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BMJ Open is committed to open peer review. As part of this commitment we make the peer review history of every article we publish publicly available. When an article is published we post the peer reviewers’ comments and the authors’ responses online. We also post the versions of the paper that were used during peer review. These are the versions that the peer review comments apply to. The versions of the paper that follow are the versions that were submitted during the peer review process. They are not the versions of record or the final published versions. They should not be cited or distributed as the published version of this manuscript. BMJ Open is an open access journal and the full, final, typeset and author-corrected version of record of the manuscript is available on our site with no access controls, subscription charges or pay-per-view fees (http://bmjopen.bmj.com). If you have any questions on BMJ Open’s open peer review process please email
For peer review onlyCorrelation of the neutrophil to lymphocyte ratio and clinical outcomes in lung cancer patients receiving immunotherapy:
a meta-analysis
Journal: BMJ Open
Manuscript ID bmjopen-2019-035031
Article Type: Original research
Date Submitted by the Author: 17-Oct-2019
Complete List of Authors: Jin, Jing; Sichuan University West China HospitalYang, Lan; Sichuan University West China HospitalLiu, Dan; Sichuan University West China HospitalLi, Weimin; Sichuan University West China Hospital,
For peer review onlyI, the Submitting Author has the right to grant and does grant on behalf of all authors of the Work (as defined in the below author licence), an exclusive licence and/or a non-exclusive licence for contributions from authors who are: i) UK Crown employees; ii) where BMJ has agreed a CC-BY licence shall apply, and/or iii) in accordance with the terms applicable for US Federal Government officers or employees acting as part of their official duties; on a worldwide, perpetual, irrevocable, royalty-free basis to BMJ Publishing Group Ltd (“BMJ”) its licensees and where the relevant Journal is co-owned by BMJ to the co-owners of the Journal, to publish the Work in this journal and any other BMJ products and to exploit all rights, as set out in our licence.
The Submitting Author accepts and understands that any supply made under these terms is made by BMJ to the Submitting Author unless you are acting as an employee on behalf of your employer or a postgraduate student of an affiliated institution which is paying any applicable article publishing charge (“APC”) for Open Access articles. Where the Submitting Author wishes to make the Work available on an Open Access basis (and intends to pay the relevant APC), the terms of reuse of such Open Access shall be governed by a Creative Commons licence – details of these licences and which Creative Commons licence will apply to this Work are set out in our licence referred to above.
Other than as permitted in any relevant BMJ Author’s Self Archiving Policies, I confirm this Work has not been accepted for publication elsewhere, is not being considered for publication elsewhere and does not duplicate material already published. I confirm all authors consent to publication of this Work and authorise the granting of this licence.
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All data in the current study were available in published articles
Acknowledgments:
This work was supported by the Transformation Projects of Sci-Tech Achievements
of Sichuan Province (2016CZYD0001), the Sci-Tech Support Program of Science and
Technology Department of Sichuan Province (2016SZ0073), the National Major Sci-
Tech Project (2017ZX10103004-012) and the National Key Development Plan for
Precision Medicine Research (2017YFC0910004).
Compliance with Ethical Standards
Ethical approval: All procedures performed in the studies involving human
participants were in accordance with the ethical standards of the institutional and/or
national research committee and with the 1964 Helsinki declaration and its later
amendment.
Reference:
1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65(1):5-29.2. Callahan MK, Postow MA, Wolchok JD. Targeting T Cell Co-receptors for Cancer Therapy. Immunity. 2016;44(5):1069-78.3. Chen YM. Immune checkpoint inhibitors for nonsmall cell lung cancer treatment. Journal of the Chinese Medical Association Jcma. 2016;80(1):7.4. Yarchoan M, Hopkins A, Jaffee EM. Tumor Mutational Burden and Response Rate to PD-1 Inhibition. The New England journal of medicine. 2017;377(25):2500-1.5. Anagnostou V, Smith KN, Forde PM, Niknafs N, Bhattacharya R, White J, et al. Evolution of Neoantigen Landscape during Immune Checkpoint Blockade in Non-Small Cell Lung Cancer. Cancer discovery. 2017;7(3):264-76.
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6. Goswami S, Basu S, Sharma P. A potential biomarker for anti-PD-1 immunotherapy. Nature medicine. 2018;24(2):123-4.7. Marchetti A, Barberis M, Franco R, De Luca G, Pace MV, Staibano S, et al. Multicenter Comparison of 22C3 PharmDx (Agilent) and SP263 (Ventana) Assays to Test PD-L1 Expression for NSCLC Patients to Be Treated with Immune Checkpoint Inhibitors. Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer. 2017;12(11):1654-63.8. Hooper CE, Lyburn ID, Searle J, Darby M, Hall T, Hall D, et al. The South West Area Mesothelioma and Pemetrexed trial: a multicentre prospective observational study evaluating novel markers of chemotherapy response and prognostication. British journal of cancer. 2015;112(7):1175-82.9. Wang X, Teng F, Kong L, Yu J. Pretreatment neutrophil-to-lymphocyte ratio as a survival predictor for small-cell lung cancer. OncoTargets and therapy. 2016;9:5761-70.10. Sanchez-Salcedo P, de-Torres JP, Martinez-Urbistondo D, Gonzalez-Gutierrez J, Berto J, Campo A, et al. The neutrophil to lymphocyte and platelet to lymphocyte ratios as biomarkers for lung cancer development. Lung cancer (Amsterdam, Netherlands). 2016;97:28-34.11. Kos M, Hocazade C, Kos FT, Uncu D, Karakas E, Dogan M, et al. Prognostic role of pretreatment platelet/lymphocyte ratio in patients with non-small cell lung cancer. Wien Klin Wochenschr. 2016;128(17-18):635-40.12. Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating summary time-to-event data into meta-analysis. Trials. 2007;8(1):16.13. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. Biometrics. 1994;50(4):1088.14. O’Rourke K, Shea B, Wells GA. Meta-Analysis of Clinical Trials: Springer New York; 2001. 397-424 p.15. Ren F, Zhao T, Liu B, Pan L. Neutrophil-lymphocyte ratio (NLR) predicted prognosis for advanced non-small-cell lung cancer (NSCLC) patients who received immune checkpoint blockade (ICB). OncoTargets and therapy. 2019;12:4235-44.16. Pavan A, Calvetti L, Dal Maso A, Attili I, Del Bianco P, Pasello G, et al. Peripheral Blood Markers Identify Risk of Immune-Related Toxicity in Advanced Non-Small Cell Lung Cancer Treated with Immune-Checkpoint Inhibitors. The oncologist. 2019.17. Passiglia F, Galvano A, Castiglia M, Incorvaia L, Calo V, Listi A, et al. Monitoring blood biomarkers to predict nivolumab effectiveness in NSCLC patients. Therapeutic advances in medical oncology. 2019;11:1758835919839928.18. Minami S, Ihara S, Ikuta S, Komuta K. Gustave Roussy Immune Score and Royal Marsden Hospital Prognostic Score Are Biomarkers of Immune-Checkpoint Inhibitor for Non-Small Cell Lung Cancer. World journal of oncology. 2019;10(2):90-100.19. Ichiki Y, Taira A, Chikaishi Y, Matsumiya H, Mori M, Kanayama M, et al. Prognostic factors of
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advanced or postoperative recurrent non-small cell lung cancer targeted with immune check point inhibitors. Journal of thoracic disease. 2019;11(4):1117-23.20. Fukui T, Okuma Y, Nakahara Y, Otani S, Igawa S, Katagiri M, et al. Activity of Nivolumab and Utility of Neutrophil-to-Lymphocyte Ratio as a Predictive Biomarker for Advanced Non-Small-Cell Lung Cancer: A Prospective Observational Study. Clinical lung cancer. 2019;20(3):208-14.e2.21. Dusselier M, Deluche E, Delacourt N, Ballouhey J, Egenod T, Melloni B, et al. Neutrophil-to-lymphocyte ratio evolution is an independent predictor of early progression of second-line nivolumab-treated patients with advanced non-small-cell lung cancers. PloS one. 2019;14(7):e0219060.22. Zer A, Sung MR, Walia P, Khoja L, Maganti M, Labbe C, et al. Correlation of Neutrophil to Lymphocyte Ratio and Absolute Neutrophil Count With Outcomes With PD-1 Axis Inhibitors in Patients With Advanced Non-Small-Cell Lung Cancer. Clinical lung cancer. 2018;19(5):426-+.23. Takeda T, Takeuchi M, Saitoh M, Takeda S. Neutrophil-to-lymphocyte ratio after four weeks of nivolumab administration as a predictive marker in patients with pretreated non-small-cell lung cancer. Thoracic cancer. 2018;9(10):1291-9.24. Svaton M, Zemanova M, Skrickova J, Jakubikova L, Kolek V, Kultan J, et al. Chronic Inflammation as a Potential Predictive Factor of Nivolumab Therapy in Non-small Cell Lung Cancer. Anticancer research. 2018;38(12):6771-82.25. Suh KJ, Kim SH, Kim YJ, Kim M, Keam B, Kim TM, et al. Post-treatment neutrophil-to-lymphocyte ratio at week 6 is prognostic in patients with advanced non-small cell lung cancers treated with anti-PD-1 antibody. Cancer Immunology Immunotherapy. 2018;67(3):459-70.26. Shiroyama T, Suzuki H, Tamiya M, Tamiya A, Tanaka A, Okamoto N, et al. Pretreatment advanced lung cancer inflammation index (ALI) for predicting early progression in nivolumab-treated patients with advanced non-small cell lung cancer. Cancer medicine. 2018;7(1):13-20.27. Russo A, Franchina T, Ricciardi GRR, Battaglia A, Scimone A, Berenato R, et al. Baseline neutrophilia, derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), and outcome in non small cell lung cancer (NSCLC) treated with Nivolumab or Docetaxel. Journal of cellular physiology. 2018;233(10):6337-43.28. Park W, Kwon D, Saravia D, Desai A, Vargas F, El Dinali M, et al. Developing a Predictive Model for Clinical Outcomes of Advanced Non-Small Cell Lung Cancer Patients Treated With Nivolumab. Clinical lung cancer. 2018;19(3):280-8.e4.29. Nakaya A, Kurata T, Yoshioka H, Takeyasu Y, Niki M, Kibata K, et al. Neutrophil-to-lymphocyte ratio as an early marker of outcomes in patients with advanced non-small-cell lung cancer treated with nivolumab. International journal of clinical oncology. 2018;23(4):634-40.30. Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, et al. Association of the Lung Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced Non-Small Cell Lung Cancer. JAMA oncology. 2018;4(3):351-7.
