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Leveraging Electronic Medical Records for Surveillance of Surgical Site Infection in a Total Joint Replacement Population

Published online by Cambridge University Press:  02 January 2015

Maria C. S. Inacio ,
Elizabeth W. Paxton ,
Yuexin Chen ,
Jessica Harris ,
Enid Eck ,
Sue Barnes ,
Robert S. Namba  and
Christopher F. Ake
Maria C. S. Inacio*
Affiliation:
Surgical Outcomes and Analysis Unit of Clinical Analysis, Kaiser Permanente, San Diego, California 92109
Elizabeth W. Paxton
Affiliation:
Surgical Outcomes and Analysis Unit of Clinical Analysis, Kaiser Permanente, San Diego, California 92109
Yuexin Chen
Affiliation:
Surgical Outcomes and Analysis Unit of Clinical Analysis, Kaiser Permanente, San Diego, California 92109
Jessica Harris
Affiliation:
Surgical Outcomes and Analysis Unit of Clinical Analysis, Kaiser Permanente, San Diego, California 92109
Enid Eck
Affiliation:
Quality and Risk Management, Infection Prevention and Control Department, Kaiser Permanente, Pasadena, California 91188
Sue Barnes
Affiliation:
Program Offices, Infection Prevention and Control Department, Kaiser Permanente, Oakland, California 94612
Robert S. Namba
Affiliation:
Department of Orthopedics, Kaiser Permanente Orange County, Irvine, California 92618
Christopher F. Ake
Affiliation:
Surgical Outcomes and Analysis Unit of Clinical Analysis, Kaiser Permanente, San Diego, California 92109
*
Surgical Outcomes and Analysis Unit of Clinical Analysis, Kaiser Permanente, 3033 Bunker Hill Street, San Diego, California 92109 ([email protected])
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Abstract

Objective.

TO evaluate whether a hybrid electronic screening algorithm using a total joint replacement (TJR) registry, electronic surgical site infection (SSI) screening, and electronic health record (EHR) review of SSI is sensitive and specific for SSI detection and reduces chart review volume for SSI surveillance.

Design.

Validation study.

Setting.

A large health maintenance organization (HMO) with 8.6 million members.

Methods.

Using codes for infection, wound complications, cellullitis, procedures related to infections, and surgeon-reported complications from the International Classification of Diseases, Ninth Revision, Clinical Modification, we screened each TJR procedure performed in our HMO between January 2006 and December 2008 for possible infections. Flagged charts were reviewed by clinical-content experts to confirm SSIs. SSIs identified by the electronic screening algorithm were compared with SSIs identified by the traditional indirect surveillance methodology currently employed in our HMO. Positive predictive values (PPVs), negative predictive values (NPVs), and specificity and sensitivity values were calculated. Absolute reduction of chart review volume was evaluated.

Results.

The algorithm identified 4,001 possible SSIs (9.5%) for the 42,173 procedures performed for our TJR patient population. A total of 440 case patients (1.04%) had SSIs (PPV, 11.0%; NPV, 100.0%). The sensitivity and specificity of the overall algorithm were 97.8% and 91.5%, respectively.

Conclusion.

An electronic screening algorithm combined with an electronic health record review of flagged cases can be used as a valid source for TJR SSI surveillance. The algorithm successfully reduced the volume of chart review for surveillance by 90.5%.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 32 , Issue 4 , April 2011 , pp. 351 - 359
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2011

