Using machine learning approaches to develop a fast and easy-to-perform diagnostic tool for patients with light chain amyloidosis: a retrospective real-world study.

Abstract

Immunoglobulin light chain (AL) amyloidosis is a severe disorder caused by the accumulation of amyloid fibrils, leading to organ failure. Early diagnosis is crucial to prevent irreversible damage, yet it remains a challenge due to nonspecific symptoms that often appear later in the disease progression. A retrospective study analyzed data collected from 133 AL amyloidosis patients and 271 non-AL patients with similar symptoms but different diagnoses between January 1st, 2017, and September 30th, 2022. Demographic data and laboratory test results were collected. Subsequently, significant features were identified by both logistic regression and independent expert clinical ability. Eventually, logistic regression and four machine learning (ML) algorithms were employed to construct a diagnostic model, utilizing fivefold cross-validation and blind set testing to identify the optimal model. The study successfully identified nine independent predictors of AL amyloidosis patients with kidney or cardiac involvement, respectively. Two models were developed to identify key features that distinguish AL amyloidosis from nephrotic syndrome and hypertrophic cardiomyopathy, respectively. The light gradient boosting machine (LightGBM) model emerged as the most effective, demonstrating superior performance with the area under curve (AUC) of 0.90 in both models, alongside high sensitivity, specificity, and F1-score. This research highlights the potential of using a machine learning-based LightGBM model to facilitate early and accurate diagnosis of AL amyloidosis. The model's effectiveness suggests it could be a valuable tool in clinical settings, aiding in the timely identification of AL amyloidosis among patients with non-specific symptoms. Further validation in diverse populations is recommended to establish its universal applicability.