Detection and risk stratification of cardiac amyloidosis patients by integration of imaging and non-imaging data using a machine learning approach

Abstract

Abstract Background With the advent of amyloid-targeting therapies, early and reliable diagnosis as well as precise risk estimation of cardiac amyloidosis (CA) have become of substantial importance. While the current diagnostic approach relies on difficult-to-standardize, visual interpretation of 99mTc-scintigraphy, risk assessment is largely based on a combination of blood and imaging parameters. Purpose Aim of this study is to assess the possibility of machine learning-based integration of scintigraphy, echocardiography, and non-imaging parameters such as blood parameters and comorbidities for the detection and risk stratification of patients with CA. Methods This study included all patients who underwent 99mTc-scintigraphy at a university affiliated tertiary care centre between 2010 and 2023. Two machine learning models were developed, and the relative predictive importance of their parameters were assessed (Figure 1): First, a model to detect patients with CA-suggestive uptake (Perugini grade 2/3) on 99mTc-scintigraphy scans; Second, a model to assess the risk of patients with CA-suggestive uptake for future heart failure hospitalization (HFH). A total of 58 features were extracted from electronic health records including blood, echocardiographic, demographic parameters and comorbidities. Scintigraphy imaging features were extracted from the raw imaging data using a deep learning approach. For the time to HFH prediction, a random survival forest machine learning model was employed. Results Overall, 12 380 consecutively enrolled patients were included, 279 (2.3%) of which were affected by CA-suggestive uptake. The machine learning model showed higher accuracy for the detection of CA (AUC 0.96 [95% CI 0.95-0.97], sensitivity 0.78 [95% CI 0.77-0.80] and specificity 0.99 [95% CI 0.99-0.99]. In the outcome analysis, the machine model showed good accuracy in predicting future HFH (C-index 0.71 [95% CI 0.68-0.75]). Parameter importance for the time to event analysis revealed right ventricular diameter, previous diagnosis of chronic heart failure and creatine kinase as the three most important predictors (Figure 2). Conclusions Detection of patients with cardiac amyloidosis and their risk estimation for heart failure can be aided using machine learning-based integration of imaging and non-imaging data. Our findings may guide novel approaches for assessing disease progression and treatment response.