RESUMO
Postoperative Delirium (POD) is the most common complication following surgery among older adults, and has been consistently associated with increased mortality and morbidity, cognitive decline, and loss of independence, as well as markedly increased health-care costs. The development of new tools to identify individuals at high risk for POD could guide clinical decision-making and enable targeted interventions to potentially decrease delirium incidence and POD-related complications. In this study, we used machine learning techniques to evaluate whether baseline (pre-operative) cognitive function and resting-state electroencephalography could be used to identify patients at risk for POD. Pre-operative resting-state EEGs and the Montreal Cognitive Assessment (MoCA) were collected from 85 patients (age = 73 ± 6.4 years) undergoing elective surgery, 12 of whom subsequently developed POD. The model with the highest f1-score for predicting delirium, a linear-discriminant analysis (LDA) model incorporating MoCA scores and occipital alpha-band EEG features, was subsequently validated in an independent, prospective cohort of 51 older adults (age ≥ 60) undergoing elective surgery, 6 of whom developed POD. The LDA-based model, with a total of 7 features, was able to predict POD with area under the receiver operating characteristic curve, specificity and accuracy all >90%, and sensitivity > 80%, in the validation cohort. Notably, models incorporating both resting-state EEG and MoCA scores outperformed those including either EEG or MoCA alone. While requiring prospective validation in larger cohorts, these results suggest that prediction of POD with high accuracy may be feasible in clinical settings using simple and widely available clinical tools.
RESUMO
BACKGROUND: There is an urgent need to identify factors that increase vulnerability to opioid addiction to help stem the opioid epidemic and develop more efficient pharmacotherapeutics. MicroRNAs are small non-coding RNAs that regulate gene expression at a posttranscriptional level and have been implicated in chronic drug-taking in humans and in rodent models. Recent evidence has shown that chronic opioid treatment regulates the microRNA miR-9. The present study was designed to test the hypothesis that miR-9 in the nucleus accumbens potentiates oxycodone addictive-like behavior. METHODS: We utilized adeno-associated virus (AAV) to overexpress miR-9 in the nucleus accumbens of male rats and tested the effects on intravenous self-administration of the highly abused prescription opioid, oxycodone, in 1-hour short-access followed by 6-h long-access sessions, the latter of which leads to escalation of drug intake. In separate rats, we assessed the effects of nucleus accumbens miR-9 overexpression on mRNA targets including RE1-silencing transcription factor (REST) and dopamine D2 receptor (DRD2), which have been shown to be regulated by drugs of abuse. RESULTS: Overexpression of miR-9 in the nucleus accumbens significantly increased oxycodone self-administration compared with rats expressing a control, scrambled microRNA. Analysis of the pattern of oxycodone intake revealed that miR-9 overexpression increased "burst" episodes of intake and decreased the inter-infusion interval. Furthermore, miR-9 overexpression decreased the expression of REST and increased DRD2 in the nucleus accumbens at time points that coincided with behavioral effects. CONCLUSIONS: These results suggest that nucleus accumbens miR-9 regulates oxycodone addictive-like behavior as well as the expression of genes that are involved in drug addiction.