Underutilization of Cardiac Therapies in Patients with Acute Ischemic Stroke and Elevated Troponin.

INTRODUCTION: Recent findings have shown that in Acute Ischemic Stroke (AIS) patients, elevated troponin is associated with increased mortality. However, due to concerns of cerebral hypoperfusion and hemorrhagic transformation, current practice has been slow to apply proven cardiac therapies to these patients. This study aims to determine this rate of utilization. MATERIALS AND METHODS: A single-center review of 83 patients with AIS and measured troponin was conducted. Patients were stratified based on elevated and non-elevated troponin. Between groups, we measured the utilization of evidence-based cardiac therapies and used a univariate logistic regression to compare outcomes of mortality, re-hospitalization, recurrent acute ischemic stroke, recurrent acute myocardial infarction, and a composite of these outcomes. RESULTS: Of 83 patients, 25 had elevated troponin and 58 had non-elevated troponin. There was no statistical difference in the use of cardiac therapies between the two groups. Adenosine diphosphate P2Y12 antagonists were infrequently used in both elevated and non-elevated troponin groups at 32% vs. 24% (p = 0.64), as were Angiotensin-Converting Enzyme Inhibitors (ACE-I) and angiotensin II receptor blockers (ARB) at 56% vs. 69% (p = 0.38). Those in the elevated troponin group encountered a statistically significant increase in composite endpoint 64% vs. 33% (Odds Ratio [OR] 7.28, 95% Confidence interval [CI] 2.19-28.88, p<0.01). CONCLUSION: Cardiac therapies are underutilized in patients with acute ischemic stroke and elevated troponin levels. In turn, this low usage may explain the increase in morbidity and mortality seen in these patients and the use of such therapies should be considered when treating this subset of patients as the cardio protective nature of these therapies may outweigh the risks associated with them in AIS patients.

A computational tool for identifying minimotifs in protein-protein interactions and improving the accuracy of minimotif predictions.

Protein-protein interactions are important to understanding cell functions; however, our theoretical understanding is limited. There is a general discontinuity between the well-accepted physical and chemical forces that drive protein-protein interactions and the large collections of identified protein-protein interactions in various databases. Minimotifs are short functional peptide sequences that provide a basis to bridge this gap in knowledge. However, there is no systematic way to study minimotifs in the context of protein-protein interactions or vice versa. Here we have engineered a set of algorithms that can be used to identify minimotifs in known protein-protein interactions and implemented this for use by scientists in Minimotif Miner. By globally testing these algorithms on verified data and on 100 individual proteins as test cases, we demonstrate the utility of these new computation tools. This tool also can be used to reduce false-positive predictions in the discovery of novel minimotifs. The statistical significance of these algorithms is demonstrated by an ROC analysis (P = 0.001).