Abstract
There is a high percentage of error in the approach of patients with joint pain by primary care physicians. An algorithm can help improve this misdiagnosis problem. Our study seeks to determine the effectiveness of an algorithm when used by primary care physicians for the diagnosis of cases of joint pain patients. A randomized clinical experiment was carried out. Primary care physicians from five cities in Colombia developed a series of clinical cases, which were presented to them through a website on their personal cell phones. Half of the doctors developed the cases using the diagnostic algorithm, and the other half developed the cases without the use of the algorithm. Main measures were proportion of correct diagnosis, number, type of laboratory and diagnostic images requested for the diagnostic approach of clinical cases. Two hundred and twenty-four primary care physicians participated. The overall proportion of cases correctly diagnosed was 37.3% higher in the intervention group; we found a greater difference in cases of spondyloarthritis (60.8%), followed by systemic lupus erythematosus with joint involvement (32.2%), rheumatoid arthritis (30.3%) and osteoarthritis (25.9%). The average number of tests requested to develop clinical cases was lower in the intervention group than in the control group, both globally and for each of the four diseases, with statistically significant differences for each of the comparisons. The diagnostic algorithm proved to be an effective tool when used by primary care physicians; the proportion of correct diagnoses increased, and the number of tests requested in the development of the cases decreased.
Similar content being viewed by others
References
Fernández-Ávila DG, Mora S, Vargas A, Díaz M, Gutiérrez J (2012) Enfoque diagnóstico y terapéutico inicial por parte de médicos no reumatólogos en un grupo de pacientes colombianos con dolor articular. J Clin Rheumatol 18:32
Fernández-Ávila DG, Ruiz ÁJ, Gil F, Mora S, Tobar C, Gutiérrez J (2018) The effect of an educational intervention, based on clinical simulation, on the diagnosis of rheumatoid arthritis and osteoarthritis. Musculoskeletal Care 16:147–151
Gamez-Nava J, Gonzalez-Lopez L, Davis P, Suárez-Almazor M (1998) Referral and diagnosis of common rheumatic diseases by primary care physicians. Rheumatology 37:1215–1219
Pacheco RD, Gatica RH, Kaliski KS (2006) Self assessment of strengths, weaknesses and self confidence of primary care physicians taking care of rheumatic diseases. Rev médica Chile 134:813–820
Aletaha D, Neogi T, Silman AJ, Funovits J, Felson DT, Bingham CO et al (2010) 2010 Rheumatoid arthritis classification criteria: an American college of rheumatology/European league against rheumatism collaborative initiative. Arthritis Rheum 62:2569–2581
Rudwaleit M, van der Heijde D, Landewe R, Akkoc N, Brandt J, Chou CT et al (2011) The assessment of Spondylo arthritis international society classification criteria for peripheral spondyloarthritis and for spondyloarthritis in general. Ann Rheum Dis 70:25–31
Aringer M, Costenbader K, Daikh D, Brinks R, Mosca M, Ramsey-Goldman R et al (2019) 2019 European league against rheumatism/American college of rheumatology classification criteria for systemic lupus erythematosus. Ann Rheum Dis 78:1151–1159
Altman R, Alarcon G, Appelrouth D, Bloch D, Borenstein D, Brandt K et al (1990) The American college of rheumatology criteria for the classification and reporting of osteoarthritis of the hand. Arthritis Rheum 33:1601–1610
Dougados M, Gossec L (2007) Classification criteria for rheumatic diseases: Why and how? Arthritis Rheum 57:1112–1115
Liddy C, Arbab-Tafti S, Moroz I, Keely E (2017) Primary care physician referral patterns in Ontario, Canada: a descriptive analysis of self-reported referral data. BMC Fam Pract 18:81–88
Wong J, Tu K, Bernatsky S, Jaakkimainen L, Thome J, Ahlualia V (2019) Quality and continuity of information between primary care physicians and rheumatologists. BMC Rheumatol 3:23
Morrison MC (1993) Teaching of musculoskeletal medicine: a survey of general practitioners and deans. Med Educ 27:245–249
Schmale GA (2005) More evidence of educational inadequacies in musculoskeletal medicine. Clin Orthop Relat Res 437:251–259
Day CS, Yeh AC (2008) Evidence of educational inadequacies in region-specific musculoskeletal medicine. Clin Orthop Relat Res 466:2542–2547
Monrad SU, Zeller JL, Craig CL, Diponio L (2011) Musculoskeletal education in US medical schools: lessons from the past and suggestions for the future. Curr Rev Musculoskelet Med 4:91–98
Euller-Ziegler L (1999) The teaching of rheumatology in undergraduate medical education in France. J Rheumatol 55:9
