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The Utility of Claims Data for Infection Surveillance following Anterior Cruciate Ligament Reconstruction1

Published online by Cambridge University Press:  10 May 2016

Michael V. Murphy
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Dongyi (Tony) Du
Affiliation:
Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, Maryland
Wei Hua
Affiliation:
Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, Maryland
Karoll J. Cortez
Affiliation:
Office of Cellular, Tissue, and Gene Therapies, Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, Maryland
Melissa G. Butler
Affiliation:
Center for Health Research–Southeast, Kaiser Permanente Georgia, Atlanta, Georgia
Robert L. Davis
Affiliation:
Center for Health Research–Southeast, Kaiser Permanente Georgia, Atlanta, Georgia
Thomas DeCoster
Affiliation:
Department of Orthopaedics and Rehabilitation, University of New Mexico School of Medicine, Albuquerque, New Mexico
Laura Johnson
Affiliation:
Center for Health Services Research, Henry Ford Health System, Detroit, Michigan
Lingling Li
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Cynthia Nakasato
Affiliation:
Center for Health Research, Kaiser Permanente Hawaii, Honolulu, Hawaii
James D. Nordin
Affiliation:
HealthPartners Institute for Education and Research, Minneapolis, Minnesota
Mayur Ramesh
Affiliation:
Center for Health Services Research, Henry Ford Health System, Detroit, Michigan
Michael Schum
Affiliation:
Health Services Research Division, LCF Research, Albuquerque, New Mexico
Ann Von Worley
Affiliation:
Health Services Research Division, LCF Research, Albuquerque, New Mexico
Craig Zinderman
Affiliation:
Office of Biostatistics and Epidemiology, Center for Biologics Evaluation and Research, Food and Drug Administration, Rockville, Maryland
Richard Platt
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Michael Klompas
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Corresponding

Abstract

Objective.

To explore the feasibility of identifying anterior cruciate ligament (ACL) allograft implantations and infections using claims.

Design.

Retrospective cohort study.

Methods.

We identified ACL reconstructions using procedure codes at 6 health plans from 2000 to 2008. We then identified potential infections using claims-based indicators of infection, including diagnoses, procedures, antibiotic dispensings, specialty consultations, emergency department visits, and hospitalizations. Patients’ medical records were reviewed to determine graft type, validate infection status, and calculate sensitivity and positive predictive value (PPV) for indicators of ACL allografts and infections.

Results.

A total of 11,778 patients with codes for ACL reconstruction were identified. After chart review, PPV for ACL reconstruction was 96% (95% confidence interval [CI], 94%–97%). Of the confirmed ACL reconstructions, 39% (95% CI, 35%–42%) used allograft tissues. The deep infection rate after ACL reconstruction was 1.0% (95% CI, 0.7%–1.4%). The odds ratio of infection for allografts versus autografts was 0.41 (95% CI, 0.19–0.78). Sensitivity of individual claims-based indicators for deep infection after ACL reconstruction ranged from 0% to 75% and PPV from 0% to 100%. Claims-based infection indicators could be combined to enhance sensitivity or PPV but not both.

Conclusions.

While claims data accurately identify ACL reconstructions, they poorly distinguish between allografts and autografts and identify infections with variable accuracy. Claims data could be useful to monitor infection trends after ACL reconstruction, with different algorithms optimized for different surveillance goals.

Infect Control Hosp Epidemiol 2014;35(6):652–659

Type
Original Articles
Copyright
© 2014 by The Society for Healthcare Epidemiology of America. All rights reserved.

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Footnotes

Presented in part: 17th Annual HMO Research Network Conference; Boston, Massachuestts; March 23–25, 2011; 18th Annual HMO Research Network Conference; Seattle, Washington; April 29–May 2, 2012.

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