BACKGROUND: Low gradient (mean gradient, MG < 40 mmHg) aortic stenosis (AS) may occur in conjunction with a small valve effective orifice area (EOA) < 1 cm². This discordant grading in AS severity raises challenges from both a diagnostic and therapeutic standpoint. We sought to identify echocardiographic markers of severe AS in normal and low flow conditions in vitro. METHODS: In an in vitro mock circulatory system, we tested 6 degrees of AS severity (3 severe and 3 non-severe), and 3 levels of stroke volume (30, 50, 70 ml), heart rate (60, 80, 100 bpm), and mean arterial pressure (100, 120, 140 mmHg). MG and EOA were measured by Doppler-echocardiography. RESULTS: In normal flow conditions, a MG of 40 mmHg corresponded to an EOA of 0.85 cm², and an EOA of 1 cm² to a MG of 28 mmHg. The presence of EOA < 0.85 cm² and MG between 24 and 40 mmHg in the low-flow condition yielded a 100% specificity for severe AS. In normal flow conditions, these thresholds were respectively < 0.9 cm² and > 36 mmHg. CONCLUSION: Low gradient AS may occur in severe AS in both low and normal flow conditions. The presence of a valve EOA < 0.85 cm² with a mean gradient ≥ 25 mmHg may be used as a criteria to confirm the presence of severe AS in presence of low flow. In patients with normal-flow, a MG ≥ 37 mmHg is generally suggestive of severe AS.

Half of the patients with heart failure (HF) have preserved ejection fraction (HFpEF). To date, there are no specific markers to distinguish this subgroup. The main objective of this work was to stratify HF patients using current biochemical markers coupled with clinical data. The cohort study included HFpEF (n = 24) and heart failure with reduced ejection fraction (HFrEF) (n = 34) patients as usually considered in clinical practice based on cardiac imaging (EF ≥ 50% for HFpEF; EF < 50% for HFrEF). Routine blood tests consisted of measuring biomarkers of renal and heart functions, inflammation, and iron metabolism. A multi-test approach and analysis of peripheral blood samples aimed to establish a computerized Machine Learning strategy to provide a blood signature to distinguish HFpEF and HFrEF. Based on logistic regression, demographic characteristics and clinical biomarkers showed no statistical significance to differentiate the HFpEF and HFrEF patient subgroups. Hence a multivariate factorial discriminant analysis, performed blindly using the data set, allowed us to stratify the two HF groups. Consequently, a Machine Learning (ML) strategy was developed using the same variables in a genetic algorithm approach. ML provided very encouraging explorative results when considering the small size of the samples applied. The accuracy and the sensitivity were high for both validation and test groups (69% and 100%, 64% and 75%, respectively). Sensitivity was 100% for the validation and 75% for the test group, whereas specificity was 44% and 55% for the validation and test groups because of the small number of samples. Lastly, the precision was acceptable, with 58% in the validation and 60% in the test group. Combining biochemical and clinical markers is an excellent entry to develop a computer classification tool to diagnose HFpEF. This translational approach is a springboard for improving new personalized treatment methods and identifying “high-yield” populations for clinical trials.