogeneity in meta-analysis should be investigated. BMJ, 1994, 309:1351-1355.
41.	Rücker G, Schwarzer G, Carpenter JR, et al. Undue reliance on I(2) in assessing heterogeneity may mislead. BMC Med Res Methodol, 2008, 8:79.
42.	Jüni P, Altman DG, Egger M. Systematic reviews in health care:Assessing the quality of controlled clinical trials. BMJ, 2001, 323:42-46.
43.	Baker WL, White CM, Cappelleri JC, et al. Understanding heterogeneity in meta-analysis:the role of meta-regression. Int J Clin Pract, 2009, 63:1426-1434.
44.	Tompson SG, Higgins JP. How should meta-regression analyses be undertaken and interpreted? Stat Med, 2002, 21:1559-1573.
45.	Phan K, Zhou JJ, Niranjan N, et al. Minimally invasive reoperative aortic valve replacement:a systematic review and meta-analysis. Ann Cardiothorac Surg, 2015, 4:15-25.
46.	Caldwell DM, Ades AE, Higgins JP. Simultaneous comparison of multiple treatments:combining direct and indirect evidence. BMJ, 2005, 331:897-900.
47.	Sutton A, Ades AE, Cooper N, et al. Use of indirect and mixed treatment comparisons for technology assessment. Pharmacoeconomics, 2008, 26:753-767.
48.	Caldwell DM, Welton NJ, Ades AE. Mixed treatment comparison analysis provides internally coherent treatment effect estimates based on overviews of reviews and can reveal inconsistency. J Clin Epidemiol, 2010, 63:875-882.
49.	Salanti G. Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis:many names, many benefits, many concerns for the next generation evidence synthesis tool. Research Synthesis Methods, 2012, 3:80-97.
50.	Jansen JP, Naci H. Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifers. BMC Med, 2013, 11:159.
51.	Caldwell DM, Gibb DM, Ades AE. Validity of indirect comparisons in meta-analysis. Lancet, 2007, 369:270-author reply 271.
52.	Sturtz S, Bender R. Unsolved issues of mixed treatment comparison meta-analysis:network size and inconsistency. Research Synthesis Methods, 2012, 3:300-311.
53.	Song F, Xiong T, Parekh-Bhurke S, et al. Inconsistency between direct and indirect comparisons of competing interventions:metaepidemiological study. BMJ, 2011, 343:d4909.
54.	Dear KB. Iterative generalized least squares for meta-analysis of survival data at multiple times. Biometrics, 1994, 50:989-1002.
55.	Arends LR, Hunink MG, Stijnen T. Meta-analysis of summary survival curve data. Stat Med, 2008, 27:4381-4396.
56.	Fiocco M, Putter H, van Houwelingen JC. Meta-analysis of pairs of survival curves under heterogeneity:a Poisson correlated gammafrailty approach. Stat Med, 2009, 28:3782-3897.
57.	Parmar MK, Torri V, Stewart L. Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Stat Med, 1998, 17:2815-2134.
58.	Tierney JF, Stewart LA, Ghersi D, et al. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials, 2007, 8:16.
59.	Guyot P, Ades AE, Ouwens MJ, et al. Enhanced secondary analysis of survival data:reconstructing the data from published KaplanMeier survival curves. BMC Med Res Methodol, 2012, 12:9.
60.	Tian DH, De Silva RP, Wang T, et al. Open surgical repair for chronic type B aortic dissection:a systematic review. Ann Cardiothorac Surg, 2014, 3:340-350.
61.	Phan K, Ha HS, Phan S, et al. New-onset atrial fbrillation following coronary bypass surgery predicts long-term mortality:a systematic review and meta-analysis. Eur J Cardiothorac Surg, 2015.[Epub ahead of print].
62.	Chalmers I. Underreporting research is scientific misconduct. JAMA, 1990, 263:1405-1408.
63.	Easterbrook PJ, Berlin JA, Gopalan R, et al. Publication bias in clinical research. Lancet, 1991, 337:867-872.
64.	Sterne JA, Sutton AJ, Ioannidis JP, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ, 2011, 343:d4002.
65.	Egger M, Davey Smith G, Schneider M, et al. Bias in meta-analysis detected by a simple, graphical test. BMJ, 1997, 315:629-634.
66.	Duval S, Tweedie R. Trim and fill:A simple funnel-plot-based method of testing and adjusting for publication bias in metaanalysis. Biometrics, 2000, 56:455-463.
