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Machine Learning Outcome Prediction in Dilated Cardiomyopathy Using Regional Left Ventricular Multiparametric Strain

The clinical presentation of idiopathic dilated cardiomyopathy (IDCM) heart failure (HF) patients who will respond to medical therapy (responders) and those who will not (non-responders) is often similar. A machine learning (ML)-based clinical tool to identify responders would prevent unnecessary surgery, while targeting non-responders for early intervention. We used regional left ventricular (LV) contractile injury patterns in ML models to identify IDCM HF non-responders. MRI-based multiparametric strain analysis was performed in 178 test subjects (140 normal subjects and 38 IDCM patients), calculating longitudinal, circumferential, and radial strain over 18 LV sub-regions for inclusion in ML analyses. Patients were identified as responders based upon symptomatic and contractile improvement on medical therapy. We tested the predictive accuracy of support vector machines (SVM), logistic regression (LR), random forest (RF), and deep neural networks (DNN). The DNN model outperformed other models, predicting response to medical therapy with an area under the receiver operating characteristic curve (AUC) of 0.94. The top features were longitudinal strain in (1) basal: anterior, posterolateral and (2) mid: posterior, anterolateral, and anteroseptal sub-regions. Regional contractile injury patterns predict response to medical therapy in IDCM HF patients, and have potential application in ML-based HF patient care.

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AUC:

Area under the curve

DENSE:

Displacement encoding with stimulated echoes

DNN:

Deep neural networks

EHR:

Electronic health record

HF:

Heart failure

IDCM:

Idiopathic dilated cardiomyopathy

LR:

Logistic regression

LV:

Left ventricle

ML:

Machine learning

NYHA:

New York Heart Association

RF:

Random forest

ROC:

Receiver operating characteristic curve

SVM:

Support vector machines

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The authors have no conflicts of interest to disclose. Relevant funding sources included National Institutes of Health 1RO1HL112804 and 1R56HL136619.

1. Department of Surgery, Division of Cardiothoracic Surgery, Barnes-Jewish Hospital, Washington University School of Medicine, Campus Box 8234, 660 S. Euclid Ave., St. Louis, MO, 63110, USA

   Robert M. MacGregor, Muhammad F. Masood, Brian P. Cupps & Michael K. Pasque

2. Institute for Informatics, Division of General Medical Sciences, Washington University School of Medicine, St. Louis, MO, USA

   Aixia Guo & Randi Foraker

3. John T. Milliken Department of Internal Medicine, Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA

   Gregory A. Ewald

1. Robert M. MacGregor
2. Aixia Guo
3. Muhammad F. Masood
4. Brian P. Cupps
5. Gregory A. Ewald
6. Michael K. Pasque
7. Randi Foraker

Correspondence to Michael K. Pasque.

Associate Editor Lakshmi Prasad Dasi oversaw the review of this article.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Below is the link to the electronic supplementary material.

MacGregor, R.M., Guo, A., Masood, M.F. et al. Machine Learning Outcome Prediction in Dilated Cardiomyopathy Using Regional Left Ventricular Multiparametric Strain. Ann Biomed Eng 49, 922–932 (2021). https://doi.org/10.1007/s10439-020-02639-1

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• Received: 09 June 2020

• Accepted: 24 September 2020

• Published: 01 October 2020

• Issue Date: February 2021

• DOI: https://doi.org/10.1007/s10439-020-02639-1

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