GAPPA: Enhancing prognosis prediction in primary aldosteronism post-adrenalectomy using graph-based modeling

Background and objective: Predicting postoperative prognosis is vital for clinical decision making in patients undergoing adrenalectomy (ADX). This study introduced GAPPA, a novel GNN-based approach, to predict postADX outcomes in patients with unilateral primary aldosteronism (UPA). The objective was to leverage the intricate dependencies between clinico-biochemical features and clinical outcomes using GNNs integrated into a bipartite graph structure to enhance prognostic prediction accuracy. Methods: We conceptualized prognostic prediction as a link prediction task on a bipartite graph, with nodes representing patients, clinico-biochemical features, and clinical outcomes, and edges denoting the connections between them. GAPPA utilizes GNNs to capture these dependencies and seamlessly integrates the outcome predictions into a graph structure. This approach was evaluated using a dataset of 640 patients with UPA who underwent unilateral ADX (uADX) between 1990 and 2022. We conducted a comparative analysis using repeated stratified five-fold cross-validation and paired t-tests to evaluate the performance of GAPPA against conventional machine learning methods and previous studies across various metrics. Results: GAPPA significantly outperformed conventional machine learning methods and previous studies (p < 0.05) across various metrics. It achieved F1-score, accuracy, sensitivity, and specificity of 71.3 % f 3.1 %, 71.1 % f 3.4 %, 69.9 % f 4.3 %, and 72.4 % f 7.2 %, respectively, with an AUC of 0.775 f 0.030. We also investigated the impact of different initialization schemes on GAPPA outcome-edge embeddings, highlighting their robustness and stability. Conclusion: GAPPA aids in preoperative prognosis assessment and facilitates patient counseling, contributing to prognostic prediction and advancing the applications of GNNs in the biomedical domain.