The utility of claims data for infection surveillance following anterior cruciate ligament reconstruction

Michael V. Murphy, Dongyi Tony Du, Wei Hua, Karoll J. Cortez, Melissa G. Butler, Robert Davis, Thomas DeCoster, Laura Johnson, Lingling Li, Cynthia Nakasato, James D. Nordin, Mayur Ramesh, Michael Schum, Ann Von Worley, Craig Zinderman, Richard Platt, Michael Klompas

Research output: Contribution to journalArticle

4 Citations (Scopus)

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. Patientsmedical 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.

Original languageEnglish (US)
Pages (from-to)652-659
Number of pages8
JournalInfection Control and Hospital Epidemiology
Volume35
Issue number6
DOIs
StatePublished - Jan 1 2014

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Anterior Cruciate Ligament Reconstruction
Infection
Allografts
Confidence Intervals
Anterior Cruciate Ligament
Autografts
Hospital Emergency Service
Hospitalization
Cohort Studies
Referral and Consultation
Retrospective Studies
Odds Ratio
Anti-Bacterial Agents

All Science Journal Classification (ASJC) codes

  • Epidemiology
  • Microbiology (medical)
  • Infectious Diseases

Cite this

The utility of claims data for infection surveillance following anterior cruciate ligament reconstruction. / Murphy, Michael V.; Du, Dongyi Tony; Hua, Wei; Cortez, Karoll J.; Butler, Melissa G.; Davis, Robert; DeCoster, Thomas; Johnson, Laura; Li, Lingling; Nakasato, Cynthia; Nordin, James D.; Ramesh, Mayur; Schum, Michael; Von Worley, Ann; Zinderman, Craig; Platt, Richard; Klompas, Michael.

In: Infection Control and Hospital Epidemiology, Vol. 35, No. 6, 01.01.2014, p. 652-659.

Research output: Contribution to journalArticle

Murphy, MV, Du, DT, Hua, W, Cortez, KJ, Butler, MG, Davis, R, DeCoster, T, Johnson, L, Li, L, Nakasato, C, Nordin, JD, Ramesh, M, Schum, M, Von Worley, A, Zinderman, C, Platt, R & Klompas, M 2014, 'The utility of claims data for infection surveillance following anterior cruciate ligament reconstruction', Infection Control and Hospital Epidemiology, vol. 35, no. 6, pp. 652-659. https://doi.org/10.1086/676430
Murphy, Michael V. ; Du, Dongyi Tony ; Hua, Wei ; Cortez, Karoll J. ; Butler, Melissa G. ; Davis, Robert ; DeCoster, Thomas ; Johnson, Laura ; Li, Lingling ; Nakasato, Cynthia ; Nordin, James D. ; Ramesh, Mayur ; Schum, Michael ; Von Worley, Ann ; Zinderman, Craig ; Platt, Richard ; Klompas, Michael. / The utility of claims data for infection surveillance following anterior cruciate ligament reconstruction. In: Infection Control and Hospital Epidemiology. 2014 ; Vol. 35, No. 6. pp. 652-659.
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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. Patientsmedical 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.",
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AU - Du, Dongyi Tony

AU - Hua, Wei

AU - Cortez, Karoll J.

AU - Butler, Melissa G.

AU - Davis, Robert

AU - DeCoster, Thomas

AU - Johnson, Laura

AU - Li, Lingling

AU - Nakasato, Cynthia

AU - Nordin, James D.

AU - Ramesh, Mayur

AU - Schum, Michael

AU - Von Worley, Ann

AU - Zinderman, Craig

AU - Platt, Richard

AU - Klompas, Michael

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N2 - 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. Patientsmedical 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.

AB - 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. Patientsmedical 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.

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