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.
Funding: This work was in part supported by funding from the National Institutes of Health (NCATS KL2TROO1429 to MCF and NIGMS R35GM147164 to CAK) and Moffitt Cancer Center and Research Institute’s Junior Scientist Partnership Award (to MCF and IBR). MAC is a trainee in the Medical Scientist Training Program supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number T32GM163993.
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BibTeX
@misc{cremer2026computational,
title = {{Computational alternative to cardiac biopsy in multiple myeloma: utilizing the shape of routine laboratory data for the classification of AL cardiac amyloidosis}},
author = {Maegan A. Cremer and Hyeju Song and Michael G. Fradley and Isaac B. Rhea and Chris A. Kieslich and Meghan C. Ferrall-Fairbanks},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.27.762298},
url = {https://www.biorxiv.org/content/10.64898/2026.10.27.762298v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Computational alternative to cardiac biopsy in multiple myeloma: utilizing the shape of routine laboratory data for the classification of AL cardiac amyloidosis
AU - Maegan A. Cremer
AU - Hyeju Song
AU - Michael G. Fradley
AU - Isaac B. Rhea
AU - Chris A. Kieslich
AU - Meghan C. Ferrall-Fairbanks
PY - 2026
DA - 2026/10/27
PB - bioRxiv
DO - 10.64898/2026.10.27.762298
UR - https://www.biorxiv.org/content/10.64898/2026.10.27.762298v1
AB - 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. Funding: This work was in part supported by funding from the National Institutes of Health (NCATS KL2TROO1429 to MCF and NIGMS R35GM147164 to CAK) and Moffitt Cancer Center and Research Institute’s Junior Scientist Partnership Award (to MCF and IBR). MAC is a trainee in the Medical Scientist Training Program supported by the National Institute of General Medical Sciences of the National Institutes of Health under award number T32GM163993.
SN - 2692-8205
ER -