Background: The cardiovascular disease is the main cause of death among diabetic patients, which makes it crucial to identify the individuals at higher risk of cardiovascular events.
Objective: To evaluate the prognostic value of scintigraphy with gated single photon emission computed tomography (SPECT) in patients with diabetes mellitus (DM) and suspected coronary artery disease.
Methods: Retrospective study with 232 diabetic patients submitted to scintigraphy with gated SPECT. Perfusion Gated SPECT (scores and number of altered segments) as well as ventricular function parameters (ejection fraction, left ventricle (LV) volume and contractility) were evaluated. Cardiac death, acute ischemic coronary syndrome, revascularization procedures or encephalic vascular accident were considered future cardiovascular events. The uni- and multivariate analyses were carried out by the multiple logistic regression model (p< 0.05).
Results: At the univariate analysis, age (p=0.02), chest angina (p=0.01), insulin therapy (p=0.02), myocardial perfusion abnormalities (p<0.0001), the number of segments involved (p=0.0001), the perfusion scores (p=0.0001), the ejection fraction (p=0.004), the final systolic volume (p=0.03) and the finding of segmental alteration at the left ventricle contractility (p<0.0001) were associated with future events at the univariate analysis. At the multivariate analysis, the male sex (p=0.007), age (p=0.03), angina (p=0.001), insulin therapy (p=0.007) and the SDS > 3 (p=0.0001) and the number of altered segments > 3 (p=0.0001) were predictors of cardiovascular events.
Conclusion: The myocardial scintigraphy with gated SPECT adds independent information to the stratification of the risk of future cardiovascular events in patients with DM and suspected coronary artery disease.
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http://dx.doi.org/10.1590/s0066-782x2008000100002 | DOI Listing |
Mol Pharm
January 2025
Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Acute myocardial infarction (MI) remains a leading cause of mortality worldwide, with inflammatory and reparative phases playing critical roles in disease progression. Currently, there is a pressing need for imaging techniques to monitor immune cell infiltration and inflammation activity during these phases. We developed a novel probe, Tc-HYNIC-mAb, utilizing a monoclonal antibody that targets the voltage-gated potassium channel 1.
View Article and Find Full Text PDFNucl Med Commun
January 2025
Division of Cardiology, Onishi Hospital, Fujioka, Japan.
Objective: Patients with chronic kidney disease (CKD) have an increased risk of adverse cardio-cerebrovascular events. The purpose of this study is to evaluate the prognostic predictors over 5 years in patients with CKD including haemodialysis.
Methods: In this multicenter, prospective cohort study performed with the Gunma-CKD SPECT Study protocol, 311 patients with CKD [estimated glomerular filtration rate (eGFR) < 60 min/ml/1.
J Interv Card Electrophysiol
January 2025
Department of Cardiovascular Medicine, National Cerebral and Cardiovascular Center, 6-1 Kishibe-Shimmachi, Suita, Osaka, 564-8565, Japan.
Background: Non-response to cardiac resynchronization therapy (CRT) is an important issue in the treatment of heart failure with reduced ejection fraction (HFrEF) and non-left bundle branch block (LBBB). Electrocardiogram-gated myocardial perfusion single-photon emission computed tomography imaging (G-MPI SPECT) is typically used to assess left ventricular (LV) dyssynchrony. This study aimed to determine whether G-MPI parameters are associated with non-responsiveness to CRT.
View Article and Find Full Text PDFEur Heart J Imaging Methods Pract
July 2024
Department of Nuclear Medicine, CHU de Caen Normandie, Normandie Univ, UNICAEN UR 4650 PSIR, Avenue Cote de Nacre, 14000 Caen, France.
J Educ Health Promot
October 2024
Adani Institute of Infrastructure Engineering, Ahmedabad, Gujarat, India.
Parkinson's disease (PD) is a neurodegenerative brain disorder that causes symptoms such as tremors, sleeplessness, behavioral problems, sensory abnormalities, and impaired mobility, according to the World Health Organization (WHO). Artificial intelligence, machine learning (ML), and deep learning (DL) have been used in recent studies (2015-2023) to improve PD diagnosis by categorizing patients and healthy controls based on similar clinical presentations. This study investigates several datasets, modalities, and data preprocessing techniques from the collected data.
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