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2. Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry

Author

Rios, Richard, Miller, Robert JH, Manral, Nipun, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp A, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Van Kriekinge, Serge D, Kavanagh, Paul B, Parekh, Tejas, Liang, Joanna X, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

Subjects
Biological Sciences, Bioinformatics and Computational Biology, Health Services and Systems, Health Sciences, Information and Computing Sciences, Applied Computing, Patient Safety, Cardiovascular, Heart Disease, Good Health and Well Being, Humans, Machine Learning, Myocardial Perfusion Imaging, Registries, Tomography, Emission-Computed, Single-Photon, Machine learning, Clinical implementation, Missing values, Prognosis, Myocardial perfusion imaging, Engineering, Medical and Health Sciences, Biomedical Engineering, Bioinformatics and computational biology, Health services and systems, Applied computing
Abstract

BackgroundMachine learning (ML) models can improve prediction of major adverse cardiovascular events (MACE), but in clinical practice some values may be missing. We evaluated the influence of missing values in ML models for patient-specific prediction of MACE risk.MethodsWe included 20,179 patients from the multicenter REFINE SPECT registry with MACE follow-up data. We evaluated seven methods for handling missing values: 1) removal of variables with missing values (ML-Remove), 2) imputation with median and unique category for continuous and categorical variables, respectively (ML-Traditional), 3) unique category for missing variables (ML-Unique), 4) cluster-based imputation (ML-Cluster), 5) regression-based imputation (ML-Regression), 6) missRanger imputation (ML-MR), and 7) multiple imputation (ML-MICE). We trained ML models with full data and simulated missing values in testing patients. Prediction performance was evaluated using area under the receiver-operating characteristic curve (AUC) and compared with a model without missing values (ML-All), expert visual diagnosis and total perfusion deficit (TPD).ResultsDuring mean follow-up of 4.7 ± 1.5 years, 3,541 patients experienced at least one MACE (3.7% annualized risk). ML-All (reference model-no missing values) had AUC 0.799 for MACE risk prediction. All seven models with missing values had lower AUC (ML-Remove: 0.778, ML-MICE: 0.774, ML-Cluster: 0.771, ML-Traditional: 0.771, ML-Regression: 0.770, ML-MR: 0.766, and ML-Unique: 0.766; p

Published
2022

3. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

Author

Williams, Michelle C., Bednarski, Bryan P., Pieszko, Konrad, Miller, Robert J. H., Kwiecinski, Jacek, Shanbhag, Aakash, Liang, Joanna X., Huang, Cathleen, Sharir, Tali, Dorbala, Sharmila, Di Carli, Marcelo F., Einstein, Andrew J., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Fish, Mathews B., Ruddy, Terrence D., Acampa, Wanda, Hauser, M. Timothy, Kaufmann, Philipp A., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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4. Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study

Author

Miller, Robert J.H., Bednarski, Bryan P., Pieszko, Konrad, Kwiecinski, Jacek, Williams, Michelle C., Shanbhag, Aakash, Liang, Joanna X., Huang, Cathleen, Sharir, Tali, Hauser, M. Timothy, Dorbala, Sharmila, Di Carli, Marcelo F., Fish, Mathews B., Ruddy, Terrence D., Bateman, Timothy M., Einstein, Andrew J., Kaufmann, Philipp A., Miller, Edward J., Sinusas, Albert J., Acampa, Wanda, Han, Donghee, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2024
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5. Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational studyResearch in context

Author

Robert J.H. Miller, Bryan P. Bednarski, Konrad Pieszko, Jacek Kwiecinski, Michelle C. Williams, Aakash Shanbhag, Joanna X. Liang, Cathleen Huang, Tali Sharir, M. Timothy Hauser, Sharmila Dorbala, Marcelo F. Di Carli, Mathews B. Fish, Terrence D. Ruddy, Timothy M. Bateman, Andrew J. Einstein, Philipp A. Kaufmann, Edward J. Miller, Albert J. Sinusas, Wanda Acampa, Donghee Han, Damini Dey, Daniel S. Berman, and Piotr J. Slomka

Subjects
Machine learning, Coronary artery disease, Cluster analysis, Myocardial perfusion, Medicine, Medicine (General), R5-920
Abstract

Summary: Background: Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction. Methods: Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction. Findings: Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64–8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53–3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40–2.36). Interpretation: Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results. Funding: This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R01HL089765 to PS]. MCW was supported by the British Heart Foundation [FS/ICRF/20/26002].

Published
2024
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6. Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Author

Pieszko, Konrad, Shanbhag, Aakash D., Singh, Ananya, Hauser, M. Timothy, Miller, Robert J. H., Liang, Joanna X., Motwani, Manish, Kwieciński, Jacek, Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Berman, Daniel S., Dey, Damini, and Slomka, Piotr J.

Published
2023
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7. Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Author

Konrad Pieszko, Aakash D. Shanbhag, Ananya Singh, M. Timothy Hauser, Robert J. H. Miller, Joanna X. Liang, Manish Motwani, Jacek Kwieciński, Tali Sharir, Andrew J. Einstein, Mathews B. Fish, Terrence D. Ruddy, Philipp A. Kaufmann, Albert J. Sinusas, Edward J. Miller, Timothy M. Bateman, Sharmila Dorbala, Marcelo Di Carli, Daniel S. Berman, Damini Dey, and Piotr J. Slomka

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

Abstract Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction – in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making.

Published
2023
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8. Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images

Author

Miller, Robert J. H., Singh, Ananya, Otaki, Yuka, Tamarappoo, Balaji K., Kavanagh, Paul, Parekh, Tejas, Hu, Lien-Hsin, Gransar, Heidi, Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo F., Liang, Joanna X., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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9. Myocardial Ischemic Burden and Differences in Prognosis Among Patients With and Without Diabetes: Results From the Multicenter International REFINE SPECT Registry

Author

Han, Donghee, Rozanski, Alan, Gransar, Heidi, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp A, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X, Hu, Lien-Hsin, Germano, Guido, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Cardiovascular, Heart Disease, Biomedical Imaging, Heart Disease - Coronary Heart Disease, Diabetes, Metabolic and endocrine, Good Health and Well Being, Aged, Angina, Unstable, Cohort Studies, Coronary Artery Disease, Diabetes Mellitus, Diabetic Angiopathies, Female, Humans, Male, Middle Aged, Myocardial Infarction, Myocardial Ischemia, Myocardial Perfusion Imaging, Prevalence, Prognosis, Propensity Score, Registries, Risk Factors, Tomography, Emission-Computed, Single-Photon, Medical and Health Sciences, Endocrinology & Metabolism, Biomedical and clinical sciences, Health sciences
Abstract

ObjectivePrevalence and prognostic impact of cardiovascular disease differ between patients with or without diabetes. We aimed to explore differences in the prevalence and prognosis of myocardial ischemia by automated quantification of total perfusion deficit (TPD) among patients with and without diabetes.Research design and methodsOf 20,418 individuals who underwent single-photon emission computed tomography myocardial perfusion imaging, 2,951 patients with diabetes were matched to 2,951 patients without diabetes based on risk factors using propensity score. TPD was categorized as TPD = 0%, 0% < TPD < 1%, 1% ≤ TPD < 5%, 5% ≤ TPD ≤ 10%, and TPD >10%. Major adverse cardiovascular events (MACE) were defined as a composite of all-cause mortality, myocardial infarction, unstable angina, or late revascularization.ResultsMACE risk was increased in patients with diabetes compared with patients without diabetes at each level of TPD above 0 (P < 0.001 for interaction). In patients with TPD >10%, patients with diabetes had greater than twice the MACE risk compared with patients without diabetes (annualized MACE rate 9.4 [95% CI 6.7-11.6] and 3.9 [95% CI 2.8-5.6], respectively, P < 0.001). Patients with diabetes with even very minimal TPD (0% < TPD < 1%) experienced a higher risk for MACE than those with 0% TPD (hazard ratio 2.05 [95% CI 1.21-3.47], P = 0.007). Patients with diabetes with a TPD of 0.5% had a similar MACE risk as patients without diabetes with a TPD of 8%.ConclusionsFor every level of TPD >0%, even a very minimal deficit of 0% < TPD < 1%, the MACE risk was higher in the patients with diabetes compared with patients without diabetes. Patients with diabetes with minimal ischemia had comparable MACE risk as patients without diabetes with significant ischemia.

Published
2020

11. Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry

Author

Han, Donghee, Rozanski, Alan, Gransar, Heidi, Tzolos, Evangelos, Miller, Robert J. H., Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X., Hu, Lien-Hsin, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2022
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13. Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT

Author

Eisenberg, Evann, Miller, Robert J. H., Hu, Lien-Hsin, Rios, Richard, Betancur, Julian, Azadani, Peyman, Han, Donghee, Sharir, Tali, Einstein, Andrew J., Bokhari, Sabahat, Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X., Otaki, Yuka, Tamarappoo, Balaji K., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2022
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14. Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning

Author

Singh, Ananya, Miller, Robert J.H., Otaki, Yuka, Kavanagh, Paul, Hauser, Michael T., Tzolos, Evangelos, Kwiecinski, Jacek, Van Kriekinge, Serge, Wei, Chih-Chun, Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Liang, Joanna X., Huang, Cathleen, Han, Donghee, Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2023
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15. Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study

Author

Betancur, Julian, Hu, Lien-Hsin, Commandeur, Frederic, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp A, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Germano, Guido, Otaki, Yuka, Liang, Joanna X, Tamarappoo, Balaji K, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

Subjects
Biomedical and Clinical Sciences, Clinical Sciences, Heart Disease, Cardiovascular, Heart Disease - Coronary Heart Disease, Clinical Research, Biomedical Imaging, 4.2 Evaluation of markers and technologies, Detection, screening and diagnosis, Aged, Coronary Artery Disease, Deep Learning, Female, Heart Ventricles, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Myocardial Perfusion Imaging, Stress, Physiological, Tomography, Emission-Computed, Single-Photon, obstructive coronary artery disease, SPECT myocardial perfusion imaging, deep learning, convolutional neural network, total perfusion deficit, Nuclear Medicine & Medical Imaging, Clinical sciences
Abstract

Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the prediction of obstructive disease from combined analysis of semiupright and supine stress MPI by deep learning (DL) as compared with standard combined total perfusion deficit (TPD). Methods: 1,160 patients without known coronary artery disease (64% male) were studied. Patients underwent stress 99mTc-sestamibi MPI with new-generation solid-state SPECT scanners in 4 different centers. All patients had on-site clinical reads and invasive coronary angiography correlations within 6 mo of MPI. Obstructive disease was defined as at least 70% narrowing of the 3 major coronary arteries and at least 50% for the left main coronary artery. Images were quantified at Cedars-Sinai. The left ventricular myocardium was segmented using standard clinical nuclear cardiology software. The contour placement was verified by an experienced technologist. Combined stress TPD was computed using sex- and camera-specific normal limits. DL was trained using polar distributions of normalized radiotracer counts, hypoperfusion defects, and hypoperfusion severities and was evaluated for prediction of obstructive disease in a novel leave-one-center-out cross-validation procedure equivalent to external validation. During the validation procedure, 4 DL models were trained using data from 3 centers and then evaluated on the 1 center left aside. Predictions for each center were merged to have an overall estimation of the multicenter performance. Results: 718 (62%) patients and 1,272 of 3,480 (37%) arteries had obstructive disease. The area under the receiver operating characteristics curve for prediction of disease on a per-patient and per-vessel basis by DL was higher than for combined TPD (per-patient, 0.81 vs. 0.78; per-vessel, 0.77 vs. 0.73; P < 0.001). With the DL cutoff set to exhibit the same specificity as the standard cutoff for combined TPD, per-patient sensitivity improved from 61.8% (TPD) to 65.6% (DL) (P < 0.05), and per-vessel sensitivity improved from 54.6% (TPD) to 59.1% (DL) (P < 0.01). With the threshold matched to the specificity of a normal clinical read (56.3%), DL had a sensitivity of 84.8%, versus 82.6% for an on-site clinical read (P = 0.3). Conclusion: DL improves automatic interpretation of MPI as compared with current quantitative methods.

