Left ventricular assist device patient selection: do risk scores help?

The History of Durable Left Ventricular Assist Devices and Comparison of Outcomes: HeartWare, HeartMate II, HeartMate 3, and the Future of Mechanical Circulatory Support

The utilization of left ventricular assist devices (LVADs) in end-stage heart failure has doubled in the past ten years and is bound to continue to increase. Since the first of these devices was approved in 1994, the technology has changed tremendously, and so has the medical and surgical management of these patients. In this review, we discuss the history of LVADs, evaluating survival and complications over time. We also aim to discuss practical aspects of the medical and surgical management of LVAD patients and future directions for outcome improvement in this population.

Creation and Validation of a Novel Sex-Specific Mortality Risk Score in LVAD Recipients

Background

Prior studies have shown that women have worse 3‐month survival after receiving a left ventricular assist device compared with men. Currently used prognostic scores, including the Heartmate II Risk Score, do not account for the increased residual risk in women. We used the IMACS (International Society for Heart and Lung Transplantation Mechanically Assisted Circulatory Support) registry to create and validate a sex‐specific risk score for early mortality in left ventricular assist device recipients.

Methods and Results

Adult patients with a continuous‐flow LVAD from the IMACS registry were randomly divided into a derivation cohort (DC; n=9113; 21% female) and a validation cohort (VC; n=6074; 21% female). The IMACS Risk Score was developed in the DC to predict 3‐month mortality, from preoperative candidate predictors selected using the Akaike information criterion, or significant sex × variable interaction. In the DC, age, cardiogenic shock at implantation, body mass index, blood urea nitrogen, bilirubin, hemoglobin, albumin, platelet count, left ventricular end‐diastolic diameter, tricuspid regurgitation, dialysis, and major infection before implantation were retained as significant predictors of 3‐month mortality. There was significant ischemic heart failure × sex and platelet count × sex interaction. For each quartile increase in IMACS risk score, men (odds ratio [OR], 1.86; 95% CI, 1.74–2.00; P<0.0001), and women (OR, 1.93; 95% CI, 1.47–2.59; P<0.0001) had higher odds of 3‐month mortality. The IMACS risk score represented a significant improvement over Heartmate II Risk Score (IMACS risk score area under the receiver operating characteristic curve: men: DC, 0.71; 95% CI, 0.69–0.73; VC, 0.69; 95% CI, 0.66–0.72; women: DC, 0.73; 95% CI, 0.70–0.77; VC, 0.71 [95% CI, 0.66–0.76; P<0.01 for improvement in receiver operating characteristic) and provided excellent risk calibration in both sexes. Removal of sex‐specific interaction terms resulted in significant loss of model fit.

Conclusions

A sex‐specific risk score provides excellent risk prediction in LVAD recipients.

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.