Risk prediction models for delirium in the intensive care unit after cardiac surgery: a systematic review and independent external validation

Abstract

Numerous risk prediction models are available for predicting delirium after cardiac surgery, but few have been directly compared with one another or been validated in an independent data set. We conducted a systematic review to identify validated risk prediction models of delirium (using the Confusion Assessment Method-Intensive Care Unit tool) after cardiac surgery and assessed the transportability of the risk prediction models on a prospective cohort of 600 consecutive patients undergoing cardiac surgery at a university hospital in Hong Kong from July 2013 to July 2015. The discrimination (c-statistic), calibration (GiViTI calibration belt), and clinical usefulness (decision curve analysis) of the risk prediction models were examined in a stepwise manner. Three published high-quality intensive care unit delirium risk prediction models (n=5939) were identified: Katznelson, the original PRE-DELIRIC, and the international recalibrated PRE-DELIRIC model. Delirium occurred in 83 patients (13.8%, 95% CI: 11.2-16.9%). After updating the intercept and regression coefficients in the Katznelson model, there was fair discrimination (0.62, 95% CI: 0.58-0.66) and good calibration. As the original PRE-DELIRIC model was already validated externally and recalibrated in six countries, we performed a logistic calibration on the recalibrated model and found acceptable discrimination (0.75, 95% CI: 0.72-0.79) and good calibration. Decision curve analysis demonstrated that the recalibrated PRE-DELIRIC risk model was marginally more clinically useful than the Katznelson model. Current models predict delirium risk in the intensive care unit after cardiac surgery with only fair to moderate accuracy and are insufficient for routine clinical use.

The effect of dexmedetomidine on delirium and agitation in patients in intensive care: systematic review and meta‐analysis with trial sequential analysis

“…The variable incidence is due to: different patient baseline characteristics; types of ICU (medical, surgical or both); illness severity; screening tools for diagnosis of delirium; and concomitant medications . Several international studies have suggested a strong association between delirium and short‐term/long‐term mortality . In keeping with previous observational studies , the incidence of delirium in our review was 18.5% in the control group and 7.6% in the dexmedetomidine group, the difference being statistically significant.…”

“…The incidence of delirium varies widely from 16.1% to 83.3% in ICU patients, especially among those undergoing mechanical ventilation (60–80%) . The variable incidence is due to: different patient baseline characteristics; types of ICU (medical, surgical or both); illness severity; screening tools for diagnosis of delirium; and concomitant medications . Several international studies have suggested a strong association between delirium and short‐term/long‐term mortality .…”

Network for Investigation of Delirium across the U.S.: Advancing the Field of Delirium with a New Interdisciplinary Research Network

“…Moreover, delirium is associated with excess healthcare costs of more than $164 billion per year . Delirium can be difficult to detect and manage and is receiving greater attention in the medical literature …”

“…1,2 Delirium can be difficult to detect and manage and is receiving greater attention in the medical literature. [3][4][5] A number of factors, including cognitive impairment, infection, dehydration, and psychoactive medications, are predictive of delirium. 2 In most cases, delirium is multifactorial, which makes finding effective approaches for prevention and treatment highly challenging and research studies methodologically complex.…”

Evaluating mechanisms of postoperative delirium and cognitive dysfunction following elective spine surgery in elderly patients (CONFESS): study protocol for a prospective observational trial

“…Knowledge of, particularly modifiable, risk factors for POD and POCD is therefore imperative to enhance informed patient consent, adjust anesthetic and surgical strategies to individual risk profiles and facilitate appropriate postoperative monitoring (30). Numerous risk prediction models have been developed, yet recent studies highlight that a general application of these models in clinical routine is limited, not least because trajectories of cognitive decline are not independent of the type of surgery (20,24,31,32). For example, patients who exhibited POCD following cardiac surgery improved cognitive function after one year compared to their baseline level, which contradicts results from mixed surgical populations (24,32).…”