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.

MULTIFACTOR REGRESSION MODEL FOR PREDICTION OF CHRONIC RHINOSINUSITIS RECURRENCE.

OBJECTIVE: The aim: To propose an approach to forecasting the risk of chronic rhinosinusitis recurrence based on multivariate regression analysis for effective diagnosis and carrying out treatment and preventive measures. PATIENTS AND METHODS: Materials and methods: 104 patients aged 18 to 80, including 58 women and 46 men, diagnosed with chronic rhinosinusitis were examined. RESULTS: Results: To build a multifactorial regression model for predicting the recurrence of chronic rhinosinusitis, probable factors of the occurrence of the disease were selected. 14 possible factors were analyzed using multivariate regression analysis. 13 risk factors were selected for predicting recurrence of chronic rhinosinusitis with a significance level of less than 0.05. Histograms of the residual deviations of predicting the recurrence of chronic rhinosinusitis were obtained, which are distributed symmetrically, and a normal-probability straight line is presented, on which there are no systematic deviations. The given results confirm the statistical hypothesis that the residual deviations correspond to the normal distribution law. Residual deviations relative to the predicted values are scattered chaotically, which indicates the absence of dependence on the predicted values of the risk of recurrence of chronic rhinosinusitis. The value of the coefficient of determination was calculated, which is 0.988, which gives grounds to claim that 98.8% of the factors are taken into account in the model for predicting the recurrence of chronic rhinosinusitis and its high reliability and acceptability in general. CONCLUSION: Conclusions: The proposed model makes it possible to predict in advance potential complications and the possibility of recurrence of the studied disease.