Dataset Information

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

ABSTRACT:

Objective

Prevalence 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 methods

Of 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.

Results

MACE 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%.

Conclusions

For 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.

SUBMITTER: Han D

PROVIDER: S-EPMC6971784 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Publications

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

Han Donghee D Rozanski Alan A Gransar Heidi H Sharir Tali T Einstein Andrew J AJ Fish Mathews B MB Ruddy Terrence D TD Kaufmann Philipp A PA Sinusas Albert J AJ Miller Edward J EJ Bateman Timothy M TM Dorbala Sharmila S Di Carli Marcelo M Liang Joanna X JX Hu Lien-Hsin LH Germano Guido G Dey Damini D Berman Daniel S DS Slomka Piotr J PJ

Diabetes care 20191127 2

<h4>Objective</h4>Prevalence 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.<h4>Research design and methods</h4>Of 20,418 individuals who underwent single-photon emission computed tomography myocardial perfusion imaging, 2,951 patients with diabetes were match ...[more]

PMID: 31776140

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

Project description:BackgroundRecent evidence shows an association between coronavirus disease 2019 (COVID-19) infection and a severe inflammatory syndrome in children. Cardiovascular magnetic resonance (CMR) data about myocardial injury in children are limited to small cohorts. The aim of this multicenter, international registry is to describe clinical and cardiac characteristics of multisystem inflammatory syndrome in children (MIS-C) associated with COVID-19 using CMR so as to better understand the real extent of myocardial damage in this vulnerable cohort.Methods and resultsHundred-eleven patients meeting the World Health Organization criteria for MIS-C associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), having clinical cardiac involvement and having received CMR imaging scan were included from 17 centers. Median age at disease onset was 10.0 years (IQR 7.0-13.8). The majority of children had COVID-19 serology positive (98%) with 27% of children still having both, positive serology and polymerase chain reaction (PCR). CMR was performed at a median of 28 days (19-47) after onset of symptoms. Twenty out of 111 (18%) patients had CMR criteria for acute myocarditis (as defined by the Lake Louise Criteria) with 18/20 showing subepicardial late gadolinium enhancement (LGE). CMR myocarditis was significantly associated with New York Heart Association class IV (p = 0.005, OR 6.56 (95%-CI 1.87-23.00)) and the need for mechanical support (p = 0.039, OR 4.98 (95%-CI 1.18-21.02)). At discharge, 11/111 (10%) patients still had left ventricular systolic dysfunction.ConclusionNo CMR evidence of myocardial damage was found in most of our MIS-C cohort. Nevertheless, acute myocarditis is a possible manifestation of MIS-C associated with SARS-CoV-2 with CMR evidence of myocardial necrosis in 18% of our cohort. CMR may be an important diagnostic tool to identify a subset of patients at risk for cardiac sequelae and more prone to myocardial damage.Clinical trial registrationThe study has been registered on ClinicalTrials.gov, Identifier NCT04455347, registered on 01/07/2020, retrospectively registered.

Dataset Information

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

Objective

Research design and methods

Results

Conclusions

Publications

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

Similar Datasets

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