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31. Maymani H, Hess K, Groisberg R, Hong DS, Naing A, Piha-Paul S, et al. Predicting outcomes in patients with advanced non-small cell lung cancer enrolled in early phase immunotherapy trials. Lung cancer (Amsterdam, Netherlands). 2018;120:137-41.32. Kiriu T, Yamamoto M, Nagano T, Hazama D, Sekiya R, Katsurada M, et al. The time-series behavior of neutrophil-to-lymphocyte ratio is useful as a predictive marker in non-small cell lung cancer. PloS one. 2018;13(2):e0193018.33. Khunger M, Patil PD, Khunger A, Li M, Hu B, Rakshit S, et al. Post-treatment changes in hematological parameters predict response to nivolumab monotherapy in non-small cell lung cancer patients. PloS one. 2018;13(10):e0197743.34. Inomata M, Hirai T, Seto Z, Tokui K, Taka C, Okazawa S, et al. Clinical Parameters for Predicting the Survival in Patients with Squamous and Non-squamous-cell NSCLC Receiving PD-1 Inhibitor Therapy. Pathology oncology research : POR. 2018.35. Facchinetti F, Veneziani M, Buti S, Gelsomino F, Squadrilli A, Bordi P, et al. Clinical and hematologic parameters address the outcomes of non-small-cell lung cancer patients treated with nivolumab. Immunotherapy. 2018;10(8):681-94.36. Diem S, Schmid S, Krapf M, Flatz L, Born D, Jochum W, et al. Neutrophil-to-Lymphocyte ratio (NLR) and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer (NSCLC) treated with nivolumab. Lung cancer (Amsterdam, Netherlands). 2017;111:176-81.37. Bagley SJ, Kothari S, Aggarwal C, Bauml JM, Alley EW, Evans TL, et al. Pretreatment neutrophil-to-lymphocyte ratio as a marker of outcomes in nivolumab-treated patients with advanced non-small-cell lung cancer. Lung cancer (Amsterdam, Netherlands). 2017;106:1-7.38. Ito A, Kondo S, Tada K, Kitano S. Clinical Development of Immune Checkpoint Inhibitors. Biomed Res Int. 2015;2015:605478.39. Carbone DP, Reck M, Paz-Ares L, Creelan B, Horn L, Steins M, et al. First-Line Nivolumab in Stage IV or Recurrent Non-Small-Cell Lung Cancer. The New England journal of medicine. 2017;376(25):2415-26.40. Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, et al. Association of the Lung Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced Non-Small Cell Lung Cancer. JAMA Oncol. 2018;4(3):351-7.41. Nakaya A, Kurata T, Yoshioka H, Takeyasu Y, Niki M, Kibata K, et al. Neutrophil-to-lymphocyte ratio as an early marker of outcomes in patients with advanced non-small-cell lung cancer treated with nivolumab. Int J Clin Oncol. 2018.42. Zer A, Sung MR, Walia P, Khoja L, Maganti M, Labbe C, et al. Correlation of Neutrophil to Lymphocyte Ratio and Absolute Neutrophil Count With Outcomes With PD-1 Axis Inhibitors in Patients With Advanced Non-Small-Cell Lung Cancer. Clin Lung Cancer. 2018.43. Park W, Kwon D, Saravia D, Desai A, Vargas F, El Dinali M, et al. Developing a Predictive Model for
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Clinical Outcomes of Advanced Non-Small Cell Lung Cancer Patients Treated With Nivolumab. Clinical Lung Cancer. 2018;19(3):280-8.e4.44. Fukui T, Okuma Y, Nakahara Y, Otani S, Igawa S, Katagiri M, et al. Activity of Nivolumab and Utility of Neutrophil-to-Lymphocyte Ratio as a Predictive Biomarker for Advanced Non–Small-Cell Lung Cancer: A Prospective Observational Study. Clinical Lung Cancer. 2018.45. Russo A, Franchina T, Ricciardi GRR, Battaglia A, Scimone A, Berenato R, et al. Baseline neutrophilia, derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), and outcome in non small cell lung cancer (NSCLC) treated with Nivolumab or Docetaxel. J Cell Physiol. 2018.46. Sacdalan DB, Lucero JA, Sacdalan DL. Prognostic utility of baseline neutrophil-to-lymphocyte ratio in patients receiving immune checkpoint inhibitors: a review and meta-analysis. OncoTargets and therapy. 2018;11:955-65.47. Jiang T, Bai Y, Zhou F, Li W, Gao G, Su C, et al. Clinical value of neutrophil-to-lymphocyte ratio in patients with non-small-cell lung cancer treated with PD-1/PD-L1 inhibitors. Lung cancer (Amsterdam, Netherlands). 2019;130:76-83.48. Kargl J, Busch SE, Yang GH, Kim KH, Hanke ML, Metz HE, et al. Neutrophils dominate the immune cell composition in non-small cell lung cancer. Nature communications. 2017;8:14381.