Published
2019

17. Automated quantitative analysis of CZT SPECT stratifies cardiovascular risk in the obese population: Analysis of the REFINE SPECT registry

Author

Klein, Eyal, Miller, Robert J. H., Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Otaki, Yuka, Gransar, Heidi, Liang, Joanna X., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2022
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18. Clinical Deployment of Explainable Artificial Intelligence of SPECT for Diagnosis of Coronary Artery Disease

Author

Otaki, Yuka, Singh, Ananya, Kavanagh, Paul, Miller, Robert J.H., Parekh, Tejas, Tamarappoo, Balaji K., Sharir, Tali, Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Dorbala, Sharmila, Di Carli, Marcelo, Cadet, Sebastien, Liang, Joanna X., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2022
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19. The Updated Registry of Fast Myocardial Perfusion Imaging with Next-Generation SPECT (REFINE SPECT 2.0).

Author

Miller, Robert J.H., Lemley, Mark, Shanbhag, Aakash, Ramirez, Giselle, Liang, Joanna X., Builoff, Valerie, Kavanagh, Paul, Sharir, Tali, Hauser, M. Timothy, Ruddy, Terrence D., Fish, Mathews B., Bateman, Timothy M., Acampa, Wanda, Einstein, Andrew J., Dorbala, Sharmila, Di Carli, Marcelo F., Feher, Attila, Miller, Edward J., Sinusas, Albert J., and Halcox, Julian

Published
2024
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20. Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT A Multicenter Study

Author

Betancur, Julian, Commandeur, Frederic, Motlagh, Mahsaw, Sharir, Tali, Einstein, Andrew J, Bokhari, Sabahat, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Germano, Guido, Otaki, Yuka, Tamarappoo, Balaji K, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Clinical Sciences, Biomedical Imaging, Clinical Research, Heart Disease, Cardiovascular, Heart Disease - Coronary Heart Disease, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Aged, Aged, 80 and over, Coronary Circulation, Coronary Stenosis, Deep Learning, Female, Humans, Image Interpretation, Computer-Assisted, Male, Middle Aged, Myocardial Perfusion Imaging, Organophosphorus Compounds, Organotechnetium Compounds, Predictive Value of Tests, Radiopharmaceuticals, Registries, Technetium Tc 99m Sestamibi, Tomography, Emission-Computed, Single-Photon, convolutional neural network, deep learning, obstructive coronary artery disease, SPECT myocardial perfusion imaging, Cardiorespiratory Medicine and Haematology, Cardiovascular System & Hematology, Cardiovascular medicine and haematology, Clinical sciences
Abstract

OBJECTIVES:The study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion deficit (TPD). BACKGROUND:Deep convolutional neural networks trained with a large multicenter population may provide improved prediction of per-patient and per-vessel coronary artery disease from single-photon emission computed tomography MPI. METHODS:A total of 1,638 patients (67% men) without known coronary artery disease, undergoing stress 99mTc-sestamibi or tetrofosmin MPI with new generation solid-state scanners in 9 different sites, with invasive coronary angiography performed within 6 months of MPI, were studied. Obstructive disease was defined as ≥70% narrowing of coronary arteries (≥50% for left main artery). Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. Stress TPD was computed using sex- and camera-specific normal limits. Deep learning was trained using raw and quantitative polar maps and evaluated for prediction of obstructive stenosis in a stratified 10-fold cross-validation procedure. RESULTS:A total of 1,018 (62%) patients and 1,797 of 4,914 (37%) arteries had obstructive disease. Area under the receiver-operating characteristic curve for disease prediction by deep learning was higher than for TPD (per patient: 0.80 vs. 0.78; per vessel: 0.76 vs. 0.73: p < 0.01). With deep learning threshold set to the same specificity as TPD, per-patient sensitivity improved from 79.8% (TPD) to 82.3% (deep learning) (p < 0.05), and per-vessel sensitivity improved from 64.4% (TPD) to 69.8% (deep learning) (p < 0.01). CONCLUSIONS:Deep learning has the potential to improve automatic interpretation of MPI as compared with current clinical methods.

Published
2018

21. Impact of incomplete ventricular coverage on diagnostic performance of myocardial perfusion imaging

Author

Sharif, Behzad, Motwani, Manish, Arsanjani, Reza, Dharmakumar, Rohan, Fish, Mathews B, Germano, Guido, Li, Debiao, Berman, Daniel S, and Slomka, Piotr

Subjects
Biomedical and Clinical Sciences, Cardiovascular Medicine and Haematology, Cardiovascular, Heart Disease, Biomedical Imaging, Heart Disease - Coronary Heart Disease, Clinical Research, Detection, screening and diagnosis, 4.2 Evaluation of markers and technologies, Aged, Area Under Curve, Coronary Angiography, Coronary Artery Disease, Coronary Circulation, Coronary Vessels, Databases, Factual, Female, Heart Ventricles, Humans, Image Interpretation, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Models, Cardiovascular, Myocardial Perfusion Imaging, Patient-Specific Modeling, Predictive Value of Tests, ROC Curve, Reproducibility of Results, Retrospective Studies, Severity of Illness Index, Tomography, Emission-Computed, Single-Photon, Myocardial ischemia, Myocardial perfusion imaging, Cardiac magnetic resonance, Coronary artery disease, Myocardial ischemic burden, Whole heart imaging, Cardiorespiratory Medicine and Haematology, Nuclear Medicine & Medical Imaging, Cardiovascular medicine and haematology
Abstract

In the context of myocardial perfusion imaging (MPI) with cardiac magnetic resonance (CMR), there is ongoing debate on the merits of using technically complex acquisition methods to achieve whole-heart spatial coverage, rather than conventional 3-slice acquisition. An adequately powered comparative study is difficult to achieve given the requirement for two separate stress CMR studies in each patient. The aim of this work is to draw relevant conclusions from SPECT MPI by comparing whole-heart versus simulated 3-slice coverage in a large existing dataset. SPECT data from 651 patients with suspected coronary artery disease who underwent invasive angiography were analyzed. A computational approach was designed to model 3-slice MPI by retrospective subsampling of whole- heart data. For both whole-heart and 3-slice approaches, the diagnostic performance and the stress total perfusion deficit (TPD) score-a measure of ischemia extent/severity-were quantified and compared. Diagnostic accuracy for the 3-slice and whole-heart approaches were similar (area under the curve: 0.843 vs. 0.855, respectively; P = 0.07). The majority (54%) of cases missed by 3-slice imaging had primarily apical ischemia. Whole-heart and 3-slice TPD scores were strongly correlated (R2 = 0.93, P

Published
2018

22. Clinical phenotypes among patients with normal cardiac perfusion using unsupervised learning: a retrospective observational study

Author

Miller, Robert J H, Bednarski, Bryan P, Pieszko, Konrad, Kwiecinski, Jacek, Williams, Michelle C, Shanbhag, Aakash, Liang, Joanna X, Huang, Cathleen, Sharir, Tali, Hauser, M Timothy, Dorbala, Sharmila, Di Carli, Marcelo F, Fish, Mathews B, Ruddy, Terrence D, Bateman, Timothy M, Einstein, Andrew J, Kaufmann, Philipp A; https://orcid.org/0000-0002-9451-5210, Miller, Edward J, Sinusas, Albert J, Acampa, Wanda, Han, Donghee, Dey, Damini, Berman, Daniel S, Slomka, Piotr J, Miller, Robert J H, Bednarski, Bryan P, Pieszko, Konrad, Kwiecinski, Jacek, Williams, Michelle C, Shanbhag, Aakash, Liang, Joanna X, Huang, Cathleen, Sharir, Tali, Hauser, M Timothy, Dorbala, Sharmila, Di Carli, Marcelo F, Fish, Mathews B, Ruddy, Terrence D, Bateman, Timothy M, Einstein, Andrew J, Kaufmann, Philipp A; https://orcid.org/0000-0002-9451-5210, Miller, Edward J, Sinusas, Albert J, Acampa, Wanda, Han, Donghee, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

Abstract

BACKGROUND Myocardial perfusion imaging (MPI) is one of the most common cardiac scans and is used for diagnosis of coronary artery disease and assessment of cardiovascular risk. However, the large majority of MPI patients have normal results. We evaluated whether unsupervised machine learning could identify unique phenotypes among patients with normal scans and whether those phenotypes were associated with risk of death or myocardial infarction. METHODS Patients from a large international multicenter MPI registry (10 sites) with normal perfusion by expert visual interpretation were included in this cohort analysis. The training population included 9849 patients, and external testing population 12,528 patients. Unsupervised cluster analysis was performed, with separate training and external testing cohorts, to identify clusters, with four distinct phenotypes. We evaluated the clinical and imaging features of clusters and their associations with death or myocardial infarction. FINDINGS Patients in Clusters 1 and 2 almost exclusively underwent exercise stress, while patients in Clusters 3 and 4 mostly required pharmacologic stress. In external testing, the risk for Cluster 4 patients (20.2% of population, unadjusted hazard ratio [HR] 6.17, 95% confidence interval [CI] 4.64-8.20) was higher than the risk associated with pharmacologic stress (HR 3.03, 95% CI 2.53-3.63), or previous myocardial infarction (HR 1.82, 95% CI 1.40-2.36). INTERPRETATION Unsupervised learning identified four distinct phenotypes of patients with normal perfusion scans, with a significant proportion of patients at very high risk of myocardial infarction or death. Our results suggest a potential role for patient phenotyping to improve risk stratification of patients with normal imaging results. FUNDING This work was supported by the National Heart, Lung, and Blood Institute at the National Institutes of Health [R35HL161195 to PS]. The REFINE SPECT database was supported by the National Heart, Lung

Published
2024

23. Upper reference limits of transient ischemic dilation ratio for different protocols on new-generation cadmium zinc telluride cameras: A report from REFINE SPECT registry

Author

Hu, Lien-Hsin, Sharir, Tali, Miller, Robert J.H., Einstein, Andrew J., Fish, Mathews B., Ruddy, Terrence D., Dorbala, Sharmila, Di Carli, Marcelo, Kaufmann, Philipp A., Sinusas, Albert J., Miller, Edward J., Bateman, Timothy M., Betancur, Julian, Germano, Guido, Liang, Joanna X., Commandeur, Frederic, Azadani, Peyman N., Gransar, Heidi, Otaki, Yuka, Tamarappoo, Balaji K., Dey, Damini, Berman, Daniel S., and Slomka, Piotr J.

