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Neuroscience and Neuroanesthesiology

The Association Between Preoperative Frailty and Postoperative Delirium After Cardiac Surgery

Brown, Charles H. IV MD, MHS*; Max, Laura BA*; LaFlam, Andrew BS*; Kirk, Lou MD*; Gross, Alden PhD; Arora, Rakesh MD, PhD; Neufeld, Karin MD, MPH§; Hogue, Charles W. MD*; Walston, Jeremy MD; Pustavoitau, Aliaksei MD, MHS*

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doi: 10.1213/ANE.0000000000001271
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Delirium after cardiac surgery is a common1 and costly complication that increases the risk of morbidity,2 mortality,3 and cognitive decline.4 Effective treatment of delirium is difficult,5 so prevention is critical. However, widespread prevention efforts in low-risk patients may have negative consequences, including increased resource utilization5 or medication side effects.6 Thus identifying high-risk patients is critical to target delirium-prevention strategies.

Frailty assessment is commonly used to characterize the most vulnerable subset of older adults with decreased reserve across multiple organ systems.7 The most widely used physiologic definition of frailty (Fried criteria)8 incorporates assessment of gait, strength, weight loss, exhaustion, and activity and is thought to identify older adults at elevated risk for poor outcomes. In community-based cohort studies, frailty has been associated with falls,9 disability,10 institutionalization,11 and death,11 whereas after surgery, frailty has been associated with increased postoperative complications and length of stay.12 Since delirium is thought to result from the interplay of an acute insult on a vulnerable patient,5 we hypothesized that patients with preoperative frailty (a validated geriatric marker of patient vulnerability) would be at higher risk for delirium after cardiac surgery.



This study was approved by the Johns Hopkins IRB (Baltimore, MD), and written informed consent was obtained from all participants.


This prospective observational pilot study enrolled patients between July 2010 and April 2013 at a tertiary care hospital. Inclusion criteria were age >55 years old and undergoing only coronary artery bypass graft (CABG) surgery. Exclusion criteria were emergency surgery, reoperations, acute illness besides coronary disease, or preoperative inotropic/vasoactive medication usage. Sample size was initially calculated for the primary question of demonstrating a difference in length of stay based on serum levels of a protein active in cellular senescence. As shown in Appendix 1, during enrollment, 262 patients were screened, 178 patients were eligible, 60 patients enrolled, and 55 patients completed the study (3 withdrew, 1 had unplanned valve surgery, and 1 had no staff availability).

Frailty Assessment

Trained research coordinators performed frailty assessments during the preoperative clinic visit (median, 1 day; range, 1–14 days before surgery) using the well-validated scale from Fried et al8 (Appendix 2) evaluating 5 domains: (1) shrinking, defined as unintentional weight loss of ≥10 pounds in the last year; (2) weakness, determined by grip strength, adjusted for gender and body mass index; (3) exhaustion, determined by 2 questions from the modified 10-item Center for Epidemiological Studies-Depression scale13; (4) low physical activity, determined by the modified Minnesota Leisure Time Activities Questionnaire14; and (5) slowed walking speed, as measured at normal pace over 15 feet. Each domain yielded a score of 0 or 1 based on cutoffs previously described,8 with frail patients defined as a total score of ≥3. Patients with scores of 0 (nonfrail) and 1 to 2 (potentially prefrail) were categorized together as the nonfrail group.

Outcome Assessment

Delirium was assessed using a rigorous and validated chart review method, which was the most validated method feasible at the time of this study.15 This method has the advantage of allowing assessment of the entire hospital course16 (including nights and weekends of an often long hospitalization [median, 6 days; range, 27 days in this sample]). It likely preferentially identifies hyperactive delirium,16 the subtype that has been associated with danger to self,17 more falls,18 and worse outcomes in some studies.19 According to the methodology proposed by Inouye et al,15 a research abstractor (trained in delirium assessment by a psychiatrist with recognized expertise in delirium) searched all sections of the entire medical record for any mention of key terms, with evidence of acute onset or change, to support a delirium diagnosis based on the following question: “Is there any evidence from the chart of acute confusional state (eg, delirium, mental status change, inattention, disorientation, hallucinations, agitation, inappropriate behavior, etc)?” These data were recorded on a specific data form. A 3-person panel with active involvement/training in delirium research reviewed abstractions to determine the final diagnosis. Other organ-specific complications were assessed using Society for Thoracic Surgeons database criteria,a with a composite outcome defined as any neurologic, cardiac, pulmonary, renal, infectious, or vascular complication.


