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  • Online Resource  (5)
  • Oxford University Press (OUP)  (5)
  • Kaufmann, Philipp A  (5)
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  • Online Resource  (5)
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  • Oxford University Press (OUP)  (5)
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  • 1
    Online Resource
    Online Resource
    Impact of cardiac size on SPECT myocardial perfusion imaging performance: Insights from the REFINE-SPECT registry
    Oxford University Press (OUP) ; 2024
    In:  European Heart Journal - Cardiovascular Imaging ( 2024-03-06)
    Details
    In: European Heart Journal - Cardiovascular Imaging, Oxford University Press (OUP), ( 2024-03-06)
    Abstract: Variation in diagnostic performance of SPECT myocardial perfusion imaging (MPI) has been observed, yet the impact of cardiac size has not been well characterized. We assessed whether low left ventricular volume influences SPECT MPI’s ability to detect obstructive coronary artery disease (CAD), and its interaction with age and sex. Methods and Results A total of 2,066 patients without known CAD (67% male, 64.7 ± 11.2 years) across 9 institutions underwent SPECT MPI with solid-state scanners followed by coronary angiography as part of the REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT. Area under receiver-operating characteristic curve (AUC) analyses evaluated performance of quantitative and visual assessments according to cardiac size (end- diastolic volume [EDV]; & lt; 20th vs. ≥ 20th population or sex-specific percentiles), age ( & lt;75 vs. ≥ 75 years), and sex. Significantly decreased performance was observed in patients with low EDV compared to those without (AUC: population 0.72 vs. 0.78, p = 0.03; sex-specific 0.72 vs. 0.79, p = 0.01) and elderly patients compared to younger patients (AUC 0.72 vs. 0.78, p = 0.03), whereas males and females demonstrated similar AUC (0.77 vs. 0.76, p = 0.67). The reduction in accuracy attributed to lower volumes was primarily observed in males (sex-specific threshold: EDV 0.69 vs. 0.79, p = 0.01). Accordingly, a significant decrease in AUC, sensitivity, specificity, and negative predictive value for quantitative and visual assessments was noted in patients with at least two characteristics of low EDV, elderly age, or male sex. Conclusions Detection of CAD with SPECT MPI is negatively impacted by small cardiac size, most notably in elderly and male patients.
    Type of Medium: Online Resource
    ISSN: 2047-2404 , 2047-2412
    URL: Article
    DOI: 10.1093/ehjci/jeae055
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2024
    detail.hit.zdb_id: 2042482-6
    detail.hit.zdb_id: 2647943-6
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  • 2
    Online Resource
    Online Resource
    Transient ischaemic dilation and post-stress wall motion abnormality increase risk in patients with less than moderate ischaemia: analysis of the REFINE SPECT registry
    Oxford University Press (OUP) ; 2020
    In:  European Heart Journal - Cardiovascular Imaging Vol. 21, No. 5 ( 2020-05-01), p. 567-575
    Details
    In: European Heart Journal - Cardiovascular Imaging, Oxford University Press (OUP), Vol. 21, No. 5 ( 2020-05-01), p. 567-575
    Abstract: Ischaemia on single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is strongly associated with cardiovascular risk. Transient ischaemic dilation (TID) and post-stress wall motion abnormalities (WMA) are non-perfusion markers of ischaemia with incremental prognostic utility. Using a large, multicentre SPECT MPI registry, we assessed the degree to which these features increased the risk of major adverse cardiovascular events (MACE) in patients with less than moderate ischaemia. Methods and results  Ischaemia was quantified with total perfusion deficit using semiautomated software and classified as: none ( & lt;1%), minimal (1 to & lt;5%), mild (5 to & lt;10%), moderate (10 to & lt;15%), and severe (≥15%). Univariable and multivariable Cox proportional hazard analyses were used to assess associations between high-risk imaging features and MACE. We included 16 578 patients, mean age 64.2 and median follow-up 4.7 years. During follow-up, 1842 patients experienced at least one event. Patients with mild ischaemia and TID were more likely to experience MACE compared with patients without TID [adjusted hazard ratio (HR) 1.42, P = 0.023], with outcomes not significantly different from patients with moderate ischaemia without other high-risk features (unadjusted HR 1.15, P = 0.556). There were similar findings in patients with post-stress WMA. However, in multivariable analysis of patients with mild ischaemia, TID (adjusted HR 1.50, P = 0.037), but not WMA, was independently associated with increased MACE. Conclusion  In patients with mild ischaemia, TID or post-stress WMA identify groups of patients with outcomes similar to patients with moderate ischaemia. Whether these combinations identify patients who may derive benefit from revascularization deserves further investigation.
