Development of score models for severe course risk assessment of febrile form of tick-borne encephalitis

   Background. There is no current generally accepted classification and clinical approaches to assess the clinical course severity of tick-borne encephalitis.
   The aim is to develop models for the quantitative severity scores of the febrile form of tick-borne encephalitis using the most informative predictors.
   Materials and methods. In accordance with clinical criteria of the height and duration of fever, two groups of 9 and 30 patients (52.2 ± 4.3 and 49.5 ± 3.9 years old) with severe and non-severe febrile form of tick-borne encephalitis were formed. The study design was a retrospective case-control study. Statistical significance assessment of variables was carried out using the chi-square test. The odds ratios were also calculated. The development of logistic regression models was carried out using Statistica 12.0 modules.
   Results. A questionnaire of febrile form of tick-borne encephalitis severity score was developed using the height of fever and 11 most informative clinical predictors including arthralgia, chills, sleep disturbance, dizziness, orbital pain, nausea, photophobia, myalgia, severe headache, vomiting and meningism. To predict the severe clinical course of febrile form of tick-borne encephalitis, two logistic regression models were developed with or without severity score assessment of fever syndrome (coefficients α and β were –12.13 and 0.94 and –5.78 and 0.75 for models 1 and 2, respectively). Models 1 and 2 demonstrated a high predicted probability for cut-off values equal to 14 and 9 points and excellent quality of prediction test (AUC are equal to 0.99 and 0.98, respectively).
   Conclusions. The models of severity score demonstrated high predicted probabilities to predict severe clinical course of febrile form of tick-borne encephalitis, which can be used in clinical practice and for scientific purposes.

1. Poltoratskaya N. V., Poltoratskaya T. N., Pankina T. M., Shikhin A. V., Sizova N. N., Deyneko O. Yu. Analysis of the epidemiological situation on tick-borne encephalitis and ixodid tick-borne borreliosis in Tomsk region. Medical Parasitology and Parasitic Diseases. 2021; (1): 9-15. (In Russ.). doi: 10.33092/0025-8326mp2021.1.9-15

2. Timonin A. V., Shirokostup S. V. Retrospective analysis of the incidence of tick-borne encephalitis in the Siberian federal district. Universum: Meditsina i farmakologiya. 2021; 11 (82): 19-20. (In Russ.). doi: 10.32743/UniMed.2021.82.11.12408

3. Zhukova N. G., Poltoratskaya T. N., Sizova N. N., Poltoratskaya N. V., Pankina T. M., Shikhin A. V., et al. Tick-borne encephalitis in the Tomsk region. Therapy. 2021; 5 (47): 14-21. (In Russ.). doi: 10.18565/therapy.2021.5.14-21

4. Bogovic P., Strle F. Tick-borne encephalitis: A review of epidemiology, clinical characteristics, and management. World J Clin Cases. 2015; 3 (5): 430-441. doi: 10.12998/wjcc.v3.i5.430

5. Ruzek D., Avšič Županc T., Borde J., Chrdle A., Eyer L., Karganova G., et al. Tick-borne encephalitis in Europe and Russia: Review of pathogenesis, clinical features, therapy, and vaccines. Antiviral Res. 2019; 164: 23-51. doi: 10.1016/j.antiviral.2019.01.014

6. Votyakov V. I., Zlobin V. I., Mishaeva N. P. Tick-borne encephalitis of Eurasia (environmental issues, molecular epidemiology, nosology, evolution). Novosibirsk: Nauka; 2002. (In Russ.).

7. Zlobin V. I., Dzhioev Yu. P., Kozlova I. V., Demina T. V., Verkhozina M. M., Aitov K. A., et al. Tick-borne encephalitis in Russia: Issues of etiology, epidemiology and prevention. Zhurnal infektsionnoy patologii. 2018; 23 (1-4): 19-20. (In Russ.).

8. Savinova Yu. S. European subtype of tick-borne encephalitis virus. Literature review. Acta biomedica scientifica. 2021; 6 (4): 100-113. (In Russ.). doi: 10.29413/ABS.2021-6.4.9

9. Zhukova N. G., Komandenko N. I., Podoplekina L. E. Tick-borne encephalitis in the Tomsk region (etiology, epidemiology, clinic picture, diagnosis, prevention, treatment). Tomsk: STT; 2002. (In Russ.).

10. Kuznetsova V. G., Krasnova E. I., Paturina N. G. Tick-borne encephalitis in the clinical practice of a physician. Lechaschi vrach. 2015; (1): 20-26. (In Russ.).

11. Gordeets A. V., Anikina O. L., Erokhina L. G., Myl’nikova E. Yu. Febrile form of tick-borne encephalitis in children of Primorye. Children Infections. 2005; 4 (1): 47-48. (in Russ.).

12. Borovikov V. P. Popular introduction to contemporary data analysis in STATISTICA: Guidelines for universities. Moscow; 2013. (In Russ.).

13. Lakin G. F. Biometrics: study guide. Moscow: Vysshaya shkola; 1990. (In Russ.).

14. Bergsma W. A bias-correction for Cramér’s V and Tschuprow’s T. J Korean Stat Soc. 2013; 42 (3): 323-328. doi: 10.1016/j.jkss.2012.10.002

15. Tolles J., Meurer W. J. Logistic regression: Relating patient characteristics to outcomes. JAMA. 2016; 316 (5): 533-534. doi: 10.1001/jama.2016.7653

16. Zweig M. H., Campbell G. Receiver-operating characteristic (ROC) plots: A fundamental evaluation tool in clinical medicine. Clin Chem. 1993; 39 (4): 561-577.

17. Nadezhdina M. V., Toporkova M. G., Gurariy N. M., Makhneva N. A. Clinic and optimization of treatment of acute forms of tick-borne encephalitis. Bulletin of Siberian Medicine. 2008; 7 (5-1): 263-269. (In Russ.). doi: 10.20538/1682-0363-2008-5-1-263-269

18. Nikolenko V. V., Vorob’eva N. N., Merkur’eva E. V., Nikolenko A. V., Suslina O. A., Okishev M. A., et al. Clinicoepidemiological features of tick-borne encephalitis course in Perm krai. Perm Medical Journal. 2020; 37 (3): 18-25. (In Russ.). doi: 10.17816/pmj37318-25

19. Bogovic P., Logar M., Avsic-Zupanc T., Strle F., Lotric-Furlan S. Quantitative evaluation of the severity of acute illness in adult patients with tick-borne encephalitis. Biomed Res Int. 2014; 2014: 841027. doi: 10.1155/2014/841027

20. Skogman B. H., Sjöwall J., Lindgren P. E. The NeBoP score – a clinical prediction test for evaluation of children with Lyme Neuroborreliosis in Europe. BMC Pediatr. 2015; 15: 214. doi: 10.1186/s12887-015-0537-y

21. Tuan N. M., Nhan H. T., Chau N. V., Hung N. T., Tuan H. M., Tram T. V., et al. Sensitivity and specificity of a novel classifier for the early diagnosis of dengue. PLoS Negl Trop Dis. 2015; 9 (4): e0003638. doi: 10.1371/journal.pntd.0003638

22. De Sanctis V., Bedair E. M. A., Soliman A. T., Nair A. P., Al Masalamani M. A., Yassin M. Proposed scoring system for evaluating clinicoradiological severity of COVID-19 using plain chest X-ray (CXR) changes (CO X-RADS): Preliminary results. Acta Biomed. 2020; 91 (4): e2020172. doi: 10.23750/abm.v91i4.10664