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Non-invasive detection of severe PH in lung disease using magnetic resonance imaging.
Alkhanfar, Dheyaa; Dwivedi, Krit; Alandejani, Faisal; Shahin, Yousef; Alabed, Samer; Johns, Chris; Garg, Pankaj; Thompson, A A Roger; Rothman, Alexander M K; Hameed, Abdul; Charalampopoulos, Athanasios; Wild, Jim M; Condliffe, Robin; Kiely, David G; Swift, Andrew J.
Afiliação
  • Alkhanfar D; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.
  • Dwivedi K; INSIGNEO, Institute for in Silico Medicine, University of Sheffield, Sheffield, United Kingdom.
  • Alandejani F; Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom.
  • Shahin Y; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.
  • Alabed S; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.
  • Johns C; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.
  • Garg P; Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom.
  • Thompson AAR; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.
  • Rothman AMK; Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom.
  • Hameed A; Department of Clinical Radiology, Sheffield Teaching Hospitals, Sheffield, United Kingdom.
  • Charalampopoulos A; Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.
  • Wild JM; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.
  • Condliffe R; Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.
  • Kiely DG; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, Sheffield, United Kingdom.
  • Swift AJ; Sheffield Pulmonary Vascular Disease Unit, Royal Hallamshire Hospital, Sheffield Teaching Hospitals NHS Foundation Trust, Sheffield, United Kingdom.
Front Cardiovasc Med ; 10: 1016994, 2023.
Article em En | MEDLINE | ID: mdl-37139140
Introduction: Severe pulmonary hypertension (mean pulmonary artery pressure ≥35 mmHg) in chronic lung disease (PH-CLD) is associated with high mortality and morbidity. Data suggesting potential response to vasodilator therapy in patients with PH-CLD is emerging. The current diagnostic strategy utilises transthoracic Echocardiography (TTE), which can be technically challenging in some patients with advanced CLD. The aim of this study was to evaluate the diagnostic role of MRI models to diagnose severe PH in CLD. Methods: 167 patients with CLD referred for suspected PH who underwent baseline cardiac MRI, pulmonary function tests and right heart catheterisation were identified. In a derivation cohort (n = 67) a bi-logistic regression model was developed to identify severe PH and compared to a previously published multiparameter model (Whitfield model), which is based on interventricular septal angle, ventricular mass index and diastolic pulmonary artery area. The model was evaluated in a test cohort. Results: The CLD-PH MRI model [= (-13.104) + (13.059 * VMI)-(0.237 * PA RAC) + (0.083 * Systolic Septal Angle)], had high accuracy in the test cohort (area under the ROC curve (0.91) (p < 0.0001), sensitivity 92.3%, specificity 70.2%, PPV 77.4%, and NPV 89.2%. The Whitfield model also had high accuracy in the test cohort (area under the ROC curve (0.92) (p < 0.0001), sensitivity 80.8%, specificity 87.2%, PPV 87.5%, and NPV 80.4%. Conclusion: The CLD-PH MRI model and Whitfield model have high accuracy to detect severe PH in CLD, and have strong prognostic value.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Reino Unido

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Reino Unido