Symptom-Hemodynamic Mismatch and Heart Failure Event Risk

Area of Science:

• Cardiology
• Clinical Research
• Data Science

Background:

• Heart failure (HF) is a complex condition with varied symptoms and hemodynamic profiles.
• Understanding these variations is crucial for effective patient management.

Purpose of the Study:

• To identify distinct patient profiles by integrating physical/psychological symptoms and hemodynamic data.
• To quantify the 180-day risk of adverse events across these identified profiles.

Main Methods:

• Secondary analysis of prospective cohort data.
• Latent class mixture modeling to define symptom-hemodynamic profiles.
• Cox proportional hazards modeling to assess event risk.

Main Results:

• Three distinct profiles were identified: concordant symptoms/hemodynamics (17.9%), severe symptoms/average hemodynamics (17.9%), and mild symptoms/poor hemodynamics (64.2%).
• Both mismatch profiles (severe symptoms or poor hemodynamics) showed significantly increased risk for HF events or death compared to the concordant profile.
• Patients with severe symptoms had a 3.38-fold increased risk (P=.033); those with poor hemodynamics had a 3.48-fold increased risk (P=.016).

Conclusions:

• A small proportion of heart failure patients exhibit concordant symptoms and hemodynamics.
• Symptom-hemodynamic mismatch in HF is linked to a substantially higher risk of adverse healthcare utilization and mortality.