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Observational Study
. 2025 Jul;21(7):e70516.
doi: 10.1002/alz.70516.

Identifying subphenotypes of patients undergoing post-operative delirium assessment

Emily Margaret Louise Bowman  1   2   3 , Daniel F McAuley  2 , Bernadette McGuinness  1 , Anthony P Passmore  1 , David Beverland  4 , Henrik Zetterberg  5   6   7   8   9   10 , Jonathan M Schott  5   11 , Amanda Heslegrave  5   6 , Elena Veleva  5 , Rhiannon Laban  5 , Aoife Sweeney  1 , Emma L Cunningham  1
Affiliations
Observational Study

Identifying subphenotypes of patients undergoing post-operative delirium assessment

Emily Margaret Louise Bowman et al. Alzheimers Dement. 2025 Jul.

Abstract

Introduction: Delirium has heterogeneous etiologies and clinical presentations and is often associated with poor outcomes. Its pathophysiological mechanisms remain largely hypothetical and without targeted pharmacological treatment. This work investigates subphenotypes of patients undergoing delirium assessment based on clinical features and fluid biomarkers.

Methods: We performed latent class analysis of an observational cohort of older adults undergoing elective surgery.

Results: Two classes were identified, both containing individuals experiencing delirium symptoms, with a higher number in Class 1 (p < 0.001). Class 1 were older, less educated, and had more depression (p < 0.001). They performed worse in all pre-operative cognitive assessments (p < 0.001) and had more markers of central nervous system damage: cerebrospinal fluid glial fibrillary acidic protein, neurofilament light chain, and soluble triggering receptor expressed on myeloid cells 2 (p < 0.001); plasma phosphorylated tau (p = 0.024); and amyloid beta 42/40 ratio (p < 0.001). Class 2 experienced more pain (p = 0.006) and received more morphine equivalents (p = 0.018).

Discussion: Delirium and neighboring phenotypes should be investigated thoroughly in the newly dawning era of precision medicine, to establish novel treatments.

Highlights: Latent class analysis identified two subphenotypes of patients. Both groups contained patients with delirium or its individual symptoms. Groups differed by age, education, depression, independent living, and pain levels. Groups differed by pre-operative and post-operative cognition. Groups differed by biomarker levels of neurodegeneration and neuronal injury.

Keywords: altered consciousness; biomarkers; cognition; cognitive change; delirium; endotypes; glial fibrillary acidic protein; inattention; latent class analysis; machine learning; neurofilament light chain; phenotyping; post‐operative; post‐operative delirium; subphenotypes; unsupervised clustering.

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Conflict of interest statement

E.L.C. has received grant funding from Alzheimer's Research UK. H.Z. is a Wallenberg Scholar and a Distinguished Professor at the Swedish Research Council supported by grants from the Swedish Research Council (#2023‐00356, #2022‐01018 and #2019‐02397), the European Union's Horizon Europe research and innovation programme under grant agreement No 101053962, Swedish State Support for Clinical Research (#ALFGBG‐71320), the National Institute for Health and Care Research University College London Hospitals Biomedical Research Centre, and the UK Dementia Research Institute at UCL (UKDRI‐1003). All other authors declare that they have no competing interests. Author disclosures are available in the supporting information.

Figures

FIGURE 1
FIGURE 1
A mosaic plot displaying the distribution of occurrences of individual delirium symptoms (inattention and altered consciousness) and full delirium across the two latent classes.
FIGURE 2
FIGURE 2
A line graph of the two identified classes or subphenotypes, separated by the mean z score of each continuous variable. LCA, latent class analysis
FIGURE 3
FIGURE 3
Bar charts showing the difference in the proportion of participants falling into each category between the two classes. ALTCON, Altered Level of Consciousness; ASA, American Society of Anesthesiologists Physical Status Classification System; CAM, Confusion Assessment Method; CAM/Rep, Confusion Assessment Method and reports; DIAB, diabetes; HYPERTEN, hypertension; INATTEN, inattention; INTPENT, Intersecting Pentagons; SURG, Type of Surgery; ThreeSC, Three Step Command.
See this image and copyright information in PMC

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