A multicenter evaluation of the HeartMate 3 risk score

Predictive Accuracy of HeartMate 3 Risk Score After the Heart Transplant Allocation Change

The HeartMate 3 risk score (HM3RS) was developed from the Multicenter Study of MagLev Technology in Patients Undergoing Mechanical Circulatory Support Therapy with HeartMate 3 (MOMENTUM 3) clinical trial to predict 1 and 2 year mortality after left ventricular assist device implantation. However, it has not been validated in a real-world population, especially after the heart transplant allocation system change on October 18, 2018. In this multicenter retrospective analysis, we found that HM3RS did not predict 1 and 2 year outcomes in the contemporary era, highlighting the need to revise this risk prediction tool in the real-world setting.

Dynamic Risk Estimation of Adverse Events in Ambulatory LVAD Patients: A MOMENTUM 3 Analysis

BACKGROUND

Hemocompatibility-related adverse events affect patients after left ventricular assist device (LVAD) implantation but are hard to predict.

OBJECTIVES

Dynamic risk modeling with a multistate model can predict risk of gastrointestinal bleeding (GIB), stroke, or death in ambulatory patients.

METHODS

This was a secondary analysis of the MOMENTUM 3 (Multicenter Study of MagLev Technology in Patients Undergoing Mechanical Circulatory Support Therapy with HeartMate 3) trial. HeartMate 3 LVAD recipients who survived to hospital discharge and were followed for up to 2 years. A total of 145 variables were included in the multistate model with multivariate logistic regression. Model performance was assessed with the area under the curve in a holdout validation cohort. A risk stratification tool was created by dividing patients into categories of predicted risk using the final model variables and associated OR.

RESULTS

Among 2,056 LVAD patients, the median age was 59.4 years (20.4% women, 28.6% Black). At 2 years, the incidence of GIB, stroke, and death was 25.6%, 6.0%, and 12.3%, respectively. The multistate model included 39 total variables to predict risk of GIB (16 variables), stroke (10 variables), and death (19 variables). When ambulatory patients were classified according to their risk category, the 30-day observed event rate in the highest risk group for GIB, stroke, or death was 26.9%, 1.8%, and 4.8%, respectively. The multistate model predicted GIB, stroke, and death at any 30-day period with an area under the curve of 0.70, 0.69, and 0.86, respectively.

CONCLUSIONS

The multistate model informs 30-day risk in ambulatory LVAD recipients and allows recalculation of risk as new patient-specific data become available. The model allows for accurate risk stratification that predicts impending adverse events and may guide clinical decision making. (MOMENTUM 3 IDE Clinical Study Protocol; NCT02224755).

The Role of Artificial Intelligence and Machine Learning in the Prediction of Right Heart Failure after Left Ventricular Assist Device Implantation: A Comprehensive Review

One of the most challenging and prevalent side effects of LVAD implantation is that of right heart failure (RHF) that may develop afterwards. The purpose of this study is to review and highlight recent advances in the uses of AI in evaluating RHF after LVAD implantation. The available literature was scanned using certain key words (artificial intelligence, machine learning, left ventricular assist device, prediction of right heart failure after LVAD) was scanned within Pubmed, Web of Science, and Google Scholar databases. Conventional risk scoring systems were also summarized, with their pros and cons being included in the results section of this study in order to provide a useful contrast with AI-based models. There are certain interesting and innovative ML approaches towards RHF prediction among the studies reviewed as well as more straightforward approaches that identified certain important predictive clinical parameters. Despite their accomplishments, the resulting AUC scores were far from ideal for these methods to be considered fully sufficient. The reasons for this include the low number of studies, standardized data availability, and lack of prospective studies. Another topic briefly discussed in this study is that relating to the ethical and legal considerations of using AI-based systems in healthcare. In the end, we believe that it would be beneficial for clinicians to not ignore these developments despite the current research indicating more time is needed for AI-based prediction models to achieve a better performance.