Background/Purpose: 1. to assess the validity of an electronically comorbidity assessment strategy to identify comorbid conditions among RA and PsA patients in standard practice. 2. To evaluate the impact of e-comorbidity assessment on the patients’ care and adherence to therapy.

Methods: A cohort of 448 RA and 437 PsA subjects with varying disease duration met the following inclusion criteria: RA diagnosed according to ACR/EULAR criteria and PsA according to CASPAR criteria, started a DMARD/biologic agent, continued therapy >6 months, and followed longitudinally from baseline to follow-up (mean time 24months). Electronic patients reported comorbidities questionnaire according to a RACI [1] and PsACI [2] was implemented as part of electronic patient reported outcome measures tool. The sensitivity, specificity, positive and negative predictive values of the electronic data entry and calculated comorbidity risk were compared to ICD-10 medical record (reference standard) and rheumatology clinic visits outcomes. A control group of 241 RA patients and 252 PsA patients managed according to standard protocols were also assessed and monitored for 2 years. Primary end point: no inferiority of outcomes of the electronic and standard formats. Secondary end point: the patients’ adherence to their medications and actions taken to assess and manage the comorbidity risk.

Results: The sensitivity for identifying comorbidities using the electronic approach ranged from a minimum of 94% for atlanto-axial sublaxation to a maximum of 100% for cardiovascular risk (median, 99.2%; interquartile range [IQR]: 96%-100%). Sensitivities for extracting comorbidities using ICD-10 codes ranged from a minimum of 8% for Anxiety to 100% for tumors (median, 66%; IQR: 50%-74%); whereas, sensitivities for extracting comorbidities using clinic outcomes data ranged from a minimum of 4% for falls risk to 100% for diabetes and tumors (median, 38%; IQR: 32%-54%). The median PPV and NPV were 97.7% (IQR: 96-100%) and 99.6% (IQR: 99-100%) for the e-comorbidity tool Vs 61.8% (IQR: 41%-76%) and 97.4% (IQR: 91%-98%) for the ICD-10 codes, respectively. The patients’ adherence to anti-rheumatic therapy was significantly (p<0.1) higher in the studied group whereas stopping DMARDs for intolerability was significantly (p<0.01) higher in the control group. Number of procedure/ screening tests for comorbidity risk assessment was significantly higher in the e-comorbidity group (P< 0.001).

Conclusion: e-comorbidity assessment offered a specific and dynamic approach tailored to the patient’s needs over the 2-years study period, which is applicable in standard practice. Patient reported e-comorbidity outperformed the standard medical recording systems and can have a role in healthcare management and research. Reclassifying RA patients according to their comorbidity risk would have a positive impact on their adherence to therapy, early assessment of comorbidities with subsequent preventive or treatment decisions. References:

1. Rheumatoid Arthritis Comorbidity Index. El Miedany et al. Ann Rheum Dis 2016; 75(Suppl2): 154
2. Psoriatic arthritis Comorbidity Index. El Miedany et al. Ann Rheum Dis 2016; 75(Suppl2): 89

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/e-comorbidity-evaluation-of-the-validity-of-electronic-comorbidity-assessment-in-identifying-comorbid-conditions-among-patients-with-rheumatoid-and-psoriatic-arthritis/