Onetti CM (1999) Undergraduate education in rheumatology in Latin America. J Rheumatol 55:22–23
Chamurlieva M, Korotaeva T, Loginova E (2016) THU0453 High prevalence of psoriatic arthritis misdiagnoses in psoriasis patients in russian dermatological clinics based on psoriasis epidemiology screening tool (PEST), rheumatological evaluation and the caspar criteria. Ann Rheum Dis 75:355–356
Jin D, Zhao L, Yan X, Liu J, Zhao Y (2013) The re-evaluation of 140 patients diagnosed as ankylosing spondylitis and nonradiographic axial spondyloarthritis. Zhonghua nei ke za zhi 52:920–923
Di Franco M, Iannuccelli C, Bazzichi L, Atzeni F, Consensi A, Salaffi F et al (2011) Misdiagnosis in fibromyalgia: a multicentre study. Clin Exp Rheumatol 29:S104–S108
Mosca M, Costenbader KH, Johnson SR, Lorenzoni V, Sebastiani G, Hoyer B et al (2019) How do patients with newly diagnosed systemic lupus erythematosus present? A multicenter cohort of early systemic lupus erythematosus to inform the development of new classification criteria. Arthritis Rheumatol 71:91–98
Sturrock RD (2000) Gout Easy to misdiagnose. BMJ 320:132–133
Spiera H (1987) Osteoarthritis as a misdiagnosis in elderly patients. Geriatrics 42:37–42
Rudwaleit M, van der Heijde D, Khan MA, Braun J, Sieper J (2004) How to diagnose axial spondyloarthritis early. Ann Rheum Dis 63:535–543
van den Berg R, de Hooge M, Rudwaleit M, Sieper J, van Gaalen F, Reijinierse M et al (2013) ASAS modification of the Berlin algorithm for diagnosing axial spondyloarthritis: results from the SPondyloArthritis caught early (SPACE)-cohort and from the assessment of SpondyloArthritis international Society (ASAS)-cohort. Ann Rheum Dis 72:1646–1653
van Steenbergen HW, Aletaha D, Beaart-van de Voorde LJJ, Brouwer E, Codreanu C, Combe B et al (2017) EULAR definition of arthralgia suspicious for progression to rheumatoid arthritis. Ann Rheum Dis 76:491–496
van der Helm-vanMil AHM, le Cessie S, van Dongen H, Breedveld FC, Toes REM, Huizinga TWJ (2007) A prediction rule for disease outcome in patients with Recent-onset undifferentiated arthritis: How to guide individual treatment decisions. Arthritis Rheum 56:433–440
Visser H, le Cessie S, Vos K, Breedveld FC, Hazes JMW (2002) How to diagnose rheumatoid arthritis early: a prediction model for persistent (erosive) arthritis. Arthritis Rheum 46:357–365
Alves C, Luime JJ, van Zeben D, Huisman A-M, Weel AEAM, Barendregt PJ et al (2011) Diagnostic performance of the ACR/EULAR 2010 criteria for rheumatoid arthritis and two diagnostic algorithms in an early arthritis clinic (REACH). Ann Rheum Dis 70:1645–1647
Suarez-Almazor ME, Gonzalez-Lopez L, Gamez-Nava JI, Belseck E, Kendall CJ, Davis P (1998) Utilization and predictive value of laboratory tests in patients referred to rheumatologists by primary care physicians. J Rheumatol 25:1980–1985
Tampoia M, Brescia V, Fontana A, Zucano A, Morrone LF, Pansini N (2007) Application of a combined protocol for rational request and utilization of antibody assays improves clinical diagnostic efficacy in autoimmune rheumatic disease. Arch Pathol Lab Med 131:112–116
Solomon DH, Shmerling RH, Schur PH, Lew R, Fiskio J, Bates DW (1999) A computer based intervention to reduce unnecessary serologic testing. J Rheumatol 26:2578–2584
Man A, Shojania K, Phoon C, Pal J, de Badyn MH, Pi D et al (2013) An evaluation of autoimmune antibody testing patterns in a Canadian health region and an evaluation of a laboratory algorithm aimed at reducing unnecessary testing. Clin Rheumatol 32:601–608
Bonaguri C, Melegari A, Ballabio A, Parmeggiani M, Russo A, Battistelli L et al (2011) Italian multicentre study for application of a diagnostic algorithm in autoantibody testing for autoimmune rheumatic disease: Conclusive results. Autoimmun Rev 11:1–5
Melegari A, Bonaguri C (2014) Harmonization of autoimmune diagnostics with antinuclear antibody testing algorithm: approach of appropriateness and clinical relevance. Isr Med Assoc J 16:640–642
Funding
This study had no external funding.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by DF-Á, MXR, CR, LR, and ES. The first draft of the manuscript was written by DF-Á, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. All co-authors take full responsibility for all aspects of the study.
Corresponding author
Ethics declarations
Conflict of interest
DG. Fernández-Ávila, MX. Rojas, C. Ramírez, L. Rodelo, and E. Soriano declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Fernández-Ávila, D.G., Rojas, M.X., Ramírez, C. et al. Effectiveness of the use of an algorithm in the diagnostic approach of joint pain patients by primary care physicians. Rheumatol Int 40, 1857–1864 (2020). https://doi.org/10.1007/s00296-020-04552-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00296-020-04552-1