Reduced Heart Rate Response during Drug-Induced Stress Is Related to the Severity of Perfusion Defect

Introduction: During the pharmacological stress test with dipyridamole, a normal hemodynamic response is slightly reduced blood pressure and raised heart rate (HR). However, sometimes the HR response is reduced. This study investigated the relationship between the HR response during the dipyridamole stress test and the severity of the perfusion defects using Thallium 201 myocardial perfusion imaging. Methods: We enrolled 50 patients undergoing dipyridamole stress at the nuclear cardiology Lab, Main University Hospital of Alexandria. Standard dipyridamole protocol (infusion over 4 min) and standard thallium 201 protocol (2 min after dipyridamole infusion) were followed. If the heart rate (HR) ratio (peak HR/rest HR) was 1.20 or less, it was considered a reduced response. Total perfusion defect (TPD), summed stress score (SSS), summed rest score (SRS), and summed difference score (SDS) for myocardial perfusion were calculated. Patients with reduced HR response and normal HR responses were compared and independent predictors of reduced HR response were determined. Results: About 56% of patients had reduced HR response;which included a high number of patients with a history of dyslipidemia and diabetes mellitus. The reduced HR response group showed lower SSS, SRS. The analysis showed that the independent predictors of reduced HR response were rest HR, SSS, and diabetes mellitus. Conclusions: Reduction in the HR response during the dipyridamole-induced stress test is related to the severity of perfusion defect, diabetes mellitus, and Dyslipidemia.

Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes

BACKGROUND The prevalence of type 2 diabetes(T2D)has been increasing dramatically in recent decades,and 47.5%of T2D patients will die of cardiovascular disease.Thallium-201 myocardial perfusion scan(MPS)is a precise and noninvasive method to detect coronary artery disease(CAD).Most previous studies used traditional logistic regression(LGR)to evaluate the risks for abnormal CAD.Rapidly developing machine learning(Mach-L)techniques could potentially outperform LGR in capturing non-linear relationships.AIM To aims were:(1)Compare the accuracy of Mach-L methods and LGR;and(2)Found the most important factors for abnormal TMPS.METHODS 556 T2D were enrolled in the study(287 men and 269 women).Demographic and biochemistry data were used as independent variables and the sum of stressed score derived from MPS scan was the dependent variable.Subjects with a MPS score≥9 were defined as abnormal.In addition to traditional LGR,classification and regression tree(CART),random forest,Naïve Bayes,and eXtreme gradient boosting were also applied.Sensitivity,specificity,accuracy and area under the receiver operation curve were used to evaluate the respective accuracy of LGR and Mach-L methods.RESULTS Except for CART,the other Mach-L methods outperformed LGR,with gender,body mass index,age,low-density lipoprotein cholesterol,glycated hemoglobin and smoking emerging as the most important factors to predict abnormal MPS.CONCLUSION Four Mach-L methods are found to outperform LGR in predicting abnormal TMPS in Chinese T2D,with the most important risk factors being gender,body mass index,age,low-density lipoprotein cholesterol,glycated hemoglobin and smoking.