Figure Legends
Figure 1 Flow chart of study selection
Figure 2 Forest plot of the association between NLR and OS in patients with lung
cancer receiving immunotherapy
Figure 3 Subgroup analysis of the relationship NLR and OS in patients with lung
Figure 6 Sensitivity analysis on OS (A) and PFS (B)
Table
Table 1 The basic characteristic of enrolled studiesStudy Year Country Ethnicity Sample size MFP M/F NLR at baseline
Russo A 2018 Italy European 28 17 25/3 NMZer A 2018 America American 88 5.3 43/45 NLR>4:56.8%Nakaya A 2018 Japan Asian 101 8.9 77/24 NLR≥3:46.5%Maymani H 2018 America American 74 12.3 36/38 NLR>6:20.3%Mezquita L 2018 Europe European 161 12 100/61 NLR>3:39%Diem S 2017 Europe European 52 NM 29/23 5.0(2.7-8.3) *Bagley SJ 2017 America American 175 NM 80/95 NLR≥5:58%Fukui T 2018 Japan Asian 52 10.9 37/15 NLR≥5:34.6%Park W 2018 America American 159 11.5 82/77 4.3(0.5-24.1) *Ren, F 2019 China Asian 147 2.6 94/53 NLR>2.5:59.9%
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Pavan, A 2019 Italy European 184 56.3 125/59 NLR≥3:57.5%Passiglia, F 2019 Italy European 45 9.1 32/13 NLR>3.3:51.1%Minami, S 2019 Japan Asian 76 NM 49/27 NLR≥6:14.5%Ichiki, Y. 2019 Japan Asian 44 4.83 38/6 NMDusselier, M. 2019 France European 59 NM 44/15 NLR>5:62.7%Takeda, T. 2018 Japan Asian 30 NM 19/11 NLR>5:30%Svaton, M 2018 Czech Republic European 120 NM 71/49 NLR>3.8:50%Suh, Koung Jin 2018 Korea Asian 54 26.2 42/12 NLR>5:14.8%Shiroyama, Takayuki 2018 Japan Asian 201 12.4 135/66 NLR>4:39.3%Kiriu, T 2018 Japan Asian 19.00 NM 19 NLR>5:31.6%Khunger, M 2018 America American 109 30 56/53 NLR≥5:50.5%Inomata, M 2018 Japan Asian 36 NM 27/9 NLR≥5:44.4%Facchinetti, F 2018 Italy European 54 12.6 45/9 NM
Study SCC% Treatment lines Outcome Study design
Cut-off IO
Russo A 60.71% at least second line therapy OS/PFS RO 3 NZer A 17.05% at least second line therapy OS/PFS/DCR RO 4 NMNakaya A 36.63% at least second line therapy PFS/irAEs RO 3 NMaymani H 16.22% including first line therapy OS/PFS RO 6 N/P/DMezquita L 28.57% at least second line therapy OS/PFS RO 3 N/E/A/DDiem S 34.62% including first line therapy OS/PFS RO 5 NBagley SJ 24.00% at least second line therapy OS/PFS RO 5 NFukui T 30.77% at least second line therapy OS/PFS/irAEs PO 5 NPark W 24.53% including first line therapy OS/PFS RO 5 NRen, F 42.18% at least second line therapy OS/PFS RO 2.5 N/PPavan, A 32.07% including first line therapy OS/PFS/irAEs RO 3 N/P/APassiglia, F 44.44% at least second line therapy OS/TTP RO 3.3 NMinami, S 23.68% at least second line therapy OS/PFS RO 6 N/P/AIchiki, Y. 65.91% including first line therapy OS/PFS/irAEs RO NM N/PDusselier, M. 20.34% at least second line therapy OS RO 5 NTakeda, T. 30.00% at least second line therapy PFS RO 5 NSvaton, M 33.33% at least second line therapy OS/PFS RO 3.8 NSuh, Koung Jin 31.48% including first line therapy OS/PFS/irAEs RO 5 N/PShiroyama, Takayuki 30.35% at least second line therapy PFS/RR RO 4 N
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Kiriu, T 31.58% at least second line therapy OS/PFS/TTF RO 5 NKhunger, M 23.85% at least second line therapy OS RO 5 NInomata, M 44.44% at least second line therapy PFS RO 5 N/PFacchinetti, F 48.15% at least second line therapy OS/PFS/TTF/DP PO 4 N
Abbreviation: NLR: neutrophil to lymphocyte ratio; NM: not mentioned; M/F:
male/female; MFP: Median follow-up (month); SCC%: Proportion of Squamous cell
Subgroup analysis of the relationship NLR and OS in patients with lung cancer receiving immunotherapy Abbreviation: ICI: Immune-Checkpoint Inhibitor; M/F: male/female; SCC%: Proportion of Squamous cell
carcinoma;※: the data here shows the proportion of patients whose NLR baseline levels exceeded the cutoff value; N: Nivolumab; P: Pembrolizumab; D: Durvalumab; E: Embrolizumab; A: Atezolizumab
154x99mm (300 x 300 DPI)
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Subgroup analysis of the relationship NLR and PFS in patients with lung cancer receiving immunotherapy Abbreviation: ICI: Immune-Checkpoint Inhibitor; M/F: male/female; SCC%: Proportion of Squamous cell carcinoma;※: 15 studies (20 researches) provided the data for pretreatment NLR and PFS, and 5 of them
also provided posttreatment NLR and PFS; N: Nivolumab; P: Pembrolizumab; D: Durvalumab; E: Embrolizumab; A: Atezolizumab
162x99mm (300 x 300 DPI)
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Title 1 Identify the report as a systematic review, meta-analysis, or both. 1
ABSTRACT
Structured summary 2 Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number.
2-3
INTRODUCTION
Rationale 3 Describe the rationale for the review in the context of what is already known. 4
Objectives 4 Provide an explicit statement of questions being addressed with reference to participants, interventions, comparisons, outcomes, and study design (PICOS).
5
METHODS
Protocol and registration 5 Indicate if a review protocol exists, if and where it can be accessed (e.g., Web address), and, if available, provide registration information including registration number.
5-6
Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered,
language, publication status) used as criteria for eligibility, giving rationale.
6
Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.
5
Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.
5-6 and supplements
Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).
5-8
Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.
6-7
Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.
6
Risk of bias in individual studies
12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.
8
Summary measures 13 State the principal summary measures (e.g., risk ratio, difference in means). 7-8
Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I2) for each meta-analysis.
7-8
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Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).
8
Additional analyses 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.
8
RESULTS
Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram.
8-9
Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.
9-10
Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12). 11-12
Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.
8-11
Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency. 8-11
Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see Item 15). 11-12
Additional analysis 23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]). 12
DISCUSSION
Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers).
12-13
Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).
15-16
Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research. 16
FUNDING
Funding 27 Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review.
17
From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(7): e1000097. doi:10.1371/journal.pmed1000097
For more information, visit: www.prisma-statement.org.
Page 2 of 2
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For peer review onlyAssociation of the neutrophil to lymphocyte ratio and
clinical outcomes in lung cancer patients receiving immunotherapy: a meta-analysis
Journal: BMJ Open
Manuscript ID bmjopen-2019-035031.R1
Article Type: Original research
Date Submitted by the Author: 14-Feb-2020
Complete List of Authors: Jin, Jing; Sichuan University West China HospitalYang, Lan; Sichuan University West China HospitalLiu, Dan; Sichuan University West China HospitalLi, Weimin; Sichuan University West China Hospital,
For peer review onlyI, the Submitting Author has the right to grant and does grant on behalf of all authors of the Work (as defined in the below author licence), an exclusive licence and/or a non-exclusive licence for contributions from authors who are: i) UK Crown employees; ii) where BMJ has agreed a CC-BY licence shall apply, and/or iii) in accordance with the terms applicable for US Federal Government officers or employees acting as part of their official duties; on a worldwide, perpetual, irrevocable, royalty-free basis to BMJ Publishing Group Ltd (“BMJ”) its licensees and where the relevant Journal is co-owned by BMJ to the co-owners of the Journal, to publish the Work in this journal and any other BMJ products and to exploit all rights, as set out in our licence.
The Submitting Author accepts and understands that any supply made under these terms is made by BMJ to the Submitting Author unless you are acting as an employee on behalf of your employer or a postgraduate student of an affiliated institution which is paying any applicable article publishing charge (“APC”) for Open Access articles. Where the Submitting Author wishes to make the Work available on an Open Access basis (and intends to pay the relevant APC), the terms of reuse of such Open Access shall be governed by a Creative Commons licence – details of these licences and which Creative Commons licence will apply to this Work are set out in our licence referred to above.
Other than as permitted in any relevant BMJ Author’s Self Archiving Policies, I confirm this Work has not been accepted for publication elsewhere, is not being considered for publication elsewhere and does not duplicate material already published. I confirm all authors consent to publication of this Work and authorise the granting of this licence.
