Machine Learning Enables Prediction of Cardiac Amyloidosis by Routine Laboratory Parameters: A Proof-of-Concept Study

Abstract

(1) Background: Cardiac amyloidosis (CA) is a rare and complex condition with poor prognosis. While novel therapies improve outcomes, many affected individuals remain undiagnosed due to a lack of awareness among clinicians. This study was undertaken to develop an expert-independent machine learning (ML) prediction model for CA relying on routinely determined laboratory parameters. (2) Methods: In a first step, we developed baseline linear models based on logistic regression. In a second step, we used an ML algorithm based on gradient tree boosting to improve our linear prediction model, and to perform non-linear prediction. Then, we compared the performance of all diagnostic algorithms. All prediction models were developed on a training cohort, consisting of patients with proven CA (positive cases, n = 121) and amyloidosis-unrelated heart failure (HF) patients (negative cases, n = 415). Performances of all prediction models were evaluated on a separate prognostic validation cohort with 37 CA-positive and 124 CA-negative patients. (3) Results: Our best model, based on gradient-boosted ensembles of decision trees, achieved an area under the receiver operating characteristic curve (ROC AUC) score of 0.86, with sensitivity and specificity of 89.2% and 78.2%, respectively. The best linear model had an ROC AUC score of 0.75, with sensitivity and specificity of 84.6 and 71.7, respectively. (4) Conclusions: Our work demonstrates that ML makes it possible to utilize basic laboratory parameters to generate a distinct CA-related HF profile compared with CA-unrelated HF patients. This proof-of-concept study opens a potential new avenue in the diagnostic workup of CA and may assist physicians in clinical reasoning.

Convolutional Neural Networks for Fully Automated Diagnosis of Cardiac Amyloidosis by Cardiac Magnetic Resonance Imaging

“…Goto et al [27] have proposed two CNN-based prediction models for ECG (CA, n = 130) and echocardiography (CA, n = 70) data, achieving 0.85 ROC AUC and 0.91 ROC AUC scores, respectively. Our group [28] built a gradient-boosted tree prediction model for routinely available lab parameters (CA, n = 121) and achieved an ROC AUC score of 0.86 on the test set. Martini et al [29] trained a CNN-based prediction model for LGE CMR images (CA, n = 107) and achieved an ROC AUC score of 0.982 on the test set.…”

Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics

“…Although machine learning-based algorithms applied to the diagnosis of amyloidosis are not new. Machine learning-based algorithms have previously been applied to identify heart failure-related cardiac amyloidosis patients from heart failure-unrelated cardiac amyloidosis patients by the utilization of basic laboratory methods [ 22 ].To the best of our knowledge, the current study is the first to apply machine-learning to proteomics-based data for subtype classification of amyloidosis. In this work, SVMs were employed as it is a robust learning algorithm for classification with high discriminative power and insensitive to outliers.…”

Recent advances in the diagnosis and management of amyloid cardiomyopathy

“…Recently, a proof-of-concept study showed that an expert-independent machine learning prediction model for CA relying on routinely determined laboratory parameters was able to generate a CA-related HF profile that may help increase disease awareness among physicians and improve timely diagnosis 34 . With the continuous improvement in machine learning techniques and the integration of imaging and laboratory parameters, this approach holds promise for the future.…”