Published
2020
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24. Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT)

Author

Slomka, Piotr J., Betancur, Julian, Liang, Joanna X., Otaki, Yuka, Hu, Lien-Hsin, Sharir, Tali, Dorbala, Sharmila, Di Carli, Marcelo, Fish, Mathews B., Ruddy, Terrence D., Bateman, Timothy M., Einstein, Andrew J., Kaufmann, Philipp A., Miller, Edward J., Sinusas, Albert J., Azadani, Peyman N., Gransar, Heidi, Tamarappoo, Balaji K., Dey, Damini, Berman, Daniel S., and Germano, Guido

Published
2020
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25. 360° ab-interno trabeculotomy in refractory primary open-angle glaucoma

Author

Sarkisian SR, Mathews B, Ding K, Patel A, and Nicek Z

Subjects
Glaucoma, intraocular pressure, micro invasive glaucoma surgery, primary open-angle glaucoma, refractory, trabeculotomy, Ophthalmology, RE1-994
Abstract

Steven R Sarkisian,1 Basil Mathews,2 Kai Ding,2 Aashka Patel,2 Zachary Nicek2 1University of Oklahoma College of Medicine, Dean McGee Eye Institute, Oklahoma City, OK, USA; 2University of Oklahoma, Oklahoma City, OK, USA Purpose: The purpose of this study was to evaluate the safety and efficacy of microinvasive glaucoma surgery (MIGS) with 360° ab-interno trabeculotomy using the TRAB360 device as a stand-alone procedure in patients with refractory primary open-angle glaucoma (POAG) and preoperative IOP ≥18 mmHg.Setting: This study evaluated patients treated in a tertiary-referral clinical practice setting.Design: This study is a retrospective analysis of 81 eyes.Methods: Patients with refractory open-angle glaucoma underwent stand-alone 360° ab-interno trabeculotomy using the TRAB360 device. Effectiveness was determined by reduction in medicated IOP and the use of medications from baseline. Safety was determined by the rate of adverse events and secondary surgical interventions. The time points assessed were baseline and postoperative day 1, week 1, and months 1, 3, 6, and 12. A subgroup analysis was performed on eyes with medicated preoperative IOP values of ≥25 mmHg.Results: The reductions in IOP from 1 day to 12 months postoperatively were statistically significant compared to baseline values. The mean reduction in IOP at 12 months was 7.3±6.7 mmHg from baseline. At 12 months, 59% eyes achieved ≥20% reduction in IOP and IOP

Published
2019

26. Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning

Author

Ananya Singh, Robert J.H. Miller, Yuka Otaki, Paul Kavanagh, Michael T. Hauser, Evangelos Tzolos, Jacek Kwiecinski, Serge Van Kriekinge, Chih-Chun Wei, Tali Sharir, Andrew J. Einstein, Mathews B. Fish, Terrence D. Ruddy, Philipp A. Kaufmann, Albert J. Sinusas, Edward J. Miller, Timothy M. Bateman, Sharmila Dorbala, Marcelo Di Carli, Joanna X. Liang, Cathleen Huang, Donghee Han, Damini Dey, Daniel S. Berman, Piotr J. Slomka, and University of Zurich

Subjects
610 Medicine & health, Radiology, Nuclear Medicine and imaging, 10181 Clinic for Nuclear Medicine, Cardiology and Cardiovascular Medicine
Abstract

Myocardial perfusion imaging (MPI) is frequently used to provide risk stratification, but methods to improve the accuracy of these predictions are needed.We developed an explainable deep learning (DL) model (HARD MACE [major adverse cardiac events]-DL) for the prediction of death or nonfatal myocardial infarction (MI) and validated its performance in large internal and external testing groups.Patients undergoing single-photon emission computed tomography MPI were included, with 20,401 patients in the training and internal testing group (5 sites) and 9,019 in the external testing group (2 different sites). HARD MACE-DL uses myocardial perfusion, motion, thickening, and phase polar maps combined with age, sex, and cardiac volumes. The primary outcome was all-cause mortality or nonfatal MI. Prognostic accuracy was evaluated using area under the receiver-operating characteristic curve (AUC).During internal testing, patients with normal perfusion and elevated HARD-MACE-DL risk were at higher risk than patients with abnormal perfusion and low HARD-MACE-DL risk (annualized event rate, 2.9% vs 1.2%; P 0.001). Patients in the highest quartile of HARD MACE-DL score had an annual rate of death or MI (4.8%) 10-fold higher than patients in the lowest quartile (0.48% per year). In external testing, the AUC for HARD MACE-DL (0.73; 95% CI: 0.71-0.75) was higher than a logistic regression model (AUC: 0.70), stress TPD (AUC: 0.65), and ischemic TPD (AUC: 0.63; all P 0.01). Calibration, a measure of how well predicted risk matches actual risk, was excellent in both groups (Brier score, 0.079 for internal and 0.070 for external).The DL model predicts death or MI directly from MPI, by estimating patient-level risk with good calibration and improved accuracy compared with traditional quantitative approaches. The model incorporates mechanisms to explain to the physician which image regions contribute to the adverse event prediction.

Published
2023
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27. Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images

Author

Robert J. H. Miller, Ananya Singh, Yuka Otaki, Balaji K. Tamarappoo, Paul Kavanagh, Tejas Parekh, Lien-Hsin Hu, Heidi Gransar, Tali Sharir, Andrew J. Einstein, Mathews B. Fish, Terrence D. Ruddy, Philipp A. Kaufmann, Albert J. Sinusas, Edward J. Miller, Timothy M. Bateman, Sharmila Dorbala, Marcelo F. Di Carli, Joanna X. Liang, Damini Dey, Daniel S. Berman, Piotr J. Slomka, University of Zurich, and Slomka, Piotr J

Subjects
2741 Radiology, Nuclear Medicine and Imaging, 610 Medicine & health, Radiology, Nuclear Medicine and imaging, 10181 Clinic for Nuclear Medicine, General Medicine, Article
Abstract

PURPOSE: Artificial intelligence (AI) has high diagnostic accuracy for coronary artery disease (CAD) from myocardial perfusion imaging (MPI). However, when trained using high-risk populations (such as patients with correlating invasive testing), the disease probability can be overestimated due to selection bias. We evaluated different strategies for training AI models to improve the calibration (accurate estimate of disease probability), using external testing. METHODS: Deep learning was trained using 828 patients from 3 sites, with MPI and invasive angiography within 6-months. Perfusion was assessed using upright (U-TPD) and supine total perfusion deficit (S-TPD). AI training without data augmentation (Model 1) was compared to training with augmentation (increased sampling) of patients without obstructive CAD (Model 2), and patients without CAD and TPD

Published
2022
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28. Decolonizing First Peoples Child Welfare

Author

Cunneen, C, Deckert, A, Porter, A, Tauri, J, Webb, R, Libesman, T, Blackstock, C, Mathews, B, King, J, Hermeston, W, Cunneen, C, Deckert, A, Porter, A, Tauri, J, Webb, R, Libesman, T, Blackstock, C, Mathews, B, King, J, and Hermeston, W

Abstract

In this chapter, we bring to light the duplicity of colonial governments’ claims to support human rights and equality of First Peoples children in Canada and Australia, while not only perpetuating but defending inequality. In Australia, this is viewed through recent law reforms and in Canada through a human rights challenge. Child protection services remain deeply discriminatory against First Peoples despite apologies, reforms, and claims by colonial governments of commitments to equality, non-discrimination and a reduction of First Peoples in child protection systems. This chapter presents the continuity in claims by First Peoples to self-determination in child protection, the diverse forms of resistance to colonial violence and how these challenge the deep-seated colonial values within Australian and Canadian child protection systems.

Published
2023

31. Time and event-specific deep learning for personalized risk assessment after cardiac perfusion imaging

Author

Pieszko, Konrad; https://orcid.org/0000-0002-5514-7750, Shanbhag, Aakash D, Singh, Ananya, Hauser, M Timothy, Miller, Robert J H, Liang, Joanna X, Motwani, Manish; https://orcid.org/0000-0003-4483-5168, Kwieciński, Jacek, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D; https://orcid.org/0000-0002-0686-5449, Kaufmann, Philipp A; https://orcid.org/0000-0002-9451-5210, Sinusas, Albert J; https://orcid.org/0000-0003-0972-9589, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Berman, Daniel S; https://orcid.org/0000-0002-3793-9578, Dey, Damini, Slomka, Piotr J; https://orcid.org/0000-0002-6110-938X, Pieszko, Konrad; https://orcid.org/0000-0002-5514-7750, Shanbhag, Aakash D, Singh, Ananya, Hauser, M Timothy, Miller, Robert J H, Liang, Joanna X, Motwani, Manish; https://orcid.org/0000-0003-4483-5168, Kwieciński, Jacek, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D; https://orcid.org/0000-0002-0686-5449, Kaufmann, Philipp A; https://orcid.org/0000-0002-9451-5210, Sinusas, Albert J; https://orcid.org/0000-0003-0972-9589, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Berman, Daniel S; https://orcid.org/0000-0002-3793-9578, Dey, Damini, and Slomka, Piotr J; https://orcid.org/0000-0002-6110-938X

Abstract

Standard clinical interpretation of myocardial perfusion imaging (MPI) has proven prognostic value for predicting major adverse cardiovascular events (MACE). However, personalizing predictions to a specific event type and time interval is more challenging. We demonstrate an explainable deep learning model that predicts the time-specific risk separately for all-cause death, acute coronary syndrome (ACS), and revascularization directly from MPI and 15 clinical features. We train and test the model internally using 10-fold hold-out cross-validation (n = 20,418) and externally validate it in three separate sites (n = 13,988) with MACE follow-ups for a median of 3.1 years (interquartile range [IQR]: 1.6, 3.6). We evaluate the model using the cumulative dynamic area under receiver operating curve (cAUC). The best model performance in the external cohort is observed for short-term prediction - in the first six months after the scan, mean cAUC for ACS and all-cause death reaches 0.76 (95% confidence interval [CI]: 0.75, 0.77) and 0.78 (95% CI: 0.78, 0.79), respectively. The model outperforms conventional perfusion abnormality measures at all time points for the prediction of death in both internal and external validations, with improvement increasing gradually over time. Individualized patient explanations are visualized using waterfall plots, which highlight the contribution degree and direction for each feature. This approach allows the derivation of individual event probability as a function of time as well as patient- and event-specific risk explanations that may help draw attention to modifiable risk factors. Such a method could help present post-scan risk assessments to the patient and foster shared decision-making.

Published
2023

32. Unsupervised learning to characterize patients with known coronary artery disease undergoing myocardial perfusion imaging

Author

Williams, Michelle Claire; https://orcid.org/0000-0003-3556-2428, Bednarski, Bryan P, Pieszko, Konrad; https://orcid.org/0000-0002-5514-7750, Miller, Robert J H; https://orcid.org/0000-0003-4676-2433, Kwiecinski, Jacek, Shanbhag, Aakash, Liang, Joanna X, Huang, Cathleen, Sharir, Tali, Dorbala, Sharmila, Di Carli, Marcelo F, Einstein, Andrew J, Sinusas, Albert J; https://orcid.org/0000-0003-0972-9589, Miller, Edward J; https://orcid.org/0000-0002-2156-5962, Bateman, Timothy M, Fish, Mathews B, Ruddy, Terrence D; https://orcid.org/0000-0002-0686-5449, Acampa, Wanda, Hauser, M Timothy, Kaufmann, Philipp A; https://orcid.org/0000-0002-9451-5210, Dey, Damini; https://orcid.org/0000-0003-2236-6970, Berman, Daniel S; https://orcid.org/0000-0002-3793-9578, Slomka, Piotr J; https://orcid.org/0000-0002-6110-938X, Williams, Michelle Claire; https://orcid.org/0000-0003-3556-2428, Bednarski, Bryan P, Pieszko, Konrad; https://orcid.org/0000-0002-5514-7750, Miller, Robert J H; https://orcid.org/0000-0003-4676-2433, Kwiecinski, Jacek, Shanbhag, Aakash, Liang, Joanna X, Huang, Cathleen, Sharir, Tali, Dorbala, Sharmila, Di Carli, Marcelo F, Einstein, Andrew J, Sinusas, Albert J; https://orcid.org/0000-0003-0972-9589, Miller, Edward J; https://orcid.org/0000-0002-2156-5962, Bateman, Timothy M, Fish, Mathews B, Ruddy, Terrence D; https://orcid.org/0000-0002-0686-5449, Acampa, Wanda, Hauser, M Timothy, Kaufmann, Philipp A; https://orcid.org/0000-0002-9451-5210, Dey, Damini; https://orcid.org/0000-0003-2236-6970, Berman, Daniel S; https://orcid.org/0000-0002-3793-9578, and Slomka, Piotr J; https://orcid.org/0000-0002-6110-938X

