Literature DB >> 32413107

Impact of ICD10 and secular changes on electronic medical record rheumatoid arthritis algorithms.

Sicong Huang1,2, Jie Huang1, Tianrun Cai1,2, Kumar P Dahal1, Andrew Cagan1,3, Zeling He1, Jacklyn Stratton1, Isaac Gorelik1, Chuan Hong4,5, Tianxi Cai1,4,5, Katherine P Liao1,4.   

Abstract

OBJECTIVE: The objective of this study was to compare the performance of an RA algorithm developed and trained in 2010 utilizing natural language processing and machine learning, using updated data containing ICD10, new RA treatments, and a new electronic medical records (EMR) system.
METHODS: We extracted data from subjects with ≥1 RA International Classification of Diseases (ICD) codes from the EMR of two large academic centres to create a data mart. Gold standard RA cases were identified from reviewing a random 200 subjects from the data mart, and a random 100 subjects who only have RA ICD10 codes. We compared the performance of the following algorithms using the original 2010 data with updated data: (i) a published 2010 RA algorithm; (ii) updated algorithm, incorporating ICD10 RA codes and new DMARDs; and (iii) published algorithm using ICD codes only, ICD RA code ≥3.
RESULTS: The gold standard RA cases had mean age 65.5 years, 78.7% female, 74.1% RF or antibodies to cyclic citrullinated peptide (anti-CCP) positive. The positive predictive value (PPV) for ≥3 RA ICD was 54%, compared with 56% in 2010. At a specificity of 95%, the PPV of the 2010 algorithm and the updated version were both 91%, compared with 94% (95% CI: 91, 96%) in 2010. In subjects with ICD10 data only, the PPV for the updated 2010 RA algorithm was 93%.
CONCLUSION: The 2010 RA algorithm validated with the updated data with similar performance characteristics as the 2010 data. While the 2010 algorithm continued to perform better than the rule-based approach, the PPV of the latter also remained stable over time.
© The Author(s) 2020. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For permissions, please email: journals.permissions@oup.com.

Entities:  

Keywords:  electronic medical record; machine learning; rheumatoid arthritis

Mesh:

Year:  2020        PMID: 32413107      PMCID: PMC7733719          DOI: 10.1093/rheumatology/keaa198

Source DB:  PubMed          Journal:  Rheumatology (Oxford)        ISSN: 1462-0324            Impact factor:   7.580


  25 in total

1.  Identification of rheumatoid arthritis patients using an administrative database: a Veterans Affairs study.

Authors:  Bernard Ng; Fawad Aslam; Nancy J Petersen; Hong-Jen Yu; Maria E Suarez-Almazor
Journal:  Arthritis Care Res (Hoboken)       Date:  2012-10       Impact factor: 4.794

2.  Phenome-Wide Association Study of Rheumatoid Arthritis Subgroups Identifies Association Between Seronegative Disease and Fibromyalgia.

Authors:  Jayanth Doss; Huan Mo; Robert J Carroll; Leslie J Crofford; Joshua C Denny
Journal:  Arthritis Rheumatol       Date:  2017-02       Impact factor: 10.995

3.  Electronic medical records for discovery research in rheumatoid arthritis.

Authors:  Katherine P Liao; Tianxi Cai; Vivian Gainer; Sergey Goryachev; Qing Zeng-treitler; Soumya Raychaudhuri; Peter Szolovits; Susanne Churchill; Shawn Murphy; Isaac Kohane; Elizabeth W Karlson; Robert M Plenge
Journal:  Arthritis Care Res (Hoboken)       Date:  2010-08       Impact factor: 4.794

4.  Accuracy of Veterans Administration databases for a diagnosis of rheumatoid arthritis.

Authors:  Jasvinder A Singh; Aaron R Holmgren; Siamak Noorbaloochi
Journal:  Arthritis Rheum       Date:  2004-12-15

5.  Lipid and lipoprotein levels and trend in rheumatoid arthritis compared to the general population.

Authors:  Katherine P Liao; Tianxi Cai; Vivian S Gainer; Andrew Cagan; Shawn N Murphy; Chihchin Liu; Susanne Churchill; Stanley Y Shaw; Isaac Kohane; Daniel H Solomon; Robert M Plenge; Elizabeth W Karlson
Journal:  Arthritis Care Res (Hoboken)       Date:  2013-12       Impact factor: 4.794

