Clinical kidney journal · 2026
Identification of intradialytic hemodynamic phenotypes using latent profile analysis.
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Baroz Frédéric, Suri Rita S, Sharma Abhinav, Tom Alexander, Takano Tomoko, Brophy James M, Beaubien-Souligny William, Mavrakanas Thomas A
Аннотация
BACKGROUND: Intradialytic blood pressure variations are frequent and associated with poor clinical outcomes. However, the use of arbitrary, inconsistent diagnostic definitions limits cross-study comparability and hinders the development of evidence-based knowledge guiding management. This study aimed to bypass rigid, threshold-based diagnostic criteria using latent profile analysis, an unsupervised clustering method, to identify data-driven hemodynamic phenotypes in incident hemodialysis patients and explore their prognostic utility.
METHODS: We applied latent profile analysis to intradialytic hemodynamic time-series data from a retrospective cohort of incident maintenance hemodialysis patients from 2017 to 2022. Sixteen patient-level indicators capturing baseline hemodynamics were manually engineered. We established internal validity via cross-validation, identified baseline clinical predictors of profile membership, and assessed prognostic utility against standard diagnostic definitions using Cox proportional hazards models.
RESULTS: A four-profile model was selected based on fit diagnostics and clinical interpretation and demonstrated cross-validation robustness. Profile 1 exhibited early nadirs with a late-session recovery. Profile 2 displayed a highly oscillatory pattern. Profile 3 demonstrated remarkable hemodynamic stability. Profile 4 featured frequent, unrecovered early nadirs. While results from the survival analyses were non-significant, profiles yielded divergent point estimates and demonstrated comparable discrimination and statistical fit to standard definition-based models.
CONCLUSIONS: Unsupervised clustering effectively uncovers distinct hemodynamic phenotypes, bypassing the limitations of rigid diagnostic thresholds. Data-driven profiling provides a rigorous taxonomy to improve personalized clinical decision-making and reduce heterogeneity in future interventional studies.
METHODS: We applied latent profile analysis to intradialytic hemodynamic time-series data from a retrospective cohort of incident maintenance hemodialysis patients from 2017 to 2022. Sixteen patient-level indicators capturing baseline hemodynamics were manually engineered. We established internal validity via cross-validation, identified baseline clinical predictors of profile membership, and assessed prognostic utility against standard diagnostic definitions using Cox proportional hazards models.
RESULTS: A four-profile model was selected based on fit diagnostics and clinical interpretation and demonstrated cross-validation robustness. Profile 1 exhibited early nadirs with a late-session recovery. Profile 2 displayed a highly oscillatory pattern. Profile 3 demonstrated remarkable hemodynamic stability. Profile 4 featured frequent, unrecovered early nadirs. While results from the survival analyses were non-significant, profiles yielded divergent point estimates and demonstrated comparable discrimination and statistical fit to standard definition-based models.
CONCLUSIONS: Unsupervised clustering effectively uncovers distinct hemodynamic phenotypes, bypassing the limitations of rigid diagnostic thresholds. Data-driven profiling provides a rigorous taxonomy to improve personalized clinical decision-making and reduce heterogeneity in future interventional studies.