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2 1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65(1):5-29.3 2. Callahan MK, Postow MA, Wolchok JD. Targeting T Cell Co-receptors for Cancer Therapy. Immunity. 4 2016;44(5):1069-78.5 3. Chen YM. Immune checkpoint inhibitors for nonsmall cell lung cancer treatment. Journal of the 6 Chinese Medical Association Jcma. 2016;80(1):7.7 4. Yarchoan M, Hopkins A, Jaffee EM. Tumor Mutational Burden and Response Rate to PD-1 8 Inhibition. The New England journal of medicine. 2017;377(25):2500-1.9 5. Anagnostou V, Smith KN, Forde PM, Niknafs N, Bhattacharya R, White J, et al. Evolution of
10 Neoantigen Landscape during Immune Checkpoint Blockade in Non-Small Cell Lung Cancer. Cancer 11 discovery. 2017;7(3):264-76.12 6. Goswami S, Basu S, Sharma P. A potential biomarker for anti-PD-1 immunotherapy. Nature 13 medicine. 2018;24(2):123-4.14 7. Marchetti A, Barberis M, Franco R, De Luca G, Pace MV, Staibano S, et al. Multicenter Comparison 15 of 22C3 PharmDx (Agilent) and SP263 (Ventana) Assays to Test PD-L1 Expression for NSCLC Patients to 16 Be Treated with Immune Checkpoint Inhibitors. Journal of thoracic oncology : official publication of the 17 International Association for the Study of Lung Cancer. 2017;12(11):1654-63.18 8. Hooper CE, Lyburn ID, Searle J, Darby M, Hall T, Hall D, et al. The South West Area Mesothelioma 19 and Pemetrexed trial: a multicentre prospective observational study evaluating novel markers of 20 chemotherapy response and prognostication. British journal of cancer. 2015;112(7):1175-82.21 9. Wang X, Teng F, Kong L, Yu J. Pretreatment neutrophil-to-lymphocyte ratio as a survival predictor 22 for small-cell lung cancer. OncoTargets and therapy. 2016;9:5761-70.23 10. Sanchez-Salcedo P, de-Torres JP, Martinez-Urbistondo D, Gonzalez-Gutierrez J, Berto J, Campo A, 24 et al. The neutrophil to lymphocyte and platelet to lymphocyte ratios as biomarkers for lung cancer 25 development. Lung cancer (Amsterdam, Netherlands). 2016;97:28-34.26 11. Kos M, Hocazade C, Kos FT, Uncu D, Karakas E, Dogan M, et al. Prognostic role of pretreatment 27 platelet/lymphocyte ratio in patients with non-small cell lung cancer. Wien Klin Wochenschr. 28 2016;128(17-18):635-40.29 12. Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating 30 summary time-to-event data into meta-analysis. Trials. 2007;8(1):16.31 13. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. 32 Biometrics. 1994;50(4):1088.33 14. O’Rourke K, Shea B, Wells GA. Meta-Analysis of Clinical Trials: Springer New York; 2001. 397-424 34 p.35 15. Ren F, Zhao T, Liu B, Pan L. Neutrophil-lymphocyte ratio (NLR) predicted prognosis for advanced
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1 non-small-cell lung cancer (NSCLC) patients who received immune checkpoint blockade (ICB). 2 OncoTargets and therapy. 2019;12:4235-44.3 16. Pavan A, Calvetti L, Dal Maso A, Attili I, Del Bianco P, Pasello G, et al. Peripheral Blood Markers 4 Identify Risk of Immune-Related Toxicity in Advanced Non-Small Cell Lung Cancer Treated with 5 Immune-Checkpoint Inhibitors. The oncologist. 2019.6 17. Passiglia F, Galvano A, Castiglia M, Incorvaia L, Calo V, Listi A, et al. Monitoring blood biomarkers 7 to predict nivolumab effectiveness in NSCLC patients. Therapeutic advances in medical oncology. 8 2019;11:1758835919839928.9 18. Minami S, Ihara S, Ikuta S, Komuta K. Gustave Roussy Immune Score and Royal Marsden Hospital
10 Prognostic Score Are Biomarkers of Immune-Checkpoint Inhibitor for Non-Small Cell Lung Cancer. 11 World journal of oncology. 2019;10(2):90-100.12 19. Ichiki Y, Taira A, Chikaishi Y, Matsumiya H, Mori M, Kanayama M, et al. Prognostic factors of 13 advanced or postoperative recurrent non-small cell lung cancer targeted with immune check point 14 inhibitors. Journal of thoracic disease. 2019;11(4):1117-23.15 20. Fukui T, Okuma Y, Nakahara Y, Otani S, Igawa S, Katagiri M, et al. Activity of Nivolumab and Utility 16 of Neutrophil-to-Lymphocyte Ratio as a Predictive Biomarker for Advanced Non-Small-Cell Lung Cancer: 17 A Prospective Observational Study. Clinical lung cancer. 2019;20(3):208-14.e2.18 21. Dusselier M, Deluche E, Delacourt N, Ballouhey J, Egenod T, Melloni B, et al. Neutrophil-to-19 lymphocyte ratio evolution is an independent predictor of early progression of second-line nivolumab-20 treated patients with advanced non-small-cell lung cancers. PloS one. 2019;14(7):e0219060.21 22. Zer A, Sung MR, Walia P, Khoja L, Maganti M, Labbe C, et al. Correlation of Neutrophil to 22 Lymphocyte Ratio and Absolute Neutrophil Count With Outcomes With PD-1 Axis Inhibitors in Patients 23 With Advanced Non-Small-Cell Lung Cancer. Clinical lung cancer. 2018;19(5):426-+.24 23. Takeda T, Takeuchi M, Saitoh M, Takeda S. Neutrophil-to-lymphocyte ratio after four weeks of 25 nivolumab administration as a predictive marker in patients with pretreated non-small-cell lung cancer. 26 Thoracic cancer. 2018;9(10):1291-9.27 24. Svaton M, Zemanova M, Skrickova J, Jakubikova L, Kolek V, Kultan J, et al. Chronic Inflammation 28 as a Potential Predictive Factor of Nivolumab Therapy in Non-small Cell Lung Cancer. Anticancer 29 research. 2018;38(12):6771-82.30 25. Suh KJ, Kim SH, Kim YJ, Kim M, Keam B, Kim TM, et al. Post-treatment neutrophil-to-lymphocyte 31 ratio at week 6 is prognostic in patients with advanced non-small cell lung cancers treated with anti-32 PD-1 antibody. Cancer Immunology Immunotherapy. 2018;67(3):459-70.33 26. Shiroyama T, Suzuki H, Tamiya M, Tamiya A, Tanaka A, Okamoto N, et al. Pretreatment advanced 34 lung cancer inflammation index (ALI) for predicting early progression in nivolumab-treated patients 35 with advanced non-small cell lung cancer. Cancer medicine. 2018;7(1):13-20.36 27. Russo A, Franchina T, Ricciardi GRR, Battaglia A, Scimone A, Berenato R, et al. Baseline neutrophilia,
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1 derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), and outcome in non 2 small cell lung cancer (NSCLC) treated with Nivolumab or Docetaxel. Journal of cellular physiology. 3 2018;233(10):6337-43.4 28. Park W, Kwon D, Saravia D, Desai A, Vargas F, El Dinali M, et al. Developing a Predictive Model for 5 Clinical Outcomes of Advanced Non-Small Cell Lung Cancer Patients Treated With Nivolumab. Clinical 6 lung cancer. 2018;19(3):280-8.e4.7 29. Nakaya A, Kurata T, Yoshioka H, Takeyasu Y, Niki M, Kibata K, et al. Neutrophil-to-lymphocyte ratio 8 as an early marker of outcomes in patients with advanced non-small-cell lung cancer treated with 9 nivolumab. International journal of clinical oncology. 2018;23(4):634-40.