Abstract

PURPOSE Patients with known coronary artery disease (CAD) comprise a heterogenous population with varied clinical and imaging characteristics. Unsupervised machine learning can identify new risk phenotypes in an unbiased fashion. We use cluster analysis to risk-stratify patients with known CAD undergoing single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI). METHODS From 37,298 patients in the REFINE SPECT registry, we identified 9221 patients with known coronary artery disease. Unsupervised machine learning was performed using clinical (23), acquisition (17), and image analysis (24) parameters from 4774 patients (internal cohort) and validated with 4447 patients (external cohort). Risk stratification for all-cause mortality was compared to stress total perfusion deficit (< 5%, 5-10%, ≥10%). RESULTS Three clusters were identified, with patients in Cluster 3 having a higher body mass index, more diabetes mellitus and hypertension, and less likely to be male, have dyslipidemia, or undergo exercise stress imaging (p < 0.001 for all). In the external cohort, during median follow-up of 2.6 [0.14, 3.3] years, all-cause mortality occurred in 312 patients (7%). Cluster analysis provided better risk stratification for all-cause mortality (Cluster 3: hazard ratio (HR) 5.9, 95% confidence interval (CI) 4.0, 8.6, p < 0.001; Cluster 2: HR 3.3, 95% CI 2.5, 4.5, p < 0.001; Cluster 1, reference) compared to stress total perfusion deficit (≥10%: HR 1.9, 95% CI 1.5, 2.5 p < 0.001; < 5%: reference). CONCLUSIONS Our unsupervised cluster analysis in patients with known CAD undergoing SPECT MPI identified three distinct phenotypic clusters and predicted all-cause mortality better than ischemia alone.

Published
2023

34. Comparison of diabetes to other prognostic predictors among patients referred for cardiac stress testing: A contemporary analysis from the REFINE SPECT Registry

Author

Mathews B. Fish, Heidi Gransar, Edward J. Miller, Terrence D. Ruddy, Damini Dey, Robert J.H. Miller, Marcelo F. Di Carli, Lien-Hsin Hu, Andrew J. Einstein, Philipp A. Kaufmann, Sharmila Dorbala, Joanna X Liang, Piotr J. Slomka, Albert J. Sinusas, Evangelos Tzolos, Donghee Han, Timothy M. Bateman, Tali Sharir, Daniel S. Berman, Alan Rozanski, University of Zurich, and Slomka, Piotr J

Subjects
medicine.medical_specialty, medicine.diagnostic_test, business.industry, Stress testing, Cardiac stress test, CAD, 610 Medicine & health, 10181 Clinic for Nuclear Medicine, medicine.disease, Article, 2705 Cardiology and Cardiovascular Medicine, Coronary artery disease, Myocardial perfusion imaging, Internal medicine, Diabetes mellitus, Propensity score matching, medicine, Cardiology, 2741 Radiology, Nuclear Medicine and Imaging, Radiology, Nuclear Medicine and imaging, cardiovascular diseases, Cardiology and Cardiovascular Medicine, business, Mace
Abstract

BACKGROUND: Diabetes mellitus (DM) is increasingly prevalent among contemporary populations referred for cardiac stress testing, but its potency as a predictor for major adverse cardiovascular events (MACE) versus other clinical variables is not well delineated. METHODS & RESULTS: From 19,658 patients underwent SPECT-MPI, we identified 3,122 diabetic patients without known coronary artery disease (CAD) (DM+/CAD−) and 3,564 nondiabetics with known CAD (DM−/CAD+). Propensity score matching was used to control for the differences in characteristics between DM+/CAD− and DM−/CAD+ groups. There was comparable MACE in the matched DM+/CAD− and DM−/CAD+ groups (HR:1.15, 95%CI: 0.97–1.37). By Chi-square analysis, type of stress (exercise or pharmacologic), total perfusion deficit (TPD), and left ventricular function were the most potent predictors of MACE, followed by CAD and diabetes status. The combined consideration of mode of stress, TPD, and diabetes provided synergistic stratification, an 8.87-fold (HR:8.87, 95%CI: 7.27–10.82) increase in MACE among pharmacologically stressed patients with diabetes and TPD>10% (versus non-ischemic, exercised stressed patients without diabetes). CONCLUSIONS: Propensity-matched patients with diabetes and no known CAD have similar MACE risk compared to patients with known CAD and no diabetes. Diabetes is synergistic with mode of stress testing and TPD in predicting the risk of cardiac stress test patients.

Published
2022

35. Prevalence and predictors of automatically quantified myocardial ischemia within a multicenter international registry

Author

Donghee Han, Alan Rozanski, Robert J. H. Miller, Tali Sharir, Andrew J. Einstein, Mathews B. Fish, Terrence D. Ruddy, Philipp A. Kaufmann, Albert J. Sinusas, Edward J. Miller, Timothy M. Bateman, Sharmila Dorbala, Marcelo Di Carli, Joanna X. Liang, Damini Dey, Daniel S. Berman, Piotr J. Slomka, University of Zurich, and Rozanski, Alan

Subjects
2741 Radiology, Nuclear Medicine and Imaging, Radiology, Nuclear Medicine and imaging, 610 Medicine & health, 10181 Clinic for Nuclear Medicine, Cardiology and Cardiovascular Medicine, Article, 2705 Cardiology and Cardiovascular Medicine
Abstract

BACKGROUND: The utility of cardiac stress testing depends on the prevalence of myocardial ischemia within candidate populations. However, a comprehensive assessment of the factors influencing frequency of myocardial ischemia within contemporary populations referred for stress testing has not been performed. METHODS: We assessed 19,690 patients undergoing nuclear stress testing from a multicenter registry. The chi-square test was used to assess the relative importance of features for predicting myocardial ischemia. RESULTS: In the overall cohort, LVEF, male gender, and rest total perfusion deficit (TPD) were the top three predictors of ischemia, followed by CAD status, age, typical angina, and CAD risk factors. Myocardial ischemia was observed in 13.6% of patients with LVEF>55%, in 26.2% of patients with LVEF 45-54%, and in 48.3% among patients with LVEF

Published
2022

36. Diagnostic safety of a machine learning-based automatic patient selection algorithm for stress-only myocardial perfusion SPECT

Author

Timothy M. Bateman, Sabahat Bokhari, Terrence D. Ruddy, Julian Betancur, Daniel S. Berman, Yuka Otaki, Donghee Han, Edward J. Miller, Sharmila Dorbala, Andrew J. Einstein, Joanna X Liang, Piotr J. Slomka, Albert J. Sinusas, Mathews B. Fish, Richard Rios, Lien-Hsin Hu, Tali Sharir, Philipp A. Kaufmann, Damini Dey, Peyman N. Azadani, Robert J.H. Miller, Marcelo F. Di Carli, Balaji Tamarappoo, Evann Eisenberg, University of Zurich, and Slomka, Piotr J

Subjects
CAD, 610 Medicine & health, Coronary Artery Disease, Machine learning, computer.software_genre, Coronary Angiography, Article, 2705 Cardiology and Cardiovascular Medicine, Coronary artery disease, Machine Learning, Myocardial perfusion imaging, Medicine, Humans, 2741 Radiology, Nuclear Medicine and Imaging, Radiology, Nuclear Medicine and imaging, Selection algorithm, Tomography, Emission-Computed, Single-Photon, medicine.diagnostic_test, Receiver operating characteristic, business.industry, Patient Selection, Myocardial Perfusion Imaging, 10181 Clinic for Nuclear Medicine, medicine.disease, Highly sensitive, Perfusion, Stenosis, Artificial intelligence, Cardiology and Cardiovascular Medicine, business, computer, Algorithms
Abstract

BACKGROUND: Stress-only myocardial perfusion imaging (MPI) markedly reduces radiation dose, scanning time, and cost. We developed an automated clinical algorithm to safely cancel unnecessary rest imaging with high sensitivity for obstructive coronary artery disease (CAD). METHODS AND RESULTS: Patients without known CAD undergoing both MPI and invasive coronary angiography from REFINE SPECT were studied. A machine learning score (MLS) for prediction of obstructive CAD was generated using stress-only MPI and pre-test clinical variables. An MLS threshold with a pre-defined sensitivity of 95% was applied to the automated patient selection algorithm. Obstructive CAD was present in 1,309/2,079 (63%) patients. MLS had higher area under the receiver-operator-characteristic curve (AUC) for prediction of CAD than reader diagnosis and TPD (0.84 vs 0.70 vs 0.78, p

Published
2022
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37. Mitigating bias in deep learning for diagnosis of coronary artery disease from myocardial perfusion SPECT images

Author

Miller, Robert J. H., primary, Singh, Ananya, additional, Otaki, Yuka, additional, Tamarappoo, Balaji K., additional, Kavanagh, Paul, additional, Parekh, Tejas, additional, Hu, Lien-Hsin, additional, Gransar, Heidi, additional, Sharir, Tali, additional, Einstein, Andrew J., additional, Fish, Mathews B., additional, Ruddy, Terrence D., additional, Kaufmann, Philipp A., additional, Sinusas, Albert J., additional, Miller, Edward J., additional, Bateman, Timothy M., additional, Dorbala, Sharmila, additional, Di Carli, Marcelo F., additional, Liang, Joanna X., additional, Dey, Damini, additional, Berman, Daniel S., additional, and Slomka, Piotr J., additional

Published
2022
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38. Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry

Author

Terrence D. Ruddy, Marcio A. Diniz, Edward J. Miller, Robert J.H. Miller, Marcelo F. Di Carli, Damini Dey, Philipp A. Kaufmann, Paul B. Kavanagh, Mathews B. Fish, Tejas Parekh, Ananya Singh, Tali Sharir, Serge D. Van Kriekinge, Sharmila Dorbala, Joanna X Liang, Daniel S. Berman, Albert J. Sinusas, Richard Rios, Lien-Hsin Hu, Timothy M. Bateman, Andrew J. Einstein, Piotr J. Slomka, Yuka Otaki, and University of Zurich

Subjects
Cardiovascular event, Physiology, Computer science, 610 Medicine & health, Coronary Artery Disease, 030204 cardiovascular system & hematology, Machine learning, computer.software_genre, Machine Learning, Set (abstract data type), 03 medical and health sciences, Myocardial perfusion imaging, 0302 clinical medicine, Physiology (medical), medicine, Humans, In patient, Registries, 030212 general & internal medicine, Tomography, Emission-Computed, Single-Photon, medicine.diagnostic_test, business.industry, Dimensionality reduction, Multivariable calculus, Myocardial Perfusion Imaging, 10181 Clinic for Nuclear Medicine, Prognosis, Ranking, Cardiovascular Diseases, Artificial intelligence, Cardiology and Cardiovascular Medicine, business, computer, Emission computed tomography
Abstract

AIMS 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 single-photon emission computed tomography (SPECT) MPI. METHODS AND RESULTS This study included 20,414 patients from the multicenter REFINE SPECT registry and 2,984 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 0.798, p = 0.18) by including 12 of 40 manually input variables and 11 of 58 imaging variables. ML-Reduced achieved comparable accuracy (AUC 0.795) 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. TRANSLATIONAL PERSPECTIVE A reduced machine learning model, with 12 out of 40 manually collected variables and 11 of 58 imaging variables, achieved >99% of the prognostic accuracy of the full model. Models with fewer manually collected features require less infrastructure to implement, are easier for physicians to utilize, and are potentially critical to ensuring broader clinical implementation. Additionally, these models can integrate mechanisms to explain patient-specific risk estimates to improve physician confidence in the machine learning prediction.

