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1 Drs. Shiping He, Ruofan Li and Shangyi Jin contributed equally to this study.
Shiping He
Footnotes
1 Drs. Shiping He, Ruofan Li and Shangyi Jin contributed equally to this study.
Affiliations
Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China
1 Drs. Shiping He, Ruofan Li and Shangyi Jin contributed equally to this study.
Shangyi Jin
Footnotes
1 Drs. Shiping He, Ruofan Li and Shangyi Jin contributed equally to this study.
Affiliations
Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China
Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China
Department of Rheumatology, The First Affiliated Hospital of Baotou Medical College, Inner Mongolia University of Science and Technology, Baotou, China
Department of Rheumatology and Immunology, Tangdu hospital of Air Force Military Medical University, Xi'an, ChinaDepartment of Clinical Immunology and Rheumatology, Xijing Hospital of Air Force Military Medical University, Xi'an, China
Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China
Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China
Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China
Department of Rheumatology and Clinical Immunology, Peking Union Medical College Hospital (PUMCH), Chinese Academy of Medical Sciences & Peking Union Medical College, National Clinical Research Center for Dermatologic and Immunologic Diseases (NCRC-DID), Ministry of Science & Technology, State Key Laboratory of Complex Severe and Rare Diseases, Key Laboratory of Rheumatology and Clinical Immunology, Ministry of Education, Beijing, China
Disease relapse is common in patients with Takayasu arteritis.
•
Disease duration <24 months, history of relapse, history of cerebrovascular events, aneurysms, ascending aorta or aortic arch involvement, number of involved arteries ≥6, elevated white blood cell count, and elevated high-sensitivity C-reactive protein level at baseline independently increased the risk of relapse.
•
With good discrimination and calibration, this prediction model can help to identify high-risk patients for relapse and assist clinical decision-making.
Abstract
Background
Takayasu arteritis (TAK) is a large-vessel vasculitis with high relapse rate. Longitudinal studies identifying risk factors of relapse are limited. We aimed to analyze the associated factors and develop a risk prediction model for relapse.
Methods
We analyzed the associated factors for relapse in a prospective cohort of 549 TAK patients from the Chinese Registry of Systemic Vasculitis cohort between June 2014 and December 2021 using univariate and multivariate Cox regression analyses. We also developed a prediction model for relapse, and stratified patients into low-, medium-, and high-risk groups. Discrimination and calibration were measured using C-index and calibration plots.
Results
At a median follow-up of 44 (IQR 26–62) months, 276 (50.3%) patients experienced relapses. History of relapse (HR 2.78 [2.14–3.60]), disease duration <24 months (HR 1.78 [1.37–2.32]), history of cerebrovascular events (HR 1.55 [1.12–2.16]), aneurysm (HR 1.49 [1.10–2.04], ascending aorta or aortic arch involvement (HR 1.37 [1.05–1.79]), elevated high-sensitivity C-reactive protein level (HR 1.34 [1.03–1.73]), elevated white blood cell count (HR 1.32 [1.03–1.69]), and the number of involved arteries ≥6 (HR 1.31 [1.00–1.72]) at baseline independently increased the risk of relapse and were included in the prediction model. The C-index of the prediction model was 0.70 (95% CI 0.67–0.74). Predictions correlated with observed outcomes on the calibration plots. Compared to the low-risk group, both medium and high-risk groups had a significantly higher relapse risk.
Conclusions
Disease relapse is common in TAK patients. This prediction model may help to identify high-risk patients for relapse and assist clinical decision-making.
Takayasu arteritis (TAK) is a chronic systemic large vessel vasculitis characterized by damage to the aorta and its major branches. TAK primarily occurs in young women in Asia, with a male-to-female ratio of approximately 1:8 [
]. Constitutional symptoms of systemic inflammation and vascular complications are the major clinical manifestations of patients with TAK. Despite improved treatment strategies, the majority of patients experience relapses. It is generally accepted that TAK should be treated with immunosuppressive therapy to control acute disease flare and maintain remission [
]. Therefore, assessing or predicting the risk of relapse and better control of disease activity are essential to improve the prognosis of TAK. Previous studies have suggested that male sex, carotidynia, renal hypertension, Numano type V TAK, and elevated C-reactive protein (CRP) level are risk factors for disease relapse [
]. However, these retrospective studies were small in sample sizes with conflicting results. Therefore, prospective longitudinal study is needed to clarify the risk factors for relapse. To address this gap, this study is aimed to comprehensively analyze the prognostic factors and develop a risk prediction model for relapse in a large prospective TAK patient cohort.
