Skip to main content
Download PDF

Effective factors on Sharp Score in patients with rheumatoid arthritis: a retrospective study

BMC Musculoskeletal Disorders volume 22, Article number: 865 (2021) Cite this article

Abstract

Background

This study aims to describe the association between sharp score and clinical indexes, bone metabolism indexes, Disease Activity Score (DAS28) and sociodemographic factors in rheumatoid arthritis (RA).

Methods

Data were collected from the HIS (hospital information system), a national inpatient database in China, with information on the patients hospitalized during the period from 2012 to 2019. The association between sharp score and effective factors were identified using multinomial logistic regression and association rule mining (ARM).

Results

Three thousand eight hundred and forty patients were included: 82.66% males, 17.34% females, mean (SD) age 56.95 (12.68) years and symptom duration 3.45 (1.09) years. Spearman correlation analysis and Association rules analysis showed that there were significant positive correlations between sharp score and effective factors. Logistic regression analysis presented that erythrocyte sedimentation rate (ESR), high-sensitivity C-reactive protein (CRP), rheumatoid factor (RF) were risk factors of sharp score. In the analysis of individual outcomes, sex, age, symptom duration, DSA28 score, RF, ever drinker, and radiographic grading of hands were influence factors of sharp score.

Conclusion

Sharp score should be taken into consideration in formulating treatment strategies in RA.

Peer Review reports

Background

Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by synovial membrane inflammation [1, 2]. Erosive joint damage and bone destruction are the most common manifestation of RA, which might induce ankylosis, malformation, even loss of normal joint function [3, 4]. Current goals of treatment in RA include achieving disease remission, reducing functional disability as well as minimizing pain [5, 6]. Erosions are the hallmark of bone destruction in RA [7, 8]. Controlling joint destruction and bone destruction have become the major objective for treating RA, because radiographic joint damage correlates strongly with long-term functional decline in RA patients [9, 10]. Radiographic grading of hands is the most commonly used method for the evaluation of different levels of bone erosion in clinical practice [11, 12]. Bone erosion score (vdH sharp score) has also been used to measure morphological parameters that quantify the bone erosion and bone destruction, giving useful information for early detection and early treatment of RA [13, 14]. Few studies to date have studied the overall impact of RA on sharp score and its effective factors. In addition, there has been no study that looks for effective factors associated with sharp score of RA on the basis of large data by doing the mining and analysis of this data.

This study retrospectively analyzed the clinical data of enrolled patients to investigate the value of sharp score and its effective factors in RA. The Spearman correlation analysis, Association rules analysis and Logistic regression analysis are methods of analysis that allows for the identification of risk factors associated with sharp score. Using these three methods, this study aims to: (a) sharp score exhibits diagnostic value for RA; and (b) analyze the role of effective factors as determinants of sharp score in RA.

Methods

Study design

We conducted this retrospective cohort study in a population of RA patients. RA was defined based on the classification criteria revised by the ACR/EULAR (American College of Rheumatology/European League Against Rheumatism) criteria in 2010 [15]. This study was reviewed and approved by the institutional committee of the First Hospital Affiliated to the Anhui University of Chinese Medicine on research ethics, and conforms to the ethical guidelines of the 1975 Declaration of Helsinki. All patients provided written informed consent for inclusion in this study.

Study population and data collection

A total of 3840 RA patients visited the First Hospital Affiliated to the Anhui University of Chinese Medicine from January 2012 to December 2019. During the follow-up, accumulated demographic and laboratory data obtained from patients’ electronic medical records were longitudinally examined. The following clinical data were obtained from all participants: age; sex; laboratory data, including ESR, CRP, RF, anti-cyclic citrullinated peptide antibody (CCP), immunoglobulins A (IGA), immunoglobulin G (IGG), immunoglobulin M (IGM), complement 3 (C3), and complement 4 (C4), bone alkaline phosphatase (BALP), osteocalcin (OC), Osteoprotegerin (OPG), receptor activator for nuclear factor-κB ligand (RANKL); DAS28 score, and sharp score.

