Literature DB >> 34170057

Development and validation of case-finding algorithms to identify prosthetic joint infections after total knee arthroplasty in Veterans Health Administration data.

Erica J Weinstein1,2, Alisa Stephens-Shields2, Bogadi Loabile1,2, Tiffany Yuh1, Randi Silibovsky1, Charles L Nelson3, Judith A O'Donnell1, Evelyn Hsieh4,5,6, Jennifer S Hanberg4,5,7, Kathleen M Akgün4,5, Janet P Tate4,5, Vincent Lo Re1,2.   

Abstract

PURPOSE: To determine the positive predictive values (PPVs) of ICD-9, ICD-10, and current procedural terminology (CPT)-based diagnostic coding algorithms to identify prosthetic joint infection (PJI) following knee arthroplasty (TKA) within the United States Veterans Health Administration.
METHODS: We identified patients with: (1) hospital discharge ICD-9 or ICD-10 diagnosis of PJI, (2) ICD-9, ICD-10, or CPT procedure code for TKA prior to PJI diagnosis, (3) CPT code for knee X-ray within ±90 days of the PJI diagnosis, and (4) at least 1 CPT code for arthrocentesis, arthrotomy, blood culture, or microbiologic procedure within ±90 days of the PJI diagnosis date. Separate samples of patients identified with the ICD-9 and ICD-10-based PJI diagnoses were obtained, stratified by TKA procedure volume at each medical center. Medical records of sampled patients were reviewed by infectious disease clinicians to adjudicate PJI events. The PPV (95% confidence interval [CI]) for the ICD-9 and ICD-10 PJI algorithms were calculated.
RESULTS: Among a sample of 80 patients meeting the ICD-9 PJI algorithm, 60 (PPV 75.0%, [CI 64.1%-84.0%]) had confirmed PJI. Among 80 patients who met the ICD-10 PJI algorithm, 68 (PPV 85.0%, [CI 75.3%-92.0%]) had a confirmed diagnosis.
CONCLUSIONS: An algorithm consisting of an ICD-9 or ICD-10 PJI diagnosis following a TKA code combined with CPT codes for a knee X-ray and either a relevant surgical procedure or microbiologic culture yielded a PPV of 75.0% (ICD-9) and 85.0% (ICD-10), for confirmed PJI events and could be considered for use in future pharmacoepidemiologic studies.
© 2021 John Wiley & Sons Ltd.

Entities:  

Keywords:  epidemiologic methods; outcomes; pharmacoepidemiology; prosthetic joint infection; total knee arthroplasty; validation studies; veteran

Mesh:

Year:  2021        PMID: 34170057      PMCID: PMC8343957          DOI: 10.1002/pds.5316

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.732


  16 in total

1.  Assessing validity of ICD-9-CM and ICD-10 administrative data in recording clinical conditions in a unique dually coded database.

Authors:  Hude Quan; Bing Li; L Duncan Saunders; Gerry A Parsons; Carolyn I Nilsson; Arif Alibhai; William A Ghali
Journal:  Health Serv Res       Date:  2008-08       Impact factor: 3.402

2.  Quantifying the Burden of Revision Total Joint Arthroplasty for Periprosthetic Infection.

Authors:  Atul F Kamath; Kevin L Ong; Edmund Lau; Vanessa Chan; Thomas P Vail; Harry E Rubash; Daniel J Berry; Kevin J Bozic
Journal:  J Arthroplasty       Date:  2015-03-31       Impact factor: 4.757

3.  Validation of the diagnosis 'prosthetic joint infection' in the Danish Hip Arthroplasty Register.

Authors:  P H Gundtoft; A B Pedersen; H C Schønheyder; S Overgaard
Journal:  Bone Joint J       Date:  2016-03       Impact factor: 5.082

4.  Diffusion, implementation, and use of Research Electronic Data Capture (REDCap) in the Veterans Health Administration (VA).

Authors:  Bonnie L Paris; Denise M Hynes
Journal:  JAMIA Open       Date:  2019-06-11

5.  Diagnostic tests 2: Predictive values.

Authors:  D G Altman; J M Bland
Journal:  BMJ       Date:  1994-07-09

6.  The REDCap consortium: Building an international community of software platform partners.

Authors:  Paul A Harris; Robert Taylor; Brenda L Minor; Veida Elliott; Michelle Fernandez; Lindsay O'Neal; Laura McLeod; Giovanni Delacqua; Francesco Delacqua; Jacqueline Kirby; Stephany N Duda
Journal:  J Biomed Inform       Date:  2019-05-09       Impact factor: 6.317

7.  Economic burden of periprosthetic joint infection in the United States.

Authors:  Steven M Kurtz; Edmund Lau; Heather Watson; Jordana K Schmier; Javad Parvizi
Journal:  J Arthroplasty       Date:  2012-05-02       Impact factor: 4.757

8.  Identification of prosthetic hip and knee joint infections using administrative databases-A validation study.

Authors:  Christopher E Kandel; Richard Jenkinson; Jessica Widdifield; Bettina E Hansen; J Roderick Davey; Matthew P Muller; Nick Daneman; Allison McGeer
Journal:  Infect Control Hosp Epidemiol       Date:  2020-09-30       Impact factor: 3.254

Review 9.  Patient-Related Risk Factors for Periprosthetic Joint Infection after Total Joint Arthroplasty: A Systematic Review and Meta-Analysis.

Authors:  Setor K Kunutsor; Michael R Whitehouse; Ashley W Blom; Andrew D Beswick
Journal:  PLoS One       Date:  2016-03-03       Impact factor: 3.240

10.  Patterns of COVID-19 testing and mortality by race and ethnicity among United States veterans: A nationwide cohort study.

Authors:  Christopher T Rentsch; Farah Kidwai-Khan; Janet P Tate; Lesley S Park; Joseph T King; Melissa Skanderson; Ronald G Hauser; Anna Schultze; Christopher I Jarvis; Mark Holodniy; Vincent Lo Re; Kathleen M Akgün; Kristina Crothers; Tamar H Taddei; Matthew S Freiberg; Amy C Justice
Journal:  PLoS Med       Date:  2020-09-22       Impact factor: 11.069

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