2. Methods
2.1 Participants
This study included TAK patients from 8 institutions of the Chinese Registry of Systemic Vasculitis (Fig. 1). All patients registered in this cohort must fulfill the 1990 American College of Rheumatology (ACR) classification criteria for TAK [
]. Patients with less than two follow-up visits and those who were followed up for less than 6 months were excluded. Other exclusion criteria were malignancy and patient with missing outcomes. All participants were in remission when they were enrolled into the study. For patients with active disease at registration, the baseline was adjusted to the time when the disease was in remission for more than three months. Demographic, laboratory, and imaging data were prospectively collected. Patients were followed up and evaluated every 3–6 months. Patients in this study were uniformly evaluated and treated based on the European League Against Rheumatism (EULAR) and ACR recommendations for the management of large vessel vasculitis (LVV) [
]. Patients with active disease were treated with glucocorticoid (GC) therapy (0.75–1.0 mg/kg/day prednisone-equivalent). The initial dose of GCs was maintained for the first 4 weeks, and then tapered to 7.5–10 mg/day for maintenance. In this study, 443 (80.7%, 443/549) patients were treated with GCs plus conventional immunosuppressive agents including methotrexate (MTX) in 98 (17.9%), cyclophosphamide (CYC) in 77 (14.0%), mycophenolate mofetil (MMF) in 56 (10.2%), leflunomide (LEF) in 35 (6.4%), azathioprine (AZA) in 4 (0.7%), and MTX plus MMF in 127 (23.1%), MTX plus CYC in 20 (3.6%), MTX plus AZA in 18 (3.3%), and MTX plus LEF in 8 (1.5%), and GCs monotherapy in 106 patients after first being diagnosed. Artery involvement were examined by computerized tomographic Angiography (CTA) for aorta and its major branches in head, neck and abdomen for each patient when they were initially diagnosed or when they were registered into the cohort. This CTA examination was repeated every 1 to 2 years for every patient in the cohort. Doppler ultrasonography for cephalic artery, subclavian arteries, common carotid arteries, internal and external carotid arteries, vertebral arteries, axillary arteries, branchial arteries, abdominal aorta, celiac artery, superior and inferior mesenteric arteries, hepatic and splenic artery, renal arteries, iliac arteries and femoral arteries were examined when the patient was first diagnosed or when registered to the cohort. Doppler ultrasonography for above arteries were repeated every 3 to 6 months. Cardiac ultrasonography was examined when diagnosed or registered and was repeated every 6 months for patients who had abnormal findings in the first examination and every year if there was no abnormal finding in the last examination. Since magnetic resonance angiography (MRA) and positron emission tomography (PET) are very expensive and not adopted as a routine examination in China, only a few patients were examined by these two image modalities. In this study, PET examination was performed for 30 patients before treatment and for 12 patients during follow-ups. Vascular involvement was defined as thickening, stenosis, occlusion, aneurysms, dilatation, or dissection of arteries due to TAK and was confirmed by CTA, MRA, or Doppler ultrasonography. This study was approved by the Ethics Committee of Peking Union Medical College Hospital and all patients provided written informed consent.