Published
2021
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40. 30-day morbidity and mortality of sleeve gastrectomy, Roux-en-Y gastric bypass and one anastomosis gastric bypass: a propensity score-matched analysis of the GENEVA data

Author

Singhal R., Cardoso V. R., Wiggins T., Super J., Ludwig C., Gkoutos G. V., Mahawar K., Pedziwiatr M., Major P., Zarzycki P., Pantelis A., Lapatsanis D. P., Stravodimos G., Matthys C., Focquet M., Vleeschouwers W., Spaventa A. G., Zerrweck C., Vitiello A., Berardi G., Musella M., Sanchez-Meza A., Cantu F. J., Mora F., Cantu M. A., Katakwar A., Reddy D. N., Elmaleh H., Hassan M., Elghandour A., Elbanna M., Osman A., Khan A., layani L., Kiran N., Velikorechin A., Solovyeva M., Melali H., Shahabi S., Agrawal A., Shrivastava A., Sharma A., Narwaria B., Narwaria M., Raziel A., Sakran N., Susmallian S., Karagoz L., Akbaba M., Piskin S. Z., Balta A. Z., Senol Z., Manno E., Iovino M. G., Qassem M., Arana-Garza S., Povoas H. P., Vilas-Boas M. L., Naumann D., Li A., Ammori B. J., Balamoun H., Salman M., Nasta A. M., Goel R., Sanchez-Aguilar H., Herrera M. F., Abou-mrad A., Cloix L., Mazzini G. S., Kristem L., Lazaro A., Campos J., Bernardo J., Gonzalez J., Trindade C., Viveiros O., Ribeiro R., Goitein D., Hazzan D., Segev L., Beck T., Reyes H., Monterrubio J., Garcia P., Benois M., Kassir R., Contine A., Elshafei M., Aktas S., Weiner S., Heidsieck T., Level L., Pinango S., Ortega P. M., Moncada R., Valenti V., Vlahovic I., Boras Z., Liagre A., Martini F., Juglard G., Motwani M., Saggu S. S., Momani H. A., Lopez L. A. A., Cortez M. A. C., Zavala R. A., D'Haese RN C., Kempeneers I., Himpens J., Lazzati A., Paolino L., Bathaei S., Bedirli A., Yavuz A., Buyukkasap C., Ozaydin S., Kwiatkowski A., Bartosiak K., Waledziak M., Santonicola A., Angrisani L., Iovino P., Palma R., Iossa A., Boru C. E., De Angelis F., Silecchia G., Hussain A., Balchandra S., Coltell I. B., Perez J. L., Bohra A., Awan A. K., Madhok B., Leeder P. C., Awad S., Al-Khyatt W., Shoma A., Elghadban H., Ghareeb S., Mathews B., Kurian M., Larentzakis A., Vrakopoulou G. Z., Albanopoulos K., Bozdag A., Lale A., Kirkil C., Dincer M., Bashir A., Haddad A., Hijleh L. A., Zilberstein B., de Marchi D. D., Souza W. P., Broden C. M., Gislason H., Shah K., Ambrosi A., Pavone G., Tartaglia N., Kona S. L. K., Kalyan K., Perez C. E. G., Botero M. A. F., Covic A., Timofte D., Maxim M., Faraj D., Tseng L., Liem R., Oren G., Dilektasli E., Yalcin I., AlMukhtar H., Hadad M. A., Mohan R., Arora N., Bedi D., Rives-Lange C., Chevallier J. -M., Poghosyan T., Sebbag H., Zinai L., Khaldi S., Mauchien C., Mazza D., Dinescu G., Rea B., Perez-Galaz F., Zavala L., Besa A., Curell A., Balibrea J. M., Vaz C., Galindo L., Silva N., Caballero J. L. E., Sebastian S. O., Marchesini J. C. D., da Fonseca Pereira R. A., Sobottka W. H., Fiolo F. E., Turchi M., Coelho A. C. J., Zacaron A. L., Barbosa A., Quinino R., Menaldi G., Paleari N., Martinez-Duartez P., de Esparza G. M. A. R., Esteban V. S., Torres A., Garcia-Galocha J. L., Josa M., Pacheco-Garcia J. M., Mayo-Ossorio M. A., Chowbey P., Soni V., de Vasconcelos Cunha H. A., Castilho M. V., Ferreira R. M. A., Barreiro T. A., Charalabopoulos A., Sdralis E., Davakis S., Bomans B., Dapri G., Van Belle K., Takieddine M., Vaneukem P., Karaca E. S. A., Karaca F. C., Sumer A., Peksen C., Savas O. A., Chousleb E., Elmokayed F., Fakhereldin I., Aboshanab H. M., Swelium T., Gudal A., Gamloo L., Ugale A., Ugale S., Boeker C., Reetz C., Hakami I. A., Mall J., Alexandrou A., Baili E., Bodnar Z., Maleckas A., Gudaityte R., Guldogan C. E., Gundogdu E., Ozmen M. M., Thakkar D., Dukkipati N., Shah P. S., Shah S. S., Adil M. T., Jambulingam P., Mamidanna R., Whitelaw D., Jain V., Veetil D. K., Wadhawan R., Torres M., Tinoco T., Leclercq W., Romeijn M., van de Pas K., Alkhazraji A. K., Taha S. A., Ustun M., Yigit T., Inam A., Burhanulhaq M., Pazouki A., Eghbali F., Kermansaravi M., Jazi A. H. D., Mahmoudieh M., Mogharehabed N., Tsiotos G., Stamou K., Rodriguez F. J. B., Navarro M. A. R., Torres O. M., Martinez S. L., Tamez E. R. M., Cornejo G. A. M., Flores J. E. G., Mohammed D. A., Elfawal M. H., Shabbir A., Guowei K., So J. B., Kaplan E. T., Kaplan M., Kaplan T., Pham D. T., Rana G., Kappus M., Gadani R., Kahitan M., Pokharel K., Osborne A., Pournaras D., Hewes J., Napolitano E., Chiappetta S., Bottino V., Dorado E., Schoettler A., Gaertner D., Fedtke K., Aguilar-Espinosa F., Aceves-Lozano S., Balani A., Nagliati C., Pennisi D., Rizzi A., Frattini F., Foschi D., Benuzzi L., Parikh C., Shah H., Pinotti E., Montuori M., Borrelli V., Dargent J., Copaescu C. A., Hutopila I., Smeu B., Witteman B., Hazebroek E., Deden L., Heusschen L., Okkema S., Aufenacker T., den Hengst W., Vening W., van der Burgh Y., Ghazal A., Ibrahim H., Niazi M., Alkhaffaf B., Altarawni M., Cesana G. C., Anselmino M., Uccelli M., Olmi S., Stier C., Akmanlar T., Sonnenberg T., Schieferbein U., Marcolini A., Awruch D., Vicentin M., de Souza Bastos E. L., Gregorio S. A., Ahuja A., Mittal T., Bolckmans R., Baratte C., Wisnewsky J. A., Genser L., Chong L., Taylor L., Ward S., Hi M. W., Heneghan H., Fearon N., Plamper A., Rheinwalt K., Geoghegan J., Ng K. C., Kaseja K., Kotowski M., Samarkandy T. A., Leyva-Alvizo A., Corzo-Culebro L., Wang C., Yang W., Dong Z., Riera M., Jain R., Hamed H., Said M., Zarzar K., Garcia M., Turkcapar A. G., Sen O., Baldini E., Conti L., Wietzycoski C., Lopes E., Pintar T., Salobir J., Aydin C., Atici S. D., Ergin A., Ciyiltepe H., Bozkurt M. A., Kizilkaya M. C., Onalan N. B. D., Zuber M. N. B. A., Wong W. J., Garcia A., Vidal L., Beisani M., Pasquier J., Vilallonga R., Sharma S., Parmar C., Lee L., Sufi P., Sinan H., Saydam M., Singhal, R., Cardoso, V. R., Wiggins, T., Super, J., Ludwig, C., Gkoutos, G. V., Mahawar, K., Pedziwiatr, M., Major, P., Zarzycki, P., Pantelis, A., Lapatsanis, D. P., Stravodimos, G., Matthys, C., Focquet, M., Vleeschouwers, W., Spaventa, A. G., Zerrweck, C., Vitiello, A., Berardi, G., Musella, M., Sanchez-Meza, A., Cantu, F. J., Mora, F., Cantu, M. A., Katakwar, A., Reddy, D. N., Elmaleh, H., Hassan, M., Elghandour, A., Elbanna, M., Osman, A., Khan, A., Layani, L., Kiran, N., Velikorechin, A., Solovyeva, M., Melali, H., Shahabi, S., Agrawal, A., Shrivastava, A., Sharma, A., Narwaria, B., Narwaria, M., Raziel, A., Sakran, N., Susmallian, S., Karagoz, L., Akbaba, M., Piskin, S. Z., Balta, A. Z., Senol, Z., Manno, E., Iovino, M. G., Qassem, M., Arana-Garza, S., Povoas, H. P., Vilas-Boas, M. L., Naumann, D., Li, A., Ammori, B. J., Balamoun, H., Salman, M., Nasta, A. M., Goel, R., Sanchez-Aguilar, H., Herrera, M. F., Abou-mrad, A., Cloix, L., Mazzini, G. S., Kristem, L., Lazaro, A., Campos, J., Bernardo, J., Gonzalez, J., Trindade, C., Viveiros, O., Ribeiro, R., Goitein, D., Hazzan, D., Segev, L., Beck, T., Reyes, H., Monterrubio, J., Garcia, P., Benois, M., Kassir, R., Contine, A., Elshafei, M., Aktas, S., Weiner, S., Heidsieck, T., Level, L., Pinango, S., Ortega, P. M., Moncada, R., Valenti, V., Vlahovic, I., Boras, Z., Liagre, A., Martini, F., Juglard, G., Motwani, M., Saggu, S. S., Momani, H. A., Lopez, L. A. A., Cortez, M. A. C., Zavala, R. A., D'Haese RN, C., Kempeneers, I., Himpens, J., Lazzati, A., Paolino, L., Bathaei, S., Bedirli, A., Yavuz, A., Buyukkasap, C., Ozaydin, S., Kwiatkowski, A., Bartosiak, K., Waledziak, M., Santonicola, A., Angrisani, L., Iovino, P., Palma, R., Iossa, A., Boru, C. E., De Angelis, F., Silecchia, G., Hussain, A., Balchandra, S., Coltell, I. B., Perez, J. L., Bohra, A., Awan, A. K., Madhok, B., Leeder, P. C., Awad, S., Al-Khyatt, W., Shoma, A., Elghadban, H., Ghareeb, S., Mathews, B., Kurian, M., Larentzakis, A., Vrakopoulou, G. Z., Albanopoulos, K., Bozdag, A., Lale, A., Kirkil, C., Dincer, M., Bashir, A., Haddad, A., Hijleh, L. A., Zilberstein, B., de Marchi, D. D., Souza, W. P., Broden, C. M., Gislason, H., Shah, K., Ambrosi, A., Pavone, G., Tartaglia, N., Kona, S. L. K., Kalyan, K., Perez, C. E. G., Botero, M. A. F., Covic, A., Timofte, D., Maxim, M., Faraj, D., Tseng, L., Liem, R., Oren, G., Dilektasli, E., Yalcin, I., Almukhtar, H., Hadad, M. A., Mohan, R., Arora, N., Bedi, D., Rives-Lange, C., Chevallier, J. -M., Poghosyan, T., Sebbag, H., Zinai, L., Khaldi, S., Mauchien, C., Mazza, D., Dinescu, G., Rea, B., Perez-Galaz, F., Zavala, L., Besa, A., Curell, A., Balibrea, J. M., Vaz, C., Galindo, L., Silva, N., Caballero, J. L. E., Sebastian, S. O., Marchesini, J. C. D., da Fonseca Pereira, R. A., Sobottka, W. H., Fiolo, F. E., Turchi, M., Coelho, A. C. J., Zacaron, A. L., Barbosa, A., Quinino, R., Menaldi, G., Paleari, N., Martinez-Duartez, P., de Esparza, G. M. A. R., Esteban, V. S., Torres, A., Garcia-Galocha, J. L., Josa, M., Pacheco-Garcia, J. M., Mayo-Ossorio, M. A., Chowbey, P., Soni, V., de Vasconcelos Cunha, H. A., Castilho, M. V., Ferreira, R. M. A., Barreiro, T. A., Charalabopoulos, A., Sdralis, E., Davakis, S., Bomans, B., Dapri, G., Van Belle, K., Takieddine, M., Vaneukem, P., Karaca, E. S. A., Karaca, F. C., Sumer, A., Peksen, C., Savas, O. A., Chousleb, E., Elmokayed, F., Fakhereldin, I., Aboshanab, H. M., Swelium, T., Gudal, A., Gamloo, L., Ugale, A., Ugale, S., Boeker, C., Reetz, C., Hakami, I. A., Mall, J., Alexandrou, A., Baili, E., Bodnar, Z., Maleckas, A., Gudaityte, R., Guldogan, C. E., Gundogdu, E., Ozmen, M. M., Thakkar, D., Dukkipati, N., Shah, P. S., Shah, S. S., Adil, M. T., Jambulingam, P., Mamidanna, R., Whitelaw, D., Jain, V., Veetil, D. K., Wadhawan, R., Torres, M., Tinoco, T., Leclercq, W., Romeijn, M., van de Pas, K., Alkhazraji, A. K., Taha, S. A., Ustun, M., Yigit, T., Inam, A., Burhanulhaq, M., Pazouki, A., Eghbali, F., Kermansaravi, M., Jazi, A. H. D., Mahmoudieh, M., Mogharehabed, N., Tsiotos, G., Stamou, K., Rodriguez, F. J. B., Navarro, M. A. R., Torres, O. M., Martinez, S. L., Tamez, E. R. M., Cornejo, G. A. M., Flores, J. E. G., Mohammed, D. A., Elfawal, M. H., Shabbir, A., Guowei, K., So, J. B., Kaplan, E. T., Kaplan, M., Kaplan, T., Pham, D. T., Rana, G., Kappus, M., Gadani, R., Kahitan, M., Pokharel, K., Osborne, A., Pournaras, D., Hewes, J., Napolitano, E., Chiappetta, S., Bottino, V., Dorado, E., Schoettler, A., Gaertner, D., Fedtke, K., Aguilar-Espinosa, F., Aceves-Lozano, S., Balani, A., Nagliati, C., Pennisi, D., Rizzi, A., Frattini, F., Foschi, D., Benuzzi, L., Parikh, C., Shah, H., Pinotti, E., Montuori, M., Borrelli, V., Dargent, J., Copaescu, C. A., Hutopila, I., Smeu, B., Witteman, B., Hazebroek, E., Deden, L., Heusschen, L., Okkema, S., Aufenacker, T., den Hengst, W., Vening, W., van der Burgh, Y., Ghazal, A., Ibrahim, H., Niazi, M., Alkhaffaf, B., Altarawni, M., Cesana, G. C., Anselmino, M., Uccelli, M., Olmi, S., Stier, C., Akmanlar, T., Sonnenberg, T., Schieferbein, U., Marcolini, A., Awruch, D., Vicentin, M., de Souza Bastos, E. L., Gregorio, S. A., Ahuja, A., Mittal, T., Bolckmans, R., Baratte, C., Wisnewsky, J. A., Genser, L., Chong, L., Taylor, L., Ward, S., Hi, M. W., Heneghan, H., Fearon, N., Plamper, A., Rheinwalt, K., Geoghegan, J., Ng, K. C., Kaseja, K., Kotowski, M., Samarkandy, T. A., Leyva-Alvizo, A., Corzo-Culebro, L., Wang, C., Yang, W., Dong, Z., Riera, M., Jain, R., Hamed, H., Said, M., Zarzar, K., Garcia, M., Turkcapar, A. G., Sen, O., Baldini, E., Conti, L., Wietzycoski, C., Lopes, E., Pintar, T., Salobir, J., Aydin, C., Atici, S. D., Ergin, A., Ciyiltepe, H., Bozkurt, M. A., Kizilkaya, M. C., Onalan, N. B. D., Zuber, M. N. B. A., Wong, W. J., Garcia, A., Vidal, L., Beisani, M., Pasquier, J., Vilallonga, R., Sharma, S., Parmar, C., Lee, L., Sufi, P., Sinan, H., Saydam, M., İstinye Üniversitesi, Tıp Fakültesi, Cerrahi Tıp Bilimleri Bölümü, Sumer, Aziz, Peksen, Caghan, and Savas, Osman Anil