Table 1.
Table 1.:
Complications Among Frail and Nonfrail Patients After Cardiac Surgerya

Demographics, comorbidities, medications, and functional capacity information were collected at baseline. Postoperative complications were prospectively collected, as described in Table 1.

Data Analysis

Statistical analyses were conducted using Stata version 12 (StataCorp, College Station, TX). There were no missing exposure/outcome data or loss to follow-up. Patient and surgical characteristics were compared using Student t tests, rank sum tests (for non-normally distributed data), χ2 tests, and Fisher exact tests (for comparisons with event frequency ≤5). Generalized linear models using the log link and Poisson distribution were used to determine the association between frailty and delirium. Age, stroke, depression, and Charlson score were a priori included in the final model based on potential associations with frailty11,12 and delirium.1 We also conducted a sensitivity analysis using propensity scores incorporated into the main regression model.20 Specifically, we calculated a propensity score using logistic regression to predict frailty status based on age, sex, alcohol, activities of daily living, instrumental activities of daily living, peripheral vascular disease, cerebrovascular disease, depression, albumin, and Charlson score.


Among 55 enrolled patients, 17 (30.9%) were frail. As shown in Table 2, frail patients were generally older, more likely to have ever smoked, had a higher prevalence of steroid and statin use, and had higher baseline Charlson scores compared with nonfrail patients. After surgery, frail patients had a higher incidence of composite complications and intensive care unit length of stay (Table 1).

Table 2.
Table 2.:
Baseline Patient Characteristicsa
Table 3.
Table 3.:
Risk of Delirium Among Frail Compared With Nonfrail Patients in Adjusted Models
Figure 1.
Figure 1.:
The incidence of delirium after cardiac surgery among frail compared with nonfrail patients. Among 38 nonfrail patients, 1 patient developed delirium, whereas among 17 frail patients, 8 patients developed delirium.

Nine patients (16.4%) developed postoperative delirium. As shown in Figure 1, the incidence of delirium was higher in frail compared with nonfrail patients (47.1% vs 2.6%; P < 0.001). In a multivariable model adjusted for age, prior stroke, depression, and quintile of Charlson score (Table 3), the risk of delirium was increased by >2.1-fold in frail compared with nonfrail patients (relative risk [RR], 18.3; 95% confidence interval [CI], 2.1–161.8; P = 0.01). A similar result was seen in propensity score–adjusted models (RR, 15.8; 95% CI, 1.8–137.2; P = 0.012). In addition, inferences were unchanged if all covariates associated with frailty listed in Table 2 at a significance level of P value <0.05 were added to the multivariable model. Among individual domains of frailty in the multivariable model, only weight loss (RR, 8.3; 95% CI, 1.8–37.2; P = 0.01) was associated with delirium.


We identified frailty in 30.9% of patients presenting for CABG surgery, with a significantly higher incidence of delirium among frail compared with nonfrail patients.

Our results extend the results of 2 prior studies with similar findings. The first study was limited to noncardiac surgery,21 in which patients had different baseline comorbidities and perioperative insults, so the results may not have been generalizable to cardiac surgery. The second well-done study22 in cardiac surgery patients used (among other frailty definitions) a modified version of the well-validated Fried frailty scale8 and included low cognitive status and depressed mood as frailty criteria. Although the modified criteria have not been as well validated as the original Fried criteria, the inclusion of cognitive and mood questions may have strengthened the observed association between frailty and delirium. The results of our study demonstrate that the physical decline captured by the original validated Fried criteria is in and of itself associated with an increased risk for delirium after cardiac surgery. However, because we did not administer formal cognitive testing, we cannot characterize the exact role of cognition in modifying or mediating the frailty/delirium association.