10 30. Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, et al. Association of the Lung 11 Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced 12 Non-Small Cell Lung Cancer. JAMA oncology. 2018;4(3):351-7.13 31. Maymani H, Hess K, Groisberg R, Hong DS, Naing A, Piha-Paul S, et al. Predicting outcomes in 14 patients with advanced non-small cell lung cancer enrolled in early phase immunotherapy trials. Lung 15 cancer (Amsterdam, Netherlands). 2018;120:137-41.16 32. Kiriu T, Yamamoto M, Nagano T, Hazama D, Sekiya R, Katsurada M, et al. The time-series behavior 17 of neutrophil-to-lymphocyte ratio is useful as a predictive marker in non-small cell lung cancer. PloS 18 one. 2018;13(2):e0193018.19 33. Khunger M, Patil PD, Khunger A, Li M, Hu B, Rakshit S, et al. Post-treatment changes in 20 hematological parameters predict response to nivolumab monotherapy in non-small cell lung cancer 21 patients. PloS one. 2018;13(10):e0197743.22 34. Inomata M, Hirai T, Seto Z, Tokui K, Taka C, Okazawa S, et al. Clinical Parameters for Predicting the 23 Survival in Patients with Squamous and Non-squamous-cell NSCLC Receiving PD-1 Inhibitor Therapy. 24 Pathology oncology research : POR. 2018.25 35. Facchinetti F, Veneziani M, Buti S, Gelsomino F, Squadrilli A, Bordi P, et al. Clinical and hematologic 26 parameters address the outcomes of non-small-cell lung cancer patients treated with nivolumab. 27 Immunotherapy. 2018;10(8):681-94.28 36. Diem S, Schmid S, Krapf M, Flatz L, Born D, Jochum W, et al. Neutrophil-to-Lymphocyte ratio (NLR) 29 and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer 30 (NSCLC) treated with nivolumab. Lung cancer (Amsterdam, Netherlands). 2017;111:176-81.31 37. Bagley SJ, Kothari S, Aggarwal C, Bauml JM, Alley EW, Evans TL, et al. Pretreatment neutrophil-to-32 lymphocyte ratio as a marker of outcomes in nivolumab-treated patients with advanced non-small-cell 33 lung cancer. Lung cancer (Amsterdam, Netherlands). 2017;106:1-7.34 38. Ito A, Kondo S, Tada K, Kitano S. Clinical Development of Immune Checkpoint Inhibitors. Biomed 35 Res Int. 2015;2015:605478.36 39. Carbone DP, Reck M, Paz-Ares L, Creelan B, Horn L, Steins M, et al. First-Line Nivolumab in Stage
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1 IV or Recurrent Non-Small-Cell Lung Cancer. The New England journal of medicine. 2017;376(25):2415-2 26.3 40. Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, et al. Association of the Lung 4 Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced 5 Non-Small Cell Lung Cancer. JAMA Oncol. 2018;4(3):351-7.6 41. Nakaya A, Kurata T, Yoshioka H, Takeyasu Y, Niki M, Kibata K, et al. Neutrophil-to-lymphocyte ratio 7 as an early marker of outcomes in patients with advanced non-small-cell lung cancer treated with 8 nivolumab. Int J Clin Oncol. 2018.9 42. Zer A, Sung MR, Walia P, Khoja L, Maganti M, Labbe C, et al. Correlation of Neutrophil to
10 Lymphocyte Ratio and Absolute Neutrophil Count With Outcomes With PD-1 Axis Inhibitors in Patients 11 With Advanced Non-Small-Cell Lung Cancer. Clin Lung Cancer. 2018.12 43. Park W, Kwon D, Saravia D, Desai A, Vargas F, El Dinali M, et al. Developing a Predictive Model for 13 Clinical Outcomes of Advanced Non-Small Cell Lung Cancer Patients Treated With Nivolumab. Clinical 14 Lung Cancer. 2018;19(3):280-8.e4.15 44. Fukui T, Okuma Y, Nakahara Y, Otani S, Igawa S, Katagiri M, et al. Activity of Nivolumab and Utility 16 of Neutrophil-to-Lymphocyte Ratio as a Predictive Biomarker for Advanced Non–Small-Cell Lung Cancer: 17 A Prospective Observational Study. Clinical Lung Cancer. 2018.18 45. Russo A, Franchina T, Ricciardi GRR, Battaglia A, Scimone A, Berenato R, et al. Baseline neutrophilia, 19 derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), and outcome in non 20 small cell lung cancer (NSCLC) treated with Nivolumab or Docetaxel. J Cell Physiol. 2018.21 46. Sacdalan DB, Lucero JA, Sacdalan DL. Prognostic utility of baseline neutrophil-to-lymphocyte ratio 22 in patients receiving immune checkpoint inhibitors: a review and meta-analysis. OncoTargets and 23 therapy. 2018;11:955-65.24 47. Jiang T, Bai Y, Zhou F, Li W, Gao G, Su C, et al. Clinical value of neutrophil-to-lymphocyte ratio in 25 patients with non-small-cell lung cancer treated with PD-1/PD-L1 inhibitors. Lung cancer (Amsterdam, 26 Netherlands). 2019;130:76-83.27 48. Kargl J, Busch SE, Yang GH, Kim KH, Hanke ML, Metz HE, et al. Neutrophils dominate the immune 28 cell composition in non-small cell lung cancer. Nature communications. 2017;8:14381.29 49. Mazieres J, Drilon A, Lusque A, Mhanna L, Cortot AB, Mezquita L, et al. Immune checkpoint 30 inhibitors for patients with advanced lung cancer and oncogenic driver alterations: results from the 31 IMMUNOTARGET registry. Ann Oncol. 2019;30(8):1321-8.
32
33 Figure Legends
34 Figure 1 Flow chart of study selection
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2 Table 1 The basic characteristics of the enrolled studiesStudy Year Country Ethnicity Sample size MFP M/F NLR at baseline
Diem S 2017 Europe European 52 NM 29/23 5.0(2.7-8.3) *Bagley SJ 2017 America American 175 NM 80/95 NLR≥5:58.0%Russo A 2018 Italy European 28 17 25/3 NMZer A 2018 America American 88 5.3 43/45 NLR>4:56.8%Nakaya A 2018 Japan Asian 101 8.9 77/24 NLR≥3:46.5%Maymani H 2018 America American 74 12.3 36/38 NLR>6:20.3%Mezquita L 2018 Europe European 161 12 100/61 NLR>3:39.0%Fukui T 2018 Japan Asian 52 10.9 37/15 NLR≥5:34.6%Park W 2018 America American 159 11.5 82/77 4.3(0.5-24.1) *Takeda T 2018 Japan Asian 30 NM 19/11 NLR>5:30.0%Svaton M 2018 Czech Republic European 120 NM 71/49 NLR>3.8:50.0%Suh Koung Jin 2018 Korea Asian 54 26.2 42/12 NLR>5:14.8%Shiroyama Takayuki 2018 Japan Asian 201 12.4 135/66 NLR>4:39.3%Kiriu T 2018 Japan Asian 19.00 NM 19 NLR>5:31.6%Khunger M 2018 America American 109 30 56/53 NLR≥5:50.5%Inomata M 2018 Japan Asian 36 NM 27/9 NLR≥5:44.4%Facchinetti F 2018 Italy European 54 12.6 45/9 NMRen F 2019 China Asian 147 2.6 94/53 NLR>2.5:59.9%Pavan A 2019 Italy European 184 56.3 125/59 NLR≥3:57.5%Passiglia F 2019 Italy European 45 9.1 32/13 NLR>3.3:51.1%Minami S 2019 Japan Asian 76 NM 49/27 NLR≥6:14.5%Ichiki Y 2019 Japan Asian 44 4.83 38/6 NMDusselier M 2019 France European 59 NM 44/15 NLR>5:62.7%Study SCC% Treatment lines Outcome Study design NOS Cutoff IODiem S 34.6% including first line therapy OS/PFS RO 6 5 NBagley SJ 24.0% at least second-line therapy OS/PFS RO 6 5 NRusso A 60.7% at least second-line therapy OS/PFS RO 7 3 NZer A 17.1% at least second-line therapy OS/PFS/DCR RO 7 4 NMNakaya A 36.6% at least second-line therapy PFS/irAEs RO 6 3 NMaymani H 16.2% including first line therapy OS/PFS RO 7 6 N/P/DMezquita L 28.6% at least second line therapy OS/PFS RO 9 3 N/E/A/DFukui T 30.8% at least second-line therapy OS/PFS/irAEs PO 7 5 N
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Park W 24.5% including first line therapy OS/PFS RO 7 5 NTakeda T 30.0% at least second-line therapy PFS RO 6 5 NSvaton M 33.3% at least second-line therapy OS/PFS RO 7 3.8 NSuh Koung Jin 31.5% including first line therapy OS/PFS/irAEs RO 8 5 N/PShiroyama Takayuki 30.4% at least second-line therapy PFS/RR RO 7 4 NKiriu T 31.5% at least second-line therapy OS/PFS/TTF RO 7 5 NKhunger M 23.9% at least second-line therapy OS RO 6 5 NInomata M 44.4% at least second-line therapy PFS RO 6 5 N/PFacchinetti F 48.2% at least second-line therapy OS/PFS/TTF/DP PO 8 4 NRen F 42.2% at least second-line therapy OS/PFS RO 6 2.5 N/PPavan A 32.1% including first line therapy OS/PFS/irAEs RO 8 3 N/P/APassiglia F 44.4% at least second-line therapy OS/TTP RO 8 3.3 NMinami S 23.7% at least second-line therapy OS/PFS RO 9 6 N/P/AIchiki Y 65.9% including first line therapy OS/PFS/irAEs RO 7 NM N/PDusselier M 20.3% at least second-line therapy OS RO 8 5 N
1
2 Abbreviations: NLR: neutrophil to lymphocyte ratio; NM: not mentioned; M/F:
3 male/female; MFP: median follow-up (months); SCC%: proportion of patients with
TITLE Title 1 Identify the report as a systematic review, meta-analysis, or both. 1
ABSTRACT Structured summary 2 Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility
criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number.
