Correlation between Adrenoceptor Expression and Clinical Parameters in Degenerated Lumbar Intervertebral Discs.

Despite advanced knowledge of the cellular and biomechanical processes of intervertebral disc degeneration (IVDD), the trigger and underlying mechanisms remain unclear. Since the sympathetic nervous system (SNS) has been shown to exhibit catabolic effects in osteoarthritis pathogenesis, it is attractive to speculate that it also influences IVDD. Therefore, we explored the adrenoceptor (AR) expression profile in human IVDs and correlated it with clinical parameters of patients. IVD samples were collected from n = 43 patients undergoing lumbar spinal fusion surgery. AR gene expression was analyzed by semi-quantitative polymerase chain reaction. Clinical parameters as well as radiological Pfirrmann and Modic classification were collected and correlated with AR expression levels. In total human IVD homogenates α1A-, α1B-, α2A-, α2B-, α2C-, ß1- and ß2-AR genes were expressed. Expression of α1A- (r = 0.439), α2A- (r = 0.346) and ß2-AR (r = 0.409) showed a positive and significant correlation with Pfirrmann grade. α1A-AR expression was significantly decreased in IVD tissue of patients with adjacent segment disease (p = 0.041). The results of this study indicate that a relationship between IVDD and AR expression exists. Thus, the SNS and its neurotransmitters might play a role in IVDD pathogenesis. The knowledge of differential AR expression in different etiologies could contribute to the development of new therapeutic approaches for IVDD.

Using Natural Language Processing of Free-Text Radiology Reports to Identify Type 1 Modic Endplate Changes.

Electronic medical record (EMR) systems provide easy access to radiology reports and offer great potential to support quality improvement efforts and clinical research. Harnessing the full potential of the EMR requires scalable approaches such as natural language processing (NLP) to convert text into variables used for evaluation or analysis. Our goal was to determine the feasibility of using NLP to identify patients with Type 1 Modic endplate changes using clinical reports of magnetic resonance (MR) imaging examinations of the spine. Identifying patients with Type 1 Modic change who may be eligible for clinical trials is important as these findings may be important targets for intervention. Four annotators identified all reports that contained Type 1 Modic change, using N = 458 randomly selected lumbar spine MR reports. We then implemented a rule-based NLP algorithm in Java using regular expressions. The prevalence of Type 1 Modic change in the annotated dataset was 10%. Results were recall (sensitivity) 35/50 = 0.70 (95% confidence interval (C.I.) 0.52-0.82), specificity 404/408 = 0.99 (0.97-1.0), precision (positive predictive value) 35/39 = 0.90 (0.75-0.97), negative predictive value 404/419 = 0.96 (0.94-0.98), and F1-score 0.79 (0.43-1.0). Our evaluation shows the efficacy of rule-based NLP approach for identifying patients with Type 1 Modic change if the emphasis is on identifying only relevant cases with low concern regarding false negatives. As expected, our results show that specificity is higher than recall. This is due to the inherent difficulty of eliciting all possible keywords given the enormous variability of lumbar spine reporting, which decreases recall, while availability of good negation algorithms improves specificity.