A Novel Algorithm for Improving the Prehospital Diagnostic Accuracy of ST-Segment Elevation Myocardial Infarction.

INTRODUCTION: Early detection of ST-segment elevation myocardial infarction (STEMI) on the prehospital electrocardiogram (ECG) improves patient outcomes. Current software algorithms optimize sensitivity but have a high false-positive rate. The authors propose an algorithm to improve the specificity of STEMI diagnosis in the prehospital setting. METHODS: A dataset of prehospital ECGs with verified outcomes was used to validate an algorithm to identify true and false-positive software interpretations of STEMI. Four criteria implicated in prior research to differentiate STEMI true positives were applied: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. The test characteristics were calculated and regression analysis was used to examine the association between the number of criteria included and test characteristics. RESULTS: There were 44,611 cases available. Of these, 1,193 were identified as STEMI by the software interpretation. Applying all four criteria had the highest positive likelihood ratio of 353 (95% CI, 201-595) and specificity of 99.96% (95% CI, 99.93-99.98), but the lowest sensitivity (14%; 95% CI, 11-17) and worst negative likelihood ratio (0.86; 95% CI, 0.84-0.89). There was a strong correlation between increased positive likelihood ratio (r2 = 0.90) and specificity (r2 = 0.85) with increasing number of criteria. CONCLUSIONS: Prehospital ECGs with a high probability of true STEMI can be accurately identified using these four criteria: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. Applying these criteria to prehospital ECGs with software interpretations of STEMI could decrease false-positive field activations, while also reducing the need to rely on transmission for physician over-read. This can have significant clinical and quality implications for Emergency Medical Services (EMS) systems.

Imaging through the pupal case of Drosophila melanogaster.

The longstanding use of Drosophila as a model for cell and developmental biology has yielded an array of tools. Together, these techniques have enabled analysis of cell and developmental biology from a variety of methodological angles. Live imaging is an emerging method for observing dynamic cell processes, such as cell division or cell motility. Having isolated mutations in uncharacterized putative cell cycle proteins it became essential to observe mitosis in situ using live imaging. Most live imaging studies in Drosophila have focused on the embryonic stages that are accessible to manipulation and observation because of their small size and optical clarity. However, in these stages the cell cycle is unusual in that it lacks one or both of the gap phases. By contrast, cells of the pupal wing of Drosophila have a typical cell cycle and undergo a period of rapid mitosis spanning about 20 hr of pupal development. It is easy to identify and isolate pupae of the appropriate stage to catch mitosis in situ. Mounting intact pupae provided the best combination of tractability and durability during imaging, allowing experiments to run for several hours with minimal impact on cell and animal viability. The method allows observation of features as small as, or smaller than, fly chromosomes. Adjustment of microscope settings and the details of mounting, allowed extension of the preparation to visualize membrane dynamics of adjacent cells and fluorescently labeled proteins such as tubulin. This method works for all tested fluorescent proteins and can capture submicron scale features over a variety of time scales. While limited to the outer 20 µm of the pupa with a conventional confocal microscope, this approach to observing protein and cellular dynamics in pupal tissues in vivo may be generally useful in the study of cell and developmental biology in these tissues.