Level of evidence in clinical practice guidelines and feasibility of generating evidence from trial emulations – CPG-EMULATE

Evidence-based clinical practice guidelines (CPGs) are essential tools in patient care, yet only 10 to 20% of recommendations are supported by high-level evidence, and this proportion is not improving over time. Randomized controlled trials (RCTs), considered the highest level of evidence, face limitations such as high costs, feasibility issues, and limited generalizability to real-world practice.

This project hypothesizes that innovative approaches using real-world data and target trial emulation could generate robust evidence for informing CPGs. The objectives are to describe the level of evidence informing the recommendations of major CPGs and its evolution over time, assess the feasibility of generating evidence using target trial emulation for recommendations not supported by high level of evidence, identify barriers and possible solutions to emulate target trials, and develop a framework to guide the generation of evidence to support decision-making.

The project is divided into three work packages (WP).

WP1: Assessment of the level of evidence of CPG recommendations over 10 years. It will include: 1) Identification of eligible clinical practice guidelines and recommendations, and 2) Development of the clinical questions.

WP2: Feasibility of target trial emulation, which will include: 1) Definition of target trials, 2) Assessment of the feasibility of target trial emulation, and 3) Identification of barriers and possible solutions for conducting target trial emulation.

WP3: Development of a framework to guide the generation of evidence to support recommendations. It will include: 1) Literature review, 2) Development of first draft of framework, and 3) Refine and finalize the framework with international experts.

This project will significantly contribute by generating higher quality evidence, improving CPG development and implementation, and strengthening evidence-based medicine to optimize patient outcomes.