Assessing the quality of the evidence underlying recommendations in type 2 diabetes' commonly used clinical practice
Enrik Enchev-Hristov1,2, Mariona Esteve-Serra1,3, Luz Maria Reyes-Céspedes3
1Medicine Department, Universitat de Girona.
Aim:
In a world of data overload, clinical practice recommendations are needed to help practitioners and patients to take evidence-based decisions. However, in the field of type 2 diabetes mellitus (T2DM) recommendations on glycemic goals and treatment choice are controversial in spite of being supported by a common body of evidence. We hypothesize that internal and external validity of this body of evidence might not be as sound as expected. The aim of the current study is to appraise the studies supporting recommendations developed by influential medical societies dealing with glycemic goals and the choice of pharmacological treatment in adults with T2DM.
Methods:
Clinical practice recommendations and their references were extracted out of eight documents developed by influential scientific societies between 2016 and 2019. Internal and external validity of each study was then appraised with standard tools and in duplicate.
Results:
A total of 114 recommendations and their underlying 233 citations were extracted. Out of these 233 citations 81 (35%) corresponded to randomized controlled trials (RCT) and 45 (20%) to systematic reviews. After systematical appraisal only four RCT (5%) and eight systematic reviews (17%) were considered to be unflawed. Indirectness (lack of generalizability) was the most common caveat identified in RCTs. Out of the 114 recommendations analyzed (32 dealing with glycemic goals and 82 with treatment choice), only 21 (18.4%) were supported by at least one high-quality study.
Conclusion:
Only one in five recommendations regarding glycemic goals or pharmacological treatment choice in T2DM is based on at least one high-quality study. Clinical practice recommendations dealing with areas of uncertainty should be formulated more transparently to enable real evidence-based decisions.
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