The primary endpoint was disease relapse. The definition of relapse was based on the National Institutes of Health (NIH) criteria proposed by Kerr et al. [
]. In the Kerr criteria, disease was considered to be active with the satisfaction of two or more criteria (other cause was excluded): a. constitutional features (e.g., fever, musculoskeletal symptoms); b. elevated erythrocyte sedimentation rate; c. feature of vascular ischemia or inflammation; and d. positive imaging results). In the 2018 EULAR recommendations for the management of LVV, disease relapse was defined as recurrence of active disease fulfilling the criteria of presence of typical signs or symptoms of active LVV and at least one of the following: a. current activity on imaging or biopsy; b. ischemic complications attributed to LVV; c. persistently elevated inflammatory markers (after other causes have been excluded). The key symptoms that suggest recurrence of TAK as the follows: constitutional symptoms (e.g., fever, arthralgia, arthritis and myalgia, decrease of body weight, fatigue, night sweating after other causes were excluded), new onset or worsening of the following symptoms: limb claudication, amaurosis fugax, decrease of eyesight, angina pectoris, abdominal pain, and neck pain after other caused were excluded.
2.3 Statistical analysis
In total, 46 clinical variables, including demographic, laboratory and imaging variables were collected at baseline. 1.6–6.2% of vascular imaging and laboratory parameters were missed. The missing values were multiply imputed by the R statistical software (MICE package), and the 5th set of imputations was used for analysis. Numerical variables were described as mean (standard deviation [SD]) or median (interquartile ranges [IQR]), and were compared using the Student's t-test or Mann–Whitney U test. Categorical variables were presented as proportions, and were compared using the chi-square or Fisher's exact tests. The reverse Kaplan–Meier method was used to estimate the median follow-up times. Univariate analyses were performed using a Cox regression model to identify significant risk factors for disease relapse. Variables that were significant at P < 0.10 were included in multivariate analysis (backward stepwise). All tests were two-sided, and statistical significance was set at P < 0.05. Statistical analyses were performed using R statistical software, version 4.2.0.
2.4 Development and validation of the prediction model
Prediction model was developed and validated following the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) [
]. All available data on the database were used to maximize the power of the results. According to the literature review and univariate Cox regression analyses, 15 candidate baseline variables, including female sex, disease duration <24 months, history of relapse, cardiac involvement, history of cerebrovascular events, aneurysm, common carotid artery involvement, subclavian artery involvement, ascending aorta or aortic arch involvement, thoracic aorta involvement, symmetrical arteries involvement, number of involved arteries ≥6, elevated white blood cell (WBC) count, elevated platelet count, and elevated high-sensitivity C-reactive protein (hsCRP), were included in a multivariate Cox regression analysis using backward stepwise selection procedure (Supplementary Table 1). Finally, 8 predictors, including disease duration <24 months, history of relapse, history of cerebrovascular events, aneurysms, ascending aorta or aortic arch involvement, number of involved arteries ≥6, elevated WBC count, and elevated hsCRP level, were selected into the prediction model. Accordingly, no interaction between the predictors was observed. The overall test of the proportionality of hazards over the follow-up period was not statistically significant.
Harrell's concordance index (C-index), calibration plots and Brier scores were used to test the model performance in discrimination and calibration. R package (pec) was used to evaluate the performance of risk prediction models in survival analysis. The Brier score is a weighted average of the squared distances between the observed survival status and the predicted survival probability of a model. The enhanced bootstrap procedure (1000 repetitions) was used for internal validation. The nomogram of prediction model was built by Cox regression to calculate the predicted survival probability. Patients were categorized into low-, medium-, and high-risk groups (3:4:3) based on the 36-month relapse-free probability calculated by nomogram. Kaplan–Meier analysis was used to compare relapse-free survival between the 3 risk groups. In addition, sensitivity analysis was conducted with complete cases to assess the potential effect of missing values on relapse-free survival.
3. Results
3.1 Clinical characteristics and overall relapses
During a median follow-up of 44 (IQR 26–62) months, 276 (50.3%) patients experienced disease relapse (Fig. 2(A)). The main manifestations of disease relapse (Fig. 2(B)) included recurrence of the key symptoms of TAK (37.3%, 103/276), presentations of vascular ischemia or inflammation (45.7%, 126/276), elevated inflammatory parameters (55.4%, 153/276), and new onset or worsening of vascular changes evidenced by vascular image modalities (44.2%, 122/276).
Kaplan–Meier curves of relapse-free survival during 84 months follow-up (A); The key clinical features of 276 patients with relapse (B); TAK, Takayasu arteritis.
