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 |

REPOSITORIES: biostudies-literature

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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.

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