Predicting postoperative delirium severity in older adults: The role of surgical risk and executive function. (23rd April 2019) - Lindroth, Heidi; Bratzke, Lisa; Twadell, Sara; Rowley, Paul; Kildow, Janie; Danner, Mara; Turner, Lily; Hernandez, Brandon; Brown, Roger; Sanders, Robert D.
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 R 2 : 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.