The demographic and clinical characteristics of the 549 patients with TAK were shown in Table 1. Disease relapses were significantly more frequent in female patients, patients with younger age at disease onset, or a history of relapse (P < 0.05). Patients with cardiac involvement, especially aortic regurgitation, had a higher rate of relapse (P < 0.05). Aortic arch and its branches involvement were common in patients with TAK and were related to the recurrence of active disease (P < 0.01).
Table 1Demographic and clinical characteristics in 549 patients with TAK.
Parameters
Total population (n = 549)
Patients without relapse (n = 273)
Patients with relapse (n = 276)
P value
Age (years), mean(SD)
32 (9.6)
33 (10.0)
31 (9.2)
0.017*
BMI (kg/m2), mean(SD)
21.9 (3.3)
21.9 (3.1)
22.0 (3.5)
0.629
Female sex, n (%)
482 (87.8)
231 (84.6)
251 (90.9)
0.033*
Smoker, n (%)
33 (6.0)
18 (6.6)
15 (5.4)
0.695
Diabetes mellitus, n (%)
12 (2.2)
7 (2.6)
5 (1.8)
0.7558
Dyslipidemia, n (%)
72 (13.1)
39 (14.3)
33 (12.0)
0.4953
History of pulmonary tuberculosis, n (%)
37 (6.7)
18 (6.6)
19 (6.9)
1.000
Age at disease onset (years), mean(SD)
26 (9.2)
27 (9.6)
26 (8.9)
0.033*
Diagnostic delay (months), median (IQR)
4 (1, 24)
4 (1, 24)
4 (1, 24)
0.538
Disease duration (months), median (IQR)
31 (12, 77)
34 (11, 78)
28 (12, 76)
0.856
History of relapse, n (%)
175 (31.9)
44 (16.1)
131 (47.5)
<0.001**
Carotidynia, n (%)
90 (16.4)
41 (15.0)
49 (17.8)
0.453
Claudication, n (%)
267 (48.6)
114 (41.8)
153 (55.4)
0.002*
History of revascularization procedures, n (%)
166 (30.2)
87 (31.9)
79 (28.6)
0.463
Musculoskeletal involvement, n (%)
86 (15.7)
36 (13.2)
50 (18.1)
0.141
Arthritis
80 (14.6)
34 (12.5)
46 (16.7)
0.201
Myalgia
16 (2.9)
5 (1.8)
11 (4.0)
0.213
Eye involvement, n (%)
67 (12.2)
39 (14.3)
28 (10.1)
0.177
Impaired vision
64 (11.7)
38 (13.9)
26 (9.4)
0.131
Loss of vision
10 (1.8)
5 (1.8)
5 (1.8)
1.000
Retinopathy
8 (1.5)
4 (1.5)
4 (1.4)
1.000
Cardiac involvement, n (%)
154 (28.1)
63 (23.1)
91 (33.0)
0.013*
Angina
29 (5.3)
16 (5.9)
13 (4.7)
0.681
Myocardial infarction
16 (2.9)
6 (2.2)
10 (3.6)
0.460
Cardiomyopathy
6 (1.1)
1 (0.4)
5 (1.8)
0.223
Heart failure
19 (3.5)
5 (1.8)
14 (5.1)
0.065
Aortic regurgitation
115 (20.9)
40 (14.7)
75 (27.2)
0.001**
Hypertension, n (%)
185 (33.7)
92 (33.7)
93 (33.7)
1.000
Renal insufficiency, n (%)
