AUTHOR=Zhang Jingya , Basnet Diksha , Du Xue , Yang Junjun , Liu Jiehui , Wu Fan , Zhang Xiaoqing , Liu Jianhui TITLE=Does cognitive frailty predict delayed neurocognitive recovery after noncardiac surgery in frail elderly individuals? Probably not JOURNAL=Frontiers in Aging Neuroscience VOLUME=14 YEAR=2022 URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2022.995781 DOI=10.3389/fnagi.2022.995781 ISSN=1663-4365 ABSTRACT=Introduction

Delayed neurocognitive recovery (DNR) is a common post-surgical complication among the elderly. Cognitive frailty (CF) is also an age-related medical syndrome. However, little is known about the association between CF and DNR. Therefore, this study aimed to study whether CF is associated with DNR in elderly patients undergoing elective noncardiac surgery, as well as to explore the potential risk factors for DNR in frail elderly individuals and construct a prediction model.

Methods

This prospective cohort study administered a battery of cognitive and frailty screening instruments for 146 individuals (≥65 years old) scheduled for elective noncardiac surgery. Screening for CF was performed at least one day before surgery, and tests for the presence of DNR were performed seven days after surgery. The association between CF and DNR was investigated. Moreover, the study subjects were randomly divided into a modeling group (70%) and a validation group (30%). Univariate and multivariate logistic regression was performed to analyze the modeling group data and identify the independent risk factors for DNR. The R software was used to construct DNR's nomogram model, verifying the model.

Results

In total, 138 individuals were eligible. Thirty-three cases were diagnosed with DNR (23.9%). No significant difference in the number of patients with CF was observed between the DNR and non-DNR groups (P > 0.05). Multivariate analysis after adjusting relevant risk factors showed that only the judgment of line orientation (JLOT) test score significantly affected the incidence of DNR. After internal validation of the constructed DNR prediction model, the area under the curve (AUC) of the forecast probability for the modeling population (n = 97) for DNR was 0.801, and the AUC for the validation set (n = 41) was 0.797. The calibration curves of both the modeling and validation groups indicate that the prediction model has good stability.

Conclusion

Cognitive frailty is not an independent risk factor in predicting DNR after noncardiac surgery in frail elderly individuals. The preoperative JLOT score is an independent risk factor for DNR in frail elderly individuals. The prediction model has a good degree of discrimination and calibration, which means that it can individually predict the risk probability of DNR in frail elderly individuals.