PIFA-N Multifactor Model to Predict Adverse Outcomes for Chronic Heart Failure Patients.

Area of Science:

• Cardiology
• Internal Medicine
• Medical Prognostics

Background:

• Chronic heart failure (CHF) patient prognosis requires personalized multifactor models.
• Existing models (SHFM, MAGGIC-HF, PREDICT-HF, BCN-Bio-HF) use inaccessible variables.
• Physicians need practical tools for assessing adverse outcomes in CHF patients.

Purpose of the Study:

• Develop a multifactor prognostic model for comorbid CHF patients.
• Assess the risk of adverse outcomes using accessible clinical and laboratory data.
• Create an efficient tool for predicting lethal outcomes in CHF patients.

Main Methods:

• Included 233 CHF patients (NYHA FC II-IV) with varying ejection fraction.
• Measured NT-proBNP, sST2, galectin-3, hepcidin, and copeptin levels.
• Conducted a 36-month prospective follow-up to determine all-cause mortality.

Main Results:

• Developed the PIFA-N model incorporating pneumonia, prior myocardial infarction, atrial fibrillation, anemia, and NT-proBNP.
• PIFA-N model demonstrated high efficiency (AUC 0.845), 77.1% sensitivity, and 77.3% specificity.
• Other tested biomarkers (sST2, galectin-3, hepcidin, copeptin) showed no significant predictive value.

Conclusions:

• The PIFA-N model offers an efficient prognostic tool for CHF patients.
• Model utilizes easily verifiable conditions and routine lab tests.
• Facilitates risk assessment without complex diagnostic procedures.