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Machine learning to predict abnormal myocardial perfusion from pre-test features

https://doi.org/10.1007/s12350-022-03012-6Get rights and content

Abstract

Background

Accurately predicting which patients will have abnormal perfusion on MPI based on pre-test clinical information may help physicians make test selection decisions. We developed and validated a machine learning (ML) model for predicting abnormal perfusion using pre-test features.

Methods

We included consecutive patients who underwent SPECT MPI, with 20,418 patients from a multi-center (5 sites) international registry in the training population and 9019 patients (from 2 separate sites) in the external testing population. The ML (extreme gradient boosting) model utilized 30 pre-test features to predict the presence of abnormal myocardial perfusion by expert visual interpretation.

Results

In external testing, the ML model had higher prediction performance for abnormal perfusion (area under receiver-operating characteristic curve [AUC] 0.762, 95% CI 0.750–0.774) compared to the clinical CAD consortium (AUC 0.689) basic CAD consortium (AUC 0.657), and updated Diamond-Forrester models (AUC 0.658, p < 0.001 for all). Calibration (validation of the continuous risk prediction) was superior for the ML model (Brier score 0.149) compared to the other models (Brier score 0.165 to 0.198, all p < 0.001).

Conclusion

ML can predict abnormal myocardial perfusion using readily available pre-test information. This model could be used to help guide physician decisions regarding non-invasive test selection.

Introduction

Myocardial perfusion imaging (MPI) is frequently used to diagnose or risk stratify patients with known or suspected coronary artery disease (CAD).1, 2, 3 Abnormal regional myocardial perfusion can be used to detect obstructive CAD with high diagnostic accuracy.4 Additionally, the presence of abnormal regional myocardial perfusion can be used to identify a group of patients with a higher risk of major adverse cardiovascular events.5 As a result of these findings, the volume of SPECT MPI has grown to 15-20 million scans performed annually worldwide.6 However, the prevalence of abnormal perfusion has been decreasing since SPECT MPI was first implemented clinically, dropping from 40.9% in 1991 to 8.7% in 2009.7 As a result, it is becoming increasingly important to ensure appropriate patient selection. This is particularly relevant since patients with normal regional myocardial perfusion on SPECT MPI may be more effectively risk stratified by using measures such as absolute myocardial blood flow8,9 or coronary artery calcification.10,11 Additionally, patients with a low likelihood of abnormal perfusion may be candidates for stress-first imaging (and evaluated for stress-only imaging) to reduce radiation exposure.12 However, SPECT (or PET) MPI could be particularly useful in patients with a high risk of ischemia since this may help target more aggressive therapies. For example, ischemia can be used to predict symptom benefit from revascularization.13,14 However, to date, most pre-test risk models have focused on a patient’s likelihood of having anatomically defined obstructive CAD rather than abnormal regional myocardial perfusion.

Most pre-test risk prediction models have also been developed using statistical regression methods.15 Machine learning (ML) has the potential to surpass these traditional methods by efficiently integrating a large number of variables. ML may also identify non-linear relationships and higher-order interactions between variables,16 which is not possible with traditional statistical methods. This approach has been used to predict risk of major adverse cardiovascular events and to identify patients for stress-only imaging who have a low probability of obstructive CAD17 or adverse events.18 In this work, we propose a ML score to predict likelihood of abnormal perfusion solely from pre-test patient factors (without imaging information). The model was trained using patients from the multi-center, international REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT),19 then tested using data from two external sites.

Section snippets

Study populations

The internal testing population from included consecutive patients from 5 sites undergoing SPECT MPI between 2009 and 2014 (n = 20,418) as previously described.19 The external testing population included 9,019 consecutive patients from the University of Calgary (n = 2985) and Oklahoma Heart Hospital (n = 6034). The study protocol complied with the Declaration of Helsinki and was approved by the institutional review boards at each participating institution. The overall study was approved by the

Results

Details of the internal and external populations are shown in Table 1. As expected, there were significant differences between the two populations. In particular, patients in the external population were older and less likely to have previous CAD. An outline of the internal and external testing procedures is shown in Figure 1.

Discussion

We developed a ML model to predict abnormal perfusion on MPI using traditional pre-test features and evaluated it with both internal cross-validation and external testing. We demonstrated that the ML model was able to incorporate pre-test clinical information to improve prediction performance for abnormal regional myocardial perfusion compared to existing risk models. All models demonstrated modest calibration, but this was superior for the ML method compared to traditional methods. These

Conclusion

ML demonstrated high prediction performance for abnormal myocardial perfusion using readily available pre-test information. This model could be used to help guide physician decisions regarding testing strategies.

New knowledge gained

Using external testing, machine learning identified patients who are more likely to have abnormal myocardial perfusion using readily available clinical information. The model demonstrated higher prediction performance and better calibration compared to traditional risk models.

Disclosures

Dr. Miller has received research support from Pfizer. Drs. Berman and Slomka participate in software royalties for QPS software at Cedars-Sinai Medical Center. Dr. Slomka has received research grant support from Siemens Medical Systems. Drs. Berman, Dorbala, Einstein, and Edward Miller have served as consultants for GE Healthcare. Dr. Einstein has served as a consultant to W. L. Gore & Associates. Dr. Dorbala has served as a consultant to Bracco Diagnostics; her institution has received grant

Funding

This research was supported in part by grant R01HL089765 from the National Heart, Lung, and Blood Institute/ National Institutes of Health (NHLBI/NIH) (PI: Piotr Slomka). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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