Oh, J

Abstract Background Cardiogenic shock (CS) is characterized by critically low cardiac output and impaired myocardial energy transfer to cardiac work. Left-ventricular (LV) cardiac mechanical efficiency (CE), defined as the fraction of total pressure-volume area (PVA) converted to external work (EW), captures how effectively the LV converts energy into stroke work. Because CE integrates load, chamber volumes, and pump performance into a single physiologic construct, it may directly reflect the energy-work mismatch that characterizes CS. The prognostic value of transthoracic echocardiography (TTE)-derived CE for in-hospital outcomes in CS remains uncertain. Purpose To determine whether TTE-derived CE measured within 24 hours of admission predicts in-hospital mortality in patients with CS, and to compare its discriminatory performance with LV ejection fraction (LVEF). Methods Data from CS patients admitted between 2007 and 2018 were analyzed. First LV end-systolic pressure (LVESP=0.9×systolic blood pressure), stroke volume (SV), and LV end-diastolic pressure (LVEDP, estimated using the Nagueh formula) was estimated. CE was then estimated based on EW (EW=(LVESP×SV)−(SV×LVEDP)), potential energy (PE=0.5×LVESP×LV end-systolic volume) and PVA (EW+PE) as CE=EW/PVA×100. We assessed CE and LVEF by TTE performed within 24 hours of admission. Patients were separated into tertiles of CE: high (>47%), moderate (36% < CE ≤47%), and low (CE ≤36%). The primary endpoint was in-hospital mortality. A key secondary endpoint was cardiac intensive care unit (CICU) mortality. Associations were tested with multivariable logistic regression (pre-specified covariates including LVEF). Results A total of 577 patients were included. Median age was 69 (IQR: 59-78) years, 38% were females and 65% had acute coronary syndrome. CICU mortality was 20% and total in-hospital mortality was 29%. Patients with low CE had a lower LVEF compared with patients with moderate or high CE (21% (IQR:19-24%) vs 31% (IQR: 28-33%) and 45% (39-51%) respectively). Median CE was 40.5% (IQR 31.8–51.0%) and was lower for in-hospital deaths (37.1% vs 42.1%, P<0.001). Patients with lower CE exhibited significantly higher CICU (P=0.003) and in-hospital mortality (P<0.001; Figure 1). Patients with CE in the lowest tertile had significantly increased risk of CICU and in-hospital death (Figure 2) compared with moderate or high CE tertiles. In multivariate analysis, every 1% decrease in CE corresponded to an 10% increased risk of CICU death and an 8% increased risk of in-hospital death. When both CE and LVEF were included in the same multivariate model, both were significant predictors of CICU death, while only CE remained a significant predictor of in-hospital death. Conclusion In patients with CS, TTE-derived CE measured within 24 hours of admission predicts early risk of death, underscoring the prognostic importance of LV energy-work coupling as a marker of disease severity beyond LVEF measurement.Figure 1: Figure 2:

Abstract Aims Heart failure is prevalent; however, there is no cost-effective screening option. Against formal echocardiography, we assessed the diagnostic performance of focused cardiac ultrasound (FoCUS), performed by novice users guided and interpreted by artificial intelligence (AI) for the assessment of left ventricular ejection fraction (LVEF). Methods and results We prospectively enrolled 496 adults referred for diagnostic echocardiography. Novice operators (without clinical or imaging experience) underwent a 4-h imaging workshop, then performed FoCUS using a point-of-care device (Philips Lumify) utilizing real-time AI-image guidance (UltraSight). Images were assessed by AI-image interpretation software (Mayo Clinic), and two expert blinded echocardiologists. Median age was 67, 39% female, and body mass index range 17–56 kg/m2. Forty-one subjects (8.3%) exhibited moderate or greater LV dilatation and 28 (5.6%) had LVEF <40%. The median scan time was 4 min (IQR 3–5). Adequate views were achieved in 95.0% and 97.4% of subjects for AI interpretation and expert analysis, respectively. A two-step diagnostic screening process for low EF in which only abnormal AI reads or uninterpretable scans were reviewed by experts (15.1%) had a sensitivity 96.2%, specificity 95.4%, PPV 54.3%, and NPV 99.8%. A subsequent prospective validation study of 344 subjects demonstrated similar results (sensitivity 100%, specificity 99.4%). Conclusion In this early feasibility investigation, AI-guidance technology embedded on a handheld device, enabled novice users without clinical or imaging experience to acquire sufficient quality images to accurately assess LVEF with minimal training. The technology evaluated in this study may hold promise for AI-guidance and interpretation to facilitate low-cost screening for cardiac dysfunction.