Subjects
Nutrition and Dietetics, Endocrinology, Diabetes and Metabolism, Gastric Bypass, Medicine (miscellaneous), nutritional and metabolic diseases, COVID-19, Gastrectomy, Humans, Morbidity, Propensity Score, Retrospective Studies, Treatment Outcome, Diabetes Mellitus, Type 2, Obesity, Morbid, Article, Diabetes Mellitus, Obesity, Morbid, Type 2
Abstract

Background There is a paucity of data comparing 30-day morbidity and mortality of sleeve gastrectomy (SG), Roux-en-Y gastric bypass (RYGB), and one anastomosis gastric bypass (OAGB). This study aimed to compare the 30-day safety of SG, RYGB, and OAGB in propensity score-matched cohorts. Materials and methods This analysis utilised data collected from the GENEVA study which was a multicentre observational cohort study of bariatric and metabolic surgery (BMS) in 185 centres across 42 countries between 01/05/2022 and 31/10/2020 during the Coronavirus Disease-2019 (COVID-19) pandemic. 30-day complications were categorised according to the Clavien–Dindo classification. Patients receiving SG, RYGB, or OAGB were propensity-matched according to baseline characteristics and 30-day complications were compared between groups. Results In total, 6770 patients (SG 3983; OAGB 702; RYGB 2085) were included in this analysis. Prior to matching, RYGB was associated with highest 30-day complication rate (SG 5.8%; OAGB 7.5%; RYGB 8.0% (p = 0.006)). On multivariate regression modelling, Insulin-dependent type 2 diabetes mellitus and hypercholesterolaemia were associated with increased 30-day complications. Being a non-smoker was associated with reduced complication rates. When compared to SG as a reference category, RYGB, but not OAGB, was associated with an increased rate of 30-day complications. A total of 702 pairs of SG and OAGB were propensity score-matched. The complication rate in the SG group was 7.3% (n = 51) as compared to 7.5% (n = 53) in the OAGB group (p = 0.68). Similarly, 2085 pairs of SG and RYGB were propensity score-matched. The complication rate in the SG group was 6.1% (n = 127) as compared to 7.9% (n = 166) in the RYGB group (p = 0.09). And, 702 pairs of OAGB and RYGB were matched. The complication rate in both groups was the same at 7.5 % (n = 53; p = 0.07). Conclusions This global study found no significant difference in the 30-day morbidity and mortality of SG, RYGB, and OAGB in propensity score-matched cohorts.

Published
2021
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42. Prognostic value of early left ventricular ejection fraction reserve during regadenoson stress solid-state SPECT-MPI

Author

Yuka Otaki, Robert J.H. Miller, Mathews B. Fish, Mark Lemley, and Piotr J. Slomka

Subjects
medicine.medical_specialty, Supine position, Ejection fraction, Proportional hazards model, business.industry, fungi, Spect mpi, Solid-state, 030204 cardiovascular system & hematology, 030218 nuclear medicine & medical imaging, Regadenoson, 03 medical and health sciences, 0302 clinical medicine, Internal medicine, Cardiology, medicine, Radiology, Nuclear Medicine and imaging, Cardiology and Cardiovascular Medicine, business, Perfusion, Mace, medicine.drug
Abstract

We hypothesized early post-stress left ventricular ejection fraction reserve (EFR) on solid-state-SPECT is associated with major cardiac adverse events (MACE). 151 patients (70 ± 12 years, male 50%) undergoing same-day rest/regadenoson stress 99mTc-sestamibi solid-state SPECT were followed for MACE. Rest imaging was performed in the upright and supine positions. Early stress imaging was started 2 minutes after the regadenoson injection in the supine position and followed by late stress acquisition in the upright position. Total perfusion deficit (TPD) and functional parameters were quantified automatically. EFR, ∆end-diastolic volume (EDV), and end-systolic volume (ESV) were calculated as the difference between stress and rest values in the same position. EFR

Published
2021
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43. Machine Learning to Predict Abnormal Myocardial Perfusion from Pre-test Features

Author

Robert J. H. Miller, M. Timothy Hauser, Tali Sharir, Andrew J. Einstein, Mathews B. Fish, Terrence D. Ruddy, Philipp A. Kaufmann, Albert J. Sinusas, Edward J. Miller, Timothy M. Bateman, Sharmila Dorbala, Marcelo Di Carli, Cathleen Huang, Joanna X. Liang, Donghee Han, Damini Dey, Daniel S. Berman, Piotr J. Slomka, University of Zurich, and Slomka, Piotr J

Subjects
Machine Learning, Perfusion, Tomography, Emission-Computed, Single-Photon, ROC Curve, Myocardial Perfusion Imaging, 2741 Radiology, Nuclear Medicine and Imaging, Humans, 610 Medicine & health, Radiology, Nuclear Medicine and imaging, 10181 Clinic for Nuclear Medicine, Cardiology and Cardiovascular Medicine, 2705 Cardiology and Cardiovascular Medicine, Article
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 9,019 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

Published
2022

44. Differences in Prognostic Value of Myocardial Perfusion Single-Photon Emission Computed Tomography Using High-Efficiency Solid-State Detector Between Men and Women in a Large International Multicenter Study

Author

Balaji K. Tamarappoo, Yuka Otaki, Tali Sharir, Lien-Hsin Hu, Heidi Gransar, Andrew J. Einstein, Mathews B. Fish, Terrence D. Ruddy, Philipp Kaufmann, Albert J. Sinusas, Edward J. Miller, Timothy M. Bateman, Sharmila Dorbala, Marcelo Di Carli, Evann Eisenberg, Joanna X. Liang, Damini Dey, Daniel S. Berman, and Piotr J. Slomka

Subjects
Male, Perfusion, Tomography, Emission-Computed, Single-Photon, Myocardial Infarction, Myocardial Perfusion Imaging, Humans, Female, Radiology, Nuclear Medicine and imaging, Coronary Artery Disease, Prognosis, Cardiology and Cardiovascular Medicine, Article
Abstract

Background: Semiquantitative assessment of stress myocardial perfusion defect has been shown to have greater prognostic value for prediction of major adverse cardiac events (MACE) in women compared with men in single-center studies with conventional single-photon emission computed tomography (SPECT) cameras. We evaluated sex-specific difference in the prognostic value of automated quantification of ischemic total perfusion defect (ITPD) and the interaction between sex and ITPD using high-efficiency SPECT cameras with solid-state detectors in an international multicenter imaging registry (REFINE SPECT [Registry of Fast Myocardial Perfusion Imaging With Next-Generation SPECT]). Methods: Rest and exercise or pharmacological stress SPECT myocardial perfusion imaging were performed in 17 833 patients from 5 centers. MACE was defined as the first occurrence of death or myocardial infarction. Total perfusion defect (TPD) at rest, stress, and ejection fraction were quantified automatically by software. ITPD was given by stressTPD-restTPD. Cox proportional hazards model was used to evaluate the association between ITPD versus MACE-free survival and expressed as a hazard ratio. Results: In 10614 men and 7219 women, with a median follow-up of 4.75 years (interquartile range, 3.7–6.1), there were 1709 MACE. In a multivariable Cox model, after adjusting for revascularization and other confounding variables, ITPD was associated with MACE (hazard ratio, 1.08 [95% CI, 1.05–1.1]; P P P Conclusions: In the international, multicenter REFINE SPECT registry, moderate and severe ischemia as quantified by ITPD from high-efficiency SPECT is associated with a worse prognosis in women compared with men.