Radiographic evaluation

Radiographs of the hands were assessed according to the Sharp method. In total, scores for the 3840 radiographs (from 3840 RA patients) were determined by two experienced rheumatologist who was blinded to the clinical data. Sixteen areas were considered for assessing erosions and joint space narrowing (JSN) for the hands. The maximum erosion score of the hands and wrists was 160. Accordingly, the maximum JSN score of the hands and wrists was 120. The sum of the erosion and JSN scores is the total Sharp/van der Heijde score (SHS) (maximum: 280). Therefore, radiographic joint destruction was quantified as the total SHS score divided by the duration of RA.

Statistical analysis

Descriptive statistics were used to summarize the demographic characteristics of the cohort. Continuous variables were expressed as mean ± standard deviation or median (interquartile range). The correlations between sharp score and clinical indexes during the study period were estimated using Spearman’s correlation coefficients. The associations of important covariates with sharp score were examined using the binary linear regression model and association rule mining (ARM) [16]. Analyses were performed using SPSS version 15.0 (SPSS Chicago, Ill, USA) and GraphPad software (version 8.0).

Results

Characteristics of the study population

The study sample was composed of 3840 RA patients, with a mean age of 56.95 years (standard deviation 12.68, range 18–95) and of whom 82.66% were female. The median number of sharp score was 20.00 (IQR: 7.00, 56.00). 3003 (78.15%) were seropositive for either RF and 3636 (94.68%) were seropositive for CCP. The mean (SD) symptom duration was 3.45 (1.09) years and the mean (SD) disease duration was 6.92 (1.20) years. The main characteristics of the study population are detailed in Table 1.

Table 1 Characteristics of study population (n = 3840)
Full size table

Spearman correlation analysis of sharp score and clinical indexes

To determine whether correlations existed between sharp score and clinical indexes, a Spearman correlation test was performed. Age, ESR, CRP, RF, IGA, IGG, IGM, C3, BALP, BGP, OPG, DAS28 were all positively correlated with sharp score, as seen in Fig. 1.

Fig. 1
figure 1

Spearman correlation analysis of sharp score and clinical indexes. Spearman correlation analyses were probed between sharp score with Age, ESR, CRP, RF, IGA, IGG, IGM, C3, BALP, BGP, OPG, and DAS28, respectively. R and p values were attached to each panel

Full size image

Association rules analysis of sharp score and clinical indexes

Association rules analysis of sharp score and clinical indexes can be found in Table 2. Set the minimum support to 80% and the minimum confidence to 80%. Through Aprior module analysis, the correlation between sharp score and clinical indexes was obtained, and the degree of lift was more than 1 and P<0.05.

Table 2 Association rules analysis of sharp score and clinical indexes
Full size table

Logistic regression analysis of sharp score and clinical indexes

Logistic regression analysis of risk factors of sharp score was carried out. Significant differences in sharp score were found between RA patients with ESR (p = 0.000), CRP (p = 0.023), RF (p = 0.000), indicating that ESR, CRP, RF were risk factors for sharp score, the higher expression of ESR, CRP, RF, the high score of sharp (Fig. 2).

Fig. 2
figure 2

Logistic regression analysis of sharp score and clinical indexes. Logistic regression analysis results showed three risk factors for sharp score: RF, CRP, and ESR

Full size image

Comparison of sharp score among different variables

As shown in Table 3, there was a higher sharp score of female patients compared to male (21.00, IQR (7.00, 60.13) vs 17.00 (IQR (7.00, 42.13)) and a high sharp score of over 50 years old compared to under 50 years old (24.00, IQR (10.00, 60.13) vs 10.00 (IQR (3.00, 45.00)). There was a higher sharp score of RF-positive compared to RF-negative (23.00, IQR (8.00, 64.63) vs 13.00 (IQR (4.38, 35.63)). There was a higher sharp score of ever-smoker compared to never-smoker (20.00, IQR (7.50, 56.50) vs 19.50 (IQR (4.50, 55.00)).

Table 3 Comparison of sharp score among different variables
Full size table

Discussion

This study was a large-sample retrospective study, which has characterized sharp score and its effective factors in RA. The role of DAS28, clinical indicators, bone metabolism markers, and sociodemographic factors as determinants of sharp score was examined. Age, ESR, CRP, RF, IGA, IGG, IGM, C3, C4, BALP, BGP, OPG, RANKL, DAS28 were associated with sharp score. ESR, CRP, RF were also risk factors of sharp score.