67.	Xie A, Phan K, Yan TD. Durability of continuous-flow lef ventricular assist devices:a systematic review. Ann Cardiothorac Surg, 2014, 3:547-556.
68.	Tsai YC, Phan K, Munkholm-Larsen S, et al. Surgical left atrial appendage occlusion during cardiac surgery for patients with atrial fbrillation:a meta-analysis. Eur J Cardiothorac Surg, 2014.[Epub ahead of print].
69.	Man-Son-Hing M, Laupacis A, O'Rourke K, et al. Determination of the clinical importance of study results. J Gen Intern Med, 2002, 17:469-476.
70.	Guyatt GH, Oxman AD, Vist GE, et al. GRADE:an emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 2008, 336:924-926.
71.	Balshem H, Helfand M, Schünemann HJ, et al. GRADE guidelines:3. Rating the quality of evidence. J Clin Epidemiol, 2011, 64:401-406.
72.	Seco M, Cao C, Modi P, et al. Systematic review of robotic minimally invasive mitral valve surgery. Ann Cardiothorac Surg, 2013, 2:704-716.

1. Oxman AD, Cook DJ, Guyatt GH. Users' guides to the medical literature. VI. How to use an overview. Evidence-Based Medicine Working Group. JAMA, 1994, 272:1367-1371.
2. Swingler GH, Volmink J, Ioannidis JP. Number of published systematic reviews and global burden of disease:database analysis. BMJ, 200, 327:1083-1084.
3. Murad MH, Montori VM. Synthesizing evidence:shifing the focus from individual studies to the body of evidence. JAMA, 2013, 309:2217-2218.
4. Cook DJ, Mulrow CD, Haynes RB. Systematic reviews:synthesis of best evidence for clinical decisions. Ann Intern Med, 1997, 126:376-380.
5. Shea BJ, Grimshaw JM, Wells GA, et al. Development of AMSTAR:a measurement tool to assess the methodological quality of systematic reviews. BMC Med Res Methodol, 2007, 7:10.
6. Wilson P, Petticrew M. Why promote the fndings of single research studies? BMJ, 2008, 336:722.
7. Petticrew M. Why certain systematic reviews reach uncertain conclusions. BMJ, 2003, 326:756-758.
8. Phan K, Tsai YC, Niranjan N, et al. Sutureless aortic valve replacement:a systematic review and meta-analysis. Ann Cardiothorac Surg, 2015, 4:100-111.
9. Armstrong R, Hall BJ, Doyle J, et al. Cochrane Update. 'Scoping the scope' of a cochrane review. J Public Health (Oxf), 2011, 33:147-150.
10. Wright CC, Sim J. Intention-to-treat approach to data from randomized controlled trials:a sensitivity analysis. J Clin Epidemiol, 2003, 56:833-842.
11. Oxman AD, Guyatt GH. A consumer's guide to subgroup analyses. Ann Intern Med, 1992, 116:78-84.
12. Horwitz RI. "Large-scale randomized evidence:large, simple trials and overviews of trials":discussion. A clinician's perspective on meta-analyses. J Clin Epidemiol, 1995, 48:41-44.
13. Eysenck HJ. Meta-analysis and its problems. BMJ, 1994, 309:789-792.
14. Cao C, Ang SC, Indraratna P, et al. Systematic review and meta-analysis of transcatheter aortic valve implantation versus surgical aortic valve replacement for severe aortic stenosis. Ann Cardiothorac Surg, 2013, 2:10-23.
15. Van der Wees P, Qaseem A, Kaila M, et al. Prospective systematic review registration:perspective from the Guidelines International Network (G-I-N). Syst Rev, 2012, 1:3.
16. Booth A, Clarke M, Dooley G, et al. PROSPERO at one year:an evaluation of its utility. Syst Rev, 2013, 2:4.
17. Smith BJ, Darzins PJ, Quinn M, et al. Modern methods of searching the medical literature. Med J Aust, 1992, 157:603-611.
18. Dickersin K, Scherer R, Lefebvre C. Identifying relevant studies for systematic reviews. BMJ, 1994, 309:1286-1291.
19. Dickersin K, Hewitt P, Mutch L, et al. Perusing the literature:comparison of MEDLINE searching with a perinatal trials database. Control Clin Trials, 1985, 6:306-317.
20. Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses:the PRISMA statement. J Clin Epidemiol, 2009, 62:1006-1012.