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References

1. Namba, RS, Chen, Y, Paxton, EW, Slipchenko, T, Fithian, DC. Outcomes of routine use of antibiotic-loaded cement in primary total knee arthroplasty. J Arthroplast 2009;24(suppl 6):44–47.Google Scholar
2. Barnes, S, Salemi, C, Fithian, D, et al. An enhanced benchmark for prosthetic joint replacement infection rates. Am J Infect Control 2006;34(10):669–672.Google Scholar
3. Lentino, JR. Prosthetic joint infections: bane of orthopedists, challenge for infectious disease specialists Clin Infect Dis 2003;36(91):1157–1161.Google Scholar
4. Ridgeway, S, Wilson, J, Charlet, A, Kafatos, G, Pearson, A, Coello, R. Infection of the surgical site after arthroplasty of the hip. J Bone Joint Surg Br 2005;87(6):844–850.Google Scholar
5. Babkin, Y, Raveh, D, Lifschitz, M, et al. Incidence and risk factors for surgical infection after total knee replacement. Scand J Infect Dis 2007;39(10):890–895.Google Scholar
6. Bozic, KJ, Ries, MD. The impact of infection after total hip arthroplasty on hospital and surgeon resource utilization. J Bone Joint Surg Am 2005;87(8):1746–1751.Google Scholar
7. Bozic, KJ, Katz, P, Cisternas, M, Ono, L, Ries, MD, Showstack, J. Hospital resource utilization for primary and revision total hip arthroplasty. J Bone Joint Surg Am 2005;87(3):570–576.Google Scholar
8. Hebert, CK, Williams, RE, Levy, RS, Barrack, RL. Cost of treating an infected total knee replacement. Clin Orthop Relat Res 1996(331):140–145.10.1097/00003086-199610000-00019Google Scholar
9. Sculco, TP. The economic impact of infected joint arthroplasty. Orthopedics 1995;18(9):871–873.Google Scholar
10. Whitehouse, JD, Friedman, ND, Kirkland, KB, Richardson, WJ, Sexton, DJ. The impact of surgical-site infections following orthopedic surgery at a community hospital and a university hospital: adverse quality of life, excess length of stay, and extra cost. Infect Control Hasp Epidemiol 2002;23(4):183–189.Google Scholar
11. Mangram, AJ, Horan, TC, Pearson, ML, Silver, LC, Jarvis, WR. Guideline for prevention of surgical site infection, 1999. Hospital Infection Control Practices Advisory Committee. Infect Control Hosp Epidemiol 1999;20(4):250–278; quiz 79-80.Google Scholar
12. Reilly, J, Noone, A, Gift, A, et al. A study of telephone screening and direct observation of surgical wound infections after discharge from hospital. J Bone Joint Surg Br 2005;87(7):997–999.Google Scholar
13. Ferraz, EM, Ferraz, AA, Coelho, HS, et al. Postdischarge surveillance for nosocomial wound infection: does judicious monitoring find cases? Am J Infect Control 1995;23(5):290–294.Google Scholar
14. Huotari, K, Agthe, N, Lyytikainen, O. Validation of surgical site infection surveillance in orthopedic procedures. Am J Infect Control 2007;35(4):216–221.Google Scholar
15. Cadwallader, HL, Toohey, M, Linton, S, Dyson, A, Riley, TV. A comparison of two methods for identifying surgical site infections following orthopaedic surgery. J Hosp Infect 2001;48(4):261–266.Google Scholar
16. Baker, C, Luce, J, Chenoweth, C, Friedman, C. Comparison of case-finding methodologies for endometritis after cesarean section. Am J Infect Control 1995;23(1):27–33.Google Scholar
17. Hirschhorn, LR, Currier, JS, Platt, R. Electronic surveillance of antibiotic exposure and coded discharge diagnoses as indicators of postoperative infection and other quality assurance measures. Infect Control Hosp Epidemiol 1993;14(1):21–28.Google Scholar
18. Chalfine, A, Cauet, D, Lin, WC, et al. Highly sensitive and efficient computer-assisted system for routine surveillance for surgical site infection. Infect Control Hosp Epidemiol 2006;27(8):794–801.Google Scholar
19. Olsen, MA, Fraser, VJ. Use of diagnosis codes and/or wound culture results for surveillance of surgical site infection after mastectomy and breast reconstruction. Infect Control Hosp Epidemiol 31(5):544–547.Google Scholar
20. Stevenson, KB, Khan, Y, Dickman, J, et al. Administrative coding data, compared with CDC/NHSN criteria, are poor indicators of health care-associated infections. Am J Infect Control 2008;36(3):155–164.Google Scholar
21. McKibben, L, Horan, TC, Tokars, JI, et al. Guidance on public reporting of healthcare-associated infections: recommendations of the Healthcare Infection Control Practices Advisory Committee. Infect Control Hosp Epidemiol 2005;26(6):580–587.Google Scholar
22. Jhung, MA, Banerjee, SN. Administrative coding data and health care-associated infections. Clin Infect Dis 2009;49(6):949–955.10.1086/605086Google Scholar
23. Patkar, NM, Curtis, JR, Teng, GG, et al. Administrative codes combined with medical records based criteria accurately identified bacterial infections among rheumatoid arthritis patients. J Clin Epidemiol 2009;62(3):321–327, 7:el-e7.Google Scholar
24. Horan, TC, Andrus, M, Dudeck, MA. CDC/NHSN surveillance definition of health care-associated infection and criteria for specific types of infections in the acute care setting. Am J Infect Control 2008;36(5):309–332.Google Scholar
25. Paxton, E, Inacio, M, Slipchenko, T, Fithian, D. The Kaiser Permanente National Total Joint Replacement Registry. Permanente J 2008;12(3):12–16.Google Scholar
26. Paxton, EW, Inacio, MC, Khatod, M, Yue, EJ, Namba, RS. Kaiser Permanente National Total Joint Replacement Registry: aligning operations with information technology. Clin Orthop Relat Res 2010;468(10):2646–2663.Google Scholar
27. Ong, KL, Kurtz, SM, Lau, E, Bozic, KJ, Berry, DJ, Parvizi, J. Prosthetic joint infection risk after total hip arthroplasty in the Medicare population. J Arthroplast 2009;24(suppl 6):105–109.Google Scholar
28. Katz, JN, Barrett, J, Mahomed, NN, Baron, JA, Wright, RJ, Losina, E. Association between hospital and surgeon procedure volume and the outcomes of total knee replacement. J Bone Joint Surg Am 2004;86-A(9):1909–1916.Google Scholar
29. Miner, AL, Losina, E, Katz, JN, Fossel, AH, Platt, R. Deep infection after total knee replacement: impact of laminar airflow systems and body exhaust suits in the modern operating room. Infect Control Hosp Epidemiol 2007;28(2):222–226.Google Scholar
30. Piatt, R, Kleinman, K, Thompson, K, et al. Using automated health plan data to assess infection risk from coronary artery bypass surgery. Emerg Infect Dis 2002;8(12):1433–1441.Google Scholar
31. Piatt, R, Yokoe, DS, Sands, KE. Automated methods for surveillance of surgical site infections. Emerg Infect Dis 2001;7(2):212–216.Google Scholar
32. Bolon, MK, Hooper, D, Stevenson, KB, et al. Improved surveillance for surgical site infections after orthopedic implantation procedures: extending applications for automated data. Clin Infect Dis 2009;48(9):1223–1229.Google Scholar
33. Leal, J, Laupland, KB. Validity of electronic surveillance systems: a systematic review. J Hosp Infect 2008;69(3):220–229.Google Scholar