Artikel
Addressing methodological challenges for the evaluation of diagnostic tests: development of clinical recommendations combining the GRADE approach and the RAND Method
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Veröffentlicht: | 10. Juli 2012 |
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Gliederung
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Background: appropriateness of a disgnostic test is determined by its capacity to impact on patients' health outcomes. Lack of evidence on clinical outcomes often leads to weak recommentations on diagnostic tests.
Objective: to test an innovative approach to produce recommendations on use of Positron Emission Tomography (PET) combining the GRADE approach for grading quality of evidence and stregth of recommendations with the RAND method of consensus.
Methods: 2 multidisciplinary panels of 40 experts convened to produce recommendations on use of PET in breast, esophageal, lung, colo-rectal and head & neck cancer. Answerable research questions were developed by positioning and comparing PET against existing diagnostic strategies (replacement, add-on, triage). Clinical rationale was based on capacity of new test (PET) to modify initial diagnosis; subsequent change in therapeutic approach; expected clinical benefits for patients. Results from the systematic review of literature on PET's diagnostic accuracy were presented with levels of evidence. Lack of direct evidence on patient-important outcomes was bypassed using GRADE's approach for eliciting experts' judgement on clinical consequences of testing positive or negative. Results of two rounds of votes were analysed using the RAND/UCLA Appropriateness Method.
Results: for 32/43 clinical indications the panels reached an agreement. The decision-making algorithm based on interaction between benefits, risks and diagnostic accuracy resulted applied in 74% of clinical questions. Agreement between expected level of appropriateness and observed (voted) level of appropriateness was good (weighted kappa=0.937).
Discussion/Implications: the GRADE approach was determinant for considering patients' outcomes and was accepted/applied by the panels, despite its complexity.