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. 2025 Feb 5;20(2):e0318332.
doi: 10.1371/journal.pone.0318332. eCollection 2025.

Prevalence, influencing factors, and prediction model construction of anemia in ankylosing spondylitis based on real-world data: An exploratory study

Yifan Gong  1 , Kun Yang  1 , Zhaoyang Geng  2 , Hongxiao Liu  1
Affiliations

Prevalence, influencing factors, and prediction model construction of anemia in ankylosing spondylitis based on real-world data: An exploratory study

Yifan Gong et al. PLoS One. .

Abstract

Objective: This study aimed to explore the prevalence and influencing factors of anemia in patients with ankylosing spondylitis (AS) using real-world data and to construct a predictive model for anemia in AS.

Methods: In November 2023, we accessed the database from China Rheumatoid Arthritis Registry of Patients with Chinese Medicine (CERTAIN). Clinical data of AS collected from the CERTAIN between March 2022 and September 2023 were analyzed. Demographic information, clinical assessment scales, and laboratory test results of the patients were collected. According to the anemia diagnostic criteria established by the World Health Organization (WHO) in 2018, patients were divided into anemia group and non-anemia group. Statistical analyses were performed using SPSS 25.0 software, including χ2 tests, independent sample t-tests to compare differences between the two groups, and multivariate stepwise logistic regression analysis to explore the influencing factors of anemia in AS. The predictive efficacy of the model was evaluated by plotting receiver operating characteristic (ROC) curves. Calibration was assessed through the Hosmer-Lemeshow goodness-of-fit test, and a calibration curve was plotted to comprehensively evaluate the predictive capability of the model.

Results: A total of 251 patients were included in this study, among which 58 cases had anemia (23.1%). There were significant differences in gender, ossification, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) indicators, and clinical assessment scale results between the two groups (P < 0.05). The results of multivariate stepwise logistic regression analysis showed that female gender, underweight, ossification, abnormal CRP and ESR were independent risk factors for anemia in AS (P < 0.05). Based on the results of multivariate stepwise logistic regression analysis, a predictive model for anemia in AS was established as Logit(P) = -5.02 + 2.041 × gender -1.11 × BMI(body mass index) category + 1.103 × ossification category + 0.942 × CRP category + 1.476 × ESR category. The ROC curve analysis showed that the area under the curve of the model for predicting anemia in AS was 0.857 (95% CI: 0.808 ~ 0.906). The Omnibus test of model coefficients yielded χ2 = 85.265, P < 0.001. The Hosmer-Lemeshow test showed χ2 = 7.005, P = 0.536 (P > 0.05).

Conclusion: Analysis of real-world AS diagnosis and treatment data showed that the prevalence of anemia in Chinese AS was 23.1%. The occurrence of anemia was closely related to female gender, underweight, ossification, and abnormal CRP and ESR. The logistic model constructed based on these indicators for predicting the risk of anemia in AS demonstrated good efficacy.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Receiver Operating Characteristic (ROC) curve of the predictive model.
ROC: Receiver Operating Characteristic; AUC: area under the receiver operating characteristic (ROC) curve.
Fig 2
Fig 2. Calibration plot.
See this image and copyright information in PMC

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