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2442. Detection of Prosthetic Hip and Knee Joint Infections Using Administrative Databases – A Validation Study

BACKGROUND: Forming large cohorts to study prosthetic joint infections (PJIs) is a challenge without an existing surgical registry, as is the case in Canada. Administrative databases are an option, yet PJI diagnostic codes are insensitive. There is a need to improve the detection of PJIs from within...

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Autores principales: Kandel, Christopher, Jenkinson, Richard, Davey, Roderick, Widdifield, Jessica, Hansen, Bettina, Muller, Matthew P, Daneman, Nick, McGeer, Allison
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6810127/
http://dx.doi.org/10.1093/ofid/ofz360.2120
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author Kandel, Christopher
Jenkinson, Richard
Davey, Roderick
Widdifield, Jessica
Hansen, Bettina
Muller, Matthew P
Daneman, Nick
McGeer, Allison
author_facet Kandel, Christopher
Jenkinson, Richard
Davey, Roderick
Widdifield, Jessica
Hansen, Bettina
Muller, Matthew P
Daneman, Nick
McGeer, Allison
author_sort Kandel, Christopher
collection PubMed
description BACKGROUND: Forming large cohorts to study prosthetic joint infections (PJIs) is a challenge without an existing surgical registry, as is the case in Canada. Administrative databases are an option, yet PJI diagnostic codes are insensitive. There is a need to improve the detection of PJIs from within administrative databases. METHODS: Individuals who had a primary arthroplasty at four hospitals in Toronto, Canada from 2010 to 2016 were identified using Canadian Classification of Health Intervention codes (based on the International Classification of Disease, Tenth Revision). Each re-admission to the same hospital until December 31, 2016 was reviewed for the presence of a PJI. The performance characteristics (sensitivity, specificity, positive and negative predictive values) of combinations of diagnostic and procedure codes when compared with the gold standard of chart review were calculated. The primary outcome was the algorithm that maximized sensitivity and positive predictive value. RESULTS: 27,843 primary arthroplasties were performed with 8595 readmissions, of which 572 involved a PJI. Median follow-up was 1258 days (interquartile range (IQR) 614–1891 days), with median time to first re-admission of 352 days (IQR range 166–725 days). PJI codes exhibited a sensitivity of 0.86 (95% confidence interval (95% CI) 0.83–0.89) and positive predictive value (PPV) of 0.89 (95% CI 0.86–0.92). The best performing algorithm is a combination of a PJI code or joint spacer insertion procedure code or insertion of a peripherally inserted central catheter along with an arthroplasty code (sensitivity 0.90, 95% CI 0.88–0.93 and PPV 0.89, 95% CI 0.86–0.91). Using timing from primary arthroplasty, spacer insertion codes and presence of a subsequent arthroplasty procedure code identified 68% (71/105) of first stage and 74% (108/146) of debridement with joint retention procedures during the first re-admission for a PJI. CONCLUSION: Combinations of diagnosis and procedure codes can reliably identify PJIs from administrative databases. Individual orthopaedic procedure codes and timing from primary arthroplasty can inform the surgical procedure performed. This PJI detection algorithm could be used for PJI surveillance and research. DISCLOSURES: All authors: No reported disclosures.
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spelling pubmed-68101272019-10-28 2442. Detection of Prosthetic Hip and Knee Joint Infections Using Administrative Databases – A Validation Study Kandel, Christopher Jenkinson, Richard Davey, Roderick Widdifield, Jessica Hansen, Bettina Muller, Matthew P Daneman, Nick McGeer, Allison Open Forum Infect Dis Abstracts BACKGROUND: Forming large cohorts to study prosthetic joint infections (PJIs) is a challenge without an existing surgical registry, as is the case in Canada. Administrative databases are an option, yet PJI diagnostic codes are insensitive. There is a need to improve the detection of PJIs from within administrative databases. METHODS: Individuals who had a primary arthroplasty at four hospitals in Toronto, Canada from 2010 to 2016 were identified using Canadian Classification of Health Intervention codes (based on the International Classification of Disease, Tenth Revision). Each re-admission to the same hospital until December 31, 2016 was reviewed for the presence of a PJI. The performance characteristics (sensitivity, specificity, positive and negative predictive values) of combinations of diagnostic and procedure codes when compared with the gold standard of chart review were calculated. The primary outcome was the algorithm that maximized sensitivity and positive predictive value. RESULTS: 27,843 primary arthroplasties were performed with 8595 readmissions, of which 572 involved a PJI. Median follow-up was 1258 days (interquartile range (IQR) 614–1891 days), with median time to first re-admission of 352 days (IQR range 166–725 days). PJI codes exhibited a sensitivity of 0.86 (95% confidence interval (95% CI) 0.83–0.89) and positive predictive value (PPV) of 0.89 (95% CI 0.86–0.92). The best performing algorithm is a combination of a PJI code or joint spacer insertion procedure code or insertion of a peripherally inserted central catheter along with an arthroplasty code (sensitivity 0.90, 95% CI 0.88–0.93 and PPV 0.89, 95% CI 0.86–0.91). Using timing from primary arthroplasty, spacer insertion codes and presence of a subsequent arthroplasty procedure code identified 68% (71/105) of first stage and 74% (108/146) of debridement with joint retention procedures during the first re-admission for a PJI. CONCLUSION: Combinations of diagnosis and procedure codes can reliably identify PJIs from administrative databases. Individual orthopaedic procedure codes and timing from primary arthroplasty can inform the surgical procedure performed. This PJI detection algorithm could be used for PJI surveillance and research. DISCLOSURES: All authors: No reported disclosures. Oxford University Press 2019-10-23 /pmc/articles/PMC6810127/ http://dx.doi.org/10.1093/ofid/ofz360.2120 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Infectious Diseases Society of America. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Abstracts
Kandel, Christopher
Jenkinson, Richard
Davey, Roderick
Widdifield, Jessica
Hansen, Bettina
Muller, Matthew P
Daneman, Nick
McGeer, Allison
2442. Detection of Prosthetic Hip and Knee Joint Infections Using Administrative Databases – A Validation Study
title 2442. Detection of Prosthetic Hip and Knee Joint Infections Using Administrative Databases – A Validation Study
title_full 2442. Detection of Prosthetic Hip and Knee Joint Infections Using Administrative Databases – A Validation Study
title_fullStr 2442. Detection of Prosthetic Hip and Knee Joint Infections Using Administrative Databases – A Validation Study
title_full_unstemmed 2442. Detection of Prosthetic Hip and Knee Joint Infections Using Administrative Databases – A Validation Study
title_short 2442. Detection of Prosthetic Hip and Knee Joint Infections Using Administrative Databases – A Validation Study
title_sort 2442. detection of prosthetic hip and knee joint infections using administrative databases – a validation study
topic Abstracts
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6810127/
http://dx.doi.org/10.1093/ofid/ofz360.2120
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