2-3
INTRODUCTION Rationale 3 Describe the rationale for the review in the context of what is already known. 4
Objectives 4 Provide an explicit statement of questions being addressed with reference to participants, interventions, comparisons, outcomes, and study design (PICOS).
5
METHODS Protocol and registration 5 Indicate if a review protocol exists, if and where it can be accessed (e.g., Web address), and, if available, provide
registration information including registration number. 5
Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.
6
Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.
5
Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.
5-6 and supplements
Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).
5-8
Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.
6-7
Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.
6-7
Risk of bias in individual studies
12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.
8
Summary measures 13 State the principal summary measures (e.g., risk ratio, difference in means). 7-8
Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I2) for each meta-analysis.
7-8
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Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).
8
Additional analyses 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.
8
RESULTS Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at
each stage, ideally with a flow diagram. 8-9
Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.
8-9
Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12). 11-12
Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.
8-11
Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency. 8-11
Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see Item 15). 11-12
Additional analysis 23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]). 12
DISCUSSION Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to
key groups (e.g., healthcare providers, users, and policy makers). 12-13
Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).
15-16
Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research. 16
FUNDING Funding 27 Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the
systematic review. 17
From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(7): e1000097. doi:10.1371/journal.pmed1000097
For more information, visit: www.prisma-statement.org.
Page 2 of 2
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For peer review onlyAssociation of the neutrophil to lymphocyte ratio and
clinical outcomes in lung cancer patients receiving immunotherapy: a meta-analysis
Journal: BMJ Open
Manuscript ID bmjopen-2019-035031.R2
Article Type: Original research
Date Submitted by the Author: 14-Apr-2020
Complete List of Authors: Jin, Jing; Sichuan University West China HospitalYang, Lan; Sichuan University West China HospitalLiu, Dan; Sichuan University West China HospitalLi, Weimin; Sichuan University West China Hospital,
For peer review onlyI, the Submitting Author has the right to grant and does grant on behalf of all authors of the Work (as defined in the below author licence), an exclusive licence and/or a non-exclusive licence for contributions from authors who are: i) UK Crown employees; ii) where BMJ has agreed a CC-BY licence shall apply, and/or iii) in accordance with the terms applicable for US Federal Government officers or employees acting as part of their official duties; on a worldwide, perpetual, irrevocable, royalty-free basis to BMJ Publishing Group Ltd (“BMJ”) its licensees and where the relevant Journal is co-owned by BMJ to the co-owners of the Journal, to publish the Work in this journal and any other BMJ products and to exploit all rights, as set out in our licence.
The Submitting Author accepts and understands that any supply made under these terms is made by BMJ to the Submitting Author unless you are acting as an employee on behalf of your employer or a postgraduate student of an affiliated institution which is paying any applicable article publishing charge (“APC”) for Open Access articles. Where the Submitting Author wishes to make the Work available on an Open Access basis (and intends to pay the relevant APC), the terms of reuse of such Open Access shall be governed by a Creative Commons licence – details of these licences and which Creative Commons licence will apply to this Work are set out in our licence referred to above.
Other than as permitted in any relevant BMJ Author’s Self Archiving Policies, I confirm this Work has not been accepted for publication elsewhere, is not being considered for publication elsewhere and does not duplicate material already published. I confirm all authors consent to publication of this Work and authorise the granting of this licence.
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2 1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2015. CA Cancer J Clin. 2015;65(1):5-29.3 2. Callahan MK, Postow MA, Wolchok JD. Targeting T Cell Co-receptors for Cancer Therapy. Immunity. 4 2016;44(5):1069-78.5 3. Chen YM. Immune checkpoint inhibitors for nonsmall cell lung cancer treatment. Journal of the 6 Chinese Medical Association Jcma. 2016;80(1):7.7 4. Yarchoan M, Hopkins A, Jaffee EM. Tumor Mutational Burden and Response Rate to PD-1 8 Inhibition. The New England journal of medicine. 2017;377(25):2500-1.9 5. Anagnostou V, Smith KN, Forde PM, Niknafs N, Bhattacharya R, White J, et al. Evolution of
10 Neoantigen Landscape during Immune Checkpoint Blockade in Non-Small Cell Lung Cancer. Cancer 11 discovery. 2017;7(3):264-76.12 6. Goswami S, Basu S, Sharma P. A potential biomarker for anti-PD-1 immunotherapy. Nature 13 medicine. 2018;24(2):123-4.14 7. Marchetti A, Barberis M, Franco R, De Luca G, Pace MV, Staibano S, et al. Multicenter Comparison 15 of 22C3 PharmDx (Agilent) and SP263 (Ventana) Assays to Test PD-L1 Expression for NSCLC Patients to 16 Be Treated with Immune Checkpoint Inhibitors. Journal of thoracic oncology : official publication of the 17 International Association for the Study of Lung Cancer. 2017;12(11):1654-63.18 8. Hooper CE, Lyburn ID, Searle J, Darby M, Hall T, Hall D, et al. The South West Area Mesothelioma 19 and Pemetrexed trial: a multicentre prospective observational study evaluating novel markers of 20 chemotherapy response and prognostication. British journal of cancer. 2015;112(7):1175-82.21 9. Wang X, Teng F, Kong L, Yu J. Pretreatment neutrophil-to-lymphocyte ratio as a survival predictor 22 for small-cell lung cancer. OncoTargets and therapy. 2016;9:5761-70.23 10. Sanchez-Salcedo P, de-Torres JP, Martinez-Urbistondo D, Gonzalez-Gutierrez J, Berto J, Campo A, 24 et al. The neutrophil to lymphocyte and platelet to lymphocyte ratios as biomarkers for lung cancer 25 development. Lung cancer (Amsterdam, Netherlands). 2016;97:28-34.26 11. Kos M, Hocazade C, Kos FT, Uncu D, Karakas E, Dogan M, et al. Prognostic role of pretreatment 27 platelet/lymphocyte ratio in patients with non-small cell lung cancer. Wien Klin Wochenschr. 28 2016;128(17-18):635-40.29 12. Tierney JF, Stewart LA, Ghersi D, Burdett S, Sydes MR. Practical methods for incorporating 30 summary time-to-event data into meta-analysis. Trials. 2007;8(1):16.31 13. Begg CB, Mazumdar M. Operating characteristics of a rank correlation test for publication bias. 32 Biometrics. 1994;50(4):1088.33 14. O’Rourke K, Shea B, Wells GA. Meta-Analysis of Clinical Trials: Springer New York; 2001. 397-424 34 p.35 15. Ren F, Zhao T, Liu B, Pan L. Neutrophil-lymphocyte ratio (NLR) predicted prognosis for advanced
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1 non-small-cell lung cancer (NSCLC) patients who received immune checkpoint blockade (ICB). 2 OncoTargets and therapy. 2019;12:4235-44.3 16. Pavan A, Calvetti L, Dal Maso A, Attili I, Del Bianco P, Pasello G, et al. Peripheral Blood Markers 4 Identify Risk of Immune-Related Toxicity in Advanced Non-Small Cell Lung Cancer Treated with 5 Immune-Checkpoint Inhibitors. The oncologist. 2019.6 17. Passiglia F, Galvano A, Castiglia M, Incorvaia L, Calo V, Listi A, et al. Monitoring blood biomarkers 7 to predict nivolumab effectiveness in NSCLC patients. Therapeutic advances in medical oncology. 8 2019;11:1758835919839928.9 18. Minami S, Ihara S, Ikuta S, Komuta K. Gustave Roussy Immune Score and Royal Marsden Hospital
