Prediction model for cardiac allograft vasculopathy: Comparison of three multivariable methods.

BACKGROUND: Cardiac allograft vasculopathy (CAV) remains an important cause of graft failure after heart transplantation (HT). Although many risk factors for CAV have been identified, there are no clinical prediction models that enable clinicians to determine each recipient's risk of CAV. METHODS: We studied a cohort of 14 328 heart transplant recipients whose data were reported to the International Society for Heart and Lung Transplantation Registry between 2000 and 2010. The cohort was divided into training (75%) and test (25%) sets. Multivariable modeling was performed in the test set using variables available at the time of heart transplant using three methods: (i) stepwise Cox proportional hazard, (ii) regularized Cox proportional hazard, and (iii) Bayesian network. RESULTS: Cardiac allograft vasculopathy developed in 4259 recipients (29.7%) at a median time of 3.0 years after HT. The regularized Cox proportional hazard model yielded the optimal performance and was also the most parsimonious. We deployed this model as an Internet-based risk calculator application. CONCLUSIONS: 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.

Assessing the impact of distal protection filter design characteristics on 30-day outcomes of carotid artery stenting procedures.

OBJECTIVE: This study aims to review retrospectively the records of patients treated with carotid artery stenting (CAS) to investigate the potential correlations between clinical variables, distal protection filter (DPF) type and characteristics, and 30-day peri-/postprocedural outcomes. METHODS: This is a multicenter, single-arm, nonrandomized retrospective study of patients who underwent filter-protected CAS in the Pittsburgh, Pennsylvania, region between July 2000 and May 2011. Analysis of peri-/postprocedural complications included myocardial infarction, transient ischemic attacks (TIA), stroke, death, and a composition of all adverse events (AEs). Filter characteristics for Accunet (Abbott Vascular, Santa Clara, Calif; n = 429 [58.8%]), Angioguard (Cordis Endovascular, Miami Lakes, Fla; n = 114 [15.6%]), FilterWire (Boston Scientific, Natick, Mass; n = 113 [15.5%]), Spider (ev3 Endovascular, Plymouth, Minn; n = 45 [6.2%]), and Emboshield (Abbott Vascular; n = 24 [3.3%]) were previously determined in vitro and were used to find correlations with CAS procedural outcomes. Both univariate and multivariate analyses were performed, as well as goodness-of-fit tests to find multivariate correlations with procedural outcomes. RESULTS: In total, 731 CAS procedures using six different DPFs were analyzed. Peri-/postprocedural AEs included 19 TIAs (2.6%), 38 strokes (5.2%), one myocardial infarction (0.1%), 19 deaths (3.6%), and a total of 61 patients with complications (8.3%). Univariate analysis for filter design characteristics showed that the composite of AE was negatively associated with both vascular resistance (P = .01) and eccentricity (P = .02) and was positively associated with porosity (P = .0007), number of pores (P = .005), and pore density (P = .001). Multivariate analysis and the goodness-of-fit test revealed that patients with a history of congestive heart failure, stroke, and TIA (each with odds ratio >1) led to a good-fit model P value of .72 for peri-/postprocedural AEs. Multivariate analysis was inconclusive for all filter design characteristics. CONCLUSIONS: The following filter design characteristics are independently significant for minimizing peri-/postprocedural AEs: higher vascular resistance, concentric in shape, greater capture efficiency, lower porosity, lower number of pores, and lower pore density. Lower porosity and smaller wall apposition were also found to be independently significant for minimization of peri-/postprocedural TIAs. This information can be used when considering the desirable design characteristics of future DPFs.).

Low Accuracy of the HeartMate Risk Score for Predicting Mortality Using the INTERMACS Registry Data.

Selection is a key determinant of clinical outcomes after left ventricular assist device (LVAD) placement in patients with end-stage heart failure. The HeartMate II risk score (HMRS) has been proposed to facilitate risk stratification and patient selection for continuous flow pumps. This study retrospectively assessed the performance of HMRS in predicting 90 day and 1 year mortality in patients within the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS). A total of 11,523 INTERMACS patients who received a continuous flow LVAD between 2010 and 2015 were retrospectively categorized per their calculated HMRS to predict their 90 day and 1 year risk of mortality. The performance of the score was evaluated by the area under curve (AUC) of the receiver operator characteristic. We also performed multiple regression analysis using variables from the HMRS calculation on the INTERMACS data. The HMRS model showed moderate discrimination for both 90 day and 1 year mortality prediction with AUCs of 61% and 59%, respectively. The predictions had similar accuracy irrespective of whether the pump was axial or centrifugal flow. Multivariable analysis using independent variables used in the original HMRS analysis revealed different set of variables to be predictive of 90 day mortality than those used to calculate HMRS. HMRS predicts both 90 day and 1 year mortality with poor discrimination when applied to a large cohort of LVAD patients. Newer risk prediction models are therefore needed to optimize the therapeutic application of LVAD therapy. Patient selection for appropriate use of LVADs is critical. Currently available risk stratification tools (HMRS) continue to be limited in their ability to accurately predict mortality after LVAD. This study highlights these limitations when applied to a large, comprehensive, multicenter database. HMRS predicts mortality with only modest discrimination when applied to a large cohort of LVAD patients. Better risk stratification tools are needed to optimize outcomes.

