Editor’s Perspective What We Already Know about This Topic What This Article Tells Us That Is New Background Postoperative delirium and postoperative cognitive dysfunction share risk factors and may co-occur, but their relationship is not well established. The primary goals of this study were to describe the prevalence of postoperative cognitive dysfunction and to investigate its association with in-hospital delirium. The authors hypothesized that delirium would be a significant risk factor for postoperative cognitive dysfunction during follow-up. Methods This study used data from an observational study of cognitive outcomes after major noncardiac surgery, the Successful Aging after Elective Surgery study. Postoperative delirium was evaluated each hospital day with confusion assessment method–based interviews supplemented by chart reviews. Postoperative cognitive dysfunction was determined using methods adapted from the International Study of Postoperative Cognitive Dysfunction. Associations between delirium and postoperative cognitive dysfunction were examined at 1, 2, and 6 months. Results One hundred thirty-four of 560 participants (24%) developed delirium during hospitalization. Slightly fewer than half (47%, 256 of 548) met the International Study of Postoperative Cognitive Dysfunction-defined threshold for postoperative cognitive dysfunction at 1 month, but this proportion decreased at 2 months (23%, 123 of 536) and 6 months (16%, 85 of 528). At each follow-up, the level of agreement between delirium and postoperative cognitive dysfunction was poor (kappa less than .08) and correlations were small (r less than .16). The relative risk of postoperative cognitive dysfunction was significantly elevated for patients with a history of postoperative delirium at 1 month (relative risk = 1.34; 95% CI, 1.07–1.67), but not 2 months (relative risk = 1.08; 95% CI, 0.72–1.64), or 6 months (relative risk = 1.21; 95% CI, 0.71–2.09). Conclusions Delirium significantly increased the risk of postoperative cognitive dysfunction in the first postoperative month; this relationship did not hold in longer-term follow-up. At each evaluation, postoperative cognitive dysfunction was more common among patients without delirium. Postoperative delirium and postoperative cognitive dysfunction may be distinct manifestations of perioperative neurocognitive deficits.

ORIGINAL RESEARCHwith a model developed using traditional stepwise logistic regression (AUC = 0.69, 95% CI 0.57-0.82). Calibration for all models and feature sets was poor. CONCLUSIONS: We developed machine learning prediction models for post-operative delirium that performed better than chance and are comparable with traditional stepwise logistic regression. Delirium proved to be a phenotype that was difficult to predict with appreciable accuracy.

BACKGROUND/OBJECTIVES Delirium manifests clinically in varying ways across settings. More than 40 instruments currently exist for characterizing the different manifestations of delirium. We evaluated all delirium identification instruments according to their psychometric properties and frequency of citation in published research. DESIGN We conducted the systematic review by searching Cumulative Index to Nursing and Allied Health Literature (CINAHL), Cochrane Library, Excerpta Medica Database (Embase), PsycINFO, PubMed, and Web of Science from January 1, 1974, to January 31, 2020, with the keywords “delirium” and “instruments,” along with their known synonyms. We selected only systematic reviews, meta‐analyses, or narrative literature reviews including multiple delirium identification instruments. MEASUREMENTS Two reviewers assessed the eligibility of articles and extracted data on all potential delirium identification instruments. Using the original publication on each instrument, the psychometric properties were examined using the Consensus‐based Standards for the Selection of Health Measurement Instruments (COSMIN) framework. RESULTS Of 2,542 articles identified, 75 met eligibility criteria, yielding 30 different delirium identification instruments. A count of citations was determined using Scopus for the original publication for each instrument. Each instrument underwent methodological quality review of psychometric properties using COSMIN definitions. An expert panel categorized key domains for delirium identification based on criteria from the Diagnostic and Statistical Manual of Mental Disorders (DSM)‐III through DSM‐5. Four instruments were notable for having at least two of three of the following: citation count of 200 or more, strong validation methodology in their original publication, and fulfillment of DSM‐5 criteria. These were, alphabetically, Confusion Assessment Method, Delirium Observation Screening Scale, Delirium Rating Scale‐Revised‐98, and Memorial Delirium Assessment Scale. CONCLUSION Four commonly used and well‐validated instruments can be recommended for clinical and research use. An important area for future investigation is to harmonize these measures to compare and combine studies on delirium.

BackgroundThis study aimed to describe the level of agreement of three commonly used delirium instruments: the Delirium Rating Scale-Revised-98 (DRS-R-98), Memorial Delirium Assessment Scale (MDAS), and Confusion Assessment Method-Severity (CAM-S).MethodsWe used data from a prospective clinical research study, in which a team of trained lay interviewers administered each instrument along with supporting interview and cognitive assessments in the same group of patients daily while in the hospital (N = 352). We used item response theory methods to co-calibrate the instruments.ResultsThe latent traits underlying the three measures, capturing the severity of a delirium assessment, had a high degree of correlation (r’s > .82). Unidimensional factor models fit well, facilitating co-calibration of the instruments. Across instruments, the less intense symptoms were generally items reflecting cognitive impairment. Although the intensity of delirium severity for most in the sample was relatively low, many of the item thresholds for the delirium severity scales are high (i.e., in the more severe range of the latent ability distribution). This indicates that even people with severe delirium may have a low probability of endorsing the highest severity categories for many items. Co-calibration enabled us to derive crosswalks to map delirium severity scores among the delirium instruments.ConclusionThese delirium instruments measure the same underlying construct of delirium severity. Relative locations of items may inform design of refined measurement instruments. Mapping of overall delirium severity scores across the delirium severity instruments enabled us to derive crosswalks, which allow scores to be translated across instruments, facilitating comparison and combination of delirium studies for integrative analysis.Electronic supplementary materialThe online version of this article (10.1186/s12874-018-0552-4) contains supplementary material, which is available to authorized users.

We created instruments to reliably measure and evaluate the burden of delirium for patients and their family caregivers. Although additional validation is indicated, these instruments provide a key first step toward measuring and improving the subjective experience of delirium for patients and their families.