Preoperative Computed Tomography Assessment of Risk of Right Ventricle Failure After Left Ventricular Assist Device Placement.
Anderson ScottSeth KligermanDiana Hernandez HernandezPaul J KimHao TranVictor G PretoriusEric D AdlerFrancisco J ContijochPublished in: ASAIO journal (American Society for Artificial Internal Organs : 1992) (2022)
Identification of patients who are at a high risk for right ventricular failure (RVF) after left ventricular assist device (LVAD) implantation is of critical importance. Conventional tools for predicting RVF, including two-dimensional echocardiography, right heart catheterization (RHC), and clinical parameters, generally have limited sensitivity and specificity. We retrospectively examined the ability of computed tomography (CT) ventricular volume measures to identify patients who experienced RVF after LVAD implantation. Between September 2017 and November 2021, 92 patients underwent LVAD surgery at our institution. Preoperative CT-derived ventricular volumes were obtained in 20 patients. Patients who underwent CT evaluation had a similar demographics and rate of RVF after LVAD as patients who did not undergo cardiac CT imaging. In the study cohort, seven of 20 (35%) patients experienced RVF (2 unplanned biventricular assist device, 5 prolonged inotropic support). Computed tomography-derived right ventricular end-diastolic and end-systolic volume indices were the strongest predictors of RVF compared with demographic, echocardiographic, and RHC data with areas under the receiver operating curve of 0.79 and 0.76, respectively. Computed tomography volumetric assessment of RV size can be performed in patients evaluated for LVAD treatment. RV measures of size provide a promising means of pre-LVAD assessment for postoperative RV failure.
Keyphrases
- computed tomography
- end stage renal disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- left ventricular
- heart failure
- prognostic factors
- peritoneal dialysis
- mycobacterium tuberculosis
- blood pressure
- machine learning
- minimally invasive
- acute coronary syndrome
- patient reported outcomes
- photodynamic therapy
- patients undergoing
- mass spectrometry
- atrial fibrillation
- deep learning
- artificial intelligence
- pulmonary arterial hypertension
- ultrasound guided
- fluorescence imaging
- data analysis