# Latent profile analysis of health status and influencing factors among patients with coronary heart disease based on patient-reported outcomes

## TL;DR

This study uses patient-reported outcomes to classify coronary heart disease patients into health categories and identifies factors influencing their health status.

## Contribution

The study introduces a classification of CHD patients using latent profile analysis and identifies key factors affecting their health status.

## Key findings

- Patients were categorized into three health profiles: stable and healthy, social psychological fluctuation, and symptom prominent instability.
- Factors like glycated hemoglobin, age, education level, and COPD significantly influence health status categories.
- Patient-reported outcomes help in precision medicine by enabling personalized interventions based on health profiles.

## Abstract

Coronary heart disease is a leading cause of mortality and disability worldwide, posing significant challenges to public health and necessitating effective strategies for improving patient outcomes and quality of life.This study aims to analyze the health status of Coronary heart disease patients using a patient-reported outcomes scale, exploring differences across five dimensions: physical health, mental health, social health, spiritual health, and specific symptoms. The goal is to provide a foundation for personalized medical interventions and health management.

This is a Cross-sectional study, 240 patients were selected for latent profile analysis to categorize their health statuses. Key influencing factors were identified through univariate analysis and multivariate logistic regression analysis.

The health status of patients was categorized into three groups, stable and healthy model (n = 146), social psychological fluctuation model (n = 78), and symptom prominent instability model (n = 16). Significant differences were observed among these concerning glycated hemoglobin, high-density lipoprotein, low-density lipoprotein, age, monthly income, education level, and comorbid chronic obstructive pulmonary disease. The health status of social psychological fluctuation model and symptom prominent instability model was independently influenced by glycated hemoglobin, age, education level, and COPD (P < 0.05).

The health status of CHD patients can be classified into distinct categories influenced by multiple factors and comorbidities. As a crucial assessment tool, PRO facilitates the categorization of patient health statuses and provides a reference for precision medicine and personalized interventions. Future efforts should focus on developing targeted interventions tailored to the specific characteristics.