10 (1.8)
7 (2.6)
3 (1.1)
0.330
History of syncope, n (%)
68 (12.4)
27 (9.9)
41 (14.9)
0.102
History of cerebrovascular events, n (%)
75 (13.7)
32 (11.7)
43 (15.6)
0.233
Vascular involvement, n (%)
Brachiocephalic trunk
217 (39.5)
92 (33.7)
125 (45.3)
0.007**
Common carotid artery
473 (86.2)
220 (80.6)
253 (91.7)
<0.001**
Subclavian artery
447 (81.4)
205 (75.1)
242 (87.7)
<0.001**
Ascending aorta or aortic arch
222 (40.4)
96 (35.2)
126 (45.7)
0.016*
Thoracic aorta
197 (35.9)
91 (33.3)
106 (38.4)
0.250
Abdominal aorta
239 (43.5)
112 (41.0)
127 (46.0)
0.275
Celiac trunk
154 (28.1)
75 (27.5)
79 (28.6)
0.838
Mesenteric artery
161 (29.3)
82 (30.0)
79 (28.6)
0.787
Renal artery
185 (33.7)
96 (35.2)
89 (32.2)
0.527
Iliac artery
32 (5.8)
14 (5.1)
18 (6.5)
0.607
Symmetrical arteries involvement
465 (84.7)
216 (79.1)
249 (90.2)
0.001*
Number of involved arteries, median (IQR)
5 (4, 7)
5 (4, 7)
6 (4, 8)
0.001*
Aneurysm
90 (16.4)
35 (12.8)
55 (19.9)
0.033
Thrombosis
21 (3.8)
9 (3.3)
12 (4.3)
0.675
WBC (109/L), mean (SD)
8.8 (3.0)
8.5 (3.0)
9.2 (3.0)
0.007**
Hemoglobin (g/L), mean (SD)
125.6 (16.8)
127.9 (16.7)
123.2 (16.6)
0.001**
Platelet (109/L), mean (SD)
276.0 (84.1)
262.3 (71.7)
289.5 (92.9)
<0.001**
HsCRP (mg/L), median (IQR)
2.4 (0.7, 8.1)
1.6 (0.6, 5.5)
4.2 (1.0, 12.4)
<0.001**
ESR (mm/h), median (IQR)
9.0 (5.0, 16.0)
7.0 (4.0, 13.0)
12.0 (6.0, 18.0)
<0.001**
Previous treatment after diagnosis, n (%)
GCs monotherapy
106 (19.3)
53 (19.4)
53 (19.2)
1.000
GCs plus immunosuppressants
443 (80.7)
220 (80.6)
223 (80.8)
Initial GCs dose, mg PDN, median (IQR)
50.0 (30.0, 60.0)
40.0 (30.0, 60.0)
50.0 (40.0, 60.0)
<0.001**
Immunosuppressants
CTX
77 (14.0)
40 (14.7)
37 (13.4)
0.104
MMF
56 (10.2)
27 (9.9)
29 (10.5)
MTX
98 (17.9)
58 (21.2)
40 (14.5)
LEF
35 (6.4)
20 (7.3)
15 (5.4)
AZA
4 (0.7)
2 (0.7)
2 (0.7)
CTX + MTX
20 (3.6)
8 (2.9)
12 (4.3)
MMF + MTX
127 (23.1)
50 (18.3)
77 (27.9)
MTX + AZA
18 (3.3)
12 (4.4)
6 (2.2)
MTX + LEF
8 (1.5)
3 (1.1)
5 (1.8)
TAK, Takayasu arteritis; SD, standard deviation; IQR, interquartile range; BMI, body mass index; WBC, white blood cell; HsCRP, high-sensitivity C-reactive protein; ESR, erythrocyte sedimentation rate; GCs, glucocorticoids; PDN prednisone; CYC, cyclophosphamide; MMF, mycophenolate mofetil; MTX, methotrexate; LEF, leflunomide; AZA, azathioprine; *, P < 0.05; **, P < 0.01.
