Arthritis & Rheumatism, Volume 63,
November 2011 Abstract Supplement

Abstracts of the American College of
Rheumatology/Association of Rheumatology Health Professionals
Annual Scientific Meeting
Chicago, Illinois November 4-9, 2011.


Detection of Adverse Events in Routine Rheumatology Practice by a New Computer Application.

Rosales1,  Zulema, Rodriguez-Cambron1,  Ana B., Abasolo1,  Lydia, Leon1,  Leticia, Fontsere1,  Oscar, Vadillo1,  Cristina, Rueda1,  J.L. Fernández

Hospital Clínico San Carlos, Madrid, Spain
Hospital Clinico San Carlos, Madrid, Spain

Background/Purpose:

There is a high risk of developing adverse events (AEs) in rheumatology practice due, mainly, to the immunosuppressive drugs used. Also, we must take into account the high workload and the difficulty of register AEs in daily clinical practice.

Our purpose was to describe the AEs collected in a routine practice of Rheumatology, by implementing a software system Reporting and Analysis for Incident Learning and Adverse Events (SNAIEA).

Methods:

We performed an observational prospective study from October 1st 2010 to May 31st 2011. All patients seen, at least once, in the Rheumatology Service at the Hospital Clínico in Madrid since the introduction of SNAIEA were included. Primary endpoint: AEs collected by SNAIEA from patients attended during the study period. We also collected a) the severity (mild, moderate, severe, fatal), b) relationship to medication (Unlikely, Possible, Probable, Certain), c) causes of AE, d) the diagnosis of patients with some type of AE in that period, e) and drugs associated with these events. We collected demographic data (age and sex) of all patients. Statistical analyses: to estimate the incidence of adverse events we used survival techniques, expressing the incidence per 100 inhabitants per year (95% CI). A description of the sociodemographic, clinical, of the patients by frequency distribution and the mean and standard deviation or median and percentiles was completed. Analyses were performed using Stata statistical package 10.0.

Results:

7539 patients were attended during follow-up. Of these, 75% were female with a mean age of 61.92 ± 16 years. There were 241 AE (table 1) (84.6% female, mean age of 62.3 ± 15.3 years) during follow-up with an incidence of 8.5 % (95% CI: 7.5–9.7). The incidence of AE by sex was 9.5% (95% CI: 8.2–10.8) in females and 5.4% (95% CI: 3.9–7.5) in males. Of the AE, 5 were severe with an incidence of 0.2% (95% CI: 00.7–00.4). In 61% of the patients the relationship of the AE to medication was certain. The main diagnose in the patients with AE was Rheumatoid Arthritis (37%) followed by osteoarthritis (13%). The most frequent drugs causing AE were classical DMARDs (46.44%) followed by opioids (10.5%) and NSAIDs (9.6%).

AENumberPercentage (%)
Gastrointestinal7835.14
General Syndrome2712.16
Mucocutaneous2310.36
Laboratory Abnormalities209.01
Neurological188.11
Eye167.21
Infections146.31
Cardiological104.50
Muscular73.15
Fractures62.70
Genitourinary31.35

Conclusion:

AEs in routine rheumatology practice are common; however the vast majority is mild. The most common adverse events were gastrointestinal. DMARDs are the drugs most associated with adverse events. Using SNAIEA has been a transition from the traditional manual model to the electronic analysis of AE, identifying those events, their severity and relationship to the medication, thus contributing to improving the quality of care.

To cite this abstract, please use the following information:
Rosales, Zulema, Rodriguez-Cambron, Ana B., Abasolo, Lydia, Leon, Leticia, Fontsere, Oscar, Vadillo, Cristina, et al; Detection of Adverse Events in Routine Rheumatology Practice by a New Computer Application. [abstract]. Arthritis Rheum 2011;63 Suppl 10 :2062
DOI:

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