In this prospective study of cardiac surgery patients delirium was extremely common. We identified four preoperative factors that were independently associated with postoperative delirium: impaired cognition, depressive symptoms, prior stroke or TIA, and abnormal albumin. With these factors, we developed a clinical prediction rule and validated this rule in a separate cohort. The rule performed well in both the derivation (C-statistic=0.74) and validation cohorts (C-statistic=0.75). When applying the risk stratification system, compared to no points, the presence of 1 point more than doubles the delirium risk, 2 points more than triples the delirium risk and the presence of 3 or more points more than quadruples delirium risk.
Our clinical prediction rule has face validity in that three of our risk factors, impaired cognition27
, prior stroke28, 29
, and depressive symptoms30
have been identified in previous studies, but also adds substantial incremental value in that we have integrated these risk factors into a prediction rule that clinicians can use to stratify overall risk. Importantly, the identification of the additional risk factor of abnormal albumin extends the previous work. Albumin level is associated with operative mortality31
and has been hypothesized to be an overall biomarker of frailty, nutritional, and functional abilities.32, 33
In a non-cardiac surgery delirium prediction rule34
, low albumin was associated with delirium, but the high missing data rate precluded its inclusion in the modeling. Additionally, albumin plays an important role in intravascular volume status and drug binding. Thus, when recorded at the time of admission, low albumin may be a laboratory variable associated with lower functional level, as well as affecting hemodynamic shifts and pharmacokinetics of cognitively active drugs.
The cardiac surgery prediction rule for delirium may provide insights into our understanding of the pathophysiology of delirium. Several factors that we identified are potentially associated with central nervous system atherosclerotic disease (prior stroke/TIA, cognitive impairment, depression).35
The two graded categories of cognitive function provides insight into the delirium risk of patients with milder degrees of cognitive impairment who do not meet the traditional dementia threshold (MMSE<24); this concept is consistent with the sub-dementia threshold frequently associated with vascular cognitive impairment. Further, there is an increasing literature on the association of vascular risk and depression.36
Thus, atherosclerosis may be a common risk factor which can predispose patients to delirium.9
Our prediction rule conforms to a widely adopted approach for evaluating delirium risk, which considers predisposing factors (present prior to surgery) and precipitating factors (occur during and after surgery).21, 34
Our overall goal was to develop a preoperative clinical prediction rule based on predisposing factors. However, we also analyzed the additive contribution of intraoperative precipitating factors. The finding that duration of anesthesia was associated with delirium after adjustment for the preoperative prediction rule, could represent worse underlying disease, more complex surgery, and/or additional exposure to anesthetics. We will consider intraoperative and postoperative precipitating factors for delirium in our future work to determine additional delirium prevention strategies. As based on previous studies, prevention of delirium in high risk patients would focus on environmental modifications, early mobilization, psychoactive medication reduction, and prevention of complications.6, 7
The rate of delirium in our study is at the higher end of published reports. There are several reasons for this. First, our study used state-of-the-art delirium detection methods including a standardized assessment which was delivered daily. This standardized delirium battery includes assessments for attention impairment which may not be identified in a routine clinical interview. The incidence of delirium after cardiac surgery varies widely (2-73%).2
Studies utilizing a standardized battery37, 38
have found a higher incidence of delirium than studies that assess delirium via chart review or nursing report.39
Second, older age can be a risk factor for postoperative delirium28-30, 34
and this study enrolled patients over age 60 resulting in a mean age of 73 years, which is older than other studies of delirium after cardiac surgery and reflects the trend toward operating on older patients.23
Finally, we included patients regardless of preoperative cognitive function. Thus, we likely have patients with vascular cognitive impairment, mild cognitive impairment, and/or dementia, who are much more likely to develop delirium.
There are several strengths to this study that warrant mention. First, the study derived and validated the prediction rule in independent cohorts at more than one medical center. The study included patients undergoing elective or urgent cardiac surgical procedures. Emergency patients would likely have a higher delirium rate, but were not included because of an inability to obtain a preoperative baseline interview. Additionally, we aggressively identified and verified preoperative characteristics that were included in the model to ensure accurate risk factor identification. MMSE and GDS took about 15 minutes to administer before surgery and were performed by trained research assistants. Additionally, the model performed similarly after excluding “off-pump” patients. Finally, our analytic approach combined data augmentation via multiple imputation and bootstrap resampling procedures for deriving the independent predictors included in the clinical prediction rule. This advanced statistical methodology may provide more stable variables for prediction-validation rules by minimizing the impact of missing data, limiting model over-fitting, and is superior to listwise deletion or regression to the mean.40, 41
There are several limitations which need to be described. First, our population consisted of patients who were mostly white and well educated (>50% with education beyond high school) and recruited at academic medical centers in a single geographical region. MMSE performance may be improved with increased education, but in this study, education was not associated with delirium. This may limit generalizibility to less educated populations, but internal validity should not be challenged. There was variability in the derivation and validation cohorts specifically in characteristics which were included in the model such as age, GDS, and albumin. However, the differences in these variables in the validation cohort bias toward the null with respect to delirium risk (lower age, less depressive symptoms, higher incidence of normal albumin, etc), and yet, the overall model performance showed no degradation between the derivation and validation cohorts. We were unable to measure all preoperative characteristics, such as carotid stenosis, which may predispose to delirium.9
Finally, the lowest risk in our prediction rule is 18-19%, which limits our ability to identify patients who might be excluded from interventions. The prediction rule performed better at predicting higher levels of delirium risk.
This study identified four cogent risk factors for delirium: MMSE, prior stroke or TIA, depression, and abnormal albumin. Delirium risk more than quadruples moving from the lowest to highest risk levels. Clinically, patients who are stratified into moderate and high risk for delirium categories would benefit from frequent delirium screening and implementation of delirium prevention strategies. Importantly, this cardiac surgery delirium prediction rule provides a method to preoperatively stratify at-risk older patients for such interventions to ultimately reduce the morbidity, mortality, and cost of postoperative delirium.