Predicting incident delirium diagnoses using data from primary-care electronic health records. (2nd April 2020)

• Record Type: Journal Article
• Authors: Bowman, Kirsty; Jones, Lindsay; Masoli, Jane; Mujica-Mota, Ruben; Strain, David; Butchart, Joe; Valderas, José M; Fortinsky, Richard H; Melzer, David; Delgado, João
• Is Part Of: Age and ageing. Volume 49:Number 3(2020)
• Page Start: 374
• Page End: 381
• Publication Date: 2020-04-02
• Subjects: delirium -- confusion -- at home -- medical records -- predictive model -- older people; Aging -- Periodicals; Geriatrics -- Periodicals; 618.97
• DOI: 10.1093/ageing/afaa006
• Languages: English
• ISSNs: 0002-0729

Abstract

Abstract: Importance: risk 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. Objectives: identify 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. Design: Stage 1: case-control; Stages 2 and 3: retrospective cohort. Setting: clinical 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. Methods: Stage 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. Results: fifty-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. Conclusions: a 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.