    Type of Medium: Online Resource
    ISSN: 2047-2404 , 2047-2412
    URL: Article
    DOI: 10.1093/ehjci/jez172
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2042482-6
    detail.hit.zdb_id: 2647943-6
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  • 3
    Online Resource
    Online Resource
    Prognostically safe stress-only single-photon emission computed tomography myocardial perfusion imaging guided by machine learning: report from REFINE SPECT
    Oxford University Press (OUP) ; 2021
    In:  European Heart Journal - Cardiovascular Imaging Vol. 22, No. 6 ( 2021-05-10), p. 705-714
    Details
    In: European Heart Journal - Cardiovascular Imaging, Oxford University Press (OUP), Vol. 22, No. 6 ( 2021-05-10), p. 705-714
    Abstract: Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) stress-only protocols reduce radiation exposure and cost but require clinicians to make immediate decisions regarding rest scan cancellation. We developed a machine learning (ML) approach for automatic rest scan cancellation and evaluated its prognostic safety. Methods and results  In total, 20 414 patients from a solid-state SPECT MPI international multicentre registry with clinical data and follow-up for major adverse cardiac events (MACE) were used to train ML for MACE prediction as a continuous probability (ML score), using 10-fold repeated hold-out testing to separate test from training data. Three ML score thresholds (ML1, ML2, and ML3) were derived by matching the cancellation rates achieved by physician interpretation and two clinical selection rules. Annual MACE rates were compared in patients selected for rest scan cancellation between approaches. Patients selected for rest scan cancellation with ML had lower annualized MACE rates than those selected by physician interpretation or clinical selection rules (ML1 vs. physician interpretation: 1.4 ± 0.1% vs. 2.1 ± 0.1%; ML2 vs. clinical selection: 1.5 ± 0.1% vs. 2.0 ± 0.1%; ML3 vs. stringent clinical selection: 0.6 ± 0.1% vs. 1.7 ± 0.1%, all P  & lt; 0.0001) at matched cancellation rates (60 ± 0.7, 64 ± 0.7, and 30 ± 0.6%). Annualized all-cause mortality rates in populations recommended for rest cancellation by physician interpretation, clinical selection approaches were higher (1.3%, 1.2%, and 1.0%, respectively) compared with corresponding ML thresholds (0.6%, 0.6%, and 0.2%). Conclusion ML, using clinical and stress imaging data, can be used to automatically recommend cancellation of rest SPECT MPI scans, while ensuring higher prognostic safety than current clinical approaches.
    Type of Medium: Online Resource
    ISSN: 2047-2404 , 2047-2412
    URL: Article
    DOI: 10.1093/ehjci/jeaa134
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2042482-6
    detail.hit.zdb_id: 2647943-6
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  • 4
    Online Resource
    Online Resource
    Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry
    Oxford University Press (OUP) ; 2022
    In:  Cardiovascular Research Vol. 118, No. 9 ( 2022-07-20), p. 2152-2164
    Details
    In: Cardiovascular Research, Oxford University Press (OUP), Vol. 118, No. 9 ( 2022-07-20), p. 2152-2164
    Abstract: Optimal 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 results This 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. Conclusion Reduced 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.
    Type of Medium: Online Resource
    ISSN: 0008-6363 , 1755-3245
    URL: Article
    DOI: 10.1093/cvr/cvab236
    RVK:
    WA 15000
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 1499917-1
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  • 5
    Online Resource
    Online Resource
    Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry
    Oxford University Press (OUP) ; 2020
    In:  European Heart Journal - Cardiovascular Imaging Vol. 21, No. 5 ( 2020-05-01), p. 549-559
    Details
    In: European Heart Journal - Cardiovascular Imaging, Oxford University Press (OUP), Vol. 21, No. 5 ( 2020-05-01), p. 549-559
    Abstract: 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  & lt; 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  & lt; 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).
    Type of Medium: Online Resource
    ISSN: 2047-2404 , 2047-2412
    URL: Article
    DOI: 10.1093/ehjci/jez177
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2042482-6
    detail.hit.zdb_id: 2647943-6
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