Published
2022
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45. A review of Australian Government funding of parenting intervention research

Author

Havighurst, SS, Chainey, C, Doyle, FL, Higgins, DJ, Mathews, B, Mazzucchelli, TG, Zimmer-Gembeck, M, Andriessen, K, Cobham, VE, Cross, D, Dadds, MR, Dawe, S, Gray, KM, Guastella, AJ, Harnett, P, Haslam, DM, Middeldorp, CM, Morawska, A, Ohan, JL, Sanders, MR, Stallman, HM, Tonge, BJ, Toumbourou, John, Turner, KMT, Williams, KE, Yap, MBH, Nicholson, JM, Havighurst, SS, Chainey, C, Doyle, FL, Higgins, DJ, Mathews, B, Mazzucchelli, TG, Zimmer-Gembeck, M, Andriessen, K, Cobham, VE, Cross, D, Dadds, MR, Dawe, S, Gray, KM, Guastella, AJ, Harnett, P, Haslam, DM, Middeldorp, CM, Morawska, A, Ohan, JL, Sanders, MR, Stallman, HM, Tonge, BJ, Toumbourou, John, Turner, KMT, Williams, KE, Yap, MBH, and Nicholson, JM

Published
2022

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

Author

Miller, Robert J H, Hauser, M Timothy, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp A, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Huang, Cathleen, Liang, Joanna X, Han, Donghee, Dey, Damini, Berman, Daniel S, Slomka, Piotr J, Miller, Robert J H, Hauser, M Timothy, Sharir, Tali, Einstein, Andrew J, Fish, Mathews B, Ruddy, Terrence D, Kaufmann, Philipp A, Sinusas, Albert J, Miller, Edward J, Bateman, Timothy M, Dorbala, Sharmila, Di Carli, Marcelo, Huang, Cathleen, Liang, Joanna X, Han, Donghee, Dey, Damini, Berman, Daniel S, and Slomka, Piotr J

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.

Published
2022

47. Differences in Prognostic Value of Myocardial Perfusion Single-Photon Emission Computed Tomography Using High-Efficiency Solid-State Detector Between Men and Women in a Large International Multicenter Study

Author

Tamarappoo, Balaji K., primary, Otaki, Yuka, additional, Sharir, Tali, additional, Hu, Lien-Hsin, additional, Gransar, Heidi, additional, Einstein, Andrew J., additional, Fish, Mathews B., additional, Ruddy, Terrence D., additional, Kaufmann, Philipp, additional, Sinusas, Albert J., additional, Miller, Edward J., additional, Bateman, Timothy M., additional, Dorbala, Sharmila, additional, Di Carli, Marcelo, additional, Eisenberg, Evann, additional, Liang, Joanna X., additional, Dey, Damini, additional, Berman, Daniel S., additional, and Slomka, Piotr J., additional

Published
2022
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48. Handling missing values in machine learning to predict patient-specific risk of adverse cardiac events: Insights from REFINE SPECT registry

Author

Rios, Richard, primary, Miller, Robert J.H., additional, Manral, Nipun, additional, Sharir, Tali, additional, Einstein, Andrew J., additional, Fish, Mathews B., additional, Ruddy, Terrence D., additional, Kaufmann, Philipp A., additional, Sinusas, Albert J., additional, Miller, Edward J., additional, Bateman, Timothy M., additional, Dorbala, Sharmila, additional, Di Carli, Marcelo, additional, Van Kriekinge, Serge D., additional, Kavanagh, Paul B., additional, Parekh, Tejas, additional, Liang, Joanna X., additional, Dey, Damini, additional, Berman, Daniel S., additional, and Slomka, Piotr J., additional

Published
2022
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49. Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging

Author

Miller, Robert J.H., primary, Kuronuma, Keiichiro, additional, Singh, Ananya, additional, Otaki, Yuka, additional, Hayes, Sean, additional, Chareonthaitawee, Panithaya, additional, Kavanagh, Paul, additional, Parekh, Tejas, additional, Tamarappoo, Balaji K, additional, Sharir, Tali, additional, Einstein, Andrew J, additional, Fish, Mathews B, additional, Ruddy, Terrence D, additional, Kaufmann, Philipp A., additional, Sinusas, Albert J, additional, Miller, Edward J, additional, Bateman, Timothy, additional, Dorbala, Sharmila, additional, Di Carli, Marcelo F, additional, Cadet, Sebastien, additional, Liang, Joanna X, additional, Dey, Damini, additional, Berman, Daniel S., additional, and Slomka, Piotr J., additional

Published
2022
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50. 30-Day morbidity and mortality of bariatric metabolic surgery in adolescence during the COVID-19 pandemic – The GENEVA study

