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Correlation of sino-nasal outcome test and nasal polyp score in dupilumab-treated chronic rhinosinusitis with nasal polyps

  • Tina Mauthe,
  • Fabio S. Ryser,
  • Catrin Brühlmann,
  • Ayla Yalamanoglu,
  • Christian Meerwein,
  • Urs C. Steiner,
  • Michael B. Soyka

摘要

Background

The alignment between objective scores and patient-reported outcome measures (PROMs) is underexplored. This study aimed to assess changes in Nasal Polyp Score (NPS) and Sino-Nasal Outcome Test (SNOT) scores in chronic rhinosinusitis with nasal polyps (CRSwNP) patients undergoing dupilumab treatment and explore correlations between these scores.

Methods

CRSwNP patients received dupilumab therapy for six months. SNOT-20 German Adapted Version (GAV)/SNOT-22 scores were assessed weekly, and NPS was measured at baseline and after one, three, and six months. Correlations were analyzed using Spearman’s rank correlation and regression analysis.

Results

69 patients were included. After one, three and six months of dupilumab therapy, SNOT and NPS scores improved significantly. Correlation analysis of SNOT and NPS showed significant correlations only within the nasal subscores, along with a weak trend for SNOT-20. Absolute changes over time lacked significance. However, correlation analysis revealed significant associations between relative changes in SNOT score and NPS, irrespective of timing, and when stratified by baseline NPS of 8, 6, and 4 (r = -0.54, p = 0.01; r = -0.44, p < 0.001; r = -0.7, p < 0.001). This was supported by linear regression modeling, suggesting potential predictive capability of NPS reduction on relative SNOT score improvement.

Conclusion

Dupilumab therapy significantly improved subjective and objective CRSwNP scores, exhibiting weak correlations in absolute values for nasal subscores. Furthermore, evidence indicated a correlation between relative changes in SNOT score and NPS, substantiated by predictive capability. This might be due to subjective perception variability, highlighting the suitability of relative change correlation analysis.