Article Text

FRI0718 Prevalence and pattern of comorbidities in chronic rheumatic and musculoskeletal diseases: the comord study
  1. NR Ziade1,2,
  2. F Fayad1,2,
  3. B Khoury3,
  4. M Zoghbi4,
  5. G Merheb5,
  6. G Abi karam1,2,
  7. K Mroue6,
  8. J Missaykeh7
  1. 1Rheumatology, Hotel Dieu de France
  2. 2Rheumatology
  3. 3Medicine
  4. 4Family Medicine, St-Joseph University, Beirut
  5. 5Medicine, ND des Secours, Jbeil
  6. 6Rheumatology, Zahra University Hospital, Beirut
  7. 7Rheumatology, Monla Hospital, Tripoli, Lebanon


Background Rheumatic and musculoskeletal diseases (RMD) frequently coexist with other conditions, resulting in multimorbidity, which may compromise arthritis management and lead to diminished quality-of-life and increased mortality. In 2016, EULAR published “Points to consider for reporting, screening for and preventing selected comorbidities in chronic inflammatory rheumatic diseases”, used to guide this study.

Objectives The primary objective is to evaluate the prevalence and pattern of comorbidities in selected RMD in Lebanese patients. The secondary objective is to evaluate the gap between recommendations and routine comorbidities' screening.

Methods COMORD is an observational, cross-sectional, multicentric national study. Consecutive RMD patients (Rheumatoid Arthritis (RA), Osteoarthritis (OA), Systemic Lupus Erythematous (SLE), Axial Spondyloarthritis (AxSpA) and Peripheral Spondyloarthritis (pSpA)) as diagnosed by the rheumatologist) were recruited at 6 practices from university hospitals and private clinics in Lebanon. Six axes of comorbidities (Cardiovascular, Malignancies, Infections, Gastrointestinal, Osteoporosis, Depression) were investigated using a case report form (patient interview). Optimal comorbidities screening was defined according to current recommendations and compared with monitoring in practice. Prevalences were presented descriptively. The number of comorbidities was correlated with predictive factors using Poisson Regression. Finally, Latent Class Analysis (LCA) was used to identify patterns of multimorbidity. All analysis were performed on IBM SPSS Statistics 23 and XLSTAT 18.07.

Results 515 patients were recruited (196 RA, 161 OA, 75 AxSpA, 45 SLE, 40 pSA). Mean age was 56y, 76% were female. There was no difference in the disease distribution between centers. The most common comorbidities were cardiovascular risk factors and diseases, followed by depression and osteoporosis. The number of comorbidities was significantly associated with age (p<0.001), obesity (p<0.001) and biotherapies (p 0.05). LCA analysis identified 3 main clusters of multimorbidity: OA, RA, AxSPA (Fig 1). The most optimal screening was found for cardiovascular risk factors (84%). DXA was prescribed in 69% of correct indications. As for malignancies, mammograms and pap smears were the most optimally prescribed (36% and 28%). Colonoscopy and dermatology visit were prescribed in 22% and 18%. Correct vaccination (influenza and pneumococcal) was found in 17% and 8%. Predictive factors for optimal screening were age, university setting, social coverage, disease duration and biotherapy.

Conclusions Comorbidities are prevalent in RMDs and follow specific multimorbidity patterns, with a predominance for cardiovascular, depression and osteoporosis. They are more frequent with age and obesity. Optimal screening needs to be improved.


  1. Baillet et al. Ann Rheum Dis 2016.

  2. Dougados et al. Ann Rheum Dis 2014.

  3. Molto et al. Ann Rheum Dis 2016.

  4. Simoes et al. Arthritis Care & Research 2017.


Disclosure of Interest None declared

Statistics from

Request Permissions

If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.