Author

Singhal R., Wiggins T., Super J., Alqahtani A., Nadler E. P., Ludwig C., Tahrani A., Mahawar K., Pedziwiatr M., Major P., Zarzycki P., Pantelis A., Lapatsanis D. P., Stravodimos G., Matthys C., Focquet M., Vleeschouwers W., Spaventa A. G., Zerrweck C., Vitiello A., Berardi G., Musella M., Sanchez-Meza A., Cantu F. J., Mora F., Cantu M. A., Katakwar A., Reddy D. N., Elmaleh H., Hassan M., Elghandour A., Elbanna M., Osman A., Khan A., Layani L., Kiran N., Velikorechin A., Solovyeva M., Melali H., Shahabi S., Agrawal A., Shrivastava A., Sharma A., Narwaria B., Narwaria M., Raziel A., Sakran N., Susmallian S., Karagoz L., Akbaba M., Piskin S. Z., Ziya A., Senol Z., Manno E., Iovino M. G., Qassem M., Arana-Garza S., Povoas H. P., Vilas-Boas M. L., Naumann D., Li A., Ammori B. J., Balamoun H., Salman M., Nasta A. M., Goel R., Sanchez-Aguilar H., Herrera M. F., Abou-Mrad A., Cloix L., Mazzini G. S., Kristem L., Lazaro A., Campos J., Bernardo J., Gonzalez J., Trindade C., Viveiros O., Ribeiro R., Goitein D., Hazzan D., Segev L., Beck T., Reyes H., Monterrubio J., Garcia P., Benois M., Kassir R., Contine A., Elshafei M., Aktas S., Weiner S., Heidsieck T., Level L., Pinango S., Ortega P. M., Moncada R., Valenti V., Vlahovic I., Boras Z., Liagre A., Martini F., Juglard G., Motwani M., Saggu S. S., Al Momani H., Lopez L. A. A., Cortez M. A. C., Zavala R. A., D'Haese C., Kempeneers I., Himpens J., Lazzati A., Paolino L., Bathaei S., Bedirli A., Yavuz A., Buyukkasap C., Ozaydin S., Kwiatkowski A., Bartosiak K., Waledziak M., Santonicola A., Angrisani L., Iovino P., Palma R., Iossa A., Boru C. E., De Angelis F., Silecchia G., Hussain A., Balchandra S., Coltell I. B., Perez J. L., Bohra A., Awan A. K., Madhok B., Leeder P. C., Awad S., Al-Khyatt W., Shoma A., Elghadban H., Ghareeb S., Mathews B., Kurian M., Larentzakis A., Vrakopoulou G. Z., Albanopoulos K., Bozdag A., Lale A., Kirkil C., Dincer M., Bashir A., Haddad A., Hijleh L. A., Zilberstein B., de Marchi D. D., Souza W. P., Broden C. M., Gislason H., Shah K., Ambrosi A., Pavone G., Tartaglia N., Kona S. L. K., Kalyan K., Perez C. E. G., Botero M. A. F., Covic A., Timofte D., Maxim M., Faraj D., Tseng L., Liem R., Oren G., Dilektasli E., Yalcin I., AlMukhtar H., Al Hadad M., Mohan R., Arora N., Bedi D., Rives-Lange C., Chevallier J. -M., Poghosyan T., Sebbag H., Zinai L., Khaldi S., Mauchien C., Mazza D., Dinescu G., Rea B., Perez-Galaz F., Zavala L., Besa A., Curell A., Balibrea J. M., Vaz C., Galindo L., Silva N., Caballero J. L. E., Sebastian S. O., Marchesini J. C. D., da Fonseca Pereira R. A., Sobottka W. H., Fiolo F. E., Turchi M., Coelho A. C. J., Zacaron A. L., Barbosa A., Quinino R., Menaldi G., Paleari N., Martinez-Duartez P., Aragon Ramirez de Esparza D. G. M., Esteban V. S., Torres A., Garcia-Galocha J. L., Josa M. I., Pacheco-Garcia J. M., Mayo-Ossorio M. A., Chowbey P., Soni V., de Vasconcelos Cunha H. A., Castilho M. V., Ferreira R. M. A., Barreiro T. A., Charalabopoulos A., Sdralis E., Davakis S., Bomans B., Dapri G., Van Belle K., MazenTakieddine, Vaneukem P., Karaca E. S. A., Karaca F. C., Sumer A., Peksen C., Savas O. A., Chousleb E., Elmokayed F., Fakhereldin I., Aboshanab H. M., Swelium T., Gudal A., Gamloo L., Ugale A., Ugale S., Boeker C., Reetz C., Hakami I. A., Mall J., Alexandrou A., Baili E., Bodnar Z., Maleckas A., Gudaityte R., Guldogan C. E., Gundogdu E., Ozmen M. M., Thakkar D., Dukkipati N., Shah P. S., Shah S. S., Adil M. T., Jambulingam P., Mamidanna R., Whitelaw D., Jain V., Veetil D. K., Wadhawan R., Torres M., Tinoco T., Leclercq W., Romeijn M., van de Pas K., Alkhazraji A. K., Taha S. A., Ustun M., Yigit T., Inam A., Burhanulhaq M., Pazouki A., Eghbali F., Kermansaravi M., Jazi A. H. D., Mahmoudieh M., Mogharehabed N., Tsiotos G., Stamou K., Barrera Rodriguez F. J., Rojas Navarro M. A., Torres O. M. O., Martinez S. L., Tamez E. R. M., Millan Cornejo G. A., Flores J. E. G., Mohammed D. A., Elfawal M. H., Shabbir A., Guowei K., So J. B. Y., Kaplan E. T., Kaplan M., Kaplan T., Pham D. T., Rana G., Kappus M., Gadani R., Kahitan M., Pokharel K., Osborne A., Pournaras D., Hewes J., Napolitano E., Chiappetta S., Bottino V., Dorado E., Schoettler A., Gaertner D., Fedtke K., Aguilar-Espinosa F., Aceves-Lozano S., Balani A., Nagliati C., Pennisi D., Rizzi A., Frattini F., Foschi D., Benuzzi L., Parikh C. H. I. R. A. G., Shah H. A. R. S. H. I. L., Pinotti E., Montuori M., Borrelli V., Dargent J., Copaescu C. A., Hutopila I., Smeu B., Witteman B., Hazebroek E., Deden L., Heusschen L., Okkema S., Aufenacker T., den Hengst W., Vening W., van der Burgh Y., Ghazal A., Ibrahim H., Niazi M., Alkhaffaf B., Altarawni M., Cesana G. C., Anselmino M., Uccelli M., Olmi S., Stier C., Akmanlar T., Sonnenberg T., Schieferbein U., Marcolini A., Awruch D., Vicentin M., de Souza Bastos E. L., Gregorio S. A., Ahuja A., Mittal T., Bolckmans R., Baratte C., Wisnewsky J. A., Genser L., Chong L., Taylor L., Ward S., Hi M. W., Heneghan H., Fearon N., Plamper A., Rheinwalt K., Geoghegan J., Ng K. C., Kaseja K., Kotowski M., Samarkandy T. A., Leyva-Alvizo A., Corzo-Culebro L., Wang C., Yang W., Dong Z., Riera M., Jain R., Hamed H., Said M., Zarzar K., Garcia M., Turkcapar A. G., Sen O., Baldini E., Conti L., Wietzycoski C., Lopes E., Pintar T., Salobir J., Aydin C., Atici S. D., Ergin A., Ciyiltepe H., Bozkurt M. A., Kizilkaya M. C., Onalan N. B. D., Zuber M. N. B. A., Wong W. J., Garcia A., Vidal L., Beisani M., Pasquier J., Vilallonga R., Sharma S., Parmar C., Lee L., Sufi P., Sinan H., Saydam M., Singhal, R., Wiggins, T., Super, J., Alqahtani, A., Nadler, E. P., Ludwig, C., Tahrani, A., Mahawar, K., Pedziwiatr, M., Major, P., Zarzycki, P., Pantelis, A., Lapatsanis, D. P., Stravodimos, G., Matthys, C., Focquet, M., Vleeschouwers, W., Spaventa, A. G., Zerrweck, C., Vitiello, A., Berardi, G., Musella, M., Sanchez-Meza, A., Cantu, F. J., Mora, F., Cantu, M. A., Katakwar, A., Reddy, D. N., Elmaleh, H., Hassan, M., Elghandour, A., Elbanna, M., Osman, A., Khan, A., Layani, L., Kiran, N., Velikorechin, A., Solovyeva, M., Melali, H., Shahabi, S., Agrawal, A., Shrivastava, A., Sharma, A., Narwaria, B., Narwaria, M., Raziel, A., Sakran, N., Susmallian, S., Karagoz, L., Akbaba, M., Piskin, S. Z., Ziya, A., Senol, Z., Manno, E., Iovino, M. G., Qassem, M., Arana-Garza, S., Povoas, H. P., Vilas-Boas, M. L., Naumann, D., Li, A., Ammori, B. J., Balamoun, H., Salman, M., Nasta, A. M., Goel, R., Sanchez-Aguilar, H., Herrera, M. F., Abou-Mrad, A., Cloix, L., Mazzini, G. S., Kristem, L., Lazaro, A., Campos, J., Bernardo, J., Gonzalez, J., Trindade, C., Viveiros, O., Ribeiro, R., Goitein, D., Hazzan, D., Segev, L., Beck, T., Reyes, H., Monterrubio, J., Garcia, P., Benois, M., Kassir, R., Contine, A., Elshafei, M., Aktas, S., Weiner, S., Heidsieck, T., Level, L., Pinango, S., Ortega, P. M., Moncada, R., Valenti, V., Vlahovic, I., Boras, Z., Liagre, A., Martini, F., Juglard, G., Motwani, M., Saggu, S. S., Al Momani, H., Lopez, L. A. A., Cortez, M. A. C., Zavala, R. A., D'Haese, C., Kempeneers, I., Himpens, J., Lazzati, A., Paolino, L., Bathaei, S., Bedirli, A., Yavuz, A., Buyukkasap, C., Ozaydin, S., Kwiatkowski, A., Bartosiak, K., Waledziak, M., Santonicola, A., Angrisani, L., Iovino, P., Palma, R., Iossa, A., Boru, C. E., De Angelis, F., Silecchia, G., Hussain, A., Balchandra, S., Coltell, I. B., Perez, J. L., Bohra, A., Awan, A. K., Madhok, B., Leeder, P. C., Awad, S., Al-Khyatt, W., Shoma, A., Elghadban, H., Ghareeb, S., Mathews, B., Kurian, M., Larentzakis, A., Vrakopoulou, G. Z., Albanopoulos, K., Bozdag, A., Lale, A., Kirkil, C., Dincer, M., Bashir, A., Haddad, A., Hijleh, L. A., Zilberstein, B., de Marchi, D. D., Souza, W. P., Broden, C. M., Gislason, H., Shah, K., Ambrosi, A., Pavone, G., Tartaglia, N., Kona, S. L. K., Kalyan, K., Perez, C. E. G., Botero, M. A. F., Covic, A., Timofte, D., Maxim, M., Faraj, D., Tseng, L., Liem, R., Oren, G., Dilektasli, E., Yalcin, I., Almukhtar, H., Al Hadad, M., Mohan, R., Arora, N., Bedi, D., Rives-Lange, C., Chevallier, J. -M., Poghosyan, T., Sebbag, H., Zinai, L., Khaldi, S., Mauchien, C., Mazza, D., Dinescu, G., Rea, B., Perez-Galaz, F., Zavala, L., Besa, A., Curell, A., Balibrea, J. M., Vaz, C., Galindo, L., Silva, N., Caballero, J. L. E., Sebastian, S. O., Marchesini, J. C. D., da Fonseca Pereira, R. A., Sobottka, W. H., Fiolo, F. E., Turchi, M., Coelho, A. C. J., Zacaron, A. L., Barbosa, A., Quinino, R., Menaldi, G., Paleari, N., Martinez-Duartez, P., Aragon Ramirez de Esparza, D. G. M., Esteban, V. S., Torres, A., Garcia-Galocha, J. L., Josa, M. I., Pacheco-Garcia, J. M., Mayo-Ossorio, M. A., Chowbey, P., Soni, V., de Vasconcelos Cunha, H. A., Castilho, M. V., Ferreira, R. M. A., Barreiro, T. A., Charalabopoulos, A., Sdralis, E., Davakis, S., Bomans, B., Dapri, G., Van Belle, K., Mazentakieddine, Vaneukem, P., Karaca, E. S. A., Karaca, F. C., Sumer, A., Peksen, C., Savas, O. A., Chousleb, E., Elmokayed, F., Fakhereldin, I., Aboshanab, H. M., Swelium, T., Gudal, A., Gamloo, L., Ugale, A., Ugale, S., Boeker, C., Reetz, C., Hakami, I. A., Mall, J., Alexandrou, A., Baili, E., Bodnar, Z., Maleckas, A., Gudaityte, R., Guldogan, C. E., Gundogdu, E., Ozmen, M. M., Thakkar, D., Dukkipati, N., Shah, P. S., Shah, S. S., Adil, M. T., Jambulingam, P., Mamidanna, R., Whitelaw, D., Jain, V., Veetil, D. K., Wadhawan, R., Torres, M., Tinoco, T., Leclercq, W., Romeijn, M., van de Pas, K., Alkhazraji, A. K., Taha, S. A., Ustun, M., Yigit, T., Inam, A., Burhanulhaq, M., Pazouki, A., Eghbali, F., Kermansaravi, M., Jazi, A. H. D., Mahmoudieh, M., Mogharehabed, N., Tsiotos, G., Stamou, K., Barrera Rodriguez, F. J., Rojas Navarro, M. A., Torres, O. M. O., Martinez, S. L., Tamez, E. R. M., Millan Cornejo, G. A., Flores, J. E. G., Mohammed, D. A., Elfawal, M. H., Shabbir, A., Guowei, K., So, J. B. Y., Kaplan, E. T., Kaplan, M., Kaplan, T., Pham, D. T., Rana, G., Kappus, M., Gadani, R., Kahitan, M., Pokharel, K., Osborne, A., Pournaras, D., Hewes, J., Napolitano, E., Chiappetta, S., Bottino, V., Dorado, E., Schoettler, A., Gaertner, D., Fedtke, K., Aguilar-Espinosa, F., Aceves-Lozano, S., Balani, A., Nagliati, C., Pennisi, D., Rizzi, A., Frattini, F., Foschi, D., Benuzzi, L., Parikh, C. H. I. R. A. G., Shah, H. A. R. S. H. I. L., Pinotti, E., Montuori, M., Borrelli, V., Dargent, J., Copaescu, C. A., Hutopila, I., Smeu, B., Witteman, B., Hazebroek, E., Deden, L., Heusschen, L., Okkema, S., Aufenacker, T., den Hengst, W., Vening, W., van der Burgh, Y., Ghazal, A., Ibrahim, H., Niazi, M., Alkhaffaf, B., Altarawni, M., Cesana, G. C., Anselmino, M., Uccelli, M., Olmi, S., Stier, C., Akmanlar, T., Sonnenberg, T., Schieferbein, U., Marcolini, A., Awruch, D., Vicentin, M., de Souza Bastos, E. L., Gregorio, S. A., Ahuja, A., Mittal, T., Bolckmans, R., Baratte, C., Wisnewsky, J. A., Genser, L., Chong, L., Taylor, L., Ward, S., Hi, M. W., Heneghan, H., Fearon, N., Plamper, A., Rheinwalt, K., Geoghegan, J., Ng, K. C., Kaseja, K., Kotowski, M., Samarkandy, T. A., Leyva-Alvizo, A., Corzo-Culebro, L., Wang, C., Yang, W., Dong, Z., Riera, M., Jain, R., Hamed, H., Said, M., Zarzar, K., Garcia, M., Turkcapar, A. G., Sen, O., Baldini, E., Conti, L., Wietzycoski, C., Lopes, E., Pintar, T., Salobir, J., Aydin, C., Atici, S. D., Ergin, A., Ciyiltepe, H., Bozkurt, M. A., Kizilkaya, M. C., Onalan, N. B. D., Zuber, M. N. B. A., Wong, W. J., Garcia, A., Vidal, L., Beisani, M., Pasquier, J., Vilallonga, R., Sharma, S., Parmar, C., Lee, L., Sufi, P., Sinan, H., and Saydam, M.

Subjects
Male, Pediatrics, medicine.medical_specialty, Coronavirus disease 2019 (COVID-19), Adolescent, Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), bariatric surgery, Context (language use), Pandemic, Medicine, Humans, Pandemics, COVID-19, pandemic, SARS-CoV-2, Nutrition and Dietetics, Manchester Cancer Research Centre, business.industry, Health Policy, ResearchInstitutes_Networks_Beacons/mcrc, Public Health, Environmental and Occupational Health, medicine.disease, Obesity, Obesity, Morbid, Treatment Outcome, Pediatrics, Perinatology and Child Health, Cohort, Female, Morbidity, business, Body mass index, Cohort study, Human
Abstract

Background: Metabolic and bariatric surgery (MBS) is an effective treatment for adolescents with severe obesity. Objectives: This study examined the safety of MBS in adolescents during the coronavirus disease 2019 (COVID-19) pandemic. Methods: This was a global, multicentre and observational cohort study of MBS performed between May 01, 2020, and October 10,2020, in 68 centres from 24 countries. Data collection included in-hospital and 30-day COVID-19 and surgery-specific morbidity/mortality. Results: One hundred and seventy adolescent patients (mean age: 17.75 ± 1.30 years), mostly females (n=122, 71.8%), underwent MBS during the study period. The mean pre-operative weight and body mass index were 122.16 ± 15.92 kg and 43.7± 7.11 kg/m2, respectively. Although majority of patients had pre-operative testing for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (n=146; 85.9%), only 42.4% (n=72) of the patients were asked to self-isolate pre-operatively. Two patients developed symptomatic SARS-CoV-2 infection post-operatively (1.2%). The overall complication rate was 5.3% (n=9). There was no mortality in this cohort. Conclusions: MBS in adolescents with obesity is safe during the COVID-19 pandemic when performed within the context of local precautionary procedures (such as pre-operative testing). The 30-day morbidity rates were similar to those reported pre-pandemic. These data will help facilitate the safe re-introduction of MBS services for this group of patients.

Published
2021
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