Detection of cardiac amyloidosis using machine learning on routine echocardiographic measurements

Abstract

Background: Cardiac amyloidosis (CA) is an underdiagnosed, progressive and lethal disease. Machine learning applied to common measurements derived from routine echocardiogram studies can inform suspicion of CA.

Objectives: Our objectives were to test a random forest (RF) model in detecting CA.

Methods: We used 3603 echocardiogram studies from 636 patients at Cedars-Sinai Medical Center to train an RF model to predict CA from echocardiographic parameters. 231 patients with CA were compared with 405 control patients with negative pyrophosphate scans or clinical diagnosis of hypertrophic cardiomyopathy. 19 common echocardiographic measurements from echocardiogram reports were used as input into the RF model. Data was split by patient into a training data set of 2882 studies from 486 patients and a test data set of 721 studies from 150 patients. The performance of the model was evaluated by area under the receiver operative curve (AUC), sensitivity, specificity and positive predictive value (PPV) on the test data set.

Results: The RF model identified CA with an AUC of 0.84, sensitivity of 0.82, specificity of 0.73 and PPV of 0.76. Some echocardiographic measurements had high missingness, suggesting gaps in measurement in routine clinical practice. Features that were large contributors to the model included mitral A-wave velocity, global longitudinal strain (GLS), left ventricle posterior wall diameter end diastolic (LVPWd) and left atrial area.

Conclusion: Machine learning on echocardiographic parameters can detect patients with CA with accuracy. Our model identified several features that were major contributors towards identifying CA including GLS, mitral A peak velocity and LVPWd. Further study is needed to evaluate its external validity and application in clinical settings.

Keywords: Cardiomyopathy, Restrictive; Diagnostic Imaging; Echocardiography.

Figure 1. Development and evaluation of a random forest model to predict cardiac amyloidosis. AoR, aortic root; AUC, area under the receiver operative curve; AV, aortic valve; EF, ejection fraction; GLS, global longitudinal strain; IVSd, interventricular septal end diastole; LA, left atrium; LVEF, left ventricular ejection fraction; LVIDd, left ventricular internal diameter end diastole; LVIDs, left ventricular internal diameter end systole; LVOT, left ventricular outflow track; LVPWd, left ventricular posterior wall end diastole; MV, mitral valve; PA, pulmonary artery; PYP, pyrophosphate; RF, random forest; SHAP, SHapley Additive exPlanations; TAPSE, tricuspid annular plane systolic excursion; TR, tricuspid regurgitation.

Figure 2. Workflow of selecting cases for RF model. Patients with PYP scans were identified and those without echocardiogram reports were excluded. The patients remaining were matched to their echocardiograms and a training set and validation set were created. PYP, pyrophosphate; RF, random forest.

Figure 3. SHAP values of RF model. This model demonstrates the differential impact of each echocardiogram feature (y-axis) to the RF model measured through SHAP value (x-axis). AoR, aortic root; AV, aortic valve; EF, ejection fraction; GLS, global longitudinal strain; IVSd, interventricular septal end diastole; LA, left atrium; LVEF, left ventricular ejection fraction; LVIDd, left ventricular internal diameter end diastole; LVIDs, left ventricular internal diameter end systole; LVOT, left ventricular outflow track; LVPWd, left ventricular posterior wall end diastole; MV, mitral valve; PA, pulmonary artery; RF, random forest; SHAP, SHapley Additive exPlanations; TAPSE, tricuspid annular plane systolic excursion; TR, tricuspid regurgitation.