OBJECTIVE: To determine the accuracy of International Classification of Diseases (ICD) code 714 for rheumatoid arthritis (RA) diagnosis in a Veterans Administration (VA) hospital database and to examine the effects of adding laboratory and pharmacy data to ICD code 714 on accuracy of RA diagnosis. METHODS: We drew a random sample of patients from all Minneapolis VA rheumatology clinic patients who had at least 1 rheumatology clinic visit between January 2001 and July 2002. Charts of 184 patients were reviewed. The gold standard for RA diagnosis was chart documentation of RA diagnosis by a rheumatologist on > or =2 visits >6 weeks apart. The data definitions of RA diagnosis included presence of ICD code 714 alone or various combinations of ICD code 714, a positive rheumatoid factor (RF), and prescription for a disease-modifying antirheumatic drug (DMARD). Accuracy of data definitions of RA was assessed by calculating sensitivity, specificity, positive and negative predictive values, and area under the receiver operator characteristics curve. RESULTS: Diagnosis by ICD code 714 had 100% sensitivity, but specificity was only 55% because of a false-positive rate of 34%. The addition of a positive RF and/or a DMARD prescription to ICD code 714 dramatically improved specificity to 83-97% and positive predictive value to 81-97%; however, sensitivity decreased to 76-88%. Diagnosis by ICD 714 alone had the highest negative predicative value of 100%. The area under the curve was the greatest when both ICD code 714 and a positive RF were included, and the least when ICD code alone was used. CONCLUSION: ICD code 714 in the VA administrative database is a very sensitive screening tool for identifying patients with RA in the rheumatology clinic population. Addition of the presence of a DMARD prescription and/or a positive RF to selection criteria improves specificity of the diagnosis.
OBJECTIVE: To determine the accuracy of International Classification of Diseases (ICD) code 714 for rheumatoid arthritis (RA) diagnosis in a Veterans Administration (VA) hospital database and to examine the effects of adding laboratory and pharmacy data to ICD code 714 on accuracy of RA diagnosis. METHODS: We drew a random sample of patients from all Minneapolis VA rheumatology clinicpatients who had at least 1 rheumatology clinic visit between January 2001 and July 2002. Charts of 184 patients were reviewed. The gold standard for RA diagnosis was chart documentation of RA diagnosis by a rheumatologist on > or =2 visits >6 weeks apart. The data definitions of RA diagnosis included presence of ICD code 714 alone or various combinations of ICD code 714, a positive rheumatoid factor (RF), and prescription for a disease-modifying antirheumatic drug (DMARD). Accuracy of data definitions of RA was assessed by calculating sensitivity, specificity, positive and negative predictive values, and area under the receiver operator characteristics curve. RESULTS: Diagnosis by ICD code 714 had 100% sensitivity, but specificity was only 55% because of a false-positive rate of 34%. The addition of a positive RF and/or a DMARD prescription to ICD code 714 dramatically improved specificity to 83-97% and positive predictive value to 81-97%; however, sensitivity decreased to 76-88%. Diagnosis by ICD 714 alone had the highest negative predicative value of 100%. The area under the curve was the greatest when both ICD code 714 and a positive RF were included, and the least when ICD code alone was used. CONCLUSION:ICD code 714 in the VA administrative database is a very sensitive screening tool for identifying patients with RA in the rheumatology clinic population. Addition of the presence of a DMARD prescription and/or a positive RF to selection criteria improves specificity of the diagnosis.
Authors: Cory B Pittman; Lisa A Davis; Angelique L Zeringue; Liron Caplan; Kent R Wehmeier; Jeffrey F Scherrer; Hong Xian; Francesca E Cunningham; Jay R McDonald; Alexis Arnold; Seth A Eisen Journal: Mayo Clin Proc Date: 2014-01 Impact factor: 7.616
Authors: Rachel Knevel; Saskia le Cessie; Chikashi C Terao; Kamil Slowikowski; Jing Cui; Tom W J Huizinga; Karen H Costenbader; Katherine P Liao; Elizabeth W Karlson; Soumya Raychaudhuri Journal: Sci Transl Med Date: 2020-05-27 Impact factor: 17.956
Authors: Lisa M Lix; Marina S Yogendran; Souradet Y Shaw; Laura E Targownick; Jennifer Jones; Osama Bataineh Journal: BMC Health Serv Res Date: 2010-02-01 Impact factor: 2.655