Garneau, Jonathan, Ramirez, Michael, Armato, Samuel G., III, Sensakovic, William F., Ford, Megan K., Poon, Colin S., Ginat, Daniel T., Starkey, Adam, Baroody, Fuad M., Pinto, Jayant M.
funding text
M.R. and J.G. were supported by the Pritzker School of Medicine; J.G. received funding from the Icahn School of Medicine; J.M.P. received funding from the National Institute on Aging and the National Institute of Allergy and Infectious Disease (AG036762; AI106683); the study was also supported in part by a Preclinical Pilot Translational Study Award from The University of Chicago Institute for Translational Medicine (UL1TR000430).
abstract
BackgroundThe Lund-Mackay (LM) staging system for chronic rhinosinusitis (CRS) does not correlate with clinical parameters, likely due to its coarse scale. We developed a Modified Lund Mackay (MLM) system, which uses a three-dimensional (3D), computerized method to quantify the volume of mucosal inflammation in the sinuses, and sought to determine whether the MLM would correlate with symptoms and disease-specific quality of life. MethodsWe obtained Total Nasal Symptom Score (TNSS) and 22-item Sino-Nasal Outcome Test (SNOT-22) data from 55 adult subjects immediately prior to sinus imaging. The volume of each sinus occupied by mucosal inflammation was measured using MATLAB algorithms created using customized, image analysis software after manual outlining of each sinus. Linear regression was used to model the relationship between the MLM and the SNOT-22 and TNSS. Correlation between the LM and MLM was tested using Spearman's rank correlation coefficient. ResultsAdjusting for age, gender, and smoking, a higher symptom burden was associated with increased sinonasal inflammation as captured by the MLM ( = 0.453, p < 0.013). As expected due to the differences in scales, the LM and MLM scores were significantly different (p < 0.011). No association between MLM and SNOT-22 scores was found. ConclusionThe MLM is one of the first imaging-based scoring systems that correlates with sinonasal symptoms. Further development of this custom software, including full automation and validation in larger samples, may yield a biomarker with great utility for both treatment of patients and outcomes assessment in clinical trials. (C) 2015 ARS-AAOA, LLC.