A graph attention and SAINT-inspired transformer model for predicting postoperative atrial fibrillation: A bidirectional external validation study

Postoperative atrial fibrillation (POAF) is a common surgical complication. We tested whether a two-stage within-patient architecture-graph attention followed by a SAINT-inspired feature Transformer-adds information beyond strong tabular models and transports across healthcare systems.

We used the independent MIMIC-IV and INSPIRE databases. Sixty leakage-controlled scalar features were encoded as tokens for piecewise-linear encoding, graph attention, feature-wise Transformer blocks, and learned pooling. Reciprocal external validation was performed in both directions. Comparators were logistic regression, random forest, XGBoost, LightGBM, and a baseline-only stack. Component ablations, dense versus training-derived sparse graphs, paired bootstrap differences, and efficiency were evaluated.

In Experiment 1 (MIMIC-IV development, INSPIRE external), GAT-SAINT had an external AUC of 0.844 (95% CI, 0.829-0.858), versus 0.839 for logistic regression; the paired difference was 0.005 (95% CI, -0.001 to 0.011). In Experiment 2 (INSPIRE development, MIMIC-IV external), its AUC was 0.791 (95% CI, 0.768-0.813), versus 0.788 for random forest (paired difference, 0.003; 95% CI, -0.008 to 0.014) and 0.797 for the baseline-only stack. External Brier scores were 0.096 and 0.162. The Experiment 2 validation-locked threshold transported poorly (sensitivity, 0.025; specificity, 0.999). Component and graph-sensitivity effects were direction-dependent, and removal of some modules was compatible with little or no external AUC loss.

Reciprocal external validation showed transportable rank discrimination but did not establish a consistent advantage over simpler tabular models or a transportable operating threshold. The architecture warrants further methodological study, but prospective validation, recalibration, and clinical-impact assessment are required before deployment.