Joint damage is very common in the early stage of RA, even within 2 years following disease onset in most patients (70–93%) [17, 18]. Therefore, the probability of erosions occurring early in RA is adequately high [8, 19]. Therefore, joint damage can trigger generate and maintain pain, which is a principle cause of disability and functional decline [20]. The research conducted by Corbett et al. manifested that the occurrence of hand erosions in the first 2 years of RA was the strongest predictor of the dysfunction after 15 years [21]. Early quantitative assessment of joint destruction and bone erosion are the first step to prevent or decrease its damage [22, 23].

In spite of the lacking of a similar study so far depicting sharp score and its effective factors in RA, a few studies have described sharp score as an important observation index and effective factor of RA [24]. LMAJansen followed early RA patients for 1 year, concluded that progression of these lesions was predicted by the number of radiographic lesions and Sharp/van der Heijde score [12]. Similar findings were also observed in a cross-sectional research of RA patients with secondary SS (sSS) by Lindsay E. Brown et al., which found that RA patients with sSS exhibiting worse joint damage was associated with higher sharp score [13]. As a part of our ongoing research on the joint destruction and bone erosion, in the present study we focused on sharp score, which might have significant diagnostic value for RA.

As reflected by analyses of Spearman correlation and Association rules, considerable positive correlations were noted between age, ESR, CRP, RF, IGA, IGG, IGM, C3, BALP, BGP, OPG, DAS28 and sharp score in our work. In addition, Logistic regression analysis elucidated ESR, CRP, and RF as risk factors for the sharp score. DAS28, clinical indicators, bone metabolism markers, and sociodemographic factors differences in sharp score outcomes remains enigmatic in the China and little has been known about the influence of sharp score. There are differences in sharp score of different genders, which showed that a higher sharp score of female patients compared to male [25]. There are different explanations of these gender-based differences, which may be the biological progression of disorders and self-perception and reporting of symptoms [26]. Higher DAS28, RF+ and radiographic grading were also associated with sharp score, which can be attributable to its link to facilitated inflammation and comorbidities [27, 28]. Symptom duration and smoking history could affect sharp score progression by changing medication adherence, health literacy, and self-care [29, 30].

Several strengths exist in our study. Initially, our work is the first retrospective research of sharp score in RA patients in China, and has a unique position in ascertaining the effective factors of sharp score in China. Secondly, as we know, this is the only research so far that has utilized three methods to identify a significant correlation between sharp score and different variables. One of the limitations of our study was the lack of multi-center and inclusion of diverse ethnic/racial groups. Furthermore, the sample size in our research is small, which may limit the discovery of remarkable differences between sharp score and different variables. Additionally, we also need to study the diagnostic accuracy and importance of magnetic resonance imaging and take it into the next research.

There is no doubt that some limitations of the current study should be realized. Firstly, although the sample size of the current study is big, it yet derived from a single-center trail, where patients were all retrospectively enrolled. It’s necessary to validate and further explore the performance of sharp score in prospective cohorts nationwide or even worldwide. The lack of a validation cohort inevitably undermines the scientific power of our study. Secondly, patients included in our study may not be representative of the general RA population in China, hence a spectrum bias may exist here. Last but not least, we do admit that the multiple r values lower than 0.2 in this study could only indicate weak correlations, despite the statistical significances we observed. However, we also believe the large-scale profile of the current study could offer insights for future researches.

In conclusion, the study has characterized the correlation of sharp score with DAS28, clinical indicator, bone metabolism markers, and sociodemographic factors, and illustrated a significant correlation between sharp score and these variables. The demonstrated differences in joint damage and bone erosion in the early stage of RA may afflict the following long-term outcomes. During the early stage of the disease, differences in sharp score should be considered in developing treatment options when patients are mostly likely to benefit from interventions. This assumes a pivotal role in improvement of disease control and functional outcomes.

Conclusions

In summary, the sharp score in patients with RA was significantly increased and closely related to disease activity. In addition to that, ESR, CRP and RF were risk factors for sharp score. So, sharp score should be considered in developing treatment options for RA.

Availability of data and materials

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation to any qualified researcher. For access to study data please contact the corresponding author.

