ArticleAge and ageing2020
Predicting incident delirium diagnoses using data from primary-care electronic health records.
Article in Age and ageing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 3 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
15 citing papers in PubMed, 3 syntheses or guidelines pooled it, 32 citations in OpenAlex.
- Delirium among older adults living at home: a systematic review of prevalence, incidence, and risk factors.The British journal of general practice : the journal of the Royal College of General Practitioners · 2025Pooled it
- The role of diabetes mellitus on delirium onset: a systematic review and meta-analysis.Cardiovascular diabetology · 2025 · on this mapPooled it
- Risk factors and risk stratification approaches for delirium screening: A Geriatric Emergency Department Guidelines 2.0 systematic review.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2024Pooled it
- Identifying delirium in older adults presenting to a primary care out-of-hours (OOH) service: a retrospective cohort study.Age and ageing · 2026Article
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
- The Role of Stress Hyperglycemia on Delirium Onset.Journal of clinical medicine · 2025Review
- Delirium risk and mortality in people with pre-existing severe mental illness: a retrospective cohort study using linked datasets in England.Psychological medicine · 2024Article
- Development and validation of a nomogram to predict postoperative delirium in older patients after major abdominal surgery: a retrospective case-control study.Perioperative medicine (London, England) · 2024Article
- Development and validation of a new drug-focused predictive risk score for postoperative delirium in orthopaedic and trauma surgery patients.BMC geriatrics · 2024Observational
- PM2.5 is linked to Alzheimer's syndrome and delirium: a mendelian randomization analysis.International journal of clinical and experimental pathology · 2024Article
- Identification of risk factors for the onset of delirium associated with COVID-19 by mining nursing records.PloS one · 2024Article
- Increased α-2,6 sialic acid on microglia in amyloid pathology is resistant to oseltamivir.GeroScience · 2023Article
- Incidence and associations of hospital delirium diagnoses in 85,979 people with severe mental illness: A data linkage study.Acta psychiatrica Scandinavica · 2023Article
- The degree of frailty as a translational measure of health in aging.Nature aging · 2021Review
- Delirium: A Marker of Vulnerability in Older People.Frontiers in aging neuroscience · 2021Review
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
Abstract
importancerisk factors for delirium in hospital inpatients are well established, but less is known about whether delirium occurring in the community or during an emergency admission to hospital care might be predicted from routine primary-care records.
objectivesidentify risk factors in primary-care electronic health records (PC-EHR) predictive of delirium occurring in the community or recorded in the initial episode in emergency hospitalisation. Test predictive performance against the cumulative frailty index.
designStage 1: case-control; Stages 2 and 3: retrospective cohort.
settingclinical practice research datalink: PC-EHR linked to hospital discharge data from England. SUBJECTS: Stage 1: 17,286 patients with delirium aged ≥60 years plus 85,607 controls. Stages 2 and 3: patients ≥ 60 years (n = 429,548 in 2015), split into calibration and validation groups.
methodsStage 1: logistic regression to identify associations of 110 candidate risk measures with delirium. Stage 2: calibrating risk factor weights. Stage 3: validation in independent sample using area under the curve (AUC) receiver operating characteristic.
resultsfifty-five risk factors were predictive, in domains including: cognitive impairment or mental illness, psychoactive drugs, frailty, infection, hyponatraemia and anticholinergic drugs. The derived model predicted 1-year incident delirium (AUC = 0.867, 0.852:0.881) and mortality (AUC = 0.846, 0.842:0.853), outperforming the frailty index (AUC = 0.761, 0.740:0.782). Individuals with the highest 10% of predicted delirium risk accounted for 55% of incident delirium over 1 year.
conclusionsa risk factor model for delirium using data in PC-EHR performed well, identifying individuals at risk of new onsets of delirium. This model has potential for supporting preventive interventions.
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