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      Patient decision support interventions for candidates considering elective surgeries: a systematic review and meta-analysis

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          Background:

          The increase in elective surgeries and varied postoperative patient outcomes has boosted the use of patient decision support interventions (PDSIs). However, evidence on the effectiveness of PDSIs are not updated. This systematic review aims to summarize the effects of PDSIs for surgical candidates considering elective surgeries and to identify their moderators with an emphasis on the type of targeted surgery.

          Design:

          Systematic review and meta-analysis.

          Methods:

          We searched eight electronic databases for randomized controlled trials evaluating PDSIs among elective surgical candidates. We documented the effects on invasive treatment choice, decision-making–related outcomes, patient-reported outcomes, and healthcare resource use. The Cochrane Risk of Bias Tool version 2 and Grading of Recommendations, Assessment, Development, and Evaluations were adopted to rate the risk of bias of individual trials and certainty of evidence, respectively. STATA 16 software was used to conduct the meta-analysis.

          Results:

          Fifty-eight trials comprising 14 981 adults from 11 countries were included. Overall, PDSIs had no effect on invasive treatment choice (risk ratio=0.97; 95% CI: 0.90, 1.04), consultation time (mean difference=0.04 min; 95% CI: −0.17, 0.24), or patient-reported outcomes, but had a beneficial effect on decisional conflict (Hedges’ g=−0.29; 95% CI: −0.41, −0.16), disease and treatment knowledge (Hedges’ g=0.32; 95% CI: 0.15, 0.49), decision-making preparedness (Hedges’ g=0.22; 95% CI: 0.09, 0.34), and decision quality (risk ratio=1.98; 95% CI: 1.15, 3.39). Treatment choice varied with surgery type and self-guided PDSIs had a greater effect on disease and treatment knowledge enhancement than clinician-delivered PDSIs.

          Conclusions:

          This review has demonstrated that PDSIs targeting individuals considering elective surgeries had benefited their decision-making by reducing decisional conflict and increasing disease and treatment knowledge, decision-making preparedness, and decision quality. These findings may be used to guide the development and evaluation of new PDSIs for elective surgical care.

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          Most cited references101

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          Bias in meta-analysis detected by a simple, graphical test

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            RoB 2: a revised tool for assessing risk of bias in randomised trials

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              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.
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                Author and article information

                Contributors
                Journal
                Int J Surg
                Int J Surg
                JS9
                International Journal of Surgery (London, England)
                Lippincott Williams & Wilkins (Hagerstown, MD )
                1743-9191
                1743-9159
                May 2023
                10 April 2023
                : 109
                : 5
                : 1382-1399
                Affiliations
                [a ]Saw Swee Hock School of Public Health, National University of Singapore, Singapore
                [b ]School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
                [c ]Alice Lee Centre for Nursing Studies, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
                [d ]Department of Orthopaedic Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
                [e ]Department of Orthopaedic Surgery, National University Hospital, National University Health System, Singapore
                Author notes
                [* ]Corresponding author. Address: 12 Science Drive 2, #10-01, Singapore 117549, Singapore. Tel: +65 6516 4966, fax: +65 6779 1489, E-mail address: ephln@ 123456nus.edu.sg (N. Luo).
                Author information
                http://orcid.org/0000-0002-5338-578X
                http://orcid.org/0000-0002-1510-3462
                http://orcid.org/0000-0001-7859-3684
                http://orcid.org/0000-0002-9264-3630
                http://orcid.org/0000-0002-0265-8215
                http://orcid.org/0000-0002-2280-8356
                http://orcid.org/0000-0002-2012-9835
                http://orcid.org/0000-0001-7980-6979
                Article
                IJS-D-22-01532 00034
                10.1097/JS9.0000000000000302
                10389624
                37026838
                7849da63-2e03-4f85-ba10-9c1a2805b628
                Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc.

                This is an open access article distributed under the Creative Commons Attribution License 4.0 (CCBY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0/

                History
                : 20 October 2022
                : 1 February 2023
                Categories
                Reviews
                Custom metadata
                T
                TRUE

                Surgery
                decisional conflict,elective surgery,meta-analysis,patient decision support interventions,systematic review,treatment choice

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