# Risk stratification and survival prediction in heart failure: from grades to scores

## TL;DR

This review discusses how heart failure risk is assessed using grading systems and scores, from traditional methods to modern machine learning tools.

## Contribution

The paper critically evaluates the evolution and current state of heart failure risk stratification tools.

## Key findings

- Traditional NYHA classification remains clinically relevant for assessing heart failure severity.
- Modern models like the Seattle Heart Failure Model and machine learning tools offer improved prognostic accuracy.
- Challenges remain in implementing and optimizing these tools in clinical practice.

## Abstract

Heart failure (HF) continues to pose a significant global health burden, necessitating accurate prognostic tools to guide patient management. This mini-review presents grading systems, frailty scales, and scoring models, followed by challenges and future directions. We traces the evolution of stratification and prognostic assessments in HF, beginning with the foundational NYHA functional classification and progressing to the advanced prognostic scores currently in use. We examine the historical significance and clinical relevance of NYHA grades, which have long been pivotal in evaluating HF severity. The review then shifts focus to contemporary prognostic scores, including the Seattle Heart Failure Model (SHFM), the Heart Failure Survival Score (HFSS), and emerging tools leveraging machine learning (ML) and big data. We explore specific challenges encountered in current clinical practice and outline future directions. By highlighting the strengths and limitations of these tools, this mini-review aims to provides a critical appraisal of stratification and scoring models for HF to inform their optimal application in clinical practice, ultimately enhancing patient care and outcomes in HF.