Validating and Updating GRASP: An Evidence-Based Framework for Grading and Assessment of Clinical Predictive Tools
Abstract Background: When selecting predictive tools, clinicians are challenged with an overwhelming and ever-growing number, most of which have never been implemented or evaluated for comparative effectiveness. To overcome this challenge, the authors developed an evidence-based framework for grading and assessment of predictive tools (GRASP). The objective of this study is to update GRASP and evaluate its reliability. Methods: An online survey was developed to collect responses of a wide international group of experts, who published studies on developing, implementing or evaluating clinical decision support tools. The interrater reliability of the framework, to assign grades to eight predictive tools by two independent users, was evaluated. Results: Among 882 invited experts, 81 provided valid responses. On a five-points Likert scale, experts overall strongly agreed to GRASP evaluation criteria (4.35). Experts strongly agreed to six criteria: predictive performance (4.87) and predictive performance levels (4.44), usability (4.68) and potential effect (4.61), post-implementation impact (4.78) and evidence direction (4.26). Experts somewhat agreed to one criterion: post-implementation impact levels (4.16). Experts were neutral about one criterion; usability is higher than potential effect (2.97). Sixty-four respondents provided recommendations to open-ended questions regarding adding, removing or changing evaluation criteria. Forty-three respondents suggested the potential effect should be higher than the usability. Experts highlighted the importance of reporting quality of studies and strength of evidence supporting grades assigned to predictive tools. Accordingly, GRASP concept and its detailed report were updated. The updated framework’s interrater reliability, to produce accurate and consistent results by two independent users, was tested and found to be initially reliable. Conclusion: The GRASP framework grades predictive tools based on critical appraisal of published evidence across three dimensions: phase of evaluation, level of evidence, and direction of evidence. The final grade of a tool is based on the highest phase of evaluation, supported by the highest level of positive evidence, or mixed evidence that supports positive conclusion. GRASP aims to provide clinicians with a high-level, evidence-based, and comprehensive, yet simple and feasible, approach to evaluate predictive tools, considering their predictive performance before implementation, usability and potential effect during planning for implementation, and post-implementation impact on healthcare outcomes.