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

  • Michael V. Murphy (a1), Dongyi (Tony) Du (a2), Wei Hua (a2), Karoll J. Cortez (a3), Melissa G. Butler (a4), Robert L. Davis (a4), Thomas DeCoster (a5), Laura Johnson (a6), Lingling Li (a1), Cynthia Nakasato (a7), James D. Nordin (a8), Mayur Ramesh (a6), Michael Schum (a9), Ann Von Worley (a9), Craig Zinderman (a2), Richard Platt (a1) and Michael Klompas (a1)...

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

Copyright

Corresponding author

Harvard Pilgrim Health Care Institute, 133 Brookline Avenue, 6th Floor, Boston, MA 02215 (michael_murphy@harvardpilgrim.org).

Footnotes

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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.

Footnotes

References

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