Non-Invasive Estimation of Right Atrial Pressure Using a Semi-Automated Echocardiographic Tool for Inferior Vena Cava Edge-Tracking.
Luca MesinPiero PolicastroStefano AlbaniChristina PetersenPaolo SciarroneClaudia TaddeiAlberto GiannoniPublished in: Journal of clinical medicine (2022)
The non-invasive estimation of right atrial pressure ( RAP ) would be a key advancement in several clinical scenarios, in which the knowledge of central venous filling pressure is vital for patients' management. The echocardiographic estimation of RAP proposed by Guidelines, based on inferior vena cava (IVC) size and respirophasic collapsibility, is exposed to operator and patient dependent variability. We propose novel methods, based on semi-automated edge-tracking of IVC size and cardiac collapsibility (cardiac caval index-CCI), tested in a monocentric retrospective cohort of patients undergoing echocardiography and right heart catheterization (RHC) within 24 h in condition of clinical and therapeutic stability (170 patients, age 64 ± 14, male 45%, with pulmonary arterial hypertension, heart failure, valvular heart disease, dyspnea, or other pathologies). IVC size and CCI were integrated with other standard echocardiographic features, selected by backward feature selection and included in a linear model (LM) and a support vector machine (SVM), which were cross-validated. Three RAP classes (low < 5 mmHg, intermediate 5-10 mmHg and high > 10 mmHg) were generated and RHC values used as comparator. LM and SVM showed a higher accuracy than Guidelines (63%, 71%, and 61% for LM, SVM, and Guidelines, respectively), promoting the integration of IVC and echocardiographic features for an improved non-invasive estimation of RAP.
Keyphrases
- neuropathic pain
- inferior vena cava
- spinal cord
- left ventricular
- pulmonary embolism
- pulmonary hypertension
- ejection fraction
- pulmonary arterial hypertension
- vena cava
- heart failure
- end stage renal disease
- left atrial
- atrial fibrillation
- patients undergoing
- newly diagnosed
- deep learning
- machine learning
- chronic kidney disease
- mitral valve
- computed tomography
- healthcare
- clinical practice
- peritoneal dialysis
- cross sectional
- catheter ablation