Risk stratification in pulmonary arterial hypertension using Bayesian analysis.

BACKGROUND: Current risk stratification tools in pulmonary arterial hypertension (PAH) are limited in their discriminatory abilities, partly due to the assumption that prognostic clinical variables have an independent and linear relationship to clinical outcomes. We sought to demonstrate the utility of Bayesian network-based machine learning in enhancing the predictive ability of an existing state-of-the-art risk stratification tool, REVEAL 2.0. METHODS: We derived a tree-augmented naïve Bayes model (titled PHORA) to predict 1-year survival in PAH patients included in the REVEAL registry, using the same variables and cut-points found in REVEAL 2.0. PHORA models were validated internally (within the REVEAL registry) and externally (in the COMPERA and PHSANZ registries). Patients were classified as low-, intermediate- and high-risk (<5%, 5-20% and >10% 12-month mortality, respectively) based on the 2015 European Society of Cardiology/European Respiratory Society guidelines. RESULTS: PHORA had an area under the curve (AUC) of 0.80 for predicting 1-year survival, which was an improvement over REVEAL 2.0 (AUC 0.76). When validated in the COMPERA and PHSANZ registries, PHORA demonstrated an AUC of 0.74 and 0.80, respectively. 1-year survival rates predicted by PHORA were greater for patients with lower risk scores and poorer for those with higher risk scores (p<0.001), with excellent separation between low-, intermediate- and high-risk groups in all three registries. CONCLUSION: Our Bayesian network-derived risk prediction model, PHORA, demonstrated an improvement in discrimination over existing models. This is reflective of the ability of Bayesian network-based models to account for the interrelationships between clinical variables on outcome, and tolerance to missing data elements when calculating predictions.

The Use of Risk Assessment Tools and Prognostic Scores in Managing Patients with Pulmonary Arterial Hypertension.

PURPOSE OF REVIEW: Pulmonary arterial hypertension (PAH) is a chronic, progressive, and incurable disease with significant morbidity and mortality. Despite increasingly available treatment options, PAH patients continue to experience disease progression and increased rates of hospitalizations due to right heart failure. Physician's ability to comprehensively assess PAH patients, determine prognosis, and monitor disease progression and response to treatment remains critical in optimizing outcomes. RECENT FINDINGS: Risk assessment in PAH should include a range of clinical, hemodynamic, and exercise parameters, performed in a serial fashion over the course of treatment. Approaches to risk assessment in PAH patients include the use of risk variables, scores, and equations that stratify the impact of both modifiable (e.g., 6-min walk distance, functional class, brain natriuretic peptide), and non-modifiable (e.g., age, gender, PAH etiology) risk factors. Such tools allow physicians to better determine prognosis, allocate treatment resources, and enhance the consistency of treatment approaches across providers. Comprehensive and accurate risk prediction is essential to make individualized treatment decisions and optimizing outcomes in PAH.

Defining Optimal Outcomes in Patients with Left Ventricular Assist Devices.

Left ventricular assist devices (LVADs) have consistently and successfully improved mortality associated with end-stage heart failure. However, the definition of an "optimal" outcome post LVAD as a benchmark remains debatable. We retrospectively examined patients in the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) between 2012 and 2016 to assess 1 year post-LVAD "optimal outcome" defined as a patient who was alive on device or transplanted, New York Heart Association functional class I/II, had no more than 2 hospitalizations at year 1, and no major adverse event. We identified the features predicting a nonoptimal outcome at 1 year. Finally, we focused on 3 years outcomes in patients implanted as destination therapy. Of the 12,566 patients in INTERMACS who received an LVAD, only 3,495 (27.8%) met our definition of optimal LVAD outcome at 1 year. These patients tended to be younger, male, and were four times more likely to be supported as bridge to transplantation. For those with optimal outcome at year 1, their chances of long-term survival were better than those who were alive at year 1, but did not meet criteria for an optimal outcome. In the destination therapy population, only 14% of patients met the definition of an optimal outcome at 3 years. Despite significantly improved survival in patients with end-stage heart failure treated with LVAD therapy, majority patients had nonoptimal outcomes at 1 and 3 years post implant, by our definition. There is a pressing need to create a benchmark to define optimal outcomes post LVAD, both in our clinical trials and practice.

Sequential Pattern Mining of Longitudinal Adverse Events After Left Ventricular Assist Device Implant.

Left ventricular assist devices (LVADs) are an increasingly common therapy for patients with advanced heart failure. However, implantation of the LVAD increases the risk of stroke, infection, bleeding, and other serious adverse events (AEs). Most post-LVAD AEs studies have focused on individual AEs in isolation, neglecting the possible interrelation, or causality between AEs. This study is the first to conduct an exploratory analysis to discover common sequential chains of AEs following LVAD implantation that are correlated with important clinical outcomes. This analysis was derived from 58,575 recorded AEs for 13,192 patients in International Registry for Mechanical Circulatory Support (INTERMACS) who received a continuous-flow LVAD between 2006 and 2015. The pattern mining procedure involved three main steps: (1) creating a bank of AE sequences by converting the AEs for each patient into a single, chronologically sequenced record, (2) grouping patients with similar AE sequences using hierarchical clustering, and (3) extracting temporal chains of AEs for each group of patients using Markov modeling. The mined results indicate the existence of seven groups of sequential chains of AEs, characterized by common types of AEs that occurred in a unique order. The groups were identified as: GRP1: Recurrent bleeding, GRP2: Trajectory of device malfunction & explant, GRP3: Infection, GRP4: Trajectories to transplant, GRP5: Cardiac arrhythmia, GRP6: Trajectory of neurological dysfunction & death, and GRP7: Trajectory of respiratory failure, renal dysfunction & death. These patterns of sequential post-LVAD AEs disclose potential interdependence between AEs and may aid prediction, and prevention, of subsequent AEs in future studies.

