Literature DB >> 19333984

Physician ability to assess rheumatoid arthritis disease activity using an electronic medical record-based disease activity calculator.

Deborah S Collier1, Richard W Grant, Greg Estey, Dominic Surrao, Henry C Chueh, Jonathan Kay.   

Abstract

OBJECTIVE: To assess physicians' concordance with Disease Activity Score in 28 joints (DAS28) categories calculated by an electronic medical record (EMR)-embedded disease activity calculator, as well as attitudes toward this application.
METHODS: Fifteen rheumatologists used the EMR-embedded disease activity calculator to predict a rheumatoid arthritis (RA) DAS28 disease activity category at the time of each clinical encounter.
RESULTS: Physician-predicted DAS28 disease activity categories ranged from high (>5.1, 15% of cohort, 66 of 429 patient visits) to moderate (>3.2-5.1, 21% of cohort, 90 of 429 patient visits) to low (2.6-3.2, 29% of cohort, 123 of 429 patient visits) to remission (<2.6, 35% of cohort, 150 of 429 patient visits). Overall concordance between calculated DAS28 results and physician-predicted RA disease activity was 64%. Using either the physician-predicted or the calculated DAS28 category as the gold standard, accuracy was greatest for patients in remission (75% and 88% accuracy, respectively) and those with high disease activity (68% and 79% accuracy, respectively), and less for patients with moderate (48% and 62% accuracy, respectively) or low disease activity (62% and 31% accuracy, respectively).
CONCLUSION: Accurate physician prediction of DAS28 remission and high disease activity categories, even without immediate availability of the erythrocyte sedimentation rate or the C-reactive protein level at the time of the visit, may be used to guide quantitatively driven outpatient RA management.

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Year:  2009        PMID: 19333984     DOI: 10.1002/art.24335

Source DB:  PubMed          Journal:  Arthritis Rheum        ISSN: 0004-3591


  7 in total

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Review 3.  Quality measurement and improvement in rheumatology: rheumatoid arthritis as a case study.

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6.  Automatic prediction of rheumatoid arthritis disease activity from the electronic medical records.

Authors:  Chen Lin; Elizabeth W Karlson; Helena Canhao; Timothy A Miller; Dmitriy Dligach; Pei Jun Chen; Raul Natanael Guzman Perez; Yuanyan Shen; Michael E Weinblatt; Nancy A Shadick; Robert M Plenge; Guergana K Savova
Journal:  PLoS One       Date:  2013-08-16       Impact factor: 3.240

7.  Using LASSO Regression to Predict Rheumatoid Arthritis Treatment Efficacy.

Authors:  David J Odgers; Natalie Tellis; Heather Hall; Michel Dumontier
Journal:  AMIA Jt Summits Transl Sci Proc       Date:  2016-07-20
  7 in total

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