{
  "abstract": "Background Pulmonary hypertension (PH) is characterised by structural vascular changes and altered haemodynamics. While cardiovascular magnetic resonance (CMR) metrics such as pulmonary artery (PA) size are established for PH detection, their ability to distinguish PH subtypes remains limited. This study evaluates the diagnostic and phenotypic value of automated CMR-derived PA slow flow.Methods In a cohort of 722 patients with the same day CMR and right heart catheterisation (RHC), automated measurements of PA volumes were derived from standard cine sequences, and slow flow metrics were derived from black blood images. PH and its subtypes were defined using invasive haemodynamics. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis, and correlation with RHC measurements was assessed using Spearman’s rank correlation.Results Automated CMR metrics demonstrated high performance for identifying PH, with PA volume showing the highest diagnostic accuracy (AUC 0.86; Sensitivity 81%, Specificity 83%). In contrast, slow flow metrics provided superior discrimination of pre-capillary PH from other PH subtypes, with strongest performance observed for differentiation from isolated post-capillary PH (AUC 0.86; Sensitivity 70%, Specificity 91%). Moreover, slow flow demonstrated the strongest association with PVR (ρ = 0.72) and cardiac index (ρ = -0.53) (table 1, figures 1 and 2).Conclusion Automated CMR derived metrics provide distinct and complementary roles in the assessment of PH. While PA volume supports detection of PH, slow flow provides additional insight into haemodynamic classification of PH subtypes. Integration of these metrics into routine CMR reporting may improve non-invasive assessment of PH. Which CMR-derived metric is most useful for distinguishing pre-capillary from post-capillary pulmonary hypertension?A. Pulmonary artery diameterB. Pulmonary artery volumeC. Slow-flow volumeAbstract 32 Table 1Abstract 32 Figure 2Abstract 32 Figure 1D. Slow-flow fraction",
  "authors": [
    {
      "affiliations": [
        "School of Medicine & Population Health, The University of Sheffield, Sheffield, United Kingdom",
        "Department of Radiological Sciences, College of Applied Medical Science, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia"
      ],
      "name": "Khalid Alghamdi"
    },
    {
      "affiliations": [
        "School of Medicine & Population Health, The University of Sheffield, Sheffield, United Kingdom",
        "Insigneo Institute, Faculty of Engineering, The University of Sheffield, Sheffield, United Kingdom"
      ],
      "name": "Alireza Hokmabadi"
    },
    {
      "affiliations": [
        "School of Medicine & Population Health, The University of Sheffield, Sheffield, United Kingdom"
      ],
      "name": "Turki Alnasser"
    },
    {
      "affiliations": [
        "School of Medicine & Population Health, The University of Sheffield, Sheffield, United Kingdom"
      ],
      "name": "Michael Sharkey"
    },
    {
      "affiliations": [
        "School of Medicine & Population Health, The University of Sheffield, Sheffield, United Kingdom",
        "Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom"
      ],
      "name": "Smitha Rajaram"
    },
    {
      "affiliations": [
        "School of Medicine & Population Health, The University of Sheffield, Sheffield, United Kingdom",
        "Insigneo Institute, Faculty of Engineering, The University of Sheffield, Sheffield, United Kingdom",
        "Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Trust, Sheffield, United Kingdom",
        "National Institute for Health and Care Research, Sheffield Biomedical Research Centre, Sheffield, UK"
      ],
      "name": "David G Kiely"
    },
    {
      "affiliations": [
        "School of Medicine & Population Health, The University of Sheffield, Sheffield, United Kingdom",
        "Insigneo Institute, Faculty of Engineering, The University of Sheffield, Sheffield, United Kingdom",
        "Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom",
        "National Institute for Health and Care Research, Sheffield Biomedical Research Centre, Sheffield, UK"
      ],
      "name": "Samer Alabed"
    },
    {
      "affiliations": [
        "School of Medicine & Population Health, The University of Sheffield, Sheffield, United Kingdom",
        "Insigneo Institute, Faculty of Engineering, The University of Sheffield, Sheffield, United Kingdom",
        "Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom",
        "National Institute for Health and Care Research, Sheffield Biomedical Research Centre, Sheffield, UK"
      ],
      "name": "Andrew J Swift"
    }
  ],
  "title": "32 Automated CMR-derived slow flow improve classification of pulmonary hypertension subtypes",
  "uid": "d297a6c9-f4b5-5372-9ceb-0497b57ac772"
}