10 Prognostic Score Are Biomarkers of Immune-Checkpoint Inhibitor for Non-Small Cell Lung Cancer. 11 World journal of oncology. 2019;10(2):90-100.12 19. Ichiki Y, Taira A, Chikaishi Y, Matsumiya H, Mori M, Kanayama M, et al. Prognostic factors of 13 advanced or postoperative recurrent non-small cell lung cancer targeted with immune check point 14 inhibitors. Journal of thoracic disease. 2019;11(4):1117-23.15 20. Fukui T, Okuma Y, Nakahara Y, Otani S, Igawa S, Katagiri M, et al. Activity of Nivolumab and Utility 16 of Neutrophil-to-Lymphocyte Ratio as a Predictive Biomarker for Advanced Non-Small-Cell Lung Cancer: 17 A Prospective Observational Study. Clinical lung cancer. 2019;20(3):208-14.e2.18 21. Dusselier M, Deluche E, Delacourt N, Ballouhey J, Egenod T, Melloni B, et al. Neutrophil-to-19 lymphocyte ratio evolution is an independent predictor of early progression of second-line nivolumab-20 treated patients with advanced non-small-cell lung cancers. PloS one. 2019;14(7):e0219060.21 22. Zer A, Sung MR, Walia P, Khoja L, Maganti M, Labbe C, et al. Correlation of Neutrophil to 22 Lymphocyte Ratio and Absolute Neutrophil Count With Outcomes With PD-1 Axis Inhibitors in Patients 23 With Advanced Non-Small-Cell Lung Cancer. Clinical lung cancer. 2018;19(5):426-+.24 23. Takeda T, Takeuchi M, Saitoh M, Takeda S. Neutrophil-to-lymphocyte ratio after four weeks of 25 nivolumab administration as a predictive marker in patients with pretreated non-small-cell lung cancer. 26 Thoracic cancer. 2018;9(10):1291-9.27 24. Svaton M, Zemanova M, Skrickova J, Jakubikova L, Kolek V, Kultan J, et al. Chronic Inflammation 28 as a Potential Predictive Factor of Nivolumab Therapy in Non-small Cell Lung Cancer. Anticancer 29 research. 2018;38(12):6771-82.30 25. Suh KJ, Kim SH, Kim YJ, Kim M, Keam B, Kim TM, et al. Post-treatment neutrophil-to-lymphocyte 31 ratio at week 6 is prognostic in patients with advanced non-small cell lung cancers treated with anti-32 PD-1 antibody. Cancer Immunology Immunotherapy. 2018;67(3):459-70.33 26. Shiroyama T, Suzuki H, Tamiya M, Tamiya A, Tanaka A, Okamoto N, et al. Pretreatment advanced 34 lung cancer inflammation index (ALI) for predicting early progression in nivolumab-treated patients 35 with advanced non-small cell lung cancer. Cancer medicine. 2018;7(1):13-20.36 27. Russo A, Franchina T, Ricciardi GRR, Battaglia A, Scimone A, Berenato R, et al. Baseline neutrophilia,
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1 derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), and outcome in non 2 small cell lung cancer (NSCLC) treated with Nivolumab or Docetaxel. Journal of cellular physiology. 3 2018;233(10):6337-43.4 28. Park W, Kwon D, Saravia D, Desai A, Vargas F, El Dinali M, et al. Developing a Predictive Model for 5 Clinical Outcomes of Advanced Non-Small Cell Lung Cancer Patients Treated With Nivolumab. Clinical 6 lung cancer. 2018;19(3):280-8.e4.7 29. Nakaya A, Kurata T, Yoshioka H, Takeyasu Y, Niki M, Kibata K, et al. Neutrophil-to-lymphocyte ratio 8 as an early marker of outcomes in patients with advanced non-small-cell lung cancer treated with 9 nivolumab. International journal of clinical oncology. 2018;23(4):634-40.
10 30. Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, et al. Association of the Lung 11 Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced 12 Non-Small Cell Lung Cancer. JAMA oncology. 2018;4(3):351-7.13 31. Maymani H, Hess K, Groisberg R, Hong DS, Naing A, Piha-Paul S, et al. Predicting outcomes in 14 patients with advanced non-small cell lung cancer enrolled in early phase immunotherapy trials. Lung 15 cancer (Amsterdam, Netherlands). 2018;120:137-41.16 32. Kiriu T, Yamamoto M, Nagano T, Hazama D, Sekiya R, Katsurada M, et al. The time-series behavior 17 of neutrophil-to-lymphocyte ratio is useful as a predictive marker in non-small cell lung cancer. PloS 18 one. 2018;13(2):e0193018.19 33. Khunger M, Patil PD, Khunger A, Li M, Hu B, Rakshit S, et al. Post-treatment changes in 20 hematological parameters predict response to nivolumab monotherapy in non-small cell lung cancer 21 patients. PloS one. 2018;13(10):e0197743.22 34. Inomata M, Hirai T, Seto Z, Tokui K, Taka C, Okazawa S, et al. Clinical Parameters for Predicting the 23 Survival in Patients with Squamous and Non-squamous-cell NSCLC Receiving PD-1 Inhibitor Therapy. 24 Pathology oncology research : POR. 2018.25 35. Facchinetti F, Veneziani M, Buti S, Gelsomino F, Squadrilli A, Bordi P, et al. Clinical and hematologic 26 parameters address the outcomes of non-small-cell lung cancer patients treated with nivolumab. 27 Immunotherapy. 2018;10(8):681-94.28 36. Diem S, Schmid S, Krapf M, Flatz L, Born D, Jochum W, et al. Neutrophil-to-Lymphocyte ratio (NLR) 29 and Platelet-to-Lymphocyte ratio (PLR) as prognostic markers in patients with non-small cell lung cancer 30 (NSCLC) treated with nivolumab. Lung cancer (Amsterdam, Netherlands). 2017;111:176-81.31 37. Bagley SJ, Kothari S, Aggarwal C, Bauml JM, Alley EW, Evans TL, et al. Pretreatment neutrophil-to-32 lymphocyte ratio as a marker of outcomes in nivolumab-treated patients with advanced non-small-cell 33 lung cancer. Lung cancer (Amsterdam, Netherlands). 2017;106:1-7.34 38. Ito A, Kondo S, Tada K, Kitano S. Clinical Development of Immune Checkpoint Inhibitors. Biomed 35 Res Int. 2015;2015:605478.36 39. Carbone DP, Reck M, Paz-Ares L, Creelan B, Horn L, Steins M, et al. First-Line Nivolumab in Stage
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1 IV or Recurrent Non-Small-Cell Lung Cancer. The New England journal of medicine. 2017;376(25):2415-2 26.3 40. Mezquita L, Auclin E, Ferrara R, Charrier M, Remon J, Planchard D, et al. Association of the Lung 4 Immune Prognostic Index With Immune Checkpoint Inhibitor Outcomes in Patients With Advanced 5 Non-Small Cell Lung Cancer. JAMA Oncol. 2018;4(3):351-7.6 41. Nakaya A, Kurata T, Yoshioka H, Takeyasu Y, Niki M, Kibata K, et al. Neutrophil-to-lymphocyte ratio 7 as an early marker of outcomes in patients with advanced non-small-cell lung cancer treated with 8 nivolumab. Int J Clin Oncol. 2018.9 42. Zer A, Sung MR, Walia P, Khoja L, Maganti M, Labbe C, et al. Correlation of Neutrophil to
10 Lymphocyte Ratio and Absolute Neutrophil Count With Outcomes With PD-1 Axis Inhibitors in Patients 11 With Advanced Non-Small-Cell Lung Cancer. Clin Lung Cancer. 2018.12 43. Park W, Kwon D, Saravia D, Desai A, Vargas F, El Dinali M, et al. Developing a Predictive Model for 13 Clinical Outcomes of Advanced Non-Small Cell Lung Cancer Patients Treated With Nivolumab. Clinical 14 Lung Cancer. 2018;19(3):280-8.e4.15 44. Fukui T, Okuma Y, Nakahara Y, Otani S, Igawa S, Katagiri M, et al. Activity of Nivolumab and Utility 16 of Neutrophil-to-Lymphocyte Ratio as a Predictive Biomarker for Advanced Non–Small-Cell Lung Cancer: 17 A Prospective Observational Study. Clinical Lung Cancer. 2018.18 45. Russo A, Franchina T, Ricciardi GRR, Battaglia A, Scimone A, Berenato R, et al. Baseline neutrophilia, 19 derived neutrophil-to-lymphocyte ratio (dNLR), platelet-to-lymphocyte ratio (PLR), and outcome in non 20 small cell lung cancer (NSCLC) treated with Nivolumab or Docetaxel. J Cell Physiol. 2018.21 46. Sacdalan DB, Lucero JA, Sacdalan DL. Prognostic utility of baseline neutrophil-to-lymphocyte ratio 22 in patients receiving immune checkpoint inhibitors: a review and meta-analysis. OncoTargets and 23 therapy. 2018;11:955-65.24 47. Jiang T, Bai Y, Zhou F, Li W, Gao G, Su C, et al. Clinical value of neutrophil-to-lymphocyte ratio in 25 patients with non-small-cell lung cancer treated with PD-1/PD-L1 inhibitors. Lung cancer (Amsterdam, 26 Netherlands). 2019;130:76-83.27 48. Kargl J, Busch SE, Yang GH, Kim KH, Hanke ML, Metz HE, et al. Neutrophils dominate the immune 28 cell composition in non-small cell lung cancer. Nature communications. 2017;8:14381.29 49. Mazieres J, Drilon A, Lusque A, Mhanna L, Cortot AB, Mezquita L, et al. Immune checkpoint 30 inhibitors for patients with advanced lung cancer and oncogenic driver alterations: results from the 31 IMMUNOTARGET registry. Ann Oncol. 2019;30(8):1321-8.
