Revascularization and outcomes in Veterans with moderate to severe ischemia on myocardial perfusion imaging

Background: The prevalence of ischemia on nuclear myocardial perfusion imaging(MPI) has been decreasing. Recent research has questioned the benefit of invasive revascularization for patients with moderate to severe ischemia. We hypothesized that patients with moderate to severe ischemia could routinely undergo successful revascularization.Methods: We analyzed data from 544 patients who underwent an MPI at a single academic Veterans Affairs Medical Center. Patients with moderate to severe ischemia, defined as a summed difference score(SDS) 8 or greater, were compared to the rest of the cohort.Results: Of the total cohort(n=544), 39 patients had MPI studies with resultant moderate to severe ischemia. Patients with ischemia were more likely to develop coronary artery disease(74.4% versus 38.8%, P<0.0001) and have successful revascularization(38.5% versus 4.0%, P<0.0001) during the following year. Revascularization was attempted in 31 patients with moderate to severe ischemia, though only 15(47%) of these attempts were successful. Ischemia was predictive of myocardial infarction(5.1% versus 0.8%, P=0.01) within 1 year.Conclusion: Moderate to severe ischemia is an uncommon finding in a contemporary nuclear laboratory. Among patients with ischemia, revascularization is typically attempted but is frequently unsuccessful.Trial registration: This trial does not appear on a registry as it is neither randomized nor prospective.

Myocardial perfusion imaging with a contrast agent at a low concentration in the diagnosis of myocardial infarction in the elderly

Objective To evaluate the clinical value of myocardial perfusion imaging with dual-source dual-energy CT and a contrast agent at a low concentration in the diagnosis of myocardial infarction in the elderly.Methods Onestop cardiac imaging with dual-source CT was conducted in 138 elderly patients diagnosed with myocardial infarction between October 2015 and May 2016.The

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model:A cohort study

BACKGROUND Liver transplant(LT)patients have become older and sicker.The rate of post-LT major adverse cardiovascular events(MACE)has increased,and this in turn raises 30-d post-LT mortality.Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients.AIM To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort.METHODS This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center.We developed a predictive model for post-LT MACE(defined as a composite outcome of stroke,new-onset heart failure,severe arrhythmia,and myocardial infarction)using the extreme gradient boosting(XGBoost)machine learning model.We addressed missing data(below 20%)for relevant variables using the k-nearest neighbor imputation method,calculating the mean from the ten nearest neighbors for each case.The modeling dataset included 83 features,encompassing patient and laboratory data,cirrhosis complications,and pre-LT cardiac assessments.Model performance was assessed using the area under the receiver operating characteristic curve(AUROC).We also employed Shapley additive explanations(SHAP)to interpret feature impacts.The dataset was split into training(75%)and testing(25%)sets.Calibration was evaluated using the Brier score.We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting.Scikit-learn and SHAP in Python 3 were used for all analyses.The supplementary material includes code for model development and a user-friendly online MACE prediction calculator.RESULTS Of the 537 included patients,23(4.46%)developed in-hospital MACE,with a mean age at transplantation of 52.9 years.The majority,66.1%,were male.The XGBoost model achieved an impressive AUROC of 0.89 during the training stage.This model exhibited accuracy,precision,recall,and F1-score values of 0.84,0.85,0.80,and 0.79,respectively.Calibration,as assessed by the Brier score,indicated excellent model calibration with a score of 0.07.Furthermore,SHAP values highlighted the significance of certain variables in predicting postoperative MACE,with negative noninvasive cardiac stress testing,use of nonselective beta-blockers,direct bilirubin levels,blood type O,and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level.These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE,making it a valuable tool for clinical practice.CONCLUSION Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE,using both cardiovascular and hepatic variables.The model demonstrated impressive performance,aligning with literature findings,and exhibited excellent calibration.Notably,our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data,reinforcing the model’s value as a reliable tool for predicting post-LT MACE in clinical practice.