Quantifying cognitive and mortality outcomes in older patients following acute illness using epidemiological and machine learning approaches

Aug 5, 2026

Cognitive and functional decompensation during acute illness in older adults remains poorly characterized, particularly how delirium is shaped by baseline premorbid cognition and how it drives long-term adverse outcomes. Using longitudinal data from the DELPHIC cohort and routine clinical data from UCLH, the thesis applies machine learning and epidemiological methods to stratify risk, quantify delirium exposure, and evaluate survival and length of stay relationships. The work demonstrates dose-dependent associations and identifies clinically useful acute illness subtypes with prognostic value.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages190

Reading Time7 h 59 min

AccessFree to download

Abstract

Introduction

Methods

DELPHIC longitudinal analysis

Extreme gradient-boosted trees for 600-day mortality

Hierarchical agglomerative clustering for structure

Results

Delirium and cognitive decline/mortality risk

Multimodal machine learning mortality prediction

Acute illness subtypes from longitudinal features

Conclusions

Impact Statement

Quantifying cognitive and mortality outcomes in older patients following acute illness using epidemiological and machine learning approaches Candidate: Dr Alex Tsui number 18164344 PhD Thesis 2022 , Institute of Cardiovascular Sciences, University College London (UCL) Supervisors: Prof Daniel Davis, Prof Nishi Chaturvedi, Prof Parashkev Nachev Declaration I, Alex Chun Kong Tsui, confirm that the work presented in my thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Abstract Introduction Cognitive and functional decompensation during acute illness in older people are poorly understood. It remains unclear how delirium, an acute confusional state reflective of cognitive decompensation, is contextualised by baseline premorbid cognition and relates to long-term adverse outcomes. High-dimensional machine learning offers a novel, feasible and enticing approach for stratifying acute illness in older people, improving treatment consistency while optimising future research design. Methods Longitudinal associations were analysed from the Delirium and Population Health Informatics Cohort (DELPHIC) study, a prospective cohort ≥70 years resident in Camden, with cognitive and functional ascertainment at baseline and 2-year followup, and daily assessments during incident hospitalisation. Second, using routine clinical data from UCLH, I constructed an extreme gradient-boosted trees predicting 600-day mortality for unselected acute admissions of oldest-old patients with mechanistic inferences. Third, hierarchical agglomerative clustering was performed to demonstrate structure within DELPHIC participants, with predictive implications for survival and length of stay. Results: i. Delirium is associated with increased rates of cognitive decline and mortality risk, in a dose-dependent manner, with an interaction between baseline cognition and delirium exposure. Those with highest delirium exposure but also best premorbid cognition have the “most to lose”. ii. High-dimensional multimodal machine learning models can predict mortality in oldest-old populations with 0.874 accuracy. The anterior cingulate and angular gyri, and extracranial soft tissue, are the highest contributory intracranial and extracranial features respectively. iii. Clinically useful acute illness subtypes in older people can be described using longitudinal clinical, functional, and biochemical features. Conclusions Interactions between baseline cognition and delirium exposure during acute illness in older patients result in divergent long-term adverse outcomes. Supervised machine learning can robustly predict mortality in in oldest-old patients, producing a valuable prognostication tool using routinely collected data, ready for clinical deployment. Preliminary findings suggest possible discernible subtypes within acute illness in older people. Impact Statement Acute illness in older people commonly results in decompensation of premorbid function, cognition and increased mortality risk. Delirium, an acute confusional state resulting from cognitive decompensation, is distressing to patients and carers, associated with long-term adverse consequences. Individualised outcome prediction using low-dimensional models in this population group had been inaccurate and unreliable, with poor reproducibility between geographical and healthcare settings. Stratification by clinical presentations with divergent consequential recovery trajectories had not been robustly articulated, with acute illness of older people commonly recognised as a single entity in clinical practice. Academically, these findings first advance the definition of delirium to reflect a longitudinal construct, which can only be fully understood by accounting for nonlinear interactions with premorbid cognition. In addition, delirium exposure should be quantified as a cumulative dose instead consideration as inc

FAQs

What problem does the thesis address about older patients during acute illness?

It examines how cognitive and functional decompensation occur in older adults and how delirium relates to baseline premorbid cognition and long-term adverse outcomes.

Which data sources and methods are used in the study?

The thesis analyzes DELPHIC cohort data with longitudinal cognitive, functional, and daily incident-hospitalization assessments, and it also builds models using routine UCLH clinical data, alongside hierarchical clustering for cohort structure.

What are the main findings regarding delirium and outcomes?

Delirium is associated with higher cognitive decline and mortality risk in a dose-dependent manner, with an interaction between baseline cognition and delirium exposure. Machine learning models can robustly predict mortality in the oldest-old group and support clinically meaningful acute illness subtypes.

Aug 5, 2026

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