Although the mechanism for our findings is unclear, geriatricians view frailty as a syndrome characterized by reduced ability to withstand stressors,7 and delirium may be one manifestation of this. From the pathophysiologic perspective, a leading hypothesis for the etiology of delirium is neuroinflammation,5,23 which can occur both peripherally and centrally after surgery, with the central component perhaps predominant.24–26 Frail patients have increased peripheral inflammation at baseline,27 which may be heightened by an inflammatory insult such as cardiopulmonary bypass. In the setting of a potentially disrupted blood-brain barrier,28 resulting neuroinflammation may increase the risk for delirium in frail patients. Indeed in mouse models, the neurologic effects of inflammation have been shown to be greater in mice with preexisting neurodegenerative disease.29 Alternatively, the central neuroinflammatory response may be greater in frail patients although this hypothesis has not been tested. Although reducing neuroinflammation may be a potential avenue for prevention or treatment of delirium, a 2014 trial30 of the antiinflammatory steroid dexamethasone administered before cardiac surgery did not show any benefit for delirium prevention, with the caveat that steroid administration itself may be a risk factor for delirium.

There are several important limitations of this study. First, we used a validated delirium assessment method, but compared with the well-known Confusion Assessment Method,31 the sensitivity has been shown to be 74% and specificity 83%.15 With an incidence of delirium in our study of 16.4%, misclassification error may be present although the error would need to be quite large to obviate the results of this study. Second, our findings may only reflect hyperactive delirium that is clinically apparent on chart review, not hypoactive delirium, which can be more difficult to diagnose but is also associated with increased mortality.32 Third, the small sample size predisposes to wide CIs and limits inclusion of potentially important confounding variables in our analysis. We did use propensity score adjustment, but a larger sample size would allow for more precise estimates. Many eligible patients were not approached or refused consent, thus introducing the risk of selection bias and limiting generalizability of the results. Nonetheless, the findings of this study are provocative in showing a link between preoperative frailty and risk for postoperative delirium. Further studies are needed to confirm this association using in-person delirium assessments as well as to examine whether a targeted delirium-prevention strategy for frail patients may improve outcomes after CABG surgery.


Patient Flowchart

Appendix 2.
Appendix 2.:
Instrument to Assess Frailty


Name: Charles H. Brown IV, MD, MHS.

Contribution: This author helped design the study, conduct the study, analyze the data, and write the manuscript.

Conflicts of Interest: Charles H. Brown IV reported no conflicts of interest.

Name: Laura Max, BA.

Contribution: This author helped conduct the study and write the manuscript.

Conflicts of Interest: Laura Max reported no conflicts of interest.

Name: Andrew LaFlam, BS.

Contribution: This author helped conduct the study and write the manuscript.

Conflicts of Interest: Andrew LaFlam reported no conflicts of interest.

Name: Lou Kirk, MD.

Contribution: This author helped write the manuscript.

Conflicts of Interest: Lou Kirk reported no conflicts of interest.

Name: Alden Gross, PhD.

Contribution: This author helped analyze the data and write the manuscript.

Conflicts of Interest: Alden Gross reported no conflicts of interest.

Name: Rakesh Arora, MD, PhD.

Contribution: This author helped write the manuscript.

Conflicts of Interest: Rakesh Arora reported a conflict of interest with Pfizer Canada Education grant unrelated to this manuscript.

Name: Karin Neufeld, MD, MPH.

Contribution: This author helped conduct the study and write the manuscript.

Conflicts of Interest: Karin Neufeld received research funding from Ornim Medical Research support unrelated to the study.

Name: Charles W. Hogue, MD.

Contribution: This author helped write the manuscript.

Conflicts of Interest: Charles W. Hogue received research funding from Covidien, Inc, and reported a conflict of interest with Ornim. Explanation: Charles Hogue has received research support from Covidien, Inc, and served on the advisory board for Ornim Medical, unrelated to this study.

Name: Jeremy Walston, MD.

Contribution: This author helped write the manuscript.

Conflicts of Interest: Jeremy Walston reported no conflicts of interest.

Name: Aliaksei Pustavoitau, MD, MHS.

Contribution: This author helped design the study, conduct the study, and write the manuscript.

Conflicts of Interest: Aliaksei Pustavoitau reported no conflicts of interest.

This manuscript was handled by: Gregory Crosby, MD.


Dr. Charles W. Hogue is the Section Editor for Cardiovascular Anesthesiology for Anesthesia & Analgesia. This manuscript was handled by Dr. Gregory Crosby, Section Editor for Neuroscience in Anesthesiology and Perioperative Medicine, and Dr. Hogue was not involved in the editorial process or decision in any way.


aSociety of Thoracic Surgeons. 2008. Adult cardiac surgery database data collection form version 2.61. Available at: Accessed February 15, 2011.


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