Annals of emergency medicine
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Observational Study
Incorporation of Serial 12-Lead Electrocardiogram with Machine Learning to Augment the Out-of-Hospital Diagnosis of Non-ST Elevation Acute Coronary Syndrome.
Ischemic electrocardiogram (ECG) changes are subtle and transient in patients with suspected non-ST-segment elevation (NSTE)-acute coronary syndrome. However, the out-of-hospital ECG is not routinely used during subsequent evaluation at the emergency department. Therefore, we sought to compare the diagnostic performance of out-of-hospital and ED ECG and evaluate the incremental gain of artificial intelligence-augmented ECG analysis. ⋯ In this study, 60% of diagnostic ST changes resolved prior to hospital arrival, making the ED ECG suboptimal for the inhospital evaluation of NSTE-ACS. Using serial ECG changes or incorporating artificial intelligence-augmented analyses would allow correctly reclassifying one in 4 patients with suspected NSTE-ACS.
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Social Z codes are International Classification of Diseases, Tenth Revision, Clinical Modification codes that provide one way of documenting social risk factors in electronic health records. Despite the utility and availability of these codes, no study has examined social Z code documentation prevalence in emergency department (ED) settings. ⋯ We found a very low prevalence of social Z code documentation in ED visits nationwide. More systematic social Z code documentation could support targeted social interventions, social risk payment adjustments, and future policy reforms.