29
views
0
recommends
+1 Recommend
1 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      The accuracy of pulse oximetry in measuring oxygen saturation by levels of skin pigmentation: a systematic review and meta-analysis

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          During the COVID-19 pandemic, there have been concerns regarding potential bias in pulse oximetry measurements for people with high levels of skin pigmentation. We systematically reviewed the effects of skin pigmentation on the accuracy of oxygen saturation measurement by pulse oximetry (SpO 2) compared with the gold standard SaO 2 measured by CO-oximetry.

          Methods

          We searched Ovid MEDLINE, Ovid Embase, EBSCO CINAHL, ClinicalTrials.gov, and WHO International Clinical Trials Registry Platform (up to December 2021) for studies with SpO 2–SaO 2 comparisons and measuring the impact of skin pigmentation or ethnicity on pulse oximetry accuracy. We performed meta-analyses for mean bias (the primary outcome in this review) and its standard deviations (SDs) across studies included for each subgroup of skin pigmentation and ethnicity and used these pooled mean biases and SDs to calculate accuracy root-mean-square ( A rms ) and 95% limits of agreement. The review was registered with the Open Science Framework ( https://osf.io/gm7ty).

          Results

          We included 32 studies (6505 participants): 15 measured skin pigmentation and 22 referred to ethnicity. Compared with standard SaO 2 measurement, pulse oximetry probably overestimates oxygen saturation in people with the high level of skin pigmentation (pooled mean bias 1.11%; 95% confidence interval 0.29 to 1.93%) and people described as Black/African American (1.52%; 0.95 to 2.09%) (moderate- and low-certainty evidence). The bias of pulse oximetry measurements for people with other levels of skin pigmentation or those from other ethnic groups is either more uncertain or suggests no overestimation. Whilst the extent of mean bias is small or negligible for all subgroups evaluated, the associated imprecision is unacceptably large (pooled SDs > 1%). When the extent of measurement bias and precision is considered jointly, pulse oximetry measurements for all the subgroups appear acceptably accurate (with A rms  < 4%).

          Conclusions

          Pulse oximetry may overestimate oxygen saturation in people with high levels of skin pigmentation and people whose ethnicity is reported as Black/African American, compared with SaO 2. The extent of overestimation may be small in hospital settings but unknown in community settings.

          Review protocol registration

          https://osf.io/gm7ty

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12916-022-02452-8.

          Related collections

          Most cited references56

          • Record: found
          • Abstract: found
          • Article: not found

          QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.

          In 2003, the QUADAS tool for systematic reviews of diagnostic accuracy studies was developed. Experience, anecdotal reports, and feedback suggested areas for improvement; therefore, QUADAS-2 was developed. This tool comprises 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of risk of bias, and the first 3 domains are also assessed in terms of concerns regarding applicability. Signalling questions are included to help judge risk of bias. The QUADAS-2 tool is applied in 4 phases: summarize the review question, tailor the tool and produce review-specific guidance, construct a flow diagram for the primary study, and judge bias and applicability. This tool will allow for more transparent rating of bias and applicability of primary diagnostic accuracy studies.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range

            Background In systematic reviews and meta-analysis, researchers often pool the results of the sample mean and standard deviation from a set of similar clinical trials. A number of the trials, however, reported the study using the median, the minimum and maximum values, and/or the first and third quartiles. Hence, in order to combine results, one may have to estimate the sample mean and standard deviation for such trials. Methods In this paper, we propose to improve the existing literature in several directions. First, we show that the sample standard deviation estimation in Hozo et al.’s method (BMC Med Res Methodol 5:13, 2005) has some serious limitations and is always less satisfactory in practice. Inspired by this, we propose a new estimation method by incorporating the sample size. Second, we systematically study the sample mean and standard deviation estimation problem under several other interesting settings where the interquartile range is also available for the trials. Results We demonstrate the performance of the proposed methods through simulation studies for the three frequently encountered scenarios, respectively. For the first two scenarios, our method greatly improves existing methods and provides a nearly unbiased estimate of the true sample standard deviation for normal data and a slightly biased estimate for skewed data. For the third scenario, our method still performs very well for both normal data and skewed data. Furthermore, we compare the estimators of the sample mean and standard deviation under all three scenarios and present some suggestions on which scenario is preferred in real-world applications. Conclusions In this paper, we discuss different approximation methods in the estimation of the sample mean and standard deviation and propose some new estimation methods to improve the existing literature. We conclude our work with a summary table (an Excel spread sheet including all formulas) that serves as a comprehensive guidance for performing meta-analysis in different situations. Electronic supplementary material The online version of this article (doi:10.1186/1471-2288-14-135) contains supplementary material, which is available to authorized users.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT

                Bookmark

                Author and article information

                Contributors
                chunhu.shi@manchester.ac.uk
                Journal
                BMC Med
                BMC Med
                BMC Medicine
                BioMed Central (London )
                1741-7015
                16 August 2022
                16 August 2022
                2022
                : 20
                : 267
                Affiliations
                [1 ]GRID grid.5379.8, ISNI 0000000121662407, School of Health Sciences, Faculty of Biology, Medicine and Health, , Manchester Academic Health Science Centre, University of Manchester, ; Jean McFarlane Building, Oxford Rd, Manchester, M13 9PL UK
                [2 ]NIHR Applied Research Collaboration Greater Manchester (ARC-GM), Manchester, UK
                [3 ]GRID grid.10025.36, ISNI 0000 0004 1936 8470, Institute of Population Health, , University of Liverpool, ; Liverpool, L69 3GF UK
                [4 ]NIHR Applied Research Collaboration North West Coast (ARC-NWC), Manchester, UK
                [5 ]GRID grid.7943.9, ISNI 0000 0001 2167 3843, Applied Health Research Hub, , University of Central Lancashire, ; Preston, UK
                [6 ]GRID grid.5379.8, ISNI 0000000121662407, NIHR School for Primary Care Research, Division of Population Health, Health Services Research and Primary Care, School of Health Sciences, Faculty of Biology, Medicine and Health, , Manchester Academic Health Science Centre, University of Manchester, ; Manchester, M13 9PL UK
                [7 ]GRID grid.5379.8, ISNI 0000000121662407, NIHR Greater Manchester Patient Safety Translational Research Centre, Division of Population Health, Health Services Research & Primary Care, University of Manchester, ; Manchester, M13 9PL UK
                [8 ]GRID grid.7943.9, ISNI 0000 0001 2167 3843, Faculty of Health, , University of Central Lancashire, ; Preston, PR1 2HE UK
                [9 ]GRID grid.5379.8, ISNI 0000000121662407, NIHR Manchester Biomedical Research Centre, , University of Manchester, ; Manchester, M13 9WL UK
                [10 ]Northern Care Alliance NHS Foundation Trust, Salford Care Organisation, Salford, M6 8HD Greater Manchester UK
                Author information
                http://orcid.org/0000-0003-0151-0451
                Article
                2452
                10.1186/s12916-022-02452-8
                9377806
                35971142
                40023be2-60ef-46ea-b045-c013554434f8
                © The Author(s) 2022

                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
                : 17 March 2022
                : 28 June 2022
                Funding
                Funded by: National Institute for Health Research Applied Research Collaboration (NIHR ARC) Greater Manchester
                Funded by: NIHR ARC North West Coast
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2022

                Medicine
                pulse oximetry,arterial blood oxygen saturation,measurement bias,skin pigmentation,ethnicity,systematic review

                Comments

                Comment on this article