| Title: |
Predicting incident delirium diagnoses using data from primary-care electronic health records |
| Authors: |
Bowman, Kirsty; Jones, Lindsay; Masoli, Jane; Mujica-Mota, Ruben; Strain, David; Butchart, Joe; Valderas, José M; Fortinsky, Richard H; Melzer, David; Delgado, João |
| Contributors: |
National Institute for Health Research; Research for Patient Benefit Programme |
| Source: |
Age and Ageing ; volume 49, issue 3, page 374-381 ; ISSN 0002-0729 1468-2834 |
| Publisher Information: |
Oxford University Press (OUP) |
| Publication Year: |
2020 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| Language: |
English |
| DOI: |
10.1093/ageing/afaa006 |
| Availability: |
https://doi.org/10.1093/ageing/afaa006; http://academic.oup.com/ageing/article-pdf/49/3/374/33394332/afaa006.pdf |
| Rights: |
http://creativecommons.org/licenses/by-nc/4.0/ |
| Accession Number: |
edsbas.8A72F4EF |
| Database: |
BASE |