A novel approach for the detection of cognitive impairment and delirium risk in older patients undergoing spine surgery
J Am Geriatr Soc. 2023 Jan;71(1):227-234. doi: 10.1111/jgs.18033. Epub 2022 Sep 20.
BACKGROUND: Postoperative delirium is a common postsurgical complication in older patients and is associated with high morbidity and mortality. The objective of this study was to determine whether a digital cognitive assessment and patient characteristics could identify those at-risk.
METHODS: Patients 65 years and older undergoing spine surgeries ≥3 h were evaluated as part of a single-center prospective observational cohort study at an academic medical center, from January 1, 2019, to December 31, 2020. Of 220 eligible patients, 161 were enrolled and 152 completed the study. The primary outcome of postoperative delirium was measured by the Confusion Assessment Method for the Intensive Care Unit or the Nursing Delirium Screening Scale, administered by trained nursing staff independent from the study protocol. Baseline cognitive impairment was identified using the tablet-based TabCAT Brain Health Assessment.
RESULTS: Of the 152 patients included in this study, 46% were women. The mean [SD] age was 72 [5.4] years. Baseline cognitive impairment was identified in 38% of participants, and 26% had postoperative delirium. In multivariable analysis, impaired Brain Health Assessment Cognitive Score (OR 2.45; 95% CI, 1.05-5.67; p = 0.037), depression (OR 4.54; 95% CI, 1.73-11.89; p = 0.002), and higher surgical complexity Tier 4 (OR 5.88; 95% CI, 1.55-22.26; p = 0.009) were associated with postoperative delirium. The multivariate model was 72% accurate for predicting postoperative delirium, compared to 45% for the electronic medical record-based risk stratification model currently in use.
CONCLUSION: In this prospective cohort study of spine surgery patients, age, cognitive impairment, depression, and surgical complexity identified patients at high risk for postoperative delirium. Integration of scalable digital assessments into preoperative workflows could identify high-risk patients, automate decision support for timely interventions that can improve patient outcomes and lower hospital costs, and provide a baseline cognitive assessment to monitor for postoperative cognitive change.
PMID:36125032 | PMC:PMC9870968 | DOI:10.1111/jgs.18033