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      Health Disparities in Extracorporeal Membrane Oxygenation Utilization and Outcomes: A Scoping Review and Methodologic Critique of the Literature*

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          Abstract

          Objectives:

          To map the scope, methodological rigor, quality, and direction of associations between social determinants of health (SDoH) and extracorporeal membrane oxygenation (ECMO) utilization or outcomes.

          Data Sources:

          PubMed, Web of Science, Embase, and Cochrane Library databases were systematically searched for citations from January 2000 to January 2023, examining socioeconomic status (SES), race, ethnicity, hospital and ECMO program characteristics, transport, and geographic location (context) with utilization and outcomes (concept) in ECMO patients (population).

          Study Selection:

          Methodology followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses scoping review extension. Two reviewers independently evaluated abstracts and full text of identified publications. Exclusion criteria included non-English, unavailable, less than 40 patients, and periprocedural or mixed mechanical support.

          Data Extraction:

          Content analysis used a standardized data extraction tool and inductive thematic analysis for author-proposed mediators of disparities. Risk of bias was assessed using the Quality in Prognosis Studies tool.

          Data Synthesis:

          Of 8,214 citations screened, 219 studies were identified. Primary analysis focuses on 148 (68%) including race/ethnicity/SES/payer variables including investigation of ECMO outcomes 114 (77%) and utilization 43 (29%). SDoH were the primary predictor in 15 (10%). Overall quality and methodologic rigor was poor with advanced statistics in 7%. Direction of associations between ECMO outcomes or utilization according to race, ethnicity, SES, or payer varied. In 38% adverse outcomes or lower use was reported in underrepresented, under-resourced or diverse populations, while improved outcomes or greater use were observed in these populations in 7%, and 55% had no statistically significant result. Only 26 studies (18%) discussed mechanistic drivers of disparities, primarily focusing on individual- and hospital-level rather than systemic/structural factors.

          Conclusions:

          Associations between ECMO utilization and outcomes with SDoH are inconsistent, complicated by population heterogeneity and analytic shortcomings with limited consideration of systemic contributors. Findings and research gaps have implications for measuring, analyzing, and interpreting SDoH in ECMO research and healthcare.

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          Most cited references185

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          PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation

          Scoping reviews, a type of knowledge synthesis, follow a systematic approach to map evidence on a topic and identify main concepts, theories, sources, and knowledge gaps. Although more scoping reviews are being done, their methodological and reporting quality need improvement. This document presents the PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews) checklist and explanation. The checklist was developed by a 24-member expert panel and 2 research leads following published guidance from the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network. The final checklist contains 20 essential reporting items and 2 optional items. The authors provide a rationale and an example of good reporting for each item. The intent of the PRISMA-ScR is to help readers (including researchers, publishers, commissioners, policymakers, health care providers, guideline developers, and patients or consumers) develop a greater understanding of relevant terminology, core concepts, and key items to report for scoping reviews.
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            Scoping studies: towards a methodological framework

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              Assessing bias in studies of prognostic factors.

              Previous work has identified 6 important areas to consider when evaluating validity and bias in studies of prognostic factors: participation, attrition, prognostic factor measurement, confounding measurement and account, outcome measurement, and analysis and reporting. This article describes the Quality In Prognosis Studies tool, which includes questions related to these areas that can inform judgments of risk of bias in prognostic research.A working group comprising epidemiologists, statisticians, and clinicians developed the tool as they considered prognosis studies of low back pain. Forty-three groups reviewing studies addressing prognosis in other topic areas used the tool and provided feedback. Most reviewers (74%) reported that reaching consensus on judgments was easy. Median completion time per study was 20 minutes; interrater agreement (κ statistic) reported by 9 review teams varied from 0.56 to 0.82 (median, 0.75). Some reviewers reported challenges making judgments across prompting items, which were addressed by providing comprehensive guidance and examples. The refined Quality In Prognosis Studies tool may be useful to assess the risk of bias in studies of prognostic factors.
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                Author and article information

                Journal
                Critical Care Medicine
                Ovid Technologies (Wolters Kluwer Health)
                0090-3493
                2023
                March 28 2023
                July 2023
                : 51
                : 7
                : 843-860
                Affiliations
                [1 ]Department of Pediatrics, Harvard Medical School, Boston, MA.
                [2 ]Department of Cardiology, Boston Children’s Hospital, Boston, MA.
                [3 ]Children’s Hospital at Westmead Clinical School, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia.
                [4 ]Medical Library, Boston Children’s Hospital, Boston, MA.
                [5 ]Department of Pediatrics, Children’s Hospital New Orleans, Louisiana State University, New Orleans, LA.
                [6 ]Nell Hodgson Woodruff School of Nursing, Emory University, Atlanta, GA.
                [7 ]Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN.
                [8 ]Department of Pediatrics, C.S. Mott Children’s Hospital, University of Michigan, Ann Arbor, MI.
                [9 ]Department of Pediatrics, University of Washington, Seattle, WA.
                [10 ]Fenwick Institute for Pediatric Health Equity and Inclusion, Boston Children’s Hospital, Boston, MA.
                Article
                10.1097/CCM.0000000000005866
                36975216
                7113be48-6193-40ba-be05-8b917a1ad9e6
                © 2023
                History

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