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Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning

  • David Haro Alonso
  • Miles N. Wernick
  • Yongyi Yang
  • Guido Germano
  • Daniel S. Berman
  • Piotr Slomka
Original Article

Abstract

Background

We developed machine-learning (ML) models to estimate a patient’s risk of cardiac death based on adenosine myocardial perfusion SPECT (MPS) and associated clinical data, and compared their performance to baseline logistic regression (LR). We demonstrated an approach to visually convey the reasoning behind a patient’s risk to provide insight to clinicians beyond that of a “black box.”

Methods

We trained multiple models using 122 potential clinical predictors (features) for 8321 patients, including 551 cases of subsequent cardiac death. Accuracy was measured by area under the ROC curve (AUC), computed within a cross-validation framework. We developed a method to display the model’s rationale to facilitate clinical interpretation.

Results

The baseline LR (AUC = 0.76; 14 features) was outperformed by all other methods. A least absolute shrinkage and selection operator (LASSO) model (AUC = 0.77; p = .045; 6 features) required the fewest features. A support vector machine (SVM) model (AUC = 0.83; p < .0001; 49 features) provided the highest accuracy.

Conclusions

LASSO outperformed LR in both accuracy and simplicity (number of features), with SVM yielding best AUC for prediction of cardiac death in patients undergoing MPS. Combined with presenting the reasoning behind the risk scores, our results suggest that ML can be more effective than LR for this application.

Keywords

Cardiac death risk model machine learning feature selection data visualization 

Abbreviations

SPECT

Single-photon emission computed tomography

MPS

Myocardial perfusion SPECT

SSS

Summed stress score

SRS

Summed rest score

LV

Left ventricular

ROC

Receiver operating characteristic

AUC

Area under the curve

SVM

Support vector machine

LASSO

Least absolute shrinkage and selection operator

LR

Logistic regression

Notes

Acknowledgments

Research reported in this article was supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health under Award Numbers R01HL122484 and R01HL089765. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Disclosure

DH.A., Y.Y., and M.N.W. from the Illinois Institute of Technology have nothing to disclose. G.G., D.S.B., and P.S. from the Cedars-Sinai Medical Center have nothing to disclose.

Supplementary material

12350_2018_1250_MOESM1_ESM.pptx (54.4 mb)
Supplementary material 1 (PPTX 55,672 kb)

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Copyright information

© American Society of Nuclear Cardiology 2018

Authors and Affiliations

  • David Haro Alonso
    • 1
  • Miles N. Wernick
    • 1
  • Yongyi Yang
    • 1
  • Guido Germano
    • 2
  • Daniel S. Berman
    • 2
  • Piotr Slomka
    • 2
  1. 1.Medical Imaging Research Center, Illinois Institute of TechnologyChicagoUSA
  2. 2.Departments of Imaging and MedicineCedars-Sinai Medical CenterLos AngelesUSA

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