#782 Latent profiles of patients undergoing maintenance hemodialysis based on hemodynamic indicators

Abstract

Abstract Background and Aims Both intradialytic hypotension and intradialytic hypertension are conditions occurring frequently during hemodialysis and are associated with unfavorable clinical outcomes. However, they remain incompletely understood and poorly defined in the literature. Since intradialytic parameters are now captured automatically in electronic medical records in many institutions, machine learning could emerge as an innovative tool for hemodialysis clinical research. Latent Profile Analysis is a model-based clustering method. It allows to identify hidden subpopulations derived from a set of observed indicator variables, maximizing between-group differences and within-group similarities. Method The objectives of this study were to identify latent profiles using a set of hand-crafted indicators and to interpret these subgroups of patients in a clinically meaningful manner. We conducted a retrospective single-center cohort study of adult patients receiving hemodialysis between January 2017 and December 2022 in Montreal. Given the highly heterogeneous nature of this population, we only enrolled incidental patients. The data were extracted from the NephroCare database, which is the clinical information system used by dialysis teams in our center. Sixteen indicators were derived from time series of blood pressure and heart rate measurements, capturing various patient-centric aspects of intradialytic hemodynamics, including trends, zeniths, nadirs, and time-related features. Latent Profile Analysis involved fitting a series of models, and the best model was selected based on fit indices, evaluation metrics, and clinical interpretation of the distribution of indicator within identified profiles. The primary analysis used complete-case data, and a sensitivity analysis assessed the impact of missing data through multiple imputation. Internal model validation was conducted using k-fold cross-validation. Results The selected model consisted of four profiles with varying variance and null covariance across indicators. Fig. 1 presents radar plots summarizing the identified profiles. Profile 1 included patients with prolonged early nadirs and a nadir-to-zenith transition pattern at the session level. Profile 2 was characterized by frequent nadirs and zeniths without a specific timing preference. Profile 3 comprised patients with infrequent blood pressure variations altogether. Profile 4 included patients with frequent early nadirs but no zeniths. The model demonstrated good generalization to unseen cases and exhibited relative robustness to missing data. Conclusion In this preliminary study, we demonstrated that Latent Profile Analysis can identify clinically meaningful subpopulations of hemodialysis patients based on hemodynamic indicators. The association between the identified profiles and hard clinical endpoints remains to be established.