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      Neurally adjusted ventilatory assist as a weaning mode for adults with invasive mechanical ventilation: a systematic review and meta-analysis

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          Abstract

          Background

          Prolonged ventilatory support is associated with poor clinical outcomes. Partial support modes, especially pressure support ventilation, are frequently used in clinical practice but are associated with patient–ventilation asynchrony and deliver fixed levels of assist. Neurally adjusted ventilatory assist (NAVA), a mode of partial ventilatory assist that reduces patient–ventilator asynchrony, may be an alternative for weaning. However, the effects of NAVA on weaning outcomes in clinical practice are unclear.

          Methods

          We searched PubMed, Embase, Medline, and Cochrane Library from 2007 to December 2020. Randomized controlled trials and crossover trials that compared NAVA and other modes were identified in this study. The primary outcome was weaning success which was defined as the absence of ventilatory support for more than 48 h. Summary estimates of effect using odds ratio (OR) for dichotomous outcomes and mean difference (MD) for continuous outcomes with accompanying 95% confidence interval (CI) were expressed.

          Results

          Seven studies ( n = 693 patients) were included. Regarding the primary outcome, patients weaned with NAVA had a higher success rate compared with other partial support modes (OR = 1.93; 95% CI 1.12 to 3.32; P = 0.02). For the secondary outcomes, NAVA may reduce duration of mechanical ventilation (MD = − 2.63; 95% CI − 4.22 to − 1.03; P = 0.001) and hospital mortality (OR = 0.58; 95% CI 0.40 to 0.84; P = 0.004) and prolongs ventilator-free days (MD = 3.48; 95% CI 0.97 to 6.00; P = 0.007) when compared with other modes.

          Conclusions

          Our study suggests that the NAVA mode may improve the rate of weaning success compared with other partial support modes for difficult to wean patients.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13054-021-03644-z.

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          Most cited references53

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          Measuring inconsistency in meta-analyses.

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            The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate health care interventions: explanation and elaboration.

            Systematic reviews and meta-analyses are essential to summarize evidence relating to efficacy and safety of health care interventions accurately and reliably. The clarity and transparency of these reports, however, is not optimal. Poor reporting of systematic reviews diminishes their value to clinicians, policy makers, and other users. Since the development of the QUOROM (QUality Of Reporting Of Meta-analysis) Statement--a reporting guideline published in 1999--there have been several conceptual, methodological, and practical advances regarding the conduct and reporting of systematic reviews and meta-analyses. Also, reviews of published systematic reviews have found that key information about these studies is often poorly reported. Realizing these issues, an international group that included experienced authors and methodologists developed PRISMA (Preferred Reporting Items for Systematic reviews and Meta-Analyses) as an evolution of the original QUOROM guideline for systematic reviews and meta-analyses of evaluations of health care interventions. The PRISMA Statement consists of a 27-item checklist and a four-phase flow diagram. The checklist includes items deemed essential for transparent reporting of a systematic review. In this Explanation and Elaboration document, we explain the meaning and rationale for each checklist item. For each item, we include an example of good reporting and, where possible, references to relevant empirical studies and methodological literature. The PRISMA Statement, this document, and the associated Web site (http://www.prisma-statement.org/) should be helpful resources to improve reporting of systematic reviews and meta-analyses.
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              Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range

              The era of big data is coming, and evidence-based medicine is attracting increasing attention to improve decision making in medical practice via integrating evidence from well designed and conducted clinical research. Meta-analysis is a statistical technique widely used in evidence-based medicine for analytically combining the findings from independent clinical trials to provide an overall estimation of a treatment effectiveness. The sample mean and standard deviation are two commonly used statistics in meta-analysis but some trials use the median, the minimum and maximum values, or sometimes the first and third quartiles to report the results. Thus, to pool results in a consistent format, researchers need to transform those information back to the sample mean and standard deviation. In this article, we investigate the optimal estimation of the sample mean for meta-analysis from both theoretical and empirical perspectives. A major drawback in the literature is that the sample size, needless to say its importance, is either ignored or used in a stepwise but somewhat arbitrary manner, e.g. the famous method proposed by Hozo et al. We solve this issue by incorporating the sample size in a smoothly changing weight in the estimators to reach the optimal estimation. Our proposed estimators not only improve the existing ones significantly but also share the same virtue of the simplicity. The real data application indicates that our proposed estimators are capable to serve as "rules of thumb" and will be widely applied in evidence-based medicine.
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                Author and article information

                Contributors
                haiboq2000@163.com
                liulingdoctor@126.com
                Journal
                Crit Care
                Critical Care
                BioMed Central (London )
                1364-8535
                1466-609X
                29 June 2021
                29 June 2021
                2021
                : 25
                : 222
                Affiliations
                [1 ]GRID grid.263826.b, ISNI 0000 0004 1761 0489, Jiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Zhongda Hospital, School of Medicine, , Southeast University, ; Nanjing, 210009 Jiangsu China
                [2 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Pediatrics, , University of Toronto, ; Toronto, Canada
                [3 ]GRID grid.17063.33, ISNI 0000 0001 2157 2938, Department of Medicine and Interdepartmental Division of Critical Care Medicine, , University of Toronto, ; Toronto, Canada
                [4 ]GRID grid.415502.7, Department of Critical Care, , Keenan Research Centre for Biomedical Science of St. Michael’s Hospital, St. Michael’s Hospital, ; 30 Bond Street, Toronto, ON M5B1W8 Canada
                [5 ]GRID grid.415502.7, Institute for Biomedical Engineering and Science Technology (iBEST), , Ryerson University and St-Michael’s Hospital, ; Toronto, Canada
                Article
                3644
                10.1186/s13054-021-03644-z
                8240429
                34187528
                2fa6b01e-a7de-4006-9280-a7224c29671d
                © The Author(s) 2021

                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
                : 8 April 2021
                : 16 June 2021
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 81870066
                Award ID: 81930058
                Award Recipient :
                Funded by: Clinical Science and Technology Specific Projects of Jiangsu Province
                Award ID: BE2020786
                Award ID: BE2019749
                Award Recipient :
                Funded by: National Science and Technology Major Project
                Award ID: 2020ZX09201015
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100004608, Natural Science Foundation of Jiangsu Province;
                Award ID: BK20171271
                Award Recipient :
                Funded by: the RS McLaughlin Foundation
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Emergency medicine & Trauma
                mechanical ventilation,neurally adjusted ventilatory assist,weaning success,patient–ventilator asynchrony

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