Factors Affecting Health Care Engagement of Patients With End-Stage Heart Failure: An Exploratory Survey Study.

Background. The decision to receive a permanent left ventricular assist device (LVAD) to treat end-stage heart failure (HF) involves understanding and weighing the risks and benefits of a highly invasive treatment. The goal of this study was to characterize end-stage HF patients across parameters that may affect their decision making and to inform the development of an LVAD decision support tool. Methods. A survey of 35 end-stage HF patients at an LVAD implant hospital was performed to characterize their information-seeking habits, interaction with physicians, technology use, numeracy, and concerns about their health. Survey responses were analyzed using descriptive statistics, grounded theory method, and Bayesian network learning. Results. Most patients indicated an interest in using some type of decision support tool (roadmap of health progression: 46%, n = 16; personal prognosis: 51%, n = 18; short videos of patients telling stories of their experiences with an LVAD: 57%, n = 20). Information patients desired in a hypothetical decision support tool fell into the following topics: prognoses for health outcomes, technical information seeking, expressing emotions, and treatment decisions. Desire for understanding their condition was closely related to whether they had difficult interpreting their electronic medical record in the past. Conclusions. Most patients reported interest in engaging in their health care decision making and seeing their prognosis and electronic health record information. Patients who were less interested in their own treatment decisions were characterized by having less success understanding their health information. Design of a decision support tool for potential LVAD patients should consider a spectrum of health literacy and include information beyond the technical specifications of LVAD support.

Risk Assessment in Patients with a Left Ventricular Assist Device Across INTERMACS Profiles Using Bayesian Analysis.

Current risk stratification models to predict outcomes after a left ventricular assist device (LVAD) are limited in scope. We assessed the performance of Bayesian models to stratify post-LVAD mortality across various International Registry for Mechanically Assisted Circulatory Support (INTERMACS or IM) Profiles, device types, and implant strategies. We performed a retrospective analysis of 10,206 LVAD patients recorded in the IM registry from 2012 to 2016. Using derived Bayesian algorithms from 8,222 patients (derivation cohort), we applied the risk-prediction algorithms to the remaining 2,055 patients (validation cohort). Risk of mortality was assessed at 1, 3, and 12 months post implant according to disease severity (IM profiles), device type (axial versus centrifugal) and strategy (bridge to transplantation or destination therapy). Fifteen percentage (n = 308) were categorized as IM profile 1, 36% (n = 752) as profile 2, 33% (n = 672) as profile 3, and 15% (n = 311) as profile 4-7 in the validation cohort. The Bayesian algorithms showed good discrimination for both short-term (1 and 3 months) and long-term (1 year) mortality for patients with severe HF (Profiles 1-3), with the receiver operating characteristic area under the curve (AUC) between 0.63 and 0.74. The algorithms performed reasonably well in both axial and centrifugal devices (AUC, 0.68-0.74), as well as bridge to transplantation or destination therapy indication (AUC, 0.66-0.73). The performance of the Bayesian models at 1 year was superior to the existing risk models. Bayesian algorithms allow for risk stratification after LVAD implantation across different IM profiles, device types, and implant strategies.

Retrospective Evaluation of Bayesian Risk Models of LVAD Mortality at a Single Implant Center.

Use of a left ventricular assist device (LVAD) can benefit patients with end stage heart failure, but only with careful patient selection. In this study, previously derived Bayesian network models for predicting LVAD patient mortality at 1, 3, and 12 months post-implant were evaluated on retrospective data from a single implant center. The models performed well at all three time points, with a receiver operating characteristic area under the curve (ROC AUC) of 78, 76, and 75%, respectively. This evaluation of model performance verifies the utility of these models in "real life" scenarios at an individual institution.

A Bayesian Model to Predict Survival After Left Ventricular Assist Device Implantation.

OBJECTIVES: This study investigates the use of a Bayesian statistical models to predict survival at various time points in patients undergoing left ventricular assist device (LVAD) implantation. BACKGROUND: LVADs are being increasingly used in patients with end-stage heart failure. Appropriate patient selection continues to be key in optimizing post-LVAD outcomes. METHODS: Data used for this study were derived from 10,277 adult patients from the INTERMACS (Inter-Agency Registry for Mechanically Assisted Circulatory Support) who had a primary LVAD implanted between January 2012 and December 2015. Risk for mortality was calculated retrospectively for various time points (1, 3, and 12 months) after LVAD implantation, using multiple pre-implantation variables. For each of these endpoints, a separate tree-augmented naïve Bayes model was constructed using the most predictive variables. RESULTS: A set of 29, 26, and 31 pre-LVAD variables were found to be predictive at 1, 3, and 12 months, respectively. Predictors of 1-month mortality included low Inter-Agency Registry for Mechanically Assisted Circulatory Support profile, number of acute events in the 48 h before surgery, temporary mechanical circulatory support, and renal and hepatic dysfunction. Variables predicting 12-month mortality included advanced age, frailty, device strategy, and chronic renal disease. The accuracy of all Bayesian models was between 76% and 87%, with an area under the receiver operative characteristics curve of between 0.70 and 0.71. CONCLUSIONS: A Bayesian prognostic model for predicting survival based on the comprehensive INTERMACS registry provided highly accurate predictions of mortality based on pre-operative variables. These models may facilitate clinical decision-making while screening candidates for LVAD therapy.

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.