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Background <p>Defining acute illness decompensation as a single entity limits individualisation of treatments for older patients. Multi-modal and high-dimensional data offer opportunities to derive quantified clusters with clinically meaningful outcomes. We tested the hypothesis that cluster-driven and high-dimensional predictors can be constructed with sufficient fidelity for clinical deployment, concurrently highlighting mechanistic insights into pathophysiological substrates of acute illness decompensation, including where this affected the brain.</p> Methods <p>Two independent prospective cohort studies, DELPHIC and DECIDE, were harmonised and utilised as train and test partitions, contributing 209 and 205 unique first-participant acute admission episodes respectively. Baseline and acute illness variables were projected using T-stochastic neighbour embedding onto a two-dimensional manifold and agglomerative hierarchical clustering designated distance-defined subtypes. Predictive performances of clusters and full models were compared for brain decompensation within admission and 2-year mortality. SHapley Additive exPlanations (SHAPs) quantified directional contributions of inputs towards high-dimensional model performances.</p> Results <p>Three broad clinical subtypes were identified in older people during decompensation, with similar contributions from baseline and acute illness variables. From baseline to cluster-driven, and then high-dimensional prediction models for brain decompensation in admission, the test area under receiver operating characteristic curve (AUROC) improved from 0.563 to 0.641 and 0.797 respectively. Sleep–wake cycle disturbance was the most important predictor of delirium in admission, while physiological fluctuations within an admission episode, in particular from cognitive domains, were significant predictors of long-term mortality after acute admission.</p> Conclusions <p>Robust, generalisable clusters with clinical utility are discernable for older patients during acute illness. Our results demonstrate proof of concept for a longitudinal approach towards defining and modelling acute illness. We illustrate the potential to maximally predict adverse outcomes with high-dimensionality and multi-modality, and highlight the importance of sleep–wake cycle disturbances as a future target in studies of delirium neuropathophysiology.</p>

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Clinical clusters during acute illness predict long-term mortality in older patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月29日

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  • A. Tsui,
  • P. Hogan,
  • H. Cheston,
  • O. Dunne,
  • D. Gardner,
  • A. McWhirter,
  • L. M. Allan,
  • S. Richardson,
  • P. Nachev,
  • D. Davis

摘要

Background

Defining acute illness decompensation as a single entity limits individualisation of treatments for older patients. Multi-modal and high-dimensional data offer opportunities to derive quantified clusters with clinically meaningful outcomes. We tested the hypothesis that cluster-driven and high-dimensional predictors can be constructed with sufficient fidelity for clinical deployment, concurrently highlighting mechanistic insights into pathophysiological substrates of acute illness decompensation, including where this affected the brain.

Methods

Two independent prospective cohort studies, DELPHIC and DECIDE, were harmonised and utilised as train and test partitions, contributing 209 and 205 unique first-participant acute admission episodes respectively. Baseline and acute illness variables were projected using T-stochastic neighbour embedding onto a two-dimensional manifold and agglomerative hierarchical clustering designated distance-defined subtypes. Predictive performances of clusters and full models were compared for brain decompensation within admission and 2-year mortality. SHapley Additive exPlanations (SHAPs) quantified directional contributions of inputs towards high-dimensional model performances.

Results

Three broad clinical subtypes were identified in older people during decompensation, with similar contributions from baseline and acute illness variables. From baseline to cluster-driven, and then high-dimensional prediction models for brain decompensation in admission, the test area under receiver operating characteristic curve (AUROC) improved from 0.563 to 0.641 and 0.797 respectively. Sleep–wake cycle disturbance was the most important predictor of delirium in admission, while physiological fluctuations within an admission episode, in particular from cognitive domains, were significant predictors of long-term mortality after acute admission.

Conclusions

Robust, generalisable clusters with clinical utility are discernable for older patients during acute illness. Our results demonstrate proof of concept for a longitudinal approach towards defining and modelling acute illness. We illustrate the potential to maximally predict adverse outcomes with high-dimensionality and multi-modality, and highlight the importance of sleep–wake cycle disturbances as a future target in studies of delirium neuropathophysiology.