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      The D-dimer level predicts the prognosis in patients with lung cancer: a systematic review and meta-analysis

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          Abstract

          Objective

          Although the significance of increased plasma D-dimer levels in activating coagulation and fibrinolysis has been reported, it is still controversial whether it can be used to predict the prognosis of lung cancer patients. This meta-analysis was performed to explore the beneficial role of plasma D-dimer as a prognostic factor in lung cancer patients according to a larger sample capacity.

          Materials and methods

          MEDLINE, EMBASE, and Cochrane Central databases were searched from inception to January 2021. The data are mainly hazard ratio(HR) with 95% confidence interval (CI) and Kaplan–Meier survival curves. The publication bias was examined by Egger’s test.

          Results

          Finally, a total of 28 studies, enrolling 8452 patients were included in the current meta-analysis. Our results showed that the OS (HR = 1.742, 95%CI:1.542–1.969, P < 0.001) and PFS (HR = 1.385, 95%CI:1.169–1.641, P = 0.003) in the high D-dimer group were significantly lower than those in the low D-dimer group. Subgroup analysis suggested that localization, detection methods and disease stage had an important effect on the prognosis.

          Conclusion

          This meta-analysis revealed that the high plasma D-dimer level leads to lower survival than in the low D-dimer level, which might provide an important clue for high plasma D-dimer level as an independent factor of poor prognosis in patients with lung cancer.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13019-021-01618-4.

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          Quantifying heterogeneity in a meta-analysis.

          Julian P T Higgins, Simon G Thompson (2002)
          The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
          Article 0 comments Cited 5575 times – based on 0 reviews     Review now
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            Practical methods for incorporating summary time-to-event data into meta-analysis

            Jayne F. Tierney, Lesley Stewart, Davina Ghersi … (2007)
            Background In systematic reviews and meta-analyses, time-to-event outcomes are most appropriately analysed using hazard ratios (HRs). In the absence of individual patient data (IPD), methods are available to obtain HRs and/or associated statistics by carefully manipulating published or other summary data. Awareness and adoption of these methods is somewhat limited, perhaps because they are published in the statistical literature using statistical notation. Methods This paper aims to 'translate' the methods for estimating a HR and associated statistics from published time-to-event-analyses into less statistical and more practical guidance and provide a corresponding, easy-to-use calculations spreadsheet, to facilitate the computational aspects. Results A wider audience should be able to understand published time-to-event data in individual trial reports and use it more appropriately in meta-analysis. When faced with particular circumstances, readers can refer to the relevant sections of the paper. The spreadsheet can be used to assist them in carrying out the calculations. Conclusion The methods cannot circumvent the potential biases associated with relying on published data for systematic reviews and meta-analysis. However, this practical guide should improve the quality of the analysis and subsequent interpretation of systematic reviews and meta-analyses that include time-to-event outcomes.
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              Enhanced secondary analysis of survival data: reconstructing the data from published Kaplan-Meier survival curves

              Patricia Guyot, AE Ades, Mario JNM Ouwens … (2012)
              Background The results of Randomized Controlled Trials (RCTs) on time-to-event outcomes that are usually reported are median time to events and Cox Hazard Ratio. These do not constitute the sufficient statistics required for meta-analysis or cost-effectiveness analysis, and their use in secondary analyses requires strong assumptions that may not have been adequately tested. In order to enhance the quality of secondary data analyses, we propose a method which derives from the published Kaplan Meier survival curves a close approximation to the original individual patient time-to-event data from which they were generated. Methods We develop an algorithm that maps from digitised curves back to KM data by finding numerical solutions to the inverted KM equations, using where available information on number of events and numbers at risk. The reproducibility and accuracy of survival probabilities, median survival times and hazard ratios based on reconstructed KM data was assessed by comparing published statistics (survival probabilities, medians and hazard ratios) with statistics based on repeated reconstructions by multiple observers. Results The validation exercise established there was no material systematic error and that there was a high degree of reproducibility for all statistics. Accuracy was excellent for survival probabilities and medians, for hazard ratios reasonable accuracy can only be obtained if at least numbers at risk or total number of events are reported. Conclusion The algorithm is a reliable tool for meta-analysis and cost-effectiveness analyses of RCTs reporting time-to-event data. It is recommended that all RCTs should report information on numbers at risk and total number of events alongside KM curves.
              Article 0 comments Cited 511 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Lianhua Ye: lhye1204@aliyun.com
                Journal
                Journal ID (nlm-ta): J Cardiothorac Surg
                Journal ID (iso-abbrev): J Cardiothorac Surg
                Title: Journal of Cardiothoracic Surgery
                Publisher: BioMed Central (London )
                ISSN (Electronic): 1749-8090
                Publication date (Electronic): 28 August 2021
                Publication date PMC-release: 28 August 2021
                Publication date Collection: 2021
                Volume: 16
                Electronic Location Identifier: 243
                Affiliations
                [1 ]GRID grid.452826.f, Department of Thoracic Surgery, , The Third Affiliated Hospital of Kunming Medical University, ; No. 519 Kunzhou Road, Xishan District, Kunming City, Yunnan Province China
                [2 ]GRID grid.459918.8, Department of Cardiothoracic Surgery, , The Sixth Affiliated Hospital of Kunming Medical University, ; Yuxi, China
                Article
                Publisher ID: 1618
                DOI: 10.1186/s13019-021-01618-4
                PMC ID: 8399789
                PubMed ID: 34454552
                SO-VID: 54135cde-a470-485c-bdd8-c4bd3bd0a2f4
                Copyright © © The Author(s) 2021
                License:

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                Date received : 16 June 2021
                Date accepted : 11 August 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, national natural science foundation of china;
                Award ID: No. 81860325
                Award Recipient :
                Funded by: high-level health technical personnel of yunnan provincial health commission.
                Award ID: No. L-2017006
                Award Recipient :
                Categories
                Subject: Review
                Custom metadata
                issue-copyright-statement © The Author(s) 2021

                ScienceOpen disciplines: Surgery
                Keywords: d-dimer,prognosis,lung cancer
                Data availability:
                ScienceOpen disciplines: Surgery
                Keywords: d-dimer, prognosis, lung cancer

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