Use of the brief Confusion Assessment Method in a veteran palliative care population: A pilot validation study

Development and validation of delirium prediction models for noncardiac surgery patients

STUDY OBJECTIVE

Postoperative delirium is associated with morbidity and mortality, and its incidence varies widely. Using known predisposing and precipitating factors, we sought to develop postoperative delirium prediction models for noncardiac surgical patients.

DESIGN

Retrospective prediction model study.

SETTING

Major quaternary medical center.

PATIENTS

Our January 2016 to June 2020 training dataset included 51,677 patients of whom 2795 patients had delirium. Our July 2020 to January 2022 validation dataset included 14,438 patients of whom 912 patients had delirium.

INTERVENTIONS

None.

MEASUREMENTS

We trained and validated two static prediction models and one dynamic delirium prediction model. For the static models, we used random survival forests and traditional Cox proportional hazard models to predict postoperative delirium from preoperative variables, or from a combination of preoperative and intraoperative variables. We also used landmark modeling to dynamically predict postoperative delirium using preoperative, intraoperative, and postoperative variables before onset of delirium.

MAIN RESULTS

In the validation analyses, the static random forest model had a c-statistic of 0.81 (95% CI: 0.79, 0.82) and a Brier score of 0.04 with preoperative variables only, and a c-statistic of 0.86 (95% CI: 0.84, 0.87) and a Brier score of 0.04 when preoperative and intraoperative variables were combined. The corresponding Cox models had similar discrimination metrics with slightly better calibration. The dynamic model - using all available data, i.e., preoperative, intraoperative and postoperative data - had an overall c-index of 0.84 (95% CI: 0.83, 0.85).

CONCLUSIONS

Using preoperative and intraoperative variables, simple static models performed as well as a dynamic delirium prediction model that also included postoperative variables. Baseline predisposing factors thus appear to contribute far more to delirium after noncardiac surgery than intraoperative or postoperative variables. Improved postoperative data capture may help improve delirium prediction and should be evaluated in future studies.

Association of Intravenous Neostigmine and Anticholinergics or Sugammadex with Postoperative Delirium: A Retrospective Cohort Study

BACKGROUND

Administration of cholinesterase inhibitors in combination with anticholinergic drugs for reversal of neuromuscular blocks may precipitate delirium through impairment of central cholinergic transmission, which could be avoided by using sugammadex. Therefore, we tested the primary hypothesis that postoperative delirium is less common when neuromuscular block is reversed with sugammadex than with neostigmine combined with glycopyrrolate or atropine.

METHODS

We conducted a single-center retrospective cohort study, analyzing all adult patients having general anesthesia for noncardiac surgery who received neostigmine or sugammadex from January 2016 to March 2022. Inverse propensity score weighting and propensity score calibration were used to adjust for appropriate confounders. Our primary outcome was presence of delirium within the first 4 days after surgery, defined as at least 1 positive brief Confusion Assessment Method (bCAM) screening. The secondary outcome was the presence of early delirium within 24 hours of surgery.

RESULTS

Among 49,468 cases in our analysis, 6881 received sugammadex and 42,587 received neostigmine. After propensity weighting, the incidence of delirium was 1.09% in the sugammadex group and 0.82% in the neostigmine group. The odds of postoperative delirium did not differ between the sugammadex and neostigmine groups, with an estimated odds ratio (95% confidence interval) of 1.33 (0.91-1.95), P = .147. A sensitivity analysis restricted to only include cases with at least 6 bCAM measurements over postoperative day (POD) 1 to 4 had consistent results, as sugammadex compared with neostigmine was associated with an estimated odds ratio for postoperative delirium of 1.20 (0.82-1.77), P = .346. Sugammadex was significantly associated with an increased incidence of early postoperative delirium, with an estimated odds ratio of 1.71 (1.07-2.72), P = .025. Further analysis showed no treatment-by-age interaction for either postoperative delirium (P = .637) or postoperative early delirium (P = .904).

CONCLUSIONS

Compared to neostigmine, use of sugammadex for reversal of neuromuscular block was not associated with an increased risk of postoperative delirium in this retrospective single-center study. Though sugammadex was associated with a statistically significant increased risk of postoperative early delirium, the difference was small and not clinically relevant, and may reflect the presence of unknown confounders.

Diagnostic accuracy of the 3-minute diagnostic interview for confusion assessment method-defined delirium in delirium detection: a systematic review and meta-analysis

BACKGROUND

Delirium is a common complication clinically and is associated with the poor outcomes, yet it is frequently unrecognised and readily disregarded. Although the 3-minute diagnostic interview for confusion assessment method-defined delirium (3D-CAM) has been used in a variety of care settings, a comprehensive evaluation of its accuracy in all available care settings has not been performed.

