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 Biomedical). 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.

Rare Germline ATM Variants Influence the Development of Chronic Lymphocytic Leukemia.

PURPOSE: Germline missense variants of unknown significance in cancer-related genes are increasingly being identified with the expanding use of next-generation sequencing. The ataxia telangiectasia-mutated (ATM) gene on chromosome 11 has more than 1,000 germline missense variants of unknown significance and is a tumor suppressor. We aimed to determine if rare germline ATM variants are more frequent in chronic lymphocytic leukemia (CLL) compared with other hematologic malignancies and if they influence the clinical characteristics of CLL. METHODS: We identified 3,128 patients (including 825 patients with CLL) in our hematologic malignancy clinic who had received clinical-grade sequencing of the entire coding region of ATM. We ascertained the comparative frequencies of germline ATM variants in categories of hematologic neoplasms, and, in patients with CLL, we determined whether these variants affected CLL-associated characteristics such as somatic 11q deletion. RESULTS: Rare germline ATM variants are present in 24% of patients with CLL, significantly greater than that in patients with other lymphoid malignancies (16% prevalence), myeloid disease (15%), or no hematologic neoplasm (14%). Patients with CLL with germline ATM variants are younger at diagnosis and twice as likely to have 11q deletion. The ATM variant p.L2307F is present in 3% of patients with CLL, is associated with a three-fold increase in rates of somatic 11q deletion, and is a hypomorph in cell-based assays. CONCLUSION: Germline ATM variants cluster within CLL and affect the phenotype of CLL that develops, implying that some of these variants (such as ATM p.L2307F) have functional significance and should not be ignored. Further studies are needed to determine whether these variants affect the response to therapy or account for some of the inherited risk of CLL.