Li, Pei-YanPei-YanLiHuang, Yu-WenYu-WenHuangVIN-CENT WUSHIH-CHIEH CHUEHCHUNG-MING CHENCHI-SHIN TSENG2024-11-272024-11-272025-01https://scholars.lib.ntu.edu.tw/handle/123456789/723294Predicting postoperative prognosis is vital for clinical decision making in patients undergoing adrenalectomy (ADX). This study introduced GAPPA, a novel GNN-based approach, to predict post-ADX 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. 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. 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 % ± 3.1 %, 71.1 % ± 3.4 %, 69.9 % ± 4.3 %, and 72.4 % ± 7.2 %, respectively, with an AUC of 0.775 ± 0.030. We also investigated the impact of different initialization schemes on GAPPA outcome-edge embeddings, highlighting their robustness and stability. GAPPA aids in preoperative prognosis assessment and facilitates patient counseling, contributing to prognostic prediction and advancing the applications of GNNs in the biomedical domain.enAdrenalectomyComputer aided diagnosisGraph neural networkPrognostics predictionUnilateral primary aldosteronismjournal article10.1016/j.artmed.2024.10302839579418