Decision Support Tool That Predicts Mortality in Bridge-to-Transplant Patients
Can a score be generated that accurately predicts 1-year mortality after heart transplantation in patients who were supported by a mechanical circulatory device?
The study investigators used the United Network for Organ Sharing (UNOS) database to collect information on 6,036 patients who were 18 years and older and with an intracorporeal mechanical circulatory device (including left ventricular assist device, right ventricular assist device, biventricular assist device, total artificial heart, or unspecified), who underwent heart transplantation. They randomly separated the patients into a derivation cohort (80%) and a validation cohort (20%). Using both donor and recipient variables, they constructed multivariable logistic regression models to predict 1-year mortality and 90-day and 3-year mortality, as well as overall survival time.
The study investigators developed a 75-point scoring system from nine recipient and four donor variables, the Transplantation Risk Index in Patients with Mechanical Circulatory Support (TRIP-MCS) score, to predict 1-year mortality. The mean age of the cohort was 51.2 years (± standard deviation [SD] 12.7), and 18.3% (n = 1,107) of the cohort was female. The average score in the validation cohort was 14.4 ± 7.7, and scores ranged from 0 to 57; these values were similar to the derivation cohort. Each 1-point increase predicts an 8.3% increase in the odds of 1-year mortality (odds ratio, 1.08; 95% confidence interval, 1.06-1.11). Low (0-10), intermediate (11-20), and high (>20) risk score cohorts were created by these investigators, with predicted average 1-year mortalities of 8.6% (± SD 3.5%, n = 360), 12.8% (± SD 4.2%, n = 517), and 31.0% (± SD 17.9%, n = 208), respectively, in the validation cohort. Recipient glomerular filtration rate <50, body mass index of 35 kg/m2 or greater, and mechanical ventilation at time of transplant were poor prognostic factors. Interestingly, neither transfusions nor percent reactive antigens added significant explanatory power to this model.
The authors concluded that their internally cross-validated risk index accurately predicts mortality in bridge-to-transplant patients.
This is an important study because of the clinical utility of this risk index. When applied, this model should allow better decisions regarding selection of patients for bridge to transplant versus destination therapy. As mechanical devices get more and more sophisticated, however, the index may have to be continuously updated.
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