The univariate and multivariate Cox regression analyses for factors related to relapse were shown in Table 2. In univariate analysis, female sex, disease duration <24 months, history of relapse, cardiac involvement (cardiomyopathy, heart failure, and aortic regurgitation), common carotid artery, subclavian artery, ascending aorta or aortic arch, thoracic aorta involvement, symmetrical arteries involvement, number of involved arteries ≥6, aneurysm, elevated WBC count, elevated platelet count, and elevated hsCRP level at baseline were associated with the recurrence of active disease (P < 0.05). In multivariate analysis, history of relapse (HR 2.78, 95% CI 2.14–3.60, P < 0.001), disease duration <24 months (HR 1.78, 95% CI 1.37–2.32, P < 0.001), history of cerebrovascular events (HR 1.55, 95% CI 1.12–2.16, P = 0.009), aneurysm (HR 1.49, 95% CI 1.10–2.04, P = 0.011), ascending aorta or aortic arch involvement (HR 1.37, 95% CI 1.05–1.79, P = 0.020), elevated hsCRP level (HR 1.34, 95% CI 1.03–1.73, P = 0.026), elevated WBC count (HR 1.32, 95% CI 1.03–1.69, P = 0.029), and number of involved arteries ≥6 (HR 1.31, 95% CI 1.00–1.72, P = 0.046) at baseline independently increased the relapse risk, and were included into the final prediction model.
Table 2Univariate and multivariate Cox regression analyses in 549 patients with TAK.
Parameters
Univariate analysis
Multivariate analysis
HR (95% CI)
P value
HR (95% CI)
P value
Age (years)
0.99 (0.98–1.00)
0.191
BMI (kg/m2)
1.01 (0.97–1.04)
0.743
Female sex
1.54 (1.02–2.33)
0.039*
Smoker
0.87 (0.52–1.47)
0.614
History of pulmonary tuberculosis
1.11 (0.69–1.77)
0.669
Age at disease onset (years)
0.99 (0.98–1.01)
0.389
Diagnostic delay <12 months
1.08 (0.85–1.39)
0.524
Disease duration <24 months
1.27 (1.00–1.62)
0.046*
1.78 (1.37–2.32)
<0.001**
History of relapse
2.39 (1.88–3.03)
<0.001**
2.78 (2.14–3.60)
<0.001**
Carotidynia
1.23 (0.90–1.68)
0.189
Claudication
1.09 (0.86–1.39)
0.471
History of revascularization procedures
0.91 (0.70–1.18)
0.485
Musculoskeletal involvement
1.28 (0.94–1.74)
0.114
Eye involvement
0.84 (0.57–1.24)
0.388
Cardiac involvement
1.29 (1.00–1.66)
0.046*
Angina
0.81 (0.46–1.41)
0.449
Myocardial infarction
1.35 (0.72–2.54)
0.355
Cardiomyopathy
2.56 (1.06–6.22)
0.038*
Heart failure
1.90 (1.11–3.25)
0.020*
Aortic regurgitation
1.42 (1.09–1.85)
0.010*
Renal insufficiency
0.56 (0.18–1.74)
0.314
History of syncope
1.31 (0.94–1.82)
0.116
History of cerebrovascular events
1.35 (0.97–1.87)
0.071
1.55 (1.12–2.16)
0.009**
Vascular involvement
Common carotid artery
1.86 (1.21–2.85)
0.004**
Subclavian artery
1.69 (1.18–2.42)
0.004**
Ascending aorta or aortic arch
1.59 (1.26–2.02)
<0.001**
1.37 (1.05–1.79)
0.020*
Thoracic aorta
1.39 (1.09–1.77)
0.008**
Abdominal aorta
1.15 (0.91–1.46)
0.243
Celiac trunk
1.07 (0.82–1.39)
0.617
Mesenteric artery
1.04 (0.80–1.35)
0.766
Renal artery
0.89 (0.69–1.15)
0.370
Iliac artery
1.27 (0.79–2.05)
0.330
Symmetrical arteries involvement
1.72 (1.15–2.55)
0.008**
Number of involved arteries ≥6
1.57 (1.24–1.99)
<0.001**
1.31 (1.00–1.72)
0.046*
Aneurysm
1.52 (1.13–2.04)
0.006**
1.49 (1.10–2.04)
0.011*
Thrombosis
1.47 (0.82–2.62)
0.195
Elevated WBC
1.45 (1.14–1.84)
0.002**
1.32 (1.03–1.69)
0.029*
Elevated platelet
1.61 (1.21–2.14)
0.001**
Elevated hsCRP
1.47 (1.14–1.88)
0.003**
1.34 (1.03–1.73)
0.026*
Elevated ESR
1.22 (0.92–1.63)
0.169
TAK, Takayasu arteritis; HR, hazard ratio; CI, confidence interval; BMI, body mass index; WBC, white blood cell; hsCRP, high-sensitivity C-reactive protein; ESR, erythrocyte sedimentation rate; *, P < 0.05; **, P < 0.01.