Abbreviations

RA:

Rheumatoid arthritis

DAS28:

Disease Activity Score

CRP:

C-reactive protein

RF:

Rheumatoid factor

ESR:

Erythrocyte sedimentation rate

CCP:

Anti-cyclic citrullinated peptide antibody

IGA:

Immunoglobulins A

IGG:

Immunoglobulin G

IGM:

Immunoglobulin M

C3:

Complement 3

C4:

Complement 4

BALP:

Bone alkaline phosphatase

OC:

Osteocalcin

OPG:

Osteoprotegerin

RANKL:

Receptor activator for nuclear factor-κB ligand

ARM:

Association rule mining

References

  1. Silman AJ, Pearson JE. Epidemiology and genetics of rheumatoid arthritis. Arthritis Res. 2002:S265–72. https://doi.org/10.1186/ar578.

  2. Small A, Wechalekar MD. Synovial biopsies in inflammatory arthritis: precision medicine in rheumatoid arthritis. Expert Rev Mol Diagn. 2020;20(3):315–25. https://doi.org/10.1080/14737159.2020.1707671.

    Article  CAS  PubMed  Google Scholar 

  3. Boissier MC, Semerano L, Challal S, Saidenberg-Kermanac'h N, Falgarone G. Rheumatoid arthritis: from autoimmunity to synovitis and joint destruction. J Autoimmun. 2012;39(3):222–8. https://doi.org/10.1016/j.jaut.2012.05.021.

    Article  CAS  PubMed  Google Scholar 

  4. Smolen JS, Aletaha D, McInnes IB. Rheumatoid arthritis. Lancet. 2016;388:2023–38. https://doi.org/10.1016/S0140-6736(16)30173-8.

    Article  CAS  PubMed  Google Scholar 

  5. Smolen JS, Landewé R, Bijlsma J, Burmester G, Chatzidionysiou K, Dougados M, et al. EULAR recommendations for the management of rheumatoid arthritis with synthetic and biological disease-modifying antirheumatic drugs: 2016 update. Ann Rheum Dis. 2017;76:960–77. https://doi.org/10.1136/annrheumdis-2016-210715.

    Article  PubMed  Google Scholar 

  6. Singh JA, Saag KG, Bridges SL, Akl EA, Bannuru RR, Sullivan MC, et al. 2015 American College of Rheumatology guideline for the treatment of rheumatoid arthritis. Arthritis Rheumatol. 2016;68(1):1–26. https://doi.org/10.1002/art.39480.

    Article  PubMed  Google Scholar 

  7. Schett G, Gravallese E. Bone erosion in rheumatoid arthritis: mechanisms, diagnosis and treatment. Nat Rev Rheumatol. 2012;8(11):656–64. https://doi.org/10.1038/nrrheum.2012.153.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Finzel S, Ohrndorf S, Englbrecht M, Stach C, Messerschmidt J, Schett G, et al. A detailed comparative study of high-resolution ultrasound and micro-computed tomography for detection of arthritic bone erosions. Arthritis Rheum. 2011;63:1231–6. https://doi.org/10.1002/art.30285.

    Article  PubMed  Google Scholar 

  9. Zijlstra TR, Bernelot Moens HJ, Bukhari MA. The rheumatoid arthritis articular damage score: first steps in developing a clinical index of long term damage in RA. Ann Rheum Dis. 2002;61(1):20–3. https://doi.org/10.1136/ard.61.1.20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kowalski E, Chung KC. Impairment and disability: Renoir’s adaptive coping strategies against rheumatoid arthritis. Hand (N Y). 2012;7(4):357–63. https://doi.org/10.1007/s11552-012-9467-4.

    Article  Google Scholar 

  11. Quintana-Duque MA, Rondon-Herrera F, Mantilla RD, Calvo-Paramo E, Yunis JJ, Varela-Nariño A, et al. Predictors of remission, erosive disease and radiographic progression in a Colombian cohort of early onset rheumatoid arthritis: a 3-year follow-up study. Clin Rheumatol. 2016;35(6):1463–73. https://doi.org/10.1007/s10067-016-3246-5.

    Article  CAS  PubMed  Google Scholar 

  12. Jansen LM, van der Horst-Bruinsma IE, van Schaardenburg D, Bezemer PD, Dijkmans BA. Predictors of radiographic joint damage in patients with early rheumatoid arthritis. Ann Rheum Dis. 2001;60(10):924–7. https://doi.org/10.1136/ard.60.10.924.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Brown LE, Frits ML, Iannaccone CK, Weinblatt ME, Shadick NA, Liao KP. Clinical characteristics of RA patients with secondary SS and association with joint damage. Rheumatology (Oxford). 2015;54(5):816–20. https://doi.org/10.1093/rheumatology/keu400.

