Hostname: page-component-cd9895bd7-mkpzs Total loading time: 0 Render date: 2024-12-29T09:56:15.207Z Has data issue: false hasContentIssue false
  • English
  • Français

Use of Medicare Diagnosis and Procedure Codes to Improve Detection of Surgical Site Infections following Hip Arthroplasty, Knee Arthroplasty, and Vascular Surgery

Published online by Cambridge University Press:  02 January 2015

Michael S. Calderwood ,
Allen Ma ,
Yosef M. Khan ,
Margaret A. Olsen ,
Dale W. Bratzler ,
Deborah S. Yokoe ,
David C. Hooper ,
Kurt Stevenson ,
Victoria J. Fraser  and
Richard Platt
...Show all authors
Michael S. Calderwood*
Affiliation:
Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Allen Ma
Affiliation:
Oklahoma Foundation for Medical Quality, Oklahoma City, Oklahoma
Yosef M. Khan
Affiliation:
Ohio State University Medical Center and College of Medicine, Columbus, Ohio
Margaret A. Olsen
Affiliation:
Washington University School of Medicine, St. Louis, Missouri
Dale W. Bratzler
Affiliation:
Oklahoma Foundation for Medical Quality, Oklahoma City, Oklahoma College of Public Health, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
Deborah S. Yokoe
Affiliation:
Brigham and Women's Hospital, Boston, Massachusetts
David C. Hooper
Affiliation:
Massachusetts General Hospital, Boston, Massachusetts
Kurt Stevenson
Affiliation:
Ohio State University Medical Center and College of Medicine, Columbus, Ohio
Victoria J. Fraser
Affiliation:
Washington University School of Medicine, St. Louis, Missouri
Richard Platt
Affiliation:
Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Susan S. Huang
Affiliation:
University of California Irvine School of Medicine, Irvine, California
*
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Healthcare Institute, 133 Brookline Avenue, 6th Floor, Boston, MA 02115 ([email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective.

To evaluate the use of routinely collected electronic health data in Medicare claims to identify surgical site infections (SSIs) following hip arthroplasty, knee arthroplasty, and vascular surgery.

Design.

Retrospective cohort study.

Setting.

Four academic hospitals that perform prospective SSI surveillance.

Methods.

We developed lists of International Classification of Diseases, Ninth Revision, and Current Procedural Terminology diagnosis and procedure codes to identify potential SSIs. We then screened for these codes in Medicare claims submitted by each hospital on patients older than 65 years of age who had undergone 1 of the study procedures during 2007. Each site reviewed medical records of patients identified by either claims codes or traditional infection control surveillance to confirm SSI using Centers for Disease Control and Prevention/ National Healthcare Safety Network criteria. We assessed the performance of both methods against all chart-confirmed SSIs identified by either method.

Results.

Claims-based surveillance detected 1.8–4.7-fold more SSIs than traditional surveillance, including detection of all previously identified cases. For hip and vascular surgery, there was a 5-fold and 1.6-fold increase in detection of deep and organ/space infections, respectively, with no increased detection of deep and organ/space infections following knee surgery. Use of claims to trigger chart review led to confirmation of SSI in 1 out of 3 charts for hip arthroplasty, 1 out of 5 charts for knee arthroplasty, and 1 out of 2 charts for vascular surgery.

Conclusion.

Claims-based SSI surveillance markedly increased the number of SSIs detected following hip arthroplasty, knee arthroplasty, and vascular surgery. It deserves consideration as a more effective approach to target chart reviews for identifying SSIs.

Infect Control Hosp Epidemiol 2012;33(1):40-49

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 33 , Issue 1 , January 2012 , pp. 40 - 49
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2012

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Klevens, RM, Edwards, JR, Richards, CL Jr, et al. Estimating health care-associated infections and deaths in U.S. hospitals, 2002. Public Health Rep 2007;122:160–166.Google Scholar
2. Scott, RD II. The Direct Medical Costs of Healthcare-Associated Infections in U.S. Hospitals and the Benefits of Prevention. Atlanta: Centers for Disease Control and Prevention, 2009. http://www.cdc.gov/ncidod/dhqp/pdf/Scott_CostPaper.pdf.Google Scholar
3. Sands, K, Vineyard, G, Piatt, R. Surgical site infections occurring after hospital discharge. J Infect Dis 1996;173:963–970.Google Scholar
4. Avato, JL, Lai, KK. Impact of postdischarge surveillance on surgical-site infection rates for coronary artery bypass procedures. Infect Control Hosp Epidemiol 2002;23:364–367.CrossRefGoogle ScholarPubMed
5. Hirschhorn, LR, Currier, JS, Piatt, 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;14:21–28.Google Scholar
6. Yokoe, DS, Shapiro, M, Simchen, E, Piatt, R. Use of antibiotic exposure to detect postoperative infections. Infect Control Hosp Epidemiol 1998;19:317–322.Google Scholar
7. Sands, K, Vineyard, G, Livingston, J, Christiansen, C, Piatt, R. Efficient identification of postdischarge surgical site infections: use of automated pharmacy dispensing information, administrative data, and medical record information. J Infect Dis 1999;179:434–441.Google Scholar
8. Sands, KE, Yokoe, DS, Hooper, DC, et al. Detection of postoperative surgical-site infections: comparison of health plan-based surveillance with hospital-based programs. Infect Control Hosp Epidemiol 2003;24:741–743.CrossRefGoogle ScholarPubMed
9. Yokoe, DS, Noskin, GA, Cunnigham, SM, et al. Enhanced identification of postoperative infections among inpatients. Emerg Infect Dis 2004;10:1924–1930.Google Scholar
10. Miner, AL, Sands, KE, Yokoe, DS, et al. Enhanced identification of postoperative infections among outpatients. Emerg Infect Dis 2004;10:1931–1937.Google Scholar
11. 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;8:1433–1441.Google Scholar
12. Huang, SS, Placzek, H, Livingston, J, et al. Use of medicare claims to rank hospitals by surgical site infection (SSI) risk following coronary artery bypass graft surgery. Infect Control Hosp Epidemiol 2011;32:775–783.CrossRefGoogle ScholarPubMed
13. QualityNet. Specifications Manual for National Hospital Inpatient Quality Measures. Version 3.3a, March 28, 2011. http://www.qualitynet.org/dcs/ContentServer?c=Page&pagename=QnetPublic%2FPage%2FQnetTier4&cid=1228760129036.Google Scholar
14. Lankiewicz, JD, Huang, SS, Yokoe, DS, Olsen, MA, Piatt, R. Beyond 30 days: does limiting the duration of SSI follow-up limit detection? In: Program and Abstracts of the 21st Annual Scientific Meeting of the Society for Healthcare Epidemiology of America (SHEA); April 1–4, 2011; Dallas. Abstract 564.Google Scholar
15. 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;36:309–332.Google Scholar
16. 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;48:1223–1229.Google Scholar
17. Yokoe, DS, Vostok, J, Olson, MA, et al. Multicenter evaluation of