Statistical modeling to determine severity of respiratory sarcoidosis and parameters associated with cardiac sarcoidosis: as a way to stratify the risk of developing pulmonary hypertension
T. P. Kalacheva,
O. A. Denisova,
N. G. Brazovskaya,
S. V. Fedosenko,
M. A. Karnaushkina,
V. L. Ostanko,
G. M. Chernyavskaya,
E. V. Kalyuzhina,
G. E. Chernogoryuk,
I. A. Palchikova,
D. S. Romanov,
I. L. Purlik,
K. A. Kulumaeva,
V. V. Kalyuzhin https://doi.org/10.20538/1682-0363-2025-1-36-44
Abstract
Aim. Using statistical modeling techniques, we aim to develop a model that optimizes the prediction of severity of sarcoidosis that affects the respiratory system (SRS) based on the identification and determination of signs (anamnestic, clinical, laboratory, instrumental examination data, etc.) associated with disease severity and subsequent stratification of the long-term risk for pulmonary hypertension (PH) development.
Materials and methods. The 12-year observational cohort comparative study included 298 participants, both male and female, who had SRS. More than 200 different patient examination parameters were analyzed. The models were built using logistic regression and linear discriminant analysis. The quality of the models was assessed by constructing a classification matrix, calculating sensitivity and specificity as well as calculating the area under ROC curve.
Results. As a result of the study, optimal classification models were developed for predicting SRS severity, constructed using various methods of statistical modelling. The models demonstrated that several characteristics, including parameters of echocardiography examination of patients (including indicators that allow for indirect diagnosis of PH), are associated with disease severity. A set of characteristics associated with particular sarcoidosis severity will allow prediction of it upon confirmation of diagnosis (individual prognosis), as well as patient management (observation or requiring the prescription of pathogenetic immunosuppressive therapy).
Conclusion. Such a complex model for predicting disease severity in patients with a non-cardiac profile (SRS) is of great importance for risk stratification in terms of PH development in patients with severe sarcoidosis. Further analysis of the features identified during model construction can help clinicians to contribute to more accurate predictions of SRS severity in real-world clinical practice.
About the Authors
T. P. Kalacheva
Siberian State Medical University
Russian Federation
Competing Interests:
Russian Federation
2, Moscow Trakt, Tomsk, 634050
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
O. A. Denisova
Siberian State Medical University
Russian Federation
Competing Interests:
Russian Federation
2, Moscow Trakt, Tomsk, 634050
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
N. G. Brazovskaya
Siberian State Medical University
Russian Federation
Competing Interests:
Russian Federation
2, Moscow Trakt, Tomsk, 634050
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
S. V. Fedosenko
Siberian State Medical University
Russian Federation
Competing Interests:
Russian Federation
2, Moscow Trakt, Tomsk, 634050
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
M. A. Karnaushkina
Patrice Lumumba Peoples’ Friendship University (RUDN University)
Russian Federation
Competing Interests:
Russian Federation
6, Miklukho-Maklaya Str., Moscow, 117198
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
V. L. Ostanko
Siberian State Medical University
Russian Federation
Competing Interests:
Russian Federation
2, Moscow Trakt, Tomsk, 634050
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
G. M. Chernyavskaya
Siberian State Medical University
Russian Federation
Competing Interests:
Russian Federation
2, Moscow Trakt, Tomsk, 634050
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
E. V. Kalyuzhina
Siberian State Medical University
Russian Federation
Competing Interests:
Russian Federation
2, Moscow Trakt, Tomsk, 634050
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
G. E. Chernogoryuk
Siberian State Medical University
Russian Federation
Competing Interests:
Russian Federation
2, Moscow Trakt, Tomsk, 634050
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
I. A. Palchikova
Tomsk Regional Clinical Hospital
Russian Federation
Competing Interests:
Russian Federation
96, I. Chernykh Str., Tomsk, 634063
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
D. S. Romanov
Siberian State Medical University
Russian Federation
Competing Interests:
Russian Federation
2, Moscow Trakt, Tomsk, 634050
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
I. L. Purlik
Siberian State Medical University
Russian Federation
Competing Interests:
Russian Federation
2, Moscow Trakt, Tomsk, 634050
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
K. A. Kulumaeva
Siberian State Medical University
Russian Federation
Competing Interests:
Russian Federation
2, Moscow Trakt, Tomsk, 634050
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
V. V. Kalyuzhin
Siberian State Medical University
Russian Federation
Competing Interests:
Russian Federation
2, Moscow Trakt, Tomsk, 634050
Competing Interests:
The authors declare the absence of obvious or potential conflicts of interest related to the publication of this article.
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For citations:
Kalacheva T.P., Denisova O.A., Brazovskaya N.G., Fedosenko S.V., Karnaushkina M.A., Ostanko V.L., Chernyavskaya G.M., Kalyuzhina E.V., Chernogoryuk G.E., Palchikova I.A., Romanov D.S., Purlik I.L., Kulumaeva K.A., Kalyuzhin V.V. Statistical modeling to determine severity of respiratory sarcoidosis and parameters associated with cardiac sarcoidosis: as a way to stratify the risk of developing pulmonary hypertension. Bulletin of Siberian Medicine. 2025;24(1):36-44. https://doi.org/10.20538/1682-0363-2025-1-36-44
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