Echocardiographic Nomogram Model: A Tool for Assessing Cardiac Involvement in Patients With Systemic Amyloidosis

Area of Science:

• Cardiology
• Medical Diagnostics
• Biomarkers

Background:

• Cardiac involvement significantly impacts prognosis in systemic amyloidosis.
• Early detection of cardiac amyloidosis (CA) in primary light chain amyloidosis (pAL) is crucial for improved patient outcomes.
• Current diagnostic methods may be invasive or lack prognostic precision.

Purpose of the Study:

• To develop and validate a predictive diagnostic model for cardiac amyloidosis (CA) in patients with primary light chain amyloidosis (pAL).
• To facilitate early detection and enhance prognostic evaluation of cardiac involvement in pAL.
• To establish a noninvasive tool for assessing cardiac involvement in systemic amyloidosis.

Main Methods:

• Retrospective analysis of 72 pAL patients (51 with cardiac involvement).
• Cardiac assessment using cardiac magnetic resonance imaging (CMR).
• Development of a predictive nomogram integrating risk factors identified by logistic regression and random forest algorithms, validated with ROC, calibration, and decision curve analyses.

Main Results:

• The nomogram incorporated relative wall thickness (RWT), E/e' sep, EFSR, and RV FAC.
• The model demonstrated strong diagnostic performance with an AUC of 0.85.
• High sensitivity (80.4%) and specificity (85.7%) were achieved in identifying cardiac involvement.

Conclusions:

• The developed nomogram offers a noninvasive and effective method for assessing cardiac involvement in systemic amyloidosis.
• This tool can significantly aid clinical decision-making and patient management strategies.
• The nomogram represents a valuable advancement in the early diagnosis and prognostic assessment of cardiac amyloidosis.