Automated detection of low ejection fraction from a one-lead electrocardiogram: application of an AI algorithm to an electrocardiogram-enabled Digital Stethoscope.

Aims: Electrocardiogram (ECG)-enabled stethoscope (ECG-Scope) acquires a single-lead ECGs during cardiac auscultation and may facilitate real-time screening for pathologies not routinely identified by cardiac auscultation alone. We previously demonstrated an artificial intelligence (AI) algorithm can identify left ventricular dysfunction (LVSD) [defined as ejection fraction (EF) ≤ 40%] with an area under the curve (AUC) of 0.91 using a 12-lead ECG. Methods and results: One hundred patients referred for clinically indicated echocardiography were prospectively recruited. ECG-Scope recordings with the patient supine and sitting were obtained in multiple electrode locations at the time of the echocardiogram. The AI algorithm for the detection of LVSD was retrained using single leads from ECG-12 and validated against ECG-Scope to determine accuracy for low EF detection (≤35%, <40%, or <50%). We evaluated the algorithm with respect to body position and lead location. Amongst 100 patients (aged 61.3 ± 13.8; 61% male, BMI: 30.0 ± 5.4), eight had EF≤40%, and six had EF 40-50%. The best single recording position was V2 with the patient supine [AUC: 0.88 (CI: 0.80-0.97) for EF≤35%, 0.85 (CI: 0.75-0.95) for EF≤40%, and 0.81 (CI: 0.71-0.90) for EF < 50%]. When using an AI model to select the recording automatically, AUC was 0.91 (CI: 0.84-0.97) for EF≤35%, 0.89 (CI: 0.83-0.96) for EF≤40%, and 0.84 (CI: 0.73-0.94) for EF < 50%. Conclusion: An AI algorithm applied to an ECG-enabled stethoscope recording in standard auscultation positions reliably detected the presence of a low EF in this prospective study of patients referred for echocardiography. The ability to screen patients with a possible low EF during routine physical examination may facilitate rapid detection of LVSD.

Discovering knowledge on pediatric fluid therapy and dysnatremias from quantitative data found in electronic medical records.

It is accepted that intravenous fluid (IVF) therapy can result in hospital-acquired dysnatremias in pediatric patients, with associated morbidity and mortality. There is interest in improving IVF therapy to prevent dysnatremias, but the optimal approach is controversial. In this study, we develop Natremia Deviation and Intravenous Renderer (NaDIR), a tool that preprocesses large volumes of electronic medical record data obtained from an academic pediatric hospital in order to analyze (1) IVF therapy, (2) the epidemiology of dysnatremias, and (3) the impact of IVFs on changes in serum sodium (ΔS(Na)). We then applied NaDIR to 3,256 inpatient records over a 3 month period, which revealed (1) a 19.9% incidence of dysnatremias, (2) a significant increase in lengths of stay associated with dysnatremias, and (3) a novel linear relationship between ΔS(Na) and IVF tonicity. This demonstrates that EMR data that can be readily analyzed to discover epidemiologic and predictive knowledge.