3.3 Performance and validation of the prediction model
The nomogram of prediction model was shown in Fig. 3(A). The C-index of the prediction model was 0.70 (95% CI 0.67–0.73). Brier scores for predicting relapse-free survival at 12 and 36 months were 0.067 and 0.144, respectively. Similar C-index was obtained (0.70, 95% CI 0.67–0.74) in the internal validation performed by bootstrapping (1000 repetitions). The predicted probabilities on the calibration plots were close to the observed probabilities at 12 and 36 months (Fig. 3(B) and (C)).
Fig. 3Nomogram and calibration plots of the prediction model.
Nomogram based on the multivariate Cox regression analysis (A); Calibration plots at 12 months (B) and 36 months (C) in the internal validation by bootstrapping (1000 repetitions).
Participants were categorized into low-, medium-, and high-risk groups (3:4:3) based on the 36-month relapse-free probability calculated by nomogram. The low-risk group scored 0–64 (36-month relapse probability <0.3), the medium-risk group scored 65–180 (36-month relapse probability 0.3–0.7), and the high-risk group scored 181–500 (36-month relapse probability >0.7). Kaplan–Meier curves showed a statistically significant difference in relapse-free survival between the three groups (P < 0.001, Fig. 4), which further verified the model prediction accuracy. The median relapse-free time of the high-risk group was 13 (IQR 7–34) months.
Fig. 4Relapse-free survival curves by risk groups.
Relapse-free survival curves of low-, medium-, and high-risk groups. The median relapse-free times of the high- and medium-risk groups was 13 months and 40 months, respectively.
Furthermore, we excluded patients with missing data (n = 59) and included patients with complete data (n = 490, Supplementary Table 2) to develop a risk prediction to assess the potential effect of missing values on relapse-free survival. A sensitivity analysis was performed with similar results of C-index (0.71 [0.68–0.74]), Brier scores at 12 and 36 months (0.069 and 0.145, respectively), and calibration plots (Supplementary Fig. 1(A) and (B)). Compared to the low-risk group, both the medium and high-risk groups had a significantly higher relapse risk (P < 0.001, Supplementary Fig. 2). All these illustrated the imputation of missing value might make less effect on the performance of this prediction model.
4. Discussion
Relapse is the main theme of TAK. Maintenance the disease in a persistent remission is the biggest challenge in the management of TAK. Therefore, identifying high-risk patients for relapse is critical to the maintenance therapy. In this study, we included 3 clinical features (disease duration, history of relapse, and history of cerebrovascular events), 3 imaging indicators (aneurysms, ascending aorta or aortic arch involvement, and number of involved arteries) and 2 serological variables (WBC count and hsCRP) in the multivariate Cox proportional hazards model. This risk prediction model performed well in discrimination and calibration, and enabled the explicit calculation of relapse probability and risk stratification. Thus, the model can assist physicians in the assessment of relapse risk and aid clinical decision-making. To the best of our knowledge, this is the largest prognostic study and the first prediction model for relapse in TAK patients.
Previous studies observed that more than half patients relapsed within the first five years after diagnosis [
]. In line with previous studies, up to 50.3% patients in our study relapsed during a median follow-up of 44 months. It was reported that male patients were prone to relapse [
]. Conversely, female patients in the present study had a higher risk of relapse than male patients (HR 1.54, 95% CI 1.02–2.33). Estrogen, genetics, and other factors may be involved in the pathogenesis of TAK, causing a high female-to-male ratio in TAK. Similarly, these factors may be involved in the recurrence of active disease. Thus, further studies are needed to explore the relationship between gender and disease relapse.
