Publications

BACKGROUND

Although analysis of cardiac magnetic resonance (CMR) images provides accurate and reproducible measurements of left ventricular (LV) volumes, these measurements are usually not performed throughout the cardiac cycle because of lack of tools that would allow such analysis within a reasonable timeframe. A fully-automated machine-learning (ML) algorithm was recently developed to automatically generate LV volume-time curves. Our aim was to validate ejection and filling parameters calculated from these curves using conventional analysis as a reference.

METHODS

We studied 21 patients undergoing clinical CMR examinations. LV volume-time curves were obtained using the ML-based algorithm (Neosoft), and independently using slice-by-slice, frame-by-frame manual tracing of the endocardial boundaries. Ejection and filling parameters derived from these curves were compared between the two techniques. For each parameter, Bland-Altman bias and limits of agreement (LOA) were expressed in percent of the mean measured value.

RESULTS

Time-volume curves were generated using the automated ML analysis within 2.5 ± 0.5 min, considerably faster than the manual analysis (43 ± 14 min per patient, including ~10 slices with 25-32 frames per slice). Time-volume curves were similar between the two techniques in magnitude and shape. Size and function parameters extracted from these curves showed no significant inter-technique differences, reflected by high correlations, small biases (<10%) and mostly reasonably narrow LOA.

CONCLUSION

ML software for dynamic LV volume measurement allows fast and accurate, fully automated analysis of ejection and filling parameters, compared to manual tracing based analysis. The ability to quickly evaluate time-volume curves is important for a more comprehensive evaluation of the patient's cardiac function.

Most published studies addressing the role of hypoxia inducible factors (HIFs) in hypoxia-induced pulmonary hypertension development employ models that may not recapitulate the clinical setting, including the use of animals with pre-existing lung/vascular defects secondary to embryonic HIF ablation or activation. Furthermore, critical questions including how and when HIF signalling contributes to hypoxia-induced pulmonary hypertension remain unanswered.Normal adult rodents in which global HIF1 or HIF2 was inhibited by inducible gene deletion or pharmacological inhibition (antisense oligonucleotides (ASO) and small molecule inhibitors) were exposed to short-term (4 days) or chronic (4-5 weeks) hypoxia. Haemodynamic studies were performed, the animals euthanised, and lungs and hearts obtained for pathological and transcriptomic analysis. Cell-type-specific HIF signals for pulmonary hypertension initiation were determined in normal pulmonary vascular cells and in mice (using cell-type-specific HIF deletion).Global deletion in mice did not prevent hypoxia-induced pulmonary hypertension at 5 weeks. Mice with global deletion did not survive long-term hypoxia. Partial deletion or -ASO (but not -ASO) reduced vessel muscularisation, increases in pulmonary arterial pressures and right ventricular hypertrophy in mice exposed to 4-5 weeks of hypoxia. A small molecule HIF2 inhibitor (PT2567) significantly attenuated early events (monocyte recruitment and vascular cell proliferation) in rats exposed to 4 days of hypoxia, as well as vessel muscularisation, tenascin C accumulation and pulmonary hypertension development in rats exposed to 5 weeks of hypoxia. , HIF2 induced a distinct set of genes in normal human pulmonary vascular endothelial cells, mediating inflammation and proliferation of endothelial cells and smooth muscle cells. Endothelial knockout prevented hypoxia-induced pulmonary hypertension in mice.Inhibition of HIF2 (but not HIF1) can provide a therapeutic approach to prevent the development of hypoxia-induced pulmonary hypertension. Future studies are needed to investigate the role of HIFs in pulmonary hypertension progression and reversal.