Abstract Body (Do not enter title and authors here):
Background: Despite the development of disease-modifying therapy (DMT) for patients with transthyretin amyloid cardiomyopathy (ATTR-CM), the rates of mortality and disease progression are still high. The conventional models to predict prognosis (e.g., the Columbia score, the National Amyloidosis Centre [NAC] staging) predate DMT and offer limited prediction in the era of DMT.

Aim: To develop a novel prognostication model in patients with ATTR-CM using plasma proteomics.

Hypothesis: Plasma proteomics improves the prediction of death and disease progression in patients with ATTR-CM beyond the conventional models.

Methods: In this prospective study, we conducted plasma proteomics profiling of 7,289 proteins in patients with ATTR-CM (n=303) at enrollment. The primary outcome was all-cause death, and the secondary was disease progression–a composite of death, heart transplant, heart failure hospitalization, and oral diuretics intensification. We randomly divided the cohort into training (2/3) and test sets (1/3). In the training set, we specified proteins to predict both outcomes using the Boruta algorithm. Using the specified proteins, we developed a random forest-based machine-learning (ML) model to predict each outcome in the training set. We compared the predictive ability of the ML model with the conventional models in the test set. We performed survival analyses between high- and low-risk groups defined by the ML models in the test set, while adjusting for the Columbia score.

Results: During a median follow-up of 3.9 [1^st–3^rd quartile: 2.2–5.9] years, 69 patients (23%) died and 124 (41%) had disease progression, despite 243 (80%) receiving DMT. In the training set, 17 proteins were specified to predict both outcomes. In the test set, the area under the receiver-operating-characteristic curve (AUC) of the ML model was 0.90 (95% confidence interval 0.84–0.96) for all-cause death and 0.82 (0.73–0.91) for disease progression (Image 1). Each ML model outperformed the conventional models in AUC, classification, and time-dependent AUC (Images 1, 2). The high-risk group in the test set specified by the ML model had a higher event rate than the low-risk group in each outcome (all-cause death, adjusted hazard ratio [aHR] 6.6 [2.5–18.0], P=0.0002; disease progression, aHR 5.7 [2.6–12.4], P<0.0001; Image 3).

Conclusion: This study first demonstrated that plasma proteomics improves the prediction of mortality and disease progression in patients with ATTR-CM in the DMT era.