Background Right ventricular failure (RVF) continues to be a major adverse event following left ventricular assist device (LVAD) implantation. This study investigates the use of a Bayesian statistical model to address the limited predictive capacity of existing risk scores derived from multivariate analyses. This is based on the hypothesis that it is necessary to consider the inter-relationships and conditional probabilities amongst independent variables to achieve sufficient statistical accuracy. Methods The data used for this study was derived from 10,909 adult patients from INTERMACS who had a primary LVAD from December 2006 – March 2014. An initial set of 176 pre-implant variables were considered. RVF post-implant was categorized as acute (<48 hours), early (48 hours–14 days) and late (>14 days) in onset. For each of these endpoints, a separate tree-augmented Naïve Bayes model was constructed using the most predictive variables using an open source Bayesian inference engine (SMILE.) Results The acute RVF model consisted of 33 variables, including: systolic pulmonary artery pressure (PAP), white blood cell count, left ventricular ejection fraction, cardiac index, sodium levels, and lymphocyte percentage. The early RVF model consisted of 34 variables, including systolic PAP, pre-albumin, LDH, INTERMACS profile, right ventricular ejection fraction, pro-B-type natriuretic peptide, age, heart rate, tricuspid regurgitation and BMI. The late RVF model included 33 variables and was mostly predicted by peripheral vascular resistance, MELD score, albumin, lymphocyte percentage, mean PAP and diastolic PAP. The accuracies of all the Bayesian models were between 91–97%, AUC between 0.83–0.90 sensitivity of 90% and specificity between 98–99%, significantly outperforming previously published risk scores. Conclusion A Bayesian prognostic model of RVF, based on the large, multi-center INTERMACS registry provided highly accurate predictions of acute, early, and late RVF based on preoperative variables. These models may facilitate clinical decision-making while screening candidates for LVAD therapy.

Existing risk assessment tools for patient selection for left ventricular assist devices (LVADs) such as the Destination Therapy Risk Score (DTRS) and HeartMate II Risk Score (HMRS) have limited predictive ability. This study aims to overcome the limitations of traditional statistical methods by performing the first application of Bayesian analysis to the comprehensive INTERMACS dataset and comparing it to HMRS. We retrospectively analyzed 8,050 continuous flow (CF) LVAD patients and 226 pre-implant variables. We then derived Bayesian models for mortality at each of five time endpoints post-implant (30 day, 90 day, 6 month, 1 year, and 2 year), achieving accuracies of 95, 90, 90, 83, and 78%, Kappa values of 0.43, 0.37, 0.37, 0.45, and 0.43, and area under the ROC of 91, 82, 82, 80 and 81% respectively. This was in comparison to the HMRS with an ROC of 57 and 60% at 90-days and 1-year, respectively. Pre-implant interventions such as dialysis, ECMO, and ventilators were major contributing risk markers. Bayesian models have the ability to reliably represent the complex causal relationships of multiple variables on clinical outcomes. Their potential to develop a reliable risk stratification tool for use in clinical decision making on LVAD patients encourages further investigation.

Plaque composition is a potentially important diagnostic feature for carotid artery stenting (CAS). The purpose of this investigation is to evaluate the reproducibility of manual border correction in intravascular ultrasound with virtual histology (VH IVUS) images. Three images each were obtained from 51 CAS datasets on which automatic border detection was corrected manually by two trained observers. Plaque was classified using the definitions from the CAPITAL (Carotid Artery Plaque Virtual Histology Evaluation) study, listed in order from least to most pathological: no plaque, pathological intimal thickening, fibroatheroma, fibrocalcific, calcified fibroatheroma, thin-cap fibroatheroma, and calcified thin-cap fibroatheroma. Inter-observer variability was quantified using both weighted and unweighted Kappa statistics. Bland-Altman analysis was used to compare the cross-sectional areas of the vessel and lumen. Agreement using necrotic core percentage as the criterion was evaluated using the unweighted Kappa statistic. Agreement between classifications of plaque type was evaluated using the weighted Kappa statistic. There was substantial agreement between the observers based on necrotic core percentage (κ = 0.63), while the agreement was moderate (κquadratic = 0.60) based on plaque classification. Due to the time-consuming nature of manual border detection, an improved automatic border detection algorithm is necessary for using VH IVUS as a diagnostic tool for assessing the suitability of patients with carotid artery occlusive disease for CAS.

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We have developed a clinical prediction model for assessing a recipient's risk of CAV using variables available at the time of HT. Application of this model may allow clinicians to determine which recipients will benefit from interventions to reduce the risk of development and progression of CAV.