Application of ROC-analysis to assess the quality of predicting the risk of chronic rhinosinusitis recurrence
1
Otolaryngology, ophtalmology and neurosurgery, I.Horbachevsky Ternopil National Medical University, Ukraine
2
Medical Informatics, I.Horbachevsky Ternopil National Medical University, Ukraine
3
Radiotechnical systems, Ternopil Ivan Puluj National Technical University, Ukraine
4
Otolaryngology, ophtalmology and neurosurgery, I.Horbachevsky Ternopil National Medical University, Ukraine, Ukraine
Submission date: 2023-08-23
Final revision date: 2023-12-26
Acceptance date: 2024-01-24
Publication date: 2024-03-30
Corresponding author
Maksym Herasymiuk
Otolaryngology, ophtalmology and neurosurgery, I.Horbachevsky Ternopil National Medical University, Ukraine
Otolaryngology, ophtalmology and neurosurgery, I.Horbachevsky Ternopil National Medical University, Ukraine
Wiadomości Lekarskie 2024;77(2):254-261
KEYWORDS
TOPICS
ABSTRACT
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.
Material and methods:
204 patients aged with a diagnosis of chronic rhinosinusitis were examined.
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
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.
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.
Material and methods:
204 patients aged with a diagnosis of chronic rhinosinusitis were examined.
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
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.
| eISSN: | 2719-342X |
| ISSN: | 0043-5147 |
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