New joint model for the dynamic prediction of heart failure in phospholamban (PLN) p.(Arg14del) positive individuals

Abstract Background PLN p.(Arg14del) positive individuals are at risk for developing arrhythmogenic cardiomyopathy which can cause severe heart failure. Future gene therapy could be lifesaving and is rapidly advancing. It is therefore of importance to identify eligible patients for this evolving treatment, with individuals risk prediction playing an imperative role in this process. Purpose To develop a dynamic prediction model for the individual risk prediction of heart failure in PLN p.(Arg14del) positive individuals. Method Data were collected of 564 PLN p.(Arg14del) positive individuals with no baseline history of heart failure or myocardial infarction. During a median follow-up of 6.3 years (interquartile range 2.9-10.0), 74 individuals experienced heart failure, defined as a composite endpoint of heart failure hospitalization, left ventricle assist device implantation, heart transplantation and heart failure-related death. For the individual prediction of time to heart failure we used a joint model which simultaneously estimates a longitudinal model for repeatedly measured left ventricle ejection fraction (LVEF) during follow-up and a Cox regression model for the hazards of the heart failure endpoint. Using the longitudinal model we predicted the LVEF-values of all patients at future time points using the estimated random-effects and these predicted LVEF values were used as a time-dependent covariate in the Cox model which also contained age at start of follow-up as predictor variable. Results The joint model trained in this study had a very good performance in discriminating the individuals at risk for heart failure, with the 5-year mean AUC of 0.86 and 10-year mean AUC of 0.84. The LVEF had a hazard ratio of 0.89 (95% confidence interval (CI) 0.86-0.93, p<0.001), and age at baseline 1.04 (95% CI 1.03-1.06], p<0.001), meaning a lower LVEF and higher age at presentation were associated with heart failure. Figure 1 shows an example of an individual risk prediction with on the x-axis the follow-up time in years, on the y-axis (left) the LVEF in % and on the y-axis (right) the predicted survival probability. Conclusion Individual risk prediction with a simple model including only LVEF and age is of great importance for identifying PLN p.(Arg14del) positive individuals who are at risk of developing heart failure. The introduction of this novel joint prediction model is the first step in identifying individuals at risk for heart failure and may aid patient selection for future gene therapy interventions.