Computational alternative to cardiac biopsy in multiple myeloma: utilizing the shape of routine laboratory data for the classification of AL cardiac amyloidosis

Abstract

Background: Patients with multiple myeloma (MM) are at increased risk for light chain cardiac amyloidosis (CA) due to high circulating levels of light chains. ALCA is a clinical diagnosis based on biopsy, cardiac imaging findings of restrictive cardiomyopathy, and serum monoclonal proteins. To overcome these challenges, we have developed machine learning models that accurately predict ALCA status while identifying key diagnostic features based on routine laboratory measurements and using limited data. Methods: Utilizing electronic medical data (EMR) data, we built support vector machine models (SVMs) using the MISTIC framework. Data including diagnosis history, treatment, and laboratory values for patients with MM or monoclonal gammopathy of unknown significance (MGUS) were collected as a retrospective cohort study at Moffit Cancer Center and Research Institute between 2014 and 2019. To utilize the wealth of information in longitudinal EMR data, we fit regression models to the EMR data and used the parameters and characteristics of the fits as features for our SVMs. We tested three different sets of features: descriptive statistics alone, parameters and characteristics of fit only, and both the descriptive statistics and parameters of fit (DescFits). A classifier using raw data at a timepoint proximal to the date of ALCA diagnostic imaging (EchoAdj), as well as analogous random forest and XGBoost models were used for comparison. Findings: SVM classifiers trained on the DescFits feature set (AUC= 1.0) outperformed blind model predictions of the EchoAdj model (AUC = 0.36) and other conventional machine learning approaches in identifying which patients were diagnosed with ALCA. The DescFits model selected features describing the temporal relationship of physiologic parameters including hematologic state, and cardiac and renal function. Interpretation: We demonstrated that the MISTIC SVM framework and the shape of EMR data can aid in identifying patients with cardiac amyloidosis without cardiac biopsy or imaging.