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      A simple index to predict healing in venous leg ulcers: a secondary analysis from four randomised controlled trials

      1 , 2 , 3 , 2 , 3
      Journal of Wound Care
      Mark Allen Group

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          Abstract

          Objective:

          To investigate whether the use of a simple baseline measurement predicts venous leg ulcer healing at 12 and 24 weeks.

          Method:

          This was a secondary analysis of a cohort of four randomised controlled trials (RCTs) of treatments adjuvant to compression. Self-reported ulcer duration, and measured ulcer length and width, to calculate estimated ulcer area, were used to obtain a Margolis index score. The score created three prognostic strata for likelihood to heal within 24 weeks, and the number of participants healed and time-to-healing were compared.

          Results:

          There were a total of 802 participants across the four RCTs—408 (50.9%) in two 12-week trials and 394 (49.1%) in two 24-week trials. The mean age of participants was 63.7±17.6 years, and 372 were female (46.4%). The Margolis index score at baseline was 0 for 320 participants (predicted normal healing); 1 for 334 participants; and 2 for 148 participants (both 1 and 2 predicted slow-to-heal). Overall, 248 (77.5%) of those participants who scored 0 at baseline healed within 24 weeks, compared with 182 (54.5%) of participants who scored 1, and 30 (20.3%) participants who scored 2. The median time-to-healing was 40 (24–62) days, 57 (35–100) days and 86.5 (56–151) days, respectively. The area under the receiver operating characteristic curve was 0.69 and 0.77, respectively, for the 12 and 24 week trials.

          Conclusion:

          A simple baseline index identifies participants with normal or slow-to-heal wounds and could be used to demonstrate prognostic balance between treatment groups in trials. This approach could also be used in clinical practice to assist with managing expectations and for early identification of patients who may best benefit from adjuvant treatments.

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          Most cited references25

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          Is Open Access

          CONSORT 2010 Statement: updated guidelines for reporting parallel group randomised trials

          The CONSORT statement is used worldwide to improve the reporting of randomised controlled trials. Kenneth Schulz and colleagues describe the latest version, CONSORT 2010, which updates the reporting guideline based on new methodological evidence and accumulating experience
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            The revised CONSORT statement for reporting randomized trials: explanation and elaboration.

            Overwhelming evidence now indicates that the quality of reporting of randomized, controlled trials (RCTs) is less than optimal. Recent methodologic analyses indicate that inadequate reporting and design are associated with biased estimates of treatment effects. Such systematic error is seriously damaging to RCTs, which boast the elimination of systematic error as their primary hallmark. Systematic error in RCTs reflects poor science, and poor science threatens proper ethical standards. A group of scientists and editors developed the CONSORT (Con solidated S tandards o f R eporting T rials) statement to improve the quality of reporting of RCTs. The statement consists of a checklist and flow diagram that authors can use for reporting an RCT. Many leading medical journals and major international editorial groups have adopted the CONSORT statement. The CONSORT statement facilitates critical appraisal and interpretation of RCTs by providing guidance to authors about how to improve the reporting of their trials. This explanatory and elaboration document is intended to enhance the use, understanding, and dissemination of the CONSORT statement. The meaning and rationale for each checklist item are presented. For most items, at least one published example of good reporting and, where possible, references to relevant empirical studies are provided. Several examples of flow diagrams are included. The CONSORT statement, this explanatory and elaboration document, and the associated Web site ( http://www.consort-statement.org ) should be helpful resources to improve reporting of randomized trials. Throughout the text, terms marked with an asterisk are defined at end of text.
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              Reliable assessment of the effects of treatment on mortality and major morbidity, I: clinical trials.

              This two-part review is intended principally for practising clinicians who want to know why some types of evidence about the effects of treatment on survival, and on other major aspects of chronic disease outcome, are much more reliable than others. Although there are a few striking examples of treatments for serious disease which really do work extremely well, most claims for big improvements turn out to be evanescent. Unrealistic expectations about the chances of discovering large treatment effects could misleadingly suggest that evidence from small randomised trials or from non-randomised studies will suffice. By contrast, the reliable assessment of any more moderate effects of treatment on major outcomes--which are usually all that can realistically be expected from most treatments for most common serious conditions--requires studies that guarantee both strict control of bias (which, in general, requires proper randomisation and appropriate analysis, with no unduly data-dependent emphasis on specific parts of the overall evidence) and strict control of random error (which, in general, requires large numbers of deaths or of some other relevant outcome). Past failures to produce such evidence, and to interpret it appropriately, have already led to many premature deaths and much unnecessary suffering.
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                Author and article information

                Journal
                Journal of Wound Care
                J Wound Care
                Mark Allen Group
                0969-0700
                2052-2916
                October 02 2023
                October 02 2023
                : 32
                : 10
                : 657-664
                Affiliations
                [1 ]School of Nursing, The University of Auckland, New Zealand
                [2 ]National Institute for Health Innovation, The University of Auckland, New Zealand
                [3 ]Department of Statistics, The University of Auckland, New Zealand
                Article
                10.12968/jowc.2023.32.10.657
                37af03a0-5049-4c71-9ecb-76b4e02b631e
                © 2023
                History

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