Validation of a consensus method for identifying delirium from hospital records
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Background: Delirium is increasingly considered to be an important determinant of trajectories of cognitive decline. Therefore, analyses of existing cohort studies measuring cognitive outcomes could benefit from methods to ascertain a retrospective delirium diagnosis. This study aimed to develop and validate such a method for delirium detection using routine medical records in UK and Ireland. Methods: A point prevalence study of delirium provided the reference-standard ratings for delirium diagnosis. Blinded to study results, clinical vignettes were compiled from participants’ medical records in a standardised manner, describing any relevant delirium symptoms recorded in the whole case record for the period leading up to case-ascertainment. An expert panel rated each vignette as unlikely, possible, or probable delirium and disagreements were resolved by consensus. Results: From 95 case records, 424 vignettes were abstracted by 5 trained clinicians. There were 29 delirium cases according to the reference standard. Median age of subjects was 76.6 years (interquartile range 54.6 to 82.5). Against the original study DSM-IV diagnosis, the chart abstraction method gave a positive likelihood ratio (LR) of 7.8 (95% CI 5.7–12.0) and the negative LR of 0.45 (95% CI 0.40–0.47) for probable delirium (sensitivity 0.58 (95% CI 0.53–0.62); specificity 0.93 (95% CI 0.90–0.95); AUC 0.86 (95% CI 0.82–0.89)). The method diagnosed possible delirium with positive LR 3.5 (95% CI 2.9–4.3) and negative LR 0.15 (95% CI 0.11–0.21) (sensitivity 0.89 (95% CI 0.85–0.91); specificity 0.75 (95% CI 0.71–0.79); AUC 0.86 (95% CI 0.80–0.89)). Conclusions: This chart abstraction method can retrospectively diagnose delirium in hospitalised patients with good accuracy. This has potential for retrospectively identifying delirium in cohort studies where routine medical records are available. This example of record linkage between hospitalisations and epidemiological data may lead to further insights into the inter-relationship between acute illness, as an exposure, for a range of chronic health outcomes.
© 2014 Kuhn et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Health Care Administration