21. Dickersin K, Chan S, Chalmers TC, et al. Publication bias and clinical trials. Control Clin Trials, 1987, 8:343-353.
22. McAuley L, Pham B, Tugwell P, et al. Does the inclusion of grey literature influence estimates of intervention effectiveness reported in meta-analyses? Lancet, 2000, 356:1228-1231.
23. Cook DJ, Guyatt GH, Ryan G, et al. Should unpublished data be included in meta-analyses? Current convictions and controversies. JAMA, 1993, 269:2749-2753.
24. Moher D, Jadad AR, Nichol G, et al. Assessing the quality of randomized controlled trials:an annotated bibliography of scales and checklists. Control Clin Trials, 1995, 16:62-73.
25. Jadad AR, Moore RA, Carroll D, et al. Assessing the quality of reports of randomized clinical trials:is blinding necessary? Control Clin Trials, 1996, 17:1-12.
26. Higgins JP, Altman DG, Gøtzsche PC, et al. Te Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ, 2011, 343:d5928.
27. Sackett DL, Rosenberg WM, Gray JA, et al. Evidence based medicine:what it is and what it isn't. BMJ, 1996, 312:71-72.
28. Begg C, Cho M, Eastwood S, et al. Improving the quality of reporting of randomized controlled trials. The CONSORT statement. JAMA, 1996, 276:637-639.
29. von Elm E, Altman DG, Egger M, et al. Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement:guidelines for reporting observational studies. BMJ, 2007, 335:806-808.
30. von Elm E, Egger M. Te scandal of poor epidemiological research. BMJ, 2004, 329:868-869.
31. Stroup DF, Berlin JA, Morton SC, et al. Meta-analysis of observational studies in epidemiology:a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA, 2000, 283:2008-2012.
32. Phan K, Ha H, Phan S, et al. Early hemodynamic performance of the third generation St Jude Trifecta aortic prosthesis:systematic review and meta-analysis. J Torac Cardiovasc Surg, 2015, [Epub ahead of print].
33. Lau J, Schmid CH, Chalmers TC. Cumulative meta-analysis of clinical trials builds evidence for exemplary medical care. J Clin Epidemiol, 1995, 48:45-60.
34. Lau J, Antman EM, Jimenez-Silva J, et al. Cumulative meta-analysis of therapeutic trials for myocardial infarction. N Engl J Med, 1992, 327:248-254.
35. Villanueva EV, Zavarsek S. Evaluating heterogeneity in cumulative meta-analyses. BMC Med Res Methodol, 2004, 4:18.
36. Phan K, Xie A, Di Eusanio M, et al. A meta-analysis of minimally invasive versus conventional sternotomy for aortic valve replacement. Ann Torac Surg, 2014, 98:1499-1511.
37. Phan K, Xie A, La Meir M, et al. Surgical ablation for treatment of atrial fbrillation in cardiac surgery:a cumulative meta-analysis of randomised controlled trials. Heart, 2014, 100:722-730.
38. Lewis S, Clarke M. Forest plots:trying to see the wood and the trees. BMJ, 2001, 322:1479-1480.
39. Phan K, Xie A, Tsai YC, et al. Ministernotomy or minithoracotomy for minimally invasive aortic valve replacement:a Bayesian network meta-analysis. Ann Cardiothorac Surg, 2015, 4:3-14.
40. Thompson SG. Why sources of heterogeneity in meta-analysis should be investigated. BMJ, 1994, 309:1351-1355.
41. Rücker G, Schwarzer G, Carpenter JR, et al. Undue reliance on I(2) in assessing heterogeneity may mislead. BMC Med Res Methodol, 2008, 8:79.
42. Jüni P, Altman DG, Egger M. Systematic reviews in health care:Assessing the quality of controlled clinical trials. BMJ, 2001, 323:42-46.
43. Baker WL, White CM, Cappelleri JC, et al. Understanding heterogeneity in meta-analysis:the role of meta-regression. Int J Clin Pract, 2009, 63:1426-1434.
44. Tompson SG, Higgins JP. How should meta-regression analyses be undertaken and interpreted? Stat Med, 2002, 21:1559-1573.
45. Phan K, Zhou JJ, Niranjan N, et al. Minimally invasive reoperative aortic valve replacement:a systematic review and meta-analysis. Ann Cardiothorac Surg, 2015, 4:15-25.
46. Caldwell DM, Ades AE, Higgins JP. Simultaneous comparison of multiple treatments:combining direct and indirect evidence. BMJ, 2005, 331:897-900.
47. Sutton A, Ades AE, Cooper N, et al. Use of indirect and mixed treatment comparisons for technology assessment. Pharmacoeconomics, 2008, 26:753-767.
