Breath volatolomics for diagnosing chronic rhinosinusitis

Abstract

Purpose: Chronic rhinosinusitis (CRS) is one of the most common chronic diseases treated by primary care physicians. It is increasingly recognized that CRS and nasal polyposis (NP) comprise several disease processes with diverse causes. Hence, subgroups of sinusitis need to be differentiated so that patients can be screened appropriately and personalized medical treatment provided.

Patients and methods: To address this need, we use a cross-reactive nanoarray based on either molecularly modified gold nanoparticles or molecularly modified single-walled carbon nanotubes, combined with pattern recognition for analyzing breath samples. Breath samples were collected from three groups of volunteers (total 71) at the Hillel Yaffe Medical Center: CRS, NP, and control.

Results: Nanoarray results discriminated between patients with sinusitis and the control group with 87% sensitivity, 83% specificity, and 85% accuracy. The system also discriminated well between the subpopulations: 1) CRS vs control (76% sensitivity, 90% specificity); 2) CRS vs NP (82% sensitivity, 71% specificity); and 3) NP vs control (71% sensitivity, 90% specificity).

Conclusion: This preliminary study shows that a nanoarray-based breath test for screening population for sinusitis-related conditions is feasible.

Keywords: breath analysis; chronic sinusitis; nasal polyposis; sensor; volatile organic compound.

Figure 1

Experimental flowchart. Note: Overall, the experiment was divided into two main phases: the first phase included the clinical steps and the second included the engineering, sensor, and data analysis steps. Abbreviations: SNOT, Sino-Nasal Outcome Test; CRS, chronic rhinosinusitis; NP, nasal polyposis.

Figure 2

The differences in SNOT-22 questionnaire scores among the three groups: nasal polyposis (n=24), chronic sinusitis (n=17), and control (n=30). (A) Total average scores and (B) average scores per question. Data are presented with standard error of mean. Abbreviation: SNOT, Sino-Nasal Outcome Test.

Figure 3

Discriminant factor analysis (DFA) models for different comparisons between the sensor measurements: (A) CRS vs control; (B) nasal polyposis vs control; (C) CRS vs nasal polyposis; and (D) sick vs control. The first two bars on the left represent the training set sample, while the subsequent four bars (true positive [TP], false negative [FN], true negative [TN], and false positive [FP]) represent validation set samples. The horizontal line corresponds to the cutoff value. Note: CV1=canonical value (the calculated values of the DFA model, reduced dimensionality). Abbreviation: CRS, chronic rhinosinusitis.