Validating Prediction Models for use in Clinical Practice: Concept, Steps, and Procedures Focusing on Hypertension Risk Prediction

Abstract

Prediction models also known as clinical prediction models are mathematical formula or equation that expresses the relationship between multiple variables and helps predict the future of an outcome using specific values of certain variables. Prediction models are extensively used in numerous areas including clinical settings and their application is large.[1] In clinical application, a prediction model helps to detect or screen high-risk subjects for asymptomatic disease for early interventions, predict a future disease to facilitate patient-doctor communication based on more objective information, assist in medical decision-making to help both doctors and patients to make an informed choice regarding the treatment, and assist in health-care services with planning and quality management.[1,2] For example, there exist many prediction models for calculating the risk of developing hypertension in the future.[3-5] While specific details may vary between prediction models, the goal and process of developing prediction models are mostly similar. Conventionally, a single prediction model is built from a dataset of individuals in whom the outcomes are known and then the developed model is applied to predict outcomes for future individuals. There are two main components of prediction modeling: model development and model validation. Once a model is developed using an appropriate modeling strategy, its utility is assessed through model validation. Investigators want to see through validation how the developed model works in a dataset that was not used to develop the model to ensure that the model’s performance is adequate for the intended purpose. Abstract