    Article  CAS  Google Scholar 

  14. Mochizuki T, Yano K, Ikari K, Hiroshima R, Sakuma Y, Momohara S. Correlation between hand bone mineral density and joint destruction in established rheumatoid arthritis. J Orthop. 2017;14(4):461–5. https://doi.org/10.1016/j.jor.2017.07.010.

    Article  PubMed  PubMed Central  Google Scholar 

  15. Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham CO, et al. 2010 rheumatoid arthritis classification criteria: an American College of Rheumatology/European League Against Rheumatism collaborative initiative. Ann Rheum Dis. 2010;69(9):1580–8. https://doi.org/10.1136/ard.2010.138461.

    Article  PubMed  Google Scholar 

  16. Fang Y, Liu J, Xin L, Sun Y, Wan L, Huang D, et al. Identifying compound effect of drugs on rheumatoid arthritis treatment based on the association rule and a random walking-based model. Biomed Res Int. 2020. https://doi.org/10.1155/2020/4031015.

  17. Möttönen TT, Hannonen P, Toivanen J, Rekonen A, Oka M. Value of joint scintigraphy in the prediction of erosiveness in early rheumatoid arthritis. Ann Rheum Dis. 1988;47(3):183–9. https://doi.org/10.1136/ard.47.3.183.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Wick MC, Lindblad S, Klareskog L, Van Vollenhoven RF. Relationship between inflammation and joint destruction in early rheumatoid arthritis: a mathematical description. Ann Rheum Dis. 2004;63(7):848–52. https://doi.org/10.1136/ard.2003.015172.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Lindqvist E, Jonsson K, Saxne T, Eberhardt K. Course of radiographic damage over 10 years in a cohort with early rheumatoid arthritis. Ann Rheum Dis. 2003;62(7):611–6. https://doi.org/10.1136/ard.62.7.611.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Závada J, Hánová P, Hurňáková J, Szczuková L, Uher M, Forejtová Š, et al. The relationship between synovitis quantified by an ultrasound 7-joint inflammation score and physical disability in rheumatoid arthritis - a cohort study. Arthritis Res Ther. 2017;19(1):5. https://doi.org/10.1186/s13075-016-1208-6.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Young A, Bielawska C, Corbett M, Roitt I. A prospective study of early onset rheumatoid arthritis over fifteen years: prognostic features and outcome. Clin Rheumatol. 1987;6(2):12–9. https://doi.org/10.1007/BF02203380.

    Article  PubMed  Google Scholar 

  22. Jawaid WB, Crosbie D, Shotton J, Reid DM, Stewart A. Use of digital x ray radiogrammetry in the assessment of joint damage in rheumatoid arthritis. Ann Rheum Dis. 2006;65(4):459–64. https://doi.org/10.1136/ard.2005.039792.

    Article  CAS  PubMed  Google Scholar 

  23. Klarlund M, Ostergaard M, Jensen KE, Madsen JL, Skjødt H, Lorenzen I. Magnetic resonance imaging, radiography, and scintigraphy of the finger joints: one year follow up of patients with early arthritis. The TIRA Group. Ann Rheum Dis. 2000;59(7):521–8. https://doi.org/10.1136/ard.59.7.521.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Forslind K, Boonen A, Albertsson K, Hafström I, Svensson B, Barfot Study Group. Hand bone loss measured by digital X-ray radiogrammetry is a predictor of joint damage in early rheumatoid arthritis. Scand J Rheumatol. 2009;38(6):431–8. https://doi.org/10.3109/03009740902939376.

    Article  CAS  PubMed  Google Scholar 

  25. van Vollenhoven RF. Sex differences in rheumatoid arthritis: more than meets the eye. BMC Med. 2009;7:12. https://doi.org/10.1186/1741-7015-7-12.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Sokka T, Toloza S, Cutolo M, Kautiainen H, Makinen H, Gogus F, et al. Women, men, and rheumatoid arthritis: analyses of disease activity, disease characteristics, and treatments in the QUEST-RA study. Arthritis Res Ther. 2009;11(1):R7. https://doi.org/10.1186/ar2591.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Yang X, Chang Y, Wei W. Endothelial dysfunction and inflammation: immunity in rheumatoid arthritis. Mediat Inflamm. 2016:6813016. https://doi.org/10.1155/2016/6813016.

