Renal Outcomes in Patients Bridged to Heart Transplant With a Left Ventricular Assist Device

BACKGROUND: Patients with end stage heart failure are increasingly being bridged to heart transplant (BTT) with mechanical circulatory support (MCS), however the association between a left ventricular assist device (LVAD) BTT strategy and post-transplant renal outcomes is unclear. The aim of this study was to analyze the association of LVAD BTT with the development of post-transplant renal failure using a large national registry. METHODS: We queried the 2009-2018 United Network for Organ Sharing (UNOS) registry for all adults undergoing first-time heart or heart-kidney transplantation and stratified patients by use of pre-transplant durable LVAD. The primary outcome of interest was post-transplant renal failure, which was evaluated with multivariable logistic regression. RESULTS: 18,307 patients met inclusion criteria including 7,887 (43%) and 10,420 (57%) that were and were not bridged to transplant with an LVAD, respectively. BTT patients had slightly better baseline renal function (eGFR 68.7 vs 65.8 mL/min, p<0.001) and were less likely to receive a heart-kidney transplant (2.7% vs 4.8%, p<0.001). On multivariable logistic regression, LVAD BTT strategy was not independently associated with post-transplant renal failure (OR 1.13, 95% CI 0.86-1.49). Similarly, LVAD BTT among patients with preoperative renal dysfunction was not associated with post-transplant renal failure (AOR 1.40, 95% CI 0.91-2.18). CONCLUSIONS: BTT with an LVAD does not appear to be associated with worse renal outcomes regardless of baseline renal function. Furthermore, an LVAD BTT strategy in patients with chronic kidney disease may enable clinicians to identify candidates suitable for isolated heart transplantation without increasing their risk for post-transplant renal failure

A Neural Computational Model of Incentive Salience

Incentive salience is a motivational property with ‘magnet-like’ qualities. When attributed to reward-predicting stimuli (cues), incentive salience triggers a pulse of ‘wanting’ and an individual is pulled toward the cues and reward. A key computational question is how incentive salience is generated during a cue re-encounter, which combines both learning and the state of limbic brain mechanisms. Learning processes, such as temporal-difference models, provide one way for stimuli to acquire cached predictive values of rewards. However, empirical data show that subsequent incentive values are also modulated on the fly by dynamic fluctuation in physiological states, altering cached values in ways requiring additional motivation mechanisms. Dynamic modulation of incentive salience for a Pavlovian conditioned stimulus (CS or cue) occurs during certain states, without necessarily requiring (re)learning about the cue. In some cases, dynamic modulation of cue value occurs during states that are quite novel, never having been experienced before, and even prior to experience of the associated unconditioned reward in the new state. Such cases can include novel drug-induced mesolimbic activation and addictive incentive-sensitization, as well as natural appetite states such as salt appetite. Dynamic enhancement specifically raises the incentive salience of an appropriate CS, without necessarily changing that of other CSs. Here we suggest a new computational model that modulates incentive salience by integrating changing physiological states with prior learning. We support the model with behavioral and neurobiological data from empirical tests that demonstrate dynamic elevations in cue-triggered motivation (involving natural salt appetite, and drug-induced intoxication and sensitization). Our data call for a dynamic model of incentive salience, such as presented here. Computational models can adequately capture fluctuations in cue-triggered ‘wanting’ only by incorporating modulation of previously learned values by natural appetite and addiction-related states.United States. Air Force Office of Scientific ResearchUnited States. National Institutes of Health (DA015188, DA017752 and MH63649