Erica Weinstein

Development and Validation of Case-Finding Algorithms to Identify Prosthetic Joint Infections After Total Knee Arthroplasty

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Erica Weinstein, Epidemiology

Authors

E Weinstein1, A Stephens-Shields2, B Loabile3, T Yuh3, R Silibovsky3, J O'Donnell3, C Nelson4, E Hsieh5, J Hanberg6, K Akgun5, J Tate7, V Lo Re III8

  1. Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
  2. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
  3. Division of Infectious Diseases, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
  4. Department of Orthopaedic Surgery, Perelman School of Medicine, the University of Pennsylvania, Philadelphia, Pennsylvania, USA
  5. Department of Medicine, Yale University School of Medicine, New Haven, Connecticut, USA
  6. Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
  7. VA Connecticut Health System, West Haven, Connecticut, USA
  8. Center for Pharmacoepidemiology Research and Training, Center for Clinical Epidemiology and Biostatistics, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA

Abstract

Purpose: To determine the positive predictive values (PPVs) of ICD-9 and ICD-10- 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) ICD-9 or ICD-10 diagnosis codes for PJI, 2) ICD-9 or ICD-10 procedure code for TKA prior to PJI diagnosis, 3) Current Procedural Terminology (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. Separate samples of patients identified with the ICD-9 and ICD-10 PJI diagnoses were obtained,  stratified by TKA procedure volume at each medical center. Medical records of sampled patients were abstracted and 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-CM PJI algorithm, 60 (PPV 75.0%, [CI 64.1-84.0%]) had confirmed PJIs. Among a sample of 80 patients who met the ICD-10-CM PJI algorithm, 68 (PPV 85.0%, [CI 75.3-92.0%]) had a confirmed diagnosis.

Conclusions: An algorithm requiring an ICD-9-CM or ICD-10-CM code for PJI following an ICD-9-CM or ICD-10-PCS diagnosis for TKA,  combined with CPT codes, had a PPV of 75% and 85%, respectively, for confirmed events and could be considered for use within epidemiologic studies.

Keywords

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