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

Pei-Yan Li; Yu-Wen Huang; Vin-Cent Wu; Jeff S Chueh; Chi-Shin Tseng; Chung-Ming Chen

doi:10.1016/j.artmed.2024.103028

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

Artif Intell Med. 2024 Nov 18:159:103028. doi: 10.1016/j.artmed.2024.103028. Online ahead of print.

Authors

Pei-Yan Li¹, Yu-Wen Huang², Vin-Cent Wu³, Jeff S Chueh⁴, Chi-Shin Tseng⁵, Chung-Ming Chen⁶

Affiliations

¹ Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan. Electronic address: d05548014@ntu.edu.tw.
² Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan. Electronic address: r10528058@ntu.edu.tw.
³ Division of Nephrology, Primary Aldosteronism Center of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan; Primary Aldosteronism Center in National Taiwan University Hospital, TAIPAI (Taiwan Primary Aldosteronism Investigation) Study Group, Taiwan. Electronic address: q91421028@ntu.edu.tw.
⁴ Department of Urology, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan; Primary Aldosteronism Center in National Taiwan University Hospital, TAIPAI (Taiwan Primary Aldosteronism Investigation) Study Group, Taiwan.
⁵ Department of Urology, National Taiwan University Hospital and College of Medicine, National Taiwan University, Taipei, Taiwan.
⁶ Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, Taipei, Taiwan. Electronic address: chung@ntu.edu.tw.

PMID: 39579418
DOI: 10.1016/j.artmed.2024.103028

Abstract

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 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.

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 % ± 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.

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.

Keywords: Adrenalectomy; Computer aided diagnosis; Graph neural network; Prognostics prediction; Unilateral primary aldosteronism.