  28. DeMizio DJ, Geraldino-Pardilla LB. Autoimmunity and inflammation link to cardiovascular disease risk in rheumatoid arthritis. Rheumatol Ther. 2020;7(1):19–33. https://doi.org/10.1007/s40744-019-00189-0.

    Article  PubMed  Google Scholar 

  29. Khoo HS, Lim YW, Vrijhoef HJ. Primary healthcare system and practice characteristics in Singapore. Asia Pac Fam Med. 2014;13(1):8. https://doi.org/10.1186/s12930-014-0008-x.

    Article  PubMed  PubMed Central  Google Scholar 

  30. Oshotse C, Zullig LL, Bosworth HB, Tu P, Lin C. Self-efficacy and adherence behaviors in rheumatoid arthritis patients. Prev Chronic Dis. 2018;15:E127. https://doi.org/10.5888/pcd15.180218.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Not applicable.

Funding

This work was supported by The National Key Research and Development Program of the Ministry of Science and Technology (2018YFC1705204); National Nature Fund Program (82074373); National Natural Youth Fund (82004102); The University Synergy Innovation Program of Anhui Province (GXXT-2020-025); Anhui Key Research and Development Program Foreign Science and Technology Cooperation Project (201904b11020011); Anhui Famous Traditional Chinese Medicine Liu Jian Studio Construction Project (Traditional Chinese Medicine Development Secret [2018] No. 11); Key Research and Development Plan Project of Anhui Province (201904a07020004); Anhui Natural Science Youth Foundation (2008085QH386); The Key Laboratory of Xin’an Ministry of Medical Education (No.2020xayx10). The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.

Author information

Authors and Affiliations

  1. Anhui University of Traditional Chinese Medicine, Hefei, 230031, Anhui Province, China

    Jianting Wen, Yanqiu Sun, Xin Wang & Jie Wang

  2. Department of Rheumatology and Immunology, First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, 230038, Anhui Province, China

    Jianting Wen, Jian Liu, Ling Xin, Lei Wan, Hui Jiang & Yue Sun

  3. Institute of Rheumatology, Anhui College of Traditional Chinese Medicine, Hefei, 230038, Anhui Province, China

    Jianting Wen, Jian Liu, Ling Xin, Lei Wan, Hui Jiang & Yue Sun

  4. Key Laboratory of Xin’an Medicine of the Ministry of Education, Anhui University of Chinese Medicine, Hefei, Anhui, 230038, P. R. China

    Jianting Wen & Jian Liu

  5. Anhui Province Key Laboratory of Modern Chinese Medicine Department of Internal Medicine Application Foundation Research and Development, Hefei, 230031, Anhui, China

    Jianting Wen, Jian Liu, Ling Xin, Lei Wan, Hui Jiang, Yue Sun, Yanqiu Sun, Xin Wang & Jie Wang

Authors
  1. Jianting Wen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jian Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ling Xin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lei Wan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Hui Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yue Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yanqiu Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xin Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jie Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

JTW, JL, HJ, LW, LX, and YS contributed to the study design. JTW contributed to data analysis, wrote the first draft, and revised the manuscript. YQS, XW, and JW contributed to the questionnaire survey on patients, specimens, and data collection. JL supervised the project and helped revise the manuscript. All authors reviewed and accepted the content of the final manuscript.

Corresponding author

Correspondence to Jian Liu.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the Ethics Committee of the First Affiliated Hospital of Anhui University of Traditional Chinese Medicine and carried out under the Helsinki Declaration. Before participating in the study, the patients filled in a written informed consent forms. A written informed consent was obtained from all the study participants.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wen, J., Liu, J., Xin, L. et al. Effective factors on Sharp Score in patients with rheumatoid arthritis: a retrospective study. BMC Musculoskelet Disord 22, 865 (2021). https://doi.org/10.1186/s12891-021-04742-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12891-021-04742-3

Keywords

  • Sharp score
  • Rheumatoid arthritis
  • Effective factors
  • Retrospective study

BMC Musculoskeletal Disorders

ISSN: 1471-2474

Contact us