OBJECTIVE

This study aimed to evaluate the diagnostic test accuracy of the 3D-CAM in delirium detection through a systematic review and meta-analysis.

METHODS

We systematically searched PubMed, EMBASE, the Cochrane Library, Web of Science, CINAHL (EBSCO) and ClinicalTrials.gov published from inception to 10 July 2022. The quality assessment of the diagnostic accuracy studies-2 tool was applied to evaluate methodological quality. A bivariate random effects model was used to pool sensitivity and specificity.

RESULTS

Seven studies with 1,350 participants and 2,499 assessments were included, which were carried out in general medical wards, intensive care units, internal medical wards, surgical wards, recovery rooms and post-anaesthesia care units. The prevalence of delirium ranged from 9.1% to 25%. The pooled sensitivity and specificity were 0.92 (95% confidence interval [CI] 0.87-0.95) and 0.95 (95% CI 0.92-0.97), respectively. The pooled positive likelihood ratio was 18.6 (95% CI 12.2-28.2), the negative likelihood ratio was 0.09 (95% CI 0.06-0.14) and the diagnostic odds ratio was 211 (95% CI 128-349). Moreover, the area under the curve was 0.97 (95% CI 0.95-0.98).

CONCLUSIONS

The 3D-CAM has good diagnostic accuracy for delirium detection in different care settings. Further analyses illustrated that it had comparable diagnostic accuracy in older adults and patients with dementia or known baseline cognitive impairment. In conclusion, the 3D-CAM is recommended for clinical delirium detection.

Validation of the 4AT tool for delirium assessment in specialist palliative care settings: protocol of a prospective diagnostic test accuracy study [version 1; peer review: 2 approved]

BACKGROUND

Delirium is a serious and distressing neuropsychiatric condition, which is prevalent across all palliative care settings. Hypoactive delirium is particularly common, but difficult to recognize, partly due to overlapping symptoms with depression and dementia. Delirium screening tools can lead to earlier identification and hence better management of patients. The 4AT (4 'A's Test) is a brief tool for delirium detection, designed for use in clinical practice. It has been validated in 17 studies in over 3,700 patients. The test is currently used in specialist palliative care units, but has not been validated in this setting. The aim of the study is to determine the diagnostic accuracy of the 4AT for delirium detection against a reference standard, in hospice inpatients.

METHODS

240 participants will be recruited from the inpatient units of two hospices in Scotland. If a patient lacks capacity to consent, agreement will be sought from a legal proxy. Each participant will complete the 4AT and a reference standard assessment based on the diagnostic delirium criteria in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). This will be supplemented by tests of cognition and attention, including reverse days of the week, counting down from 20 to 1, Vigilance 'A', the Observational Scale for Level of Arousal, the modified Richmond Agitation Sedation Scale and the Delirium Rating Scale-Revised-98. The assessments will be conducted in a randomized order by two independent clinicians, who will be blinded to the results until both are complete. Primary outcomes will be the sensitivity and specificity of the 4AT in detecting delirium.

DISCUSSION

The findings will inform clinical practice regarding delirium assessment in palliative care settings.

TRIAL REGISTRATION

ISRCTN ISRCTN97417474 (21/02/2020).

Delirium screening tools validated in the context of palliative care: A systematic review

BACKGROUND

Delirium is a distressing neuropsychiatric disorder affecting patients in palliative care. Although many delirium screening tools exist, their utility, and validation within palliative care settings has not undergone systematic review.

AIM

To systematically review studies that validate delirium screening tools conducted in palliative care settings.

DESIGN

Systematic review with narrative synthesis (PROSPERO ID: CRD42019125481). A risk of bias assessment via Quality Assessment Tool for Diagnostic Accuracy Studies-2 was performed.

DATA SOURCES

Five electronic databases were systematically searched (January 1, 1982-May 3, 2020). Quantitative studies validating a screening tool in adult palliative care patient populations were included. Studies involving alcohol withdrawal, critical or perioperative care were excluded.

RESULTS

Dual-reviewer screening of 3749 unique titles and abstracts identified 95 studies for full-text review and of these, 17 studies of 14 screening tools were included (n = 3496 patients). Data analyses revealed substantial heterogeneity in patient demographics and variability in screening and diagnostic practices that limited generalizability between study populations and care settings. A risk of bias assessment revealed methodological and reporting deficits, with only 3/17 studies at low risk of bias.

CONCLUSIONS

The processes of selecting a delirium screening tool and determining optimal screening practices in palliative care are complex. One tool is unlikely to fit the needs of the entire palliative care population across all palliative care settings. Further research should be directed at evaluating and/or adapting screening tools and practices to fit the needs of specific palliative care settings and populations.