Artificial Intelligence Driven Prehospital ECG Interpretation for the Reduction of False Positive Emergent Cardiac Catheterization Lab Activations: A Retrospective Cohort Study.

OBJECTIVES: Data suggest patients suffering acute coronary occlusion myocardial infarction (OMI) benefit from prompt primary percutaneous intervention (PPCI). Many emergency medical services (EMS) activate catheterization labs to reduce time to PPCI, but suffer a high burden of inappropriate activations. Artificial intelligence (AI) algorithms show promise to improve electrocardiogram (ECG) interpretation. The primary objective was to evaluate the potential of AI to reduce false positive activations without missing OMI. METHODS: Electrocardiograms were categorized by (1) STEMI criteria, (2) ECG integrated device software and (3) a proprietary AI algorithm (Queen of Hearts (QOH), Powerful Medical). If multiple ECGs were obtained and any one tracing was positive for a given method, that diagnostic method was considered positive. The primary outcome was OMI defined as an angiographic culprit lesion with either TIMI 0-2 flow; or TIMI 3 flow with either peak high sensitivity troponin-I > 5000 ng/L or new wall motion abnormality. The primary analysis was per-patient proportion of false positives. RESULTS: A total of 140 patients were screened and 117 met criteria. Of these, 48 met the primary outcome criteria of OMI. There were 80 positives by STEMI criteria, 88 by device algorithm, and 77 by AI software. All approaches reduced false positives, 27% for STEMI, 22% for device software, and 34% for AI (p < 0.01 for all). The reduction in false positives did not significantly differ between STEMI criteria and AI software (p = 0.19) but STEMI criteria missed 6 (5%) OMIs, while AI missed none (p = 0.01). CONCLUSIONS: In this single-center retrospective study, an AI-driven algorithm reduced false positive diagnoses of OMI compared to EMS clinician gestalt. Compared to AI (which missed no OMI), STEMI criteria also reduced false positives but missed 6 true OMI. External validation of these findings in prospective cohorts is indicated.

Nomogram for Predicting Risk of Cancer Therapy-Related Cardiac Dysfunction in Patients With Human Epidermal Growth Factor Receptor 2-Positive Breast Cancer.

BACKGROUND: Cancer therapy-related cardiac dysfunction (CTRCD) is an important treatment-limiting toxicity for patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer that adversely affects cancer and cardiovascular outcomes. Easy-to-use tools that incorporate readily accessible clinical variables for individual estimation of CTRCD risk are needed. METHODS AND RESULTS: From 2004 to 2013, 1440 patients with stage I to III HER2-positive breast cancer treated with trastuzumab-based therapy were identified. A multivariable Cox proportional hazards model was constructed to identify risk factors for CTRCD and included the 1377 patients in whom data were complete. Nine clinical variables, including age, race, body mass index, left ventricular ejection fraction, systolic blood pressure, coronary artery disease, diabetes, arrhythmia, and anthracycline exposure were built into a nomogram estimating risk of CTRCD at 1 year. The nomogram was validated for calibration and discrimination using bootstrap resampling. A total of 177 CTRCD events occurred within 1 year of HER2-targeted treatment. The nomogram for prediction of 1-year CTRCD probability demonstrated good discrimination, with a concordance index of 0.687. The predicted and observed probabilities of CTRCD were similar, demonstrating good model calibration. CONCLUSIONS: A nomogram composed of 9 readily accessible clinical variables provides an individualized 1-year risk estimate of CTRCD among women with HER2-positive breast cancer receiving HER2-targeted therapy. This nomogram represents a simple-to-use tool for clinicians and patients that can inform clinical decision-making on breast cancer treatment options, optimal frequency of cardiac surveillance, and role of cardioprotective strategies.

Early Experience With Methylprednisolone on SARS-CoV-2 Infection in the African American Population, a Retrospective Analysis.

BACKGROUND: Coronavirus disease-19 (COVID-19) is associated with acute kidney injury (AKI) and acute respiratory distress syndrome (ARDS) with high mortality rates. In African American (AA) populations, COVID-19 presentations and outcomes are more severe. NIH and Interim WHO guidelines had suggested against the use of corticosteroids unless in clinical trials until the recent publication of the RECOVERY trial. Here, we analyzed the treatment effect of methylprednisolone on patients with AKI and ARDS during the initial 2 months of COVID-19 and detail the learning effect within our institution. METHODS: Between March 1 and April 30, 2020, 75 AA patients met our inclusion criteria for ARDS and AKI, of which 37 had received corticosteroids. Twenty-eight-day mortality, improvement in PaO2/FiO2 ratio, and renal function were analyzed. The impact of methylprednisolone treatment was assessed with multivariable methods. RESULTS: Survival in the methylprednisolone group reached 51% at 21 days compared to 29% in the non-corticosteroid group (P < .001). Methylprednisolone improved the likelihood of renal function improvement. PaO2/FiO2 ratio in the methylprednisolone group improved by 73% compared to 45% in the non-corticosteroid group (P = .01). Age, gender, BMI, preexisting conditions, and other treatment factors did not show any impact on renal or PaO2/FiO2 ratio improvement. The use of anticoagulants, the month of treatment, and AKI during hospitalization also influenced outcomes. CONCLUSION: In AA COVID-19 positive patients with ARDS and AKI, IV methylprednisolone lowered the incidence of mortality and improved the likelihood of renal and lung function recovery. Further investigation with a randomized control trial of corticosteroids is warranted.