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The Necessity and Feasibility Assessment Tool of the Clinical Prediction Model for Individual Prognosis Before Its
Xiaohang Liu1, Yaguang Peng1, Nan Li2,3
1Center for Clinical Epidemiology and Evidence-Based Medicine, Beijing Children's Hospital, Capital Medical University, National Center for Children Health, Beijing, China.
Objective:
The overwhelming majority of prediction models have not been applied. An evidence-based review is needed to show that the new research is justified. This study aimed to develop an assessment tool for researchers and peer reviewers to conduct a rapid and comprehensive evaluation on the necessity and feasibility of planning clinical prediction model before its startup.
Methods:
The framework for developing quality assessment tools was followed to develop the necessity and Feasibility Assessment Tool of CLInical Prediction models for individual prognosis (FATCLIP). Firstly, the scope, framework, and item pool of the FATCLIP was identified by a steering group comprising 15 experts through a web-based meeting. Then, an iterative Delphi process was conducted to refine the FATCLIP, in which the Delphi group enrolled 34 experts from multidiscipline, including epidemiologists, statisticians, clinicians, evidence-based medicine specialists, health care administrators and academic journal editors.
Results:
Through twice steering group meetings and 2 rounds of the Delphi process, the framework of FATCLIP was determined based on expert consensus, including 6 domains and 31 signaling questions. The six domains were as follows: prediction outcome, review of existing models, candidate predictors, data, development and validation, and application and extension. At the same time, the usage manual of FATCLIP was also presented.
Conclusions:
The FATCILP aims to assist researchers and peer reviewers to detect potential challenges during the development and application of the clinical prediction model for individual prognosis before its start-up, so that the research of clinical prediction models could be efficient and avoid research waste.
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