Clinical significance of 24-hour blood pressure monitoring in prediction of hypertension development in patients with gout

Aim. The aim of the study was to develop the model for establishing early diagnosis of hypertension in patients with gout. The model was based on data of 24-hour blood pressure monitoring.

Material and Methods. A total of 69 patients with gout were enrolled in a single-stage cross-sectional prospective study. Three study groups were assigned as follows: group 1 (main group) comprised hypertensive men with gout (n = 41); group 2 (comparison group) comprised normotensive men with gout (n = 28); group 3 (control) included relatively healthy men  (n = 30). Daily blood pressure monitoring was performed on an outpatient basis using a BPLab device (Peter Telegin, Russia).

Results. The significant intergroup differences were found in the following parameters: lowest, mean, and highest 24-hour systolic blood pressure (SBP) values in patients of main and comparison groups (р < 0.001) and in patients of main and control groups (р < 0.001); mean and maximum 24-hour diastolic blood pressure (DBP) values in patients of main and comparison groups (р < 0.001) and in patients of main and control groups (р < 0.001); lowest, mean, and highest 24-hour pulse blood pressure (PBP) values in patients of main and comparison groups (р < 0.001) and in patients of main and control groups (р < 0.001); mean, and maximum 24-hour PBP values in patients of comparison and control groups (р < 0.001). Median values of the lowest, mean, and highest 24-hour SBP in hypertensive patients with gout were significantly higher than the corresponding values in normotensive patients with gout and healthy men of group 3 (p < 0.001). Median values of mean and maximum 24-hour DBP in main group were higher than the corresponding values in comparison group and control group (p < 0.001). Median values of the lowest, mean, and highest 24-hour PBP in hypertensive patients with gout exceeded the corresponding values of patients of control group (p < 0.001). Median values of the mean and maximum 24-hour PBP in main group exceeded the corresponding values of patients of comparison group (p < 0.001). Based on the binary logistic regression model, the prognostic algorithm for hypertension development in gout patients was created using the parameters of 24-hour blood pressure monitoring as predictors and the cut-off K value. If the value of K was > 0.54, then the hypertension development was predicted in gout patients. The sensitivity of developed diagnostic model was 0.84, and the specificity was 0.95.

Conclusion. Тhe proposed model, based on the assessment of average-daily values of the lowest, mean, and highest SBP, allowed to establish early diagnosis of hypertension in patients with gout with the accuracy of up to 90%.

1. Richette P., Bardin T. Gout. Lancet. 2010;375(9711):318-328. DOI: 10.1016/S0140-6736 (09) 60883-7.

2. Kuo C.F., Luo S.F. Gout: Risk of premature death in gout unchanged for years. Nat. Rev. Rheumatol. 2017;13(4):200–201. DOI: 10.1038/nrrheum.2017.27.

3. Richette P., Clerson P., Perissin L. Revisiting comorbidities in gout: A cluster analysis. Ann. Rheum. Dis. 2015;74(1):142–147. DOI: 10.1136/ annrheumdis-2013-203779.

4. Tsygankova D.P., Fedorova N.V., Krivoshapova K.E., Maksimov S.A., Indukaeva E.V., Shapovalova E.B. et al. Socio-economic risk factors of arterial hypertension in the elderly. The Siberian Journal of Clinical and Experimental Medicine. 2020;35(4):111–118 (In Russ.). DOI: 10.29001/2073-8552-2020-35-4-111-118.

5. Eliseev M.S., Novikova A.M. Comorbidity in gout and hyperuricemia: Prevalence, causes, prospects of urate-lowering therapy. Therapeutic Archive. 2019;91(5):120–128 (In Russ.). DOI: 10.26442/00403660.2019.05.000232.

6. Polyanskaya A.V., Tuzkova T.S., Patorskaya O.A. Clinical characteristics, features of comorbidity and cardiac disorders in patients with gout and arterial hypertension. Military Medicine. 2018;2:57–60 (In Russ.).

7. Feig D.I., Kang D.H., Johnson R.J. Uric acid and cardiovascular risk. N. Engl. J. Med. 2008;359:1811–1821. DOI: 10.1056/NEJMra0800885.

8. Kushnarenko N.N., Mishko M.Yu., Medvedeva T.A. Polymorphism of folate cycle genes in patients with gout in the Russian population of the Trans-Baikal Territory. The Siberian Journal of Clinical and Experimental Medicine. 2020;35(1):142–150 (In Russ.). DOI: 10.29001/2073-85522020-35-1-142-150.

9. Koshelskaya O.A., Zhuravleva O.A., Karpov R.S. Markers of chro nic kidney disease in high-risk hypertensive patients: Relationship with abnormal circadian blood pressure profile and intrarenal vascular resistance. Arterial Hypertension. 2018;24(4):478–489 (In Russ.). DOI: 10.18705/1607-419X-2018-24-4-478-489.

10. Eliseev M.S. Classification criteria for gout (ACR/ EULAR recommendations). Scientific and Practical Rheumatology. 2015;53(6):581–585 (In Russ.).

11. Wallace S.L., Robinson H., Masi A.T., Decker J.L., McCarty D.J., Yü T.F. Preliminary criteria for the classification of the acute arthritis of gout. Arthritis Rheum. 1977;20(3):895–900. DOI: 10.1002/art.1780200320.

12. Lang T.A., Altman D.G. Statistical analyses and method sinthepublished literature: The SAMPL guidelines. Medical Writing. 2016;25(3):31–36. DOI: 10.18243/eon/2016.9.7.4.

13. Mudrov V.A. Algorithms of correlation analysis of data in biomedical research using the SPSS software package. Zabaikalsky Meditsinskii Vestnik. 2020;2:169–176 (In Russ.). URL: http://zabmedvestnik.ru/arhiv-nomerov/nomer-2-za-2020-god/algoritmy-korreljacionnogo-analiza-dannyh-v-biomedicinskih-issledovanijah-s-pomoshhju-paketa-programm-spss (available: 9.07.2021).

14. Islam M.S. Ambulatory blood pressure monitoring in the diagnosis and treatment of hypertension. Adv. Exp. Med. Biol. 2017;956:109–116. DOI: 10.1007/5584_2016_177.

15. Rogoza A.N. Modern non-invasive methods of blood pressure measurement for the diagnosis of arterial hypertension and evaluation of the effectiveness of antihypertensive therapy: A manual for doctors. Мoscow: Medica; 2007:72 (In Russ.).