Advancing Perioperative Brain Health: Insights from ANESTHESIOLOGY® 2023 Abstracts

Dr. Kotaro Gunji and colleagues from Shimane University Hospital in Japan presented their abstract examining the utility of a preoperative cognitive self-screening test for the assessment of postoperative delirium (POD) risk (asamonitor.pub/4aQkR1e). Recognizing the lack of a universally accepted standardized test to diagnose cognitive decline preoperatively, the researchers implemented a preoperative self-screening cognitive test using a touchscreen (MSP-1100, Nihon Kohden, Tokyo, Japan). The retrospective analysis of data from older patients undergoing invasive surgery revealed a statistically significant association between low scores and an increased incidence of POD.

Out of the 89 patients evaluated, 42.7% developed POD. Of note, only 3.4% had a preexisting diagnosis of dementia, but the self-screening test identified 16.8% of patients as being in the low-score group, indicative of cognitive decline. The low-score group exhibited a significantly higher incidence of POD (80%) compared to the high-score group (35%). The sensitivity and specificity of the screening test were 31.6% and 94.1%, respectively.

While the self-administration of the cognitive screening makes it appealing in environments with staffing challenges, these results raise pertinent questions about the efficacy of self-screening tests in predicting POD. Although the specificity of 94.1% is noteworthy, the modest sensitivity underscores the complexity of comprehensive risk stratification for POD.

An abstract by Shelly Teng and Dr. Ira Hofer from the Icahn School of Medicine at Mount Sinai delved into the realm of machine learning models for predicting POD (asamonitor.pub/3NWKi7M). This approach has the appeal of utilizing data already available in the electronic health record (EHR) for risk stratification, potentially obviating the need for the administration of a specific screening test. Using a dataset of over 26,000 patients, logistic regression and XGBoost models demonstrated promising accuracy and specificity. Patient age, surgery length, body mass index, and preoperative vital sign data emerged as essential contributors to predictive performance, underscoring the potential of machine learning as a scalable tool.

Improving our ability to predict POD helps target the highest-risk patients for implementation of the more resource-intensive preventive measures. The reliance on EHR data prompts considerations about data quality and the need for robust validation in various clinical settings.

A prospective observational study from the David Geffen School of Medicine at UCLA, authored by Howard Hong and colleagues, explored the nuanced relationship between perioperative benzodiazepine use and POD, particularly in frail older adults (asamonitor.pub/48JJRWh). The study aimed to determine whether patients with frailty are at increased risk of POD with perioperative benzodiazepine use, compared with non-frail patients. In addition, the association between chronic preoperative benzodiazepine use and POD was examined.

Interestingly, the study did not find an association between perioperative benzodiazepine use and POD among non-frail patients. In contrast, among frail patients, both perioperative and home benzodiazepine use was associated with POD. However, only 3% of patients on chronic benzodiazepines had received perioperative benzodiazepines. The authors suggest that abstinence from benzodiazepines may be a contributing factor to POD among chronic users. An additional concerning finding was the relatively high prevalence of chronic benzodiazepine use among frail patients (26%), putting these patients at increased risk of cognitive impairment, delirium, falls, fractures, and other complications (J Am Geriatr Soc 2019;67:674-94).

These findings highlight the importance of POD prevention for frail individuals and prompt further investigation into the mechanisms underlying benzodiazepine-associated POD, in addition to potential interventions such as tapering home use of benzodiazepines before surgery.

Drs. Giovanna Patafio and George L. Tewfik at Rutgers-New Jersey Medical School sought to explore the PRECIVITY β-amyloid blood test’s association with postoperative cognitive dysfunction (POCD) (asamonitor.pub/47A21sI). Through the preoperative and postoperative measurement of Aβ42/40 ratios and calculation of Amyloid Probability Scores, the study introduces a potential biomarker for predisposition to cognitive decline. The rationale for the use of a blood test for β-amyloid protein to predict POCD was based on prior data showing an association between inhaled anesthetics and increases in β-amyloid levels in rats, in addition to the strong correlation between β-amyloid levels and cognitive decline (PLoS One 2017;12:e0175654; J Alzheimers Dis 2019;69:169-78). Although the researchers were ultimately unable to assess for POCD due to limited staffing, the identified inverse relationship between the Aβ42/40 ratio and Amyloid Probability Score invites further mechanistic exploration into the role of beta-amyloid in postoperative cognitive outcomes.

Despite study limitations, the PRECIVITY test may have potential as a tool to screen patients for POCD risk. Further research is warranted to assess long-term correlations with POCD and explore the test’s predictive capabilities in a larger patient cohort.

These four studies all examine strategies for risk assessment and prevention of postoperative adverse neurologic outcomes, with three focusing on POD and the fourth on POCD. Each of the studies focuses on a different model for risk assessment, highlighting the continuing drive for tools for risk stratification specific to brain health outcomes. Although guidelines strongly advocate for preoperative cognitive screening, adoption seems to be lagging behind, and the development of an array of tools will enable different institutions to implement screening measures tailored to their specific patient populations and resources (Anesth Analg 2018;127:1406-13; Clin Anesth 2020;62:109724).

The pursuit of preoperative cognitive screening, the integration of machine learning models, the nuanced understanding of benzodiazepine use, and the exploration of biomarkers all contribute to a deeper comprehension of perioperative brain health. As we become better at assessing perioperative neurocognitive risk, we will also be better equipped to assist patients in shared decision-making, proactive discharge planning, and targeted prevention.