Application of roc-analysis to assess the quality of predicting the risk of chronic rhinosinusitis recurrence.

Objective: Aim: To propose a new, original approach to assessing the quality of a multivariate regression model for predicting the risk of recurrence in patients with chronic rhinosinusitis based on ROC analysis with the construction of appropriate curves, estimating the area under them, as well as calculating the sensitivity, accuracy, specificity, and predictive value of a positive and negative classification results, the likelihood ratio of positive and negative patient detection results.

Patients and methods: Materials and Methods: 204 patients aged with a diagnosis of chronic rhinosinusitis were examined.

Results: Results: To build a multivariate regression model 14 probable factors of chronic rhinosinusitis occurrence were selected to determine the diagnostic value of the proposed model we calculate the sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), the likelihood ratio of a positive test (LR+), the likelihood ratio of a negative test (LR-) and prediction accuracy % of the proposed mathematical model. In order to determine the prognostic value of the risk ratio of CRS recurrence model, ROC- analysis was performed, ROC curves were obtained.

Conclusion: Conclusions: The multivariate regression model makes it possible to predict potential complications and the possibility of disease recurrence. The construction of ROC-curves allows us to assert the excellent classification quality of chronic rhinosinusitis recurrence.