A Bayesian Model to Predict Right Ventricular Failure Following Left Ventricular Assist Device Therapy.

OBJECTIVES: 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 interrelationships and conditional probabilities among independent variables to achieve sufficient statistical accuracy. BACKGROUND: Right ventricular failure (RVF) continues to be a major adverse event following left ventricular assist device (LVAD) implantation. METHODS: Data used for this study were derived from 10,909 adult patients from the Inter-Agency Registry for Mechanically Assisted Circulatory Support (INTERMACS) who had a primary LVAD implanted between December 2006 and March 2014. An initial set of 176 pre-implantation variables were considered. RVF post-implant was categorized as acute (<48 h), early (48 h to 14 daysays), 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 employing an open source Bayesian inference engine. 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, lactate dehydrogenase level, INTERMACS profile, right ventricular ejection fraction, pro-B-type natriuretic peptide, age, heart rate, tricuspid regurgitation, and body mass index. The late RVF model included 33 variables and was predicted mostly by peripheral vascular resistance, model for end-stage liver disease score, albumin level, lymphocyte percentage, and mean and diastolic PAP. The accuracy of all Bayesian models was between 91% and 97%, with an area under the receiver operator characteristics curve between 0.83 and 0.90, sensitivity of 90%, and specificity between 98% and 99%, significantly outperforming previously published risk scores. CONCLUSIONS: A Bayesian prognostic model of RVF based on the large, multicenter INTERMACS registry provided highly accurate predictions of acute, early, and late RVF on the basis of pre-operative variables. These models may facilitate clinical decision making while screening candidates for LVAD therapy.

A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality.

Existing risk assessment tools for patient selection for left ventricular assist devices (LVADs) such as the Destination Therapy Risk Score 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 Interagency Registry for Mechanically Assisted Circulatory Support dataset and comparing it to HMRS. We retrospectively analyzed 8,050 continuous flow LVAD patients and 226 preimplant variables. We then derived Bayesian models for mortality at each of five time end-points postimplant (30 days, 90 days, 6 month, 1 year, and 2 years), 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 receiver operator characteristic (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. Preimplant interventions, such as dialysis, ECMO, and ventilators were major contributing risk markers. Bayesian models have the ability to reliably represent the complex causal relations 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.

Cardiac Health Risk Stratification System (CHRiSS): a Bayesian-based decision support system for left ventricular assist device (LVAD) therapy.

This study investigated the use of Bayesian Networks (BNs) for left ventricular assist device (LVAD) therapy; a treatment for end-stage heart failure that has been steadily growing in popularity over the past decade. Despite this growth, the number of LVAD implants performed annually remains a small fraction of the estimated population of patients who might benefit from this treatment. We believe that this demonstrates a need for an accurate stratification tool that can help identify LVAD candidates at the most appropriate point in the course of their disease. We derived BNs to predict mortality at five endpoints utilizing the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) database: containing over 12,000 total enrolled patients from 153 hospital sites, collected since 2006 to the present day, and consisting of approximately 230 pre-implant clinical variables. Synthetic minority oversampling technique (SMOTE) was employed to address the uneven proportion of patients with negative outcomes and to improve the performance of the models. The resulting accuracy and area under the ROC curve (%) for predicted mortality were 30 day: 94.9 and 92.5; 90 day: 84.2 and 73.9; 6 month: 78.2 and 70.6; 1 year: 73.1 and 70.6; and 2 years: 71.4 and 70.8. To foster the translation of these models to clinical practice, they have been incorporated into a web-based application, the Cardiac Health Risk Stratification System (CHRiSS). As clinical experience with LVAD therapy continues to grow, and additional data is collected, we aim to continually update these BN models to improve their accuracy and maintain their relevance. Ongoing work also aims to extend the BN models to predict the risk of adverse events post-LVAD implant as additional factors for consideration in decision making.

Reproducibility of IVUS border detection for carotid atherosclerotic plaque assessment.

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 (κ(quadratic)=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.