Predicting postoperative delirium severity in older adults: The role of surgical risk and executive function

Abstract

Objectives: Delirium is an important postoperative complication, yet predictive risk factors for postoperative delirium severity remain elusive. We hypothesized that the NSQIP risk calculation for serious complications (NSQIP-SC) or risk of death (NSQIP-D), and cognitive tests of executive function (Trail Making Tests A and B [TMTA and TMTB]), would be predictive of postoperative delirium severity. Further, we demonstrate how advanced statistical techniques can be used to identify candidate predictors.

Methods/design: Data from an ongoing perioperative prospective cohort study of 100 adults (65 y old or older) undergoing noncardiac surgery were analyzed. In addition to NSQIP-SC, NSQIP-D, TMTA, and TMTB, participant age, sex, American Society of Anesthesiologists (ASA) score, tobacco use, surgery type, depression, Framingham risk score, and preoperative blood pressure were collected. The Delirium Rating Scale-R-98 (DRS) measured delirium severity; the Confusion Assessment Method (CAM) identified delirium. LASSO and best subsets linear regression were employed to identify predictive risk factors.

Results: Ninety-seven participants with a mean age of 71.68 ± 4.55, 55% male (31/97 CAM+, 32%), and a mean peak DRS of 21.5 ± 6.40 were analyzed. LASSO and best subsets regression identified NSQIP-SC and TMTB to predict postoperative delirium severity (P < 00.001, adjusted R² : 0.30). NSQIP-SC and TMTB were also selected as predictors for postoperative delirium incidence (AUROC 0.81, 95% CI, 0.72-0.90).

Conclusions: In this cohort, we identified NSQIP risk score for serious complications and a measure of executive function, TMT-B, to predict postoperative delirium severity using advanced modeling techniques. Future studies should investigate the utility of these variables in a formal delirium severity prediction model.

Keywords: aging; delirium; executive function; perioperative; risk; severity.

Figure 1

illustrates the Cognitive Trajectory. The relationship between cognitive abilities (predisposing, y axis) and the precipitating event, i.e. surgery over time (x-axis) is shown, with each individual trajectory displayed with a horizontal line. The dashed line, situated above the x-axis of “time”, represents the “Delirium Threshold.” (A) Trajectory #1 (gray line, numbered 1) displays an individual with maximum cognitive abilities. They have a surgery, but do not cross the “Delirium Threshold.” Trajectory #2 (blue line, numbered 2) contrasts #1 by showing an individual with decreased cognitive abilities. This individual undergoes the same surgery and crosses over the “Delirium Threshold” to experience delirium. (B) Trajectory #3 (black-dashed line, numbered 3) returns to an individual with maximum cognitive abilities. A sufficiently large precipitating event will push this individual across the “Delirium Threshold”, inducing delirium. Trajectory #1 (gray line) is transposed onto this graph to show the difference in magnitude and impact of the precipitating event. (C) When developing a prediction model for delirium, it may be important to consider not only the predisposing risk factors, but also the influence of the precipitating event. A surgical risk score such as NSQIP combines both predisposing risk and the future-precipitating event into one score, which may be optimal for postoperative delirium severity prediction.

Figure 2

displays the inclusion and exclusion criteria and a flowchart detailing study screening, recruitment, consent, and attrition numbers.

Figure 3

illustrates the postoperative delirium symptom severity prediction model. Box A is a histogram showing the data distribution of the Peak Delirium Rating Scale Score (DRS). This value was transformed using the Box-Cox Method with an optimal lambda value of 0.35 achieving a near Gaussian distribution and is shown on the histogram in Box B. Boxes C-E display the predicted burden of delirium symptoms based on the NSQIP-SC and TMTB prediction model (Box C) and univariate analysis of NSQIP-SC (Box D) and TMTB (Box E). The statistics from each regression model are shown in the upper left hand corner of each box. The univariate NSQIP-SC regression model was analyzed with 97 participants. Due to one missing assessment of TMTB, Box C and E are analyzed with 96 participants.

Figure 4

illustrates the predictive ability of the NSQIP-SC and TMTB model for postoperative delirium incidence. (A) Displays the Area Under the Receiver Operator Curve statistic (AUROC). (B) Demonstrates the predicted probability of postoperative delirium incidence based on the % NSQIP-SC score. This is holding TMTB constant at zero.