Markable Seriale

• • 252 Downloads • Abstract Despite uncertain yield, guidelines endorse routine stress myocardial perfusion imaging (MPI) for patients with suspected acute coronary syndromes, unremarkable serial electrocardiograms, and negative troponin measurements. In these patients, outcome prediction and risk stratification models could spare unnecessary testing. This study therefore investigated the use of artificial neural networks (ANN) to improve risk stratification and prediction of MPI and angiographic results.

We retrospectively identified 5354 consecutive patients referred from the emergency department for rest-stress MPI after serial negative troponins and normal ECGs. Patients were risk stratified according to thrombolysis in myocardial infarction (TIMI) scores, ischemia was defined as >5% reversible perfusion defect, and obstructive coronary artery disease was defined as >How To Install Sygic Cracked Version Fcp there. 50% angiographic obstruction. For ANN, the network architecture employed a systematic method where the number of neurons is changed incrementally, and bootstrapping was performed to evaluate the accuracy of the models. Compared to TIMI scores, ANN models provided improved discriminatory power. With regards to MPI, an ANN model could reduce testing by 59% and maintain a 96% negative predictive value (NPV) for ruling out ischemia. Application of an ANN model could also avoid 73% of invasive coronary angiograms while maintaining a 98% NPV for detecting obstructive CAD.

An online calculator for clinical use was created using these models. The ANN models improved risk stratification when compared to the TIMI score. Our calculator could also reduce downstream testing while maintaining an excellent NPV, though further study is needed before the calculator can be used clinically. Amsterdam EA, Kirk JD, Bluemke DA, Diercks D, Farkouh ME, Garvey JL, Kontos MC, McCord J, Miller TD, Morise A, Newby LK, Ruberg FL, Scordo KA, Thompson PD, American Heart Association Exercise CR, Prevention Committee of the Council on Clinical Cardiology CoCN, Interdisciplinary Council on Quality of C, Outcomes R (2010) Testing of low-risk patients presenting to the emergency department with chest pain: a scientific statement from the American Heart Association.