32
33 Figure Legends
34 Figure 1 Flow chart of study selection
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2 Table 1 The basic characteristics of the enrolled studiesStudy Year Country Ethnicity Sample size MFP M/F NLR at baseline
Diem S 2017 Europe European 52 NM 29/23 5.0(2.7-8.3) *Bagley SJ 2017 America American 175 NM 80/95 NLR≥5:58.0%Russo A 2018 Italy European 28 17 25/3 NMZer A 2018 America American 88 5.3 43/45 NLR>4:56.8%Nakaya A 2018 Japan Asian 101 8.9 77/24 NLR≥3:46.5%Maymani H 2018 America American 74 12.3 36/38 NLR>6:20.3%Mezquita L 2018 Europe European 161 12 100/61 NLR>3:39.0%Fukui T 2018 Japan Asian 52 10.9 37/15 NLR≥5:34.6%Park W 2018 America American 159 11.5 82/77 4.3(0.5-24.1) *Takeda T 2018 Japan Asian 30 NM 19/11 NLR>5:30.0%Svaton M 2018 Czech Republic European 120 NM 71/49 NLR>3.8:50.0%Suh Koung Jin 2018 Korea Asian 54 26.2 42/12 NLR>5:14.8%Shiroyama Takayuki 2018 Japan Asian 201 12.4 135/66 NLR>4:39.3%Kiriu T 2018 Japan Asian 19.00 NM 19 NLR>5:31.6%Khunger M 2018 America American 109 30 56/53 NLR≥5:50.5%Inomata M 2018 Japan Asian 36 NM 27/9 NLR≥5:44.4%Facchinetti F 2018 Italy European 54 12.6 45/9 NMRen F 2019 China Asian 147 2.6 94/53 NLR>2.5:59.9%Pavan A 2019 Italy European 184 56.3 125/59 NLR≥3:57.5%Passiglia F 2019 Italy European 45 9.1 32/13 NLR>3.3:51.1%Minami S 2019 Japan Asian 76 NM 49/27 NLR≥6:14.5%Ichiki Y 2019 Japan Asian 44 4.83 38/6 NMDusselier M 2019 France European 59 NM 44/15 NLR>5:62.7%Study SCC% Treatment lines Outcome Study design NOS Cutoff IODiem S 34.6% including first line therapy OS/PFS RO 6 5 NBagley SJ 24.0% at least second-line therapy OS/PFS RO 6 5 NRusso A 60.7% at least second-line therapy OS/PFS RO 7 3 NZer A 17.1% at least second-line therapy OS/PFS/DCR RO 7 4 NMNakaya A 36.6% at least second-line therapy PFS/irAEs RO 6 3 NMaymani H 16.2% including first line therapy OS/PFS RO 7 6 N/P/DMezquita L 28.6% at least second line therapy OS/PFS RO 9 3 N/E/A/DFukui T 30.8% at least second-line therapy OS/PFS/irAEs PO 7 5 N
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Park W 24.5% including first line therapy OS/PFS RO 7 5 NTakeda T 30.0% at least second-line therapy PFS RO 6 5 NSvaton M 33.3% at least second-line therapy OS/PFS RO 7 3.8 NSuh Koung Jin 31.5% including first line therapy OS/PFS/irAEs RO 8 5 N/PShiroyama Takayuki 30.4% at least second-line therapy PFS/RR RO 7 4 NKiriu T 31.5% at least second-line therapy OS/PFS/TTF RO 7 5 NKhunger M 23.9% at least second-line therapy OS RO 6 5 NInomata M 44.4% at least second-line therapy PFS RO 6 5 N/PFacchinetti F 48.2% at least second-line therapy OS/PFS/TTF/DP PO 8 4 NRen F 42.2% at least second-line therapy OS/PFS RO 6 2.5 N/PPavan A 32.1% including first line therapy OS/PFS/irAEs RO 8 3 N/P/APassiglia F 44.4% at least second-line therapy OS/TTP RO 8 3.3 NMinami S 23.7% at least second-line therapy OS/PFS RO 9 6 N/P/AIchiki Y 65.9% including first line therapy OS/PFS/irAEs RO 7 NM N/PDusselier M 20.3% at least second-line therapy OS RO 8 5 N
1
2 Abbreviations: NLR: neutrophil to lymphocyte ratio; NM: not mentioned; M/F:
3 male/female; MFP: median follow-up (months); SCC%: proportion of patients with
TITLE Title 1 Identify the report as a systematic review, meta-analysis, or both. 1
ABSTRACT Structured summary 2 Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility
criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number.
2-3
INTRODUCTION Rationale 3 Describe the rationale for the review in the context of what is already known. 4
Objectives 4 Provide an explicit statement of questions being addressed with reference to participants, interventions, comparisons, outcomes, and study design (PICOS).
5
METHODS Protocol and registration 5 Indicate if a review protocol exists, if and where it can be accessed (e.g., Web address), and, if available, provide
registration information including registration number. 5
Eligibility criteria 6 Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale.
6
Information sources 7 Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched.
5
Search 8 Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated.
5-6 and supplements
Study selection 9 State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis).
5-8
Data collection process 10 Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators.
6-7
Data items 11 List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made.
6-7
Risk of bias in individual studies
12 Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis.
8
Summary measures 13 State the principal summary measures (e.g., risk ratio, difference in means). 7-8
Synthesis of results 14 Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I2) for each meta-analysis.
7-8
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Risk of bias across studies 15 Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies).
8
Additional analyses 16 Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified.
8
RESULTS Study selection 17 Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at
each stage, ideally with a flow diagram. 8-9
Study characteristics 18 For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations.
8-9
Risk of bias within studies 19 Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12). 11-12
Results of individual studies 20 For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot.
8-11
Synthesis of results 21 Present results of each meta-analysis done, including confidence intervals and measures of consistency. 8-11
Risk of bias across studies 22 Present results of any assessment of risk of bias across studies (see Item 15). 11-12
Additional analysis 23 Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta-regression [see Item 16]). 12
DISCUSSION Summary of evidence 24 Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to
key groups (e.g., healthcare providers, users, and policy makers). 12-13
Limitations 25 Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias).
15-16
Conclusions 26 Provide a general interpretation of the results in the context of other evidence, and implications for future research. 16
FUNDING Funding 27 Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the
systematic review. 17
From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 6(7): e1000097. doi:10.1371/journal.pmed1000097
For more information, visit: www.prisma-statement.org.
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