Dataset Information

Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry.

ABSTRACT:

Aims

To optimize per-vessel prediction of early coronary revascularization (ECR) within 90 days after fast single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) using machine learning (ML) and introduce a method for a patient-specific explanation of ML results in a clinical setting.

Methods and results

A total of 1980 patients with suspected coronary artery disease (CAD) underwent stress/rest 99mTc-sestamibi/tetrofosmin MPI with new-generation SPECT scanners were included. All patients had invasive coronary angiography within 6 months after SPECT MPI. ML utilized 18 clinical, 9 stress test, and 28 imaging variables to predict per-vessel and per-patient ECR with 10-fold cross-validation. Area under the receiver operator characteristics curve (AUC) of ML was compared with standard quantitative analysis [total perfusion deficit (TPD)] and expert interpretation. ECR was performed in 958 patients (48%). Per-vessel, the AUC of ECR prediction by ML (AUC 0.79, 95% confidence interval (CI) [0.77, 0.80]) was higher than by regional stress TPD (0.71, [0.70, 0.73]), combined-view stress TPD (AUC 0.71, 95% CI [0.69, 0.72]), or ischaemic TPD (AUC 0.72, 95% CI [0.71, 0.74]), all P < 0.001. Per-patient, the AUC of ECR prediction by ML (AUC 0.81, 95% CI [0.79, 0.83]) was higher than that of stress TPD, combined-view TPD, and ischaemic TPD, all P < 0.001. ML also outperformed nuclear cardiologists' expert interpretation of MPI for the prediction of early revascularization performance. A method to explain ML prediction for an individual patient was also developed.

Conclusion

In patients with suspected CAD, the prediction of ECR by ML outperformed automatic MPI quantitation by TPDs (per-vessel and per-patient) or nuclear cardiologists' expert interpretation (per-patient).

SUBMITTER: Hu LH

PROVIDER: S-EPMC7167744 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry.

Hu Lien-Hsin LH Betancur Julian J Sharir Tali T Einstein Andrew J AJ Bokhari Sabahat S Fish Mathews B MB Ruddy Terrence D TD Kaufmann Philipp A PA Sinusas Albert J AJ Miller Edward J EJ Bateman Timothy M TM Dorbala Sharmila S Di Carli Marcelo M Germano Guido G Commandeur Frederic F Liang Joanna X JX Otaki Yuka Y Tamarappoo Balaji K BK Dey Damini D Berman Daniel S DS Slomka Piotr J PJ

European heart journal. Cardiovascular Imaging 20200501 5

<h4>Aims</h4>To optimize per-vessel prediction of early coronary revascularization (ECR) within 90 days after fast single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) using machine learning (ML) and introduce a method for a patient-specific explanation of ML results in a clinical setting.<h4>Methods and results</h4>A total of 1980 patients with suspected coronary artery disease (CAD) underwent stress/rest 99mTc-sestamibi/tetrofosmin MPI with new-generation SPECT ...[more]

PMID: 31317178

Similar Datasets

Project description:ObjectivesThis study compared the ability of automated myocardial perfusion imaging analysis to predict major adverse cardiac events (MACE) to that of visual analysis.BackgroundQuantitative analysis has not been compared with clinical visual analysis in prognostic studies.MethodsA total of 19,495 patients from the multicenter REFINE SPECT (REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT) study (64 ± 12 years of age, 56% males) undergoing stress Tc-99m-labeled single-photon emission computed tomography (SPECT) myocardial perfusion imaging were followed for 4.5 ± 1.7 years for MACE. Perfusion abnormalities were assessed visually and categorized as normal, probably normal, equivocal, or abnormal. Stress total perfusion deficit (TPD), quantified automatically, was categorized as TPD = 0%, TPD >0% to <1%, ≤1% to <3%, ≤3% to <5%, ≤5% to ≤10%, or TPD >10%. MACE consisted of death, nonfatal myocardial infarction, unstable angina, or late revascularization (>90 days). Kaplan-Meier and Cox proportional hazards analyses were performed to test the performance of visual and quantitative assessments in predicting MACE.ResultsDuring follow-up examinations, 2,760 (14.2%) MACE occurred. MACE rates increased with worsening of visual assessments, that is, the rate for normal MACE was 2.0%, 3.2% for probably normal, 4.2% for equivocal, and 7.4% for abnormal (all p < 0.001). MACE rates increased with increasing stress TPD from 1.3% for the TPD category of 0% to 7.8% for the TPD category of >10% (p < 0.0001). The adjusted hazard ratio (HR) for MACE increased even in equivocal assessment (HR: 1.56; 95% confidence interval [CI]: 1.37 to 1.78) and in the TPD category of ≤3% to <5% (HR: 1.74; 95% CI: 1.41 to 2.14; all p < 0.001). The rate of MACE in patients visually assessed as normal still increased from 1.3% (TPD = 0%) to 3.4% (TPD ≥5%) (p < 0.0001).ConclusionsQuantitative analysis allows precise granular risk stratification in comparison to visual reading, even for cases with normal clinical reading.

Project description:AimsOptimal risk stratification with machine learning (ML) from myocardial perfusion imaging (MPI) includes both clinical and imaging data. While most imaging variables can be derived automatically, clinical variables require manual collection, which is time-consuming and prone to error. We determined the fewest manually input and imaging variables required to maintain the prognostic accuracy for major adverse cardiac events (MACE) in patients undergoing a single-photon emission computed tomography (SPECT) MPI.Methods and resultsThis study included 20 414 patients from the multicentre REFINE SPECT registry and 2984 from the University of Calgary for training and external testing of the ML models, respectively. ML models were trained using all variables (ML-All) and all image-derived variables (including age and sex, ML-Image). Next, ML models were sequentially trained by incrementally adding manually input and imaging variables to baseline ML models based on their importance ranking. The fewest variables were determined as the ML models (ML-Reduced, ML-Minimum, and ML-Image-Reduced) that achieved comparable prognostic performance to ML-All and ML-Image. Prognostic accuracy of the ML models was compared with visual diagnosis, stress total perfusion deficit (TPD), and traditional multivariable models using area under the receiver-operating characteristic curve (AUC). ML-Minimum (AUC 0.798) obtained comparable prognostic accuracy to ML-All (AUC 0.799, P = 0.19) by including 12 of 40 manually input variables and 11 of 58 imaging variables. ML-Reduced achieved comparable accuracy (AUC 0.796) with a reduced set of manually input variables and all imaging variables. In external validation, the ML models also obtained comparable or higher prognostic accuracy than traditional multivariable models.ConclusionReduced ML models, including a minimum set of manually collected or imaging variables, achieved slightly lower accuracy compared to a full ML model but outperformed standard interpretation methods and risk models. ML models with fewer collected variables may be more practical for clinical implementation.

Dataset Information

Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry.

Aims

Methods and results

Conclusion

Publications

Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets