Table 1.

Proposed avenues to improve the success of prediction models

Steps	Avenues
Determine the objectives	Identify the clinical need and potential future use by engaging relevant stakeholders, including patients, clinicians, researchers, and administrators Carefully plan the subsequent steps and collaboration network to ensure feasibility and availability of appropriate expertise
Selection of source data for model derivation	Develop high-quality curated datasets for the specific purpose of creating generalizable prediction models Consider the exploration of new predictors such as biologic biomarkers and functional status
Model development	Avoid the use of stepwise variable selection methods that produce overfitted and nongeneralizable models Only use variables with a clear definition that can be collected prospectively in a reproducible manner Align model development with the intended purpose and future integration strategy to clinical and/or research contexts
Performance analysis	Appropriate internal validation and calibration to identify subsets in which the model may underperform
External validation	Plan for external validation strategy in advance during the design phase External validation may be repeated over time to account for a change in clinical practice Plan for interventional studies to investigate the effect of implementation in a real-life setting