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IntroductionDelayed 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.MethodsThis 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.ResultsIn 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.ConclusionCognitive 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.

Background: Postoperative cognitive dysfunction (POCD) is a general complication following cardiac and major non-cardiac surgery amongst the elderly, yet its causes and mechanisms are still unknown. The present study aimed to detect whether regional cerebral blood flow (CBF) is altered in the brain before surgery in POCD patients compared with non-POCD (NPOCD) patients, thus, CBF variation may potentially predict the occurrence of early POCD.Methods: Fifty patients scheduled for spinal stenosis surgery were enrolled in the study. All study participants completed a battery of neuropsychological tests (NPTs) by a well-trained neuropsychologist before the surgery and 1 week after the surgery. POCD was defined when the preoperative to postoperative difference of at least two of the NPTs’ |Z|-scores with reference to a control group exceeded 1.96. Pulsed arterial spin-labeling (ASL) MRI was scanned at least 1 day before surgery. The ASLtbx toolkit and SPM12 were applied to preprocess and correct the images, which were then normalized to the MNI brain template space to obtain standardized cerebral perfusion images.Results: POCD was identified in 11 out of 50 patients (22%). The CBF of the right superior temporal lobe, right and left middle cingulate gyrus, and the right hippocampus, and parahippocampal gyrus in POCD group was lower than that in NPOCD group (P < 0.001). The CBF of the pars triangularis of inferior frontal gyrus in POCD group was higher than that in NPOCD group (P < 0.001).Conclusions: These preliminary findings suggest that CBF premorbid alterations may happen in cognitively intact elderly patients that develop early POCD. Alterations of preoperative CBF might be a bio-marker for early POCD that can be detected by noninvasive MRI scans.