Machine-learning based subgroups of AL amyloidosis and cumulative incidence of mortality and end stage kidney disease.

Abstract

Immunoglobulin light chain (AL) amyloidosis is a multisystem disease with varied treatment options and disease-related outcomes. Current staging systems rely on a limited number of cardiac, renal, and plasma cell dyscrasia biomarkers. To improve prognostication for all-cause mortality and end-stage kidney disease (ESKD), we applied unsupervised machine learning using a comprehensive set of clinical and laboratory parameters. Our study cohort comprised 2067 patients with newly diagnosed, biopsy-proven AL amyloidosis from the Boston University Amyloidosis Center. Variables included 31 clinical symptoms and 28 baseline laboratory values. Our clustering algorithm identified three subgroups of AL amyloidosis (low-risk, intermediate-risk, and high-risk) with distinct clinical phenotypes and median overall survival (OS) estimates of 6.1, 3.7, and 1.2 years, respectively. The 10-year adjusted cumulative incidences of all-cause mortality were 66.8% (95% CI 63.4-70.1), 75.4% (95% CI 72.1-78.6), and 90.6% (95% CI 87.4-93.3) for low, intermediate, and high-risk subgroups. The 10-year adjusted cumulative incidences of end-stage kidney disease (ESKD) were 20.4% (95% CI 6.1-24.5), 37.6% (95% CI 31.8-43.8), and 6.7% (95% CI 2.8-11.3) for low-risk, intermediate-risk, and high-risk subgroups. Finally, we trained a classifier for external validation with high cross-validation accuracy (85% [95% CI 83-86]) using a subset of easily obtainable clinical parameters. This marks an initial stride toward integrating precision medicine into risk stratification of AL amyloidosis for both all-cause mortality and ESKD.