Predicting incident heart failure from population-based nationwide electronic health records: protocol for a model development and validation study.

ABSTRACT:

Introduction

Heart failure (HF) is increasingly common and associated with excess morbidity, mortality, and healthcare costs. Treatment of HF can alter the disease trajectory and reduce clinical events in HF. However, many cases of HF remain undetected until presentation with more advanced symptoms, often requiring hospitalisation. Predicting incident HF is challenging and statistical models are limited by performance and scalability in routine clinical practice. An HF prediction model implementable in nationwide electronic health records (EHRs) could enable targeted diagnostics to enable earlier identification of HF.

Methods and analysis

We will investigate a range of development techniques (including logistic regression and supervised machine learning methods) on routinely

SUBMITTER: Nakao YM 

PROVIDER: S-EPMC10806764 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature