ACR Meeting Abstracts

ACR Meeting Abstracts

  • Meetings
    • ACR Convergence 2024
    • ACR Convergence 2023
    • 2023 ACR/ARP PRSYM
    • ACR Convergence 2022
    • ACR Convergence 2021
    • ACR Convergence 2020
    • 2020 ACR/ARP PRSYM
    • 2019 ACR/ARP Annual Meeting
    • 2018-2009 Meetings
    • Download Abstracts
  • Keyword Index
  • Advanced Search
  • Your Favorites
    • Favorites
    • Login
    • View and print all favorites
    • Clear all your favorites
  • ACR Meetings

Abstract Number: 206

Impact of International Classification of Diseases 10th Revision Codes and Updated Medical Information on an Existing Rheumatoid Arthritis Phenotype Algorithm Using Electronic Medical Data

Sicong Huang1, Jie Huang1, Tianrun Cai2, Kumar P. Dahal3, Andrew Cagan4, Jacklyn Stratton3, Tianxi Cai5 and Katherine P. Liao3, 1Division of Rheumatology, Immunology and Allergy, Brigham and Women's Hospital, Boston, MA, 2Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, 3Brigham and Women's Hospital, Boston, MA, 4Research Computing, Partners HealthCare, Charlestown, MA, 5Harvard T.H. Chan School of Public Health, Boston, MA

Meeting: 2018 ACR/ARHP Annual Meeting

Keywords: bioinformatics and rheumatoid arthritis (RA), Electronic Health Record

  • Tweet
  • Click to email a link to a friend (Opens in new window) Email
  • Click to print (Opens in new window) Print
Session Information

Date: Sunday, October 21, 2018

Title: Epidemiology and Public Health Poster I: Rheumatoid Arthritis

Session Type: ACR Poster Session A

Session Time: 9:00AM-11:00AM

Background/Purpose:

Electronic medical records (EMRs) are increasingly being utilized for clinical research, where the phenotypes of interest are typically defined by algorithms.  Almost a decade ago, a rheumatoid arthritis (RA) phenotype algorithm using codified and narrative data extracted from the EMR using natural language processing (NLP), was developed using machine learning approaches. The objective of this study was to evaluate the temporal portability of this algorithm, with the introduction of International Classification of Diseases (ICD), 10th revision codes, as well as a new EMR system (Epic) at our institution.

 

Methods:

We studied subjects from the EMR of 2 large academic centers with ≥ 1 ICD9 RA code (714.x) or ICD10 RA code (M05.x, M06.x) and ≥ 2 clinical notes to create a database of all potential patients with RA (“RA Mart”, n = 52,728).  A random 100 subjects were selected from the RA Mart, and patients were classified as RA yes/no from medical record review to create the validation set.  We first calculated the performance characteristics of using ≥2 RA ICD9 or ICD10 RA codes to define RA compared to RA classified from chart review.  We then applied a previously published logistic regression algorithm for RA using ICD9 codes and data extracted using NLP from data fields specified in 2010.  For example, this model would not include treatments approved after 2010.  We then applied a modified algorithm incorporating ICD10 codes and additional medications to existing variable fields, e.g. number of RA ICD9 codes became number of ICD 9 or 10 RA codes. We compared performance characteristics of the original 2010 with the modified 2010 RA algorithm using the original published positive predictive value (PPV) as a benchmark. 

 

Results:

In the validation set, 41% of subjects were classified as RA. Among those with RA, mean age was 68, 76% female, and 59% were RF or anti-CCP positive; 7% of subjects only had ICD10 but not ICD9 codes.  The PPV for classifying RA using ≥2 ICD9 codes was 50%; and for using ≥2 ICD9 or ICD10 was 52% (Table). Using the exact data fields specified in the 2010 algorithm, we achieved a PPV of 93%.  When the data fields were updated with new types of data, ICD10, new treatments, the PPV remained at 93%.  In comparison, the published PPV of the algorithm was 94%, with a sensitivity of 63%. 

 

Conclusion:

We observed that an existing RA algorithm trained using machine learning approaches on EMR data was robust temporally, despite the introduction of new medical information which also updated the algorithm steps.  At this time including ICD10 had a minimal impact on classification.  The existing RA algorithm continued to perform significantly better than using ICD9 or ICD10 data alone at classifying RA.

 

 

Table. Performance characteristic of the published algorithm and modified algorithm to identify individuals with RA, as compared to codified data alone (n=100).

 

 

≥2 ICD9 RA codes

 

≥2 ICD9 or ICD10 RA codes

Published algorithm

Modified algorithm

Sensitivity

0.80

0.93

0.68

0.66

Specificity

0.44

0.41

0.97

0.97

PPV

0.50

0.52

0.93

0.93

RA: rheumatoid arthritis

PPV: positive predictive value

 

 


Disclosure: S. Huang, None; J. Huang, None; T. Cai, None; K. P. Dahal, None; A. Cagan, None; J. Stratton, None; T. Cai, None; K. P. Liao, None.

To cite this abstract in AMA style:

Huang S, Huang J, Cai T, Dahal KP, Cagan A, Stratton J, Cai T, Liao KP. Impact of International Classification of Diseases 10th Revision Codes and Updated Medical Information on an Existing Rheumatoid Arthritis Phenotype Algorithm Using Electronic Medical Data [abstract]. Arthritis Rheumatol. 2018; 70 (suppl 9). https://acrabstracts.org/abstract/impact-of-international-classification-of-diseases-10th-revision-codes-and-updated-medical-information-on-an-existing-rheumatoid-arthritis-phenotype-algorithm-using-electronic-medical-data/. Accessed .
  • Tweet
  • Click to email a link to a friend (Opens in new window) Email
  • Click to print (Opens in new window) Print

« Back to 2018 ACR/ARHP Annual Meeting

ACR Meeting Abstracts - https://acrabstracts.org/abstract/impact-of-international-classification-of-diseases-10th-revision-codes-and-updated-medical-information-on-an-existing-rheumatoid-arthritis-phenotype-algorithm-using-electronic-medical-data/

Advanced Search

Your Favorites

You can save and print a list of your favorite abstracts during your browser session by clicking the “Favorite” button at the bottom of any abstract. View your favorites »

All abstracts accepted to ACR Convergence are under media embargo once the ACR has notified presenters of their abstract’s acceptance. They may be presented at other meetings or published as manuscripts after this time but should not be discussed in non-scholarly venues or outlets. The following embargo policies are strictly enforced by the ACR.

Accepted abstracts are made available to the public online in advance of the meeting and are published in a special online supplement of our scientific journal, Arthritis & Rheumatology. Information contained in those abstracts may not be released until the abstracts appear online. In an exception to the media embargo, academic institutions, private organizations, and companies with products whose value may be influenced by information contained in an abstract may issue a press release to coincide with the availability of an ACR abstract on the ACR website. However, the ACR continues to require that information that goes beyond that contained in the abstract (e.g., discussion of the abstract done as part of editorial news coverage) is under media embargo until 10:00 AM ET on November 14, 2024. Journalists with access to embargoed information cannot release articles or editorial news coverage before this time. Editorial news coverage is considered original articles/videos developed by employed journalists to report facts, commentary, and subject matter expert quotes in a narrative form using a variety of sources (e.g., research, announcements, press releases, events, etc.).

Violation of this policy may result in the abstract being withdrawn from the meeting and other measures deemed appropriate. Authors are responsible for notifying colleagues, institutions, communications firms, and all other stakeholders related to the development or promotion of the abstract about this policy. If you have questions about the ACR abstract embargo policy, please contact ACR abstracts staff at [email protected].

Wiley

  • Online Journal
  • Privacy Policy
  • Permissions Policies
  • Cookie Preferences

© Copyright 2025 American College of Rheumatology