48. Caldwell DM, Welton NJ, Ades AE. Mixed treatment comparison analysis provides internally coherent treatment effect estimates based on overviews of reviews and can reveal inconsistency. J Clin Epidemiol, 2010, 63:875-882.
49. Salanti G. Indirect and mixed-treatment comparison, network, or multiple-treatments meta-analysis:many names, many benefits, many concerns for the next generation evidence synthesis tool. Research Synthesis Methods, 2012, 3:80-97.
50. Jansen JP, Naci H. Is network meta-analysis as valid as standard pairwise meta-analysis? It all depends on the distribution of effect modifers. BMC Med, 2013, 11:159.
51. Caldwell DM, Gibb DM, Ades AE. Validity of indirect comparisons in meta-analysis. Lancet, 2007, 369:270-author reply 271.
52. Sturtz S, Bender R. Unsolved issues of mixed treatment comparison meta-analysis:network size and inconsistency. Research Synthesis Methods, 2012, 3:300-311.
53. Song F, Xiong T, Parekh-Bhurke S, et al. Inconsistency between direct and indirect comparisons of competing interventions:metaepidemiological study. BMJ, 2011, 343:d4909.
54. Dear KB. Iterative generalized least squares for meta-analysis of survival data at multiple times. Biometrics, 1994, 50:989-1002.
55. Arends LR, Hunink MG, Stijnen T. Meta-analysis of summary survival curve data. Stat Med, 2008, 27:4381-4396.
56. Fiocco M, Putter H, van Houwelingen JC. Meta-analysis of pairs of survival curves under heterogeneity:a Poisson correlated gammafrailty approach. Stat Med, 2009, 28:3782-3897.
57. Parmar MK, Torri V, Stewart L. Extracting summary statistics to perform meta-analyses of the published literature for survival endpoints. Stat Med, 1998, 17:2815-2134.
58. Tierney JF, Stewart LA, Ghersi D, et al. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials, 2007, 8:16.
59. Guyot P, Ades AE, Ouwens MJ, et al. Enhanced secondary analysis of survival data:reconstructing the data from published KaplanMeier survival curves. BMC Med Res Methodol, 2012, 12:9.
60. Tian DH, De Silva RP, Wang T, et al. Open surgical repair for chronic type B aortic dissection:a systematic review. Ann Cardiothorac Surg, 2014, 3:340-350.
61. Phan K, Ha HS, Phan S, et al. New-onset atrial fbrillation following coronary bypass surgery predicts long-term mortality:a systematic review and meta-analysis. Eur J Cardiothorac Surg, 2015.[Epub ahead of print].
62. Chalmers I. Underreporting research is scientific misconduct. JAMA, 1990, 263:1405-1408.
63. Easterbrook PJ, Berlin JA, Gopalan R, et al. Publication bias in clinical research. Lancet, 1991, 337:867-872.
64. Sterne JA, Sutton AJ, Ioannidis JP, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ, 2011, 343:d4002.
65. Egger M, Davey Smith G, Schneider M, et al. Bias in meta-analysis detected by a simple, graphical test. BMJ, 1997, 315:629-634.
66. Duval S, Tweedie R. Trim and fill:A simple funnel-plot-based method of testing and adjusting for publication bias in metaanalysis. Biometrics, 2000, 56:455-463.
67. Xie A, Phan K, Yan TD. Durability of continuous-flow lef ventricular assist devices:a systematic review. Ann Cardiothorac Surg, 2014, 3:547-556.
68. Tsai YC, Phan K, Munkholm-Larsen S, et al. Surgical left atrial appendage occlusion during cardiac surgery for patients with atrial fbrillation:a meta-analysis. Eur J Cardiothorac Surg, 2014.[Epub ahead of print].
69. Man-Son-Hing M, Laupacis A, O'Rourke K, et al. Determination of the clinical importance of study results. J Gen Intern Med, 2002, 17:469-476.
70. Guyatt GH, Oxman AD, Vist GE, et al. GRADE:an emerging consensus on rating quality of evidence and strength of recommendations. BMJ, 2008, 336:924-926.
71. Balshem H, Helfand M, Schünemann HJ, et al. GRADE guidelines:3. Rating the quality of evidence. J Clin Epidemiol, 2011, 64:401-406.
72. Seco M, Cao C, Modi P, et al. Systematic review of robotic minimally invasive mitral valve surgery. Ann Cardiothorac Surg, 2013, 2:704-716.

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