Accurate prediction of intradialytic hypo- and hypertension in haemodialysis patients: the dual-attention Transformer model.

Background: Intradialytic hypotension (IDH) and intradialytic hypertension (IDHTN) are frequent haemodialysis complications, each independently linked to increased cardiovascular risk and all-cause mortality. Existing predictive models often fail to capture the irregular nature of real-world haemodialysis data. The study objective was to develop an advanced machine learning architecture specifically designed for irregular time-series analysis of dialysis. Methods: We conducted a retrospective analysis of 1300 haemodialysis patients, encompassing 182 111 sessions and 1 201 323 timestamped observations. Patients were randomly assigned to training (80%), validation (10%) and testing (10%) cohorts. IDH and IDHTN were defined by systolic blood pressure (BP) changes from pre-dialysis baseline: IDH is defined as a systolic BP <90 mmHg, and IDHTN is defined as an increase of ≥10 mmHg in systolic BP without the occurrence of IDH. A Transformer model with dual-attention mechanism was developed for real-time event prediction. Performance was evaluated using area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC) and F1 score. Results: In the whole cohort, the incidence of IDH and IDHTN was 0.79% and 25.60%, respectively. The Transformer model demonstrated robust predictive accuracy in the test cohort, achieving AUROCs of 0.96 for IDH and 0.88 for IDHTN (all Conclusion: Our dual-attention Transformer model enables accurate, real-time prediction of intradialytic BP abnormalities by effectively interpreting complex, asynchronous clinical data.