Development of an algorithm for diagnosing endothelial dysfunction in patients with chronic rhinosinusitis

Purpose of research. The problem of workload of doctors with an increase in the volume of informatization in medicine can be solved through the development of diagnostic algorithms. The integral approach in the work of a doctor includes not only examining the patient, collecting anamnesis, but also taking into account the results of clinical laboratory and radiation research methods.  
The purpose of the research  is to  improve the algorithm for diagnosing a patient with chronic rhinosinusitis using a mathematical model.  
Methods. When compiling  the examination algorithm,  the  requirements of GOST 19.701-90 were applied. Logistic regression was used as a mathematical method for constructing a mathematical model. Correlation analysis and ROC analysis were also used to build a mathematical model. 
Results. The results of the entire complex of examination of the patient should be entered into the medical information system and  integrated  into the medical decision support system. According to the results of  the calculation using a mathematical model, the patient is diagnosed with the presence or absence of clinical and laboratory manifestations of endothelial dysfunction. Depending on the result obtained, the tactics of further treatment of the patient are built. 
Conclusion. An algorithm for examining a patient with chronic rhinosinusitis using a mathematical model is proposed. This approach reduces the time for processing the received data for the doctor and making a decision on further patient management tactics. The developed algorithm can be used not only for diagnostic purposes, but also for monitoring the treatment of patients with chronic rhinosinusitis. The use of the algorithm can be recommended to doctors of poly- clinics and hospitals. This tactic of examining the patient reduces the processing time of the received data for the doctor and making a decision on further patient management tactics. The mathematical model does not provide an accurate diagnosis, but only estimates the likelihood of the disease. Various clinical data are also needed to make a diagnosis. 

1. Chernyaeva T.N., Nikiforova A.A., Maslova S.S., Mazur L.D. Mathematics in medicine. Stolypinskii vestnik = Stolypinsky Bulletin. 2022;(10):1–13. (In Russ.)

2. Kiselev K.V., Noeva E.A., Vybory O.N., et al. Development of an algorithm for the operation of a logical solver of an intelligent medical decision support system for instrumental diagnosis of angina pectoris. Meditsinskie tekhnologii. Otsenka i vybor = Medical Technologies. Assessment and Selection. 2019;(1):32–42. (In Russ.) https://doi.org/10.31556/2219-0678.2019.35.1.032-042

3. Parondzhanov V.D. Learn to write, read and understand algorithms. Algorithms for correct thinking. Fundamentals of algorithmization. 2nd ed. Moscow: DMK Press; 2016. 520 p. (In Russ.)

4. Sobchenko K.V., Kovalenko A.V., Koshkarov A.A., et al. Development of an algorithm for automated wavelet analysis of data on the work of the registry of a clinical oncological dispensary at the regional level. Vrach i informatsionnye tekhnologii = Doctor and Information Technologies. 2018 Oct:66–73. (In Russ.)

5. Mikheev A.E. An approach to the implementation of medical decision support systems based on the principles of a service-oriented architecture using the services of the digital medical ecosystem. Menedzher zdravookhraneniya = The Health Care Manager. 2024;(S):101–118. (In Russ.) https://doi.org/10.21045/1811-0185-2024-S-101-118

6. Kotonskiy I.N., Malina Yu.V. Method of private algorithms of doctor’s actions as a way of adapting clinical recommendations in medical practice. Meditsinskie tekhnologii. Otsenka i vybor = Medical Technologies. Assessment and Selection. 2022;(44):9–14. (In Russ.) https://doi.org/10.17116/medtech2022440119

7. Fong N. Algorithms of differential diagnosis. General complaints in adult patients. Moscow: GEOTAR-Media; 2022. 680 p. (In Russ.)

8. Korneenkov A.A., Ryazantsev S.V., Vyazemskaya E.E., Budkova M.A. Measures of informativeness of diagnostic medical technologies in otorhinolaryngology: calculation and interpretation. Rossiiskaya otorinolaringologiya = Russian Otorhinolaryngology. 2020;(19):46–55. (In Russ.) https://doi.org/10.18692/1810-4800-2020-1-46-55

9. Gabbasov A.A. Mathematical modeling of biological processes in medicine. Vestnik nauki = Bulletin of Science. 2024;5(12):914–922. (In Russ.)

10. Korneenkov A.A., Fanta I.V., Vyazemskaya E.E. Assessment of the dynamics of disease symptoms by methods of survival analysis. Rossiiskaya otorinolaringologiya = Russian Otorhinolaryngology. 2019;(18):8–14. (In Russ.) https://doi.org/10.18692/1810-4800-2019-4-8-14

11. Simonov A.N., Klyushnik T.P., Androsova L.V., Mikhailova N.M. Using cluster analysis and logistic regression to assess the risk of Alzheimer’s disease in patients with amnesic mild cognitive impairment. Zhurnal nevrologii i psikhiatrii im. S.S. Korsakova = Journal of Neurology and Psychiatry named after S.S. Korsakov. 2018;(118):40–43. (In Russ.)

12. Heinze G., Wallish K., Dunkler D. Variable selection is an overview and recommendations for a practicing statistician. Biom J. 2018;(60):431–449. https://doi.org/10.1002/bimj.201700067

13. Luchinin A.S., Lyanguzov A.V. A logistic regression model for predictivemortality in the intensive care unit: problems and solutions. Kachestvennaya klinicheskaya praktika = Qualitative Clinical Practice. 2022;(2):13–20. (In Russ.) https://doi.org/10.37489/2588-0519-2022-2-13-20

14. Okorokova T.O., Kryuchkova O.N. A logistic regression model for predicting the ineffectiveness of dual antihypertensive therapy: a prospective comparative non-randomized clinical trial. Kubanskii nauchnyi meditsinskii vestnik = Kuban Scientific Medical Bulletin. 2023;(30):54–63. (In Russ.) https://doi.org/10.25207/1608-6228-2023-30-5-54-63

15. Gel A.Y. Application of the logistic regression method based on a dataset of cancer risk factors. Molodoi uchenyi = Young Scientist. 2023;(16):6–8. (In Russ.)

16. Schober P., Vetter T.R. Logistic Regression in Medical Research. Anesthesia & Analgesia. 2021;(132):365–366. https://doi.org/10.1213/ANE.0000000000005247

17. Jong Hae Kim. Multicollinearity and misleading statistical results. Korean J. Anesthesiol. 2019;(72):558–569. https://doi.org/10.4097/kja.19087

18. Senaviratna N.A.M.R., Cooray T.M.J.A. Diagnosing Multicollinearity of Lo-gistic Regression Model. Asian Journal of Probability and Statistics. 2019;(5):1–9. https://doi.org/10.9734/ajpas/2019/v5i230132

19. Grigoriev S.G., Lobzin Yu.V., Skripchenko N.V. The role and place of logistic regression and ROC analysis in solving medical diagnostic problems. Zhurnal infektologii = Journal of Infectology. 2016;8(4):36–45. (In Russ.)

20. Mamaev A.N., Kudlay D.A. Statistical methods in medicine. Moscow: Prakticheskaya meditsina; 2021. 136 p. (In Russ.)