6.  High-throughput phenotyping with electronic medical record data using a common semi-supervised approach (PheCAP).

Authors:  Yichi Zhang; Tianrun Cai; Sheng Yu; Kelly Cho; Chuan Hong; Jiehuan Sun; Jie Huang; Yuk-Lam Ho; Ashwin N Ananthakrishnan; Zongqi Xia; Stanley Y Shaw; Vivian Gainer; Victor Castro; Nicholas Link; Jacqueline Honerlaw; Sicong Huang; David Gagnon; Elizabeth W Karlson; Robert M Plenge; Peter Szolovits; Guergana Savova; Susanne Churchill; Christopher O'Donnell; Shawn N Murphy; J Michael Gaziano; Isaac Kohane; Tianxi Cai; Katherine P Liao
Journal:  Nat Protoc       Date:  2019-11-20       Impact factor: 13.491

Review 7.  A review of approaches to identifying patient phenotype cohorts using electronic health records.

Authors:  Chaitanya Shivade; Preethi Raghavan; Eric Fosler-Lussier; Peter J Embi; Noemie Elhadad; Stephen B Johnson; Albert M Lai
Journal:  J Am Med Inform Assoc       Date:  2013-11-07       Impact factor: 4.497

8.  Association between inflammation and systolic blood pressure in RA compared to patients without RA.

Authors:  Zhi Yu; Seoyoung C Kim; Kathleen Vanni; Jie Huang; Rishi Desai; Shawn N Murphy; Daniel H Solomon; Katherine P Liao
Journal:  Arthritis Res Ther       Date:  2018-06-01       Impact factor: 5.156

9.  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

10.  Development of phenotype algorithms using electronic medical records and incorporating natural language processing.

Authors:  Katherine P Liao; Tianxi Cai; Guergana K Savova; Shawn N Murphy; Elizabeth W Karlson; Ashwin N Ananthakrishnan; Vivian S Gainer; Stanley Y Shaw; Zongqi Xia; Peter Szolovits; Susanne Churchill; Isaac Kohane
Journal:  BMJ       Date:  2015-04-24
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  5 in total

1.  The Association Between Inflammation, Incident Heart Failure, and Heart Failure Subtypes in Patients with Rheumatoid Arthritis.

Authors:  Sicong Huang; Tianrun Cai; Brittany N Weber; Zeling He; Kumar P Dahal; Chuan Hong; Jue Hou; Thany Seyok; Andrew Cagan; Marcelo F DiCarli; Jacob Joseph; Seoyoung C Kim; Daniel H Solomon; Tianxi Cai; Katherine P Liao
Journal:  Arthritis Care Res (Hoboken)       Date:  2021-10-08       Impact factor: 4.794

2.  Association of Sinusitis and Upper Respiratory Tract Diseases With Incident Rheumatoid Arthritis: A Case-control Study.

Authors:  Vanessa L Kronzer; Weixing Huang; Alessandra Zaccardelli; Cynthia S Crowson; John M Davis; Robert Vassallo; Tracy J Doyle; Elena Losina; Jeffrey A Sparks
Journal:  J Rheumatol       Date:  2021-10-15       Impact factor: 4.666

3.  Demographic, Lifestyle, and Serologic Risk Factors for Rheumatoid Arthritis (RA)-associated Bronchiectasis: Role of RA-related Autoantibodies.

Authors:  Gregory McDermott; Ritu Gill; Staci Gagne; Suzanne Byrne; Weixing Huang; Xiaosong Wang; Lauren C Prisco; Alessandra Zaccardelli; Lily W Martin; Lucy Masto; Vanessa L Kronzer; Nancy Shadick; Paul F Dellaripa; Tracy J Doyle; Jeffrey A Sparks
Journal:  J Rheumatol       Date:  2022-03-15       Impact factor: 5.346

4.  ATLAS: an automated association test using probabilistically linked health records with application to genetic studies.

Authors:  Harrison G Zhang; Boris P Hejblum; Griffin M Weber; Nathan P Palmer; Susanne E Churchill; Peter Szolovits; Shawn N Murphy; Katherine P Liao; Isaac S Kohane; Tianxi Cai
Journal:  J Am Med Inform Assoc       Date:  2021-11-25       Impact factor: 7.942

Review 5.  [Perspectives for rheumatological health services research at the German Rheumatism Research Center].

Authors:  K Albrecht; F Milatz; J Callhoff; I Redeker; K Minden; A Strangfeld; A Regierer
Journal:  Z Rheumatol       Date:  2020-12-01       Impact factor: 1.372

  5 in total

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