], we observed that disease durations <24 months was significantly associated with increased frequency of disease relapse (HR 1.78, 95% CI 1.37–2.32). In addition, patients with a history of relapse tended to have more relapses (HR 2.78, 95% CI 2.14–3.60). This suggests that treatment duration and follow-up frequency should be adjusted according to disease duration and efficacy of treatments. For those with a shorter disease duration or history of relapse, an extended course of maintenance therapy and close monitoring should be considered.
Aortic regurgitation is one of the most common complications in TAK [
Cardiovascular manifestations of Takayasu arteritis and their relationship to the disease activity: analysis of 204 Korean patients at a single center.
]. In our study, aortic regurgitation was associated with recurrence of active disease (HR 1.42, 95% CI 1.09–1.85). Additionally, history of cerebrovascular events independently increased the risk of relapse (HR 1.55, 95% CI 1.12–2.16). This suggests the arteries that responsible for brain blood supply should be closely monitored. Furthermore, previous studies found that history of cerebrovascular events and valvular heart disease were relevant to relapse, and increased the risk of poor prognosis [
]. Therefore, patients with cardiac and cerebrovascular involvement should be treated with extended maintenance therapy and close monitoring of disease activity should be applied.
Regarding vascular involvement, increased relapses were significantly associated with aortic arch and its main branches as well as thoracic aorta involvement (P < 0.01). Aneurysm (HR 1.49, 95% CI 1.10–2.04) and 6 or more arteries involved (HR 1.31, 95% CI 1.00–1.72) were associated with a higher risk of relapse. These findings were consistent with those of previous studies [
Cardiovascular manifestations of Takayasu arteritis and their relationship to the disease activity: analysis of 204 Korean patients at a single center.
]. Consistent with this finding, we found that patients with elevated hsCRP or WBC count at baseline had a 1.3-fold increased risk of relapse compared with those with normal levels at baseline in this study. Therefore, patients with TAK, especially those with aortic arch and thoracic aorta involvement, and elevated inflammatory parameters, should be monitored regularly to adjust treatment in order to decrease relapses.
There are some limitations in our study. Firstly, all participating centers are from tertiary medical centers. Patients had more severe diseases and their treatment was more challenging. This may cause patients selection bias in this study. Secondly, our study only included Chinese patients with no other ethnic groups. Consequently, whether the results of the present prediction model can be generalized to other ethnic groups needs further investigation. Finally, the follow-up time was relatively short compared to this life-long disease. Therefore, extended follow-up studies are needed to test the long-term prediction performance of this model.
5. Conclusion
In conclusion, patients with TAK are at high risk of relapse. Disease duration less than 2 years, history of relapse, history of cerebrovascular events, aneurysms, ascending aorta or aortic arch involvement, number of involved arteries ≥6, elevated WBC count, and elevated hsCRP levels at baseline are risk factors for disease relapse. The prediction model may provide useful clues to clinicians to help them to identify patients with high-risk of relapse and support individualized management.
Contributors
Shiping He, Ruofan Li and Shangyi Jin designed the research and wrote the manuscript. Hongbin Li, Xinwang Duan, Lili Pan, Lijun Wu, Yongfu Wang, Yan Zhang, and Zhenbiao Wu participated in data collection. Ruofan Li, Yunjiao Yang and Jing Li verified the data. Shiping He, Shangyi Jin and Yanhong Wang carried out data analysis. Xinping Tian and Xiaofeng Zeng helped optimize the research and supervised the study. All authors have read and approved the final manuscript.
Ethics approval
This study was approved by the Ethics Committee of Peking Union Medical College Hospital (JS-2038). All patients provided written informed consent.
Availability of data and materials
The data in this study are available from the corresponding author on reasonable request.
Declaration of Competing Interest
None declared.
Acknowledgments
The authors thank all patients, their families, and hospital staff who made this study possible.
Funding
This study was supported by CAMS Innovation Fund for Medical Sciences (CIFMS) (Grant number 2021-I2M-1-005) and National High Level Hospital Clinical Research Funding (Grant number 2022-PUMCH-B-013).
Cardiovascular manifestations of Takayasu arteritis and their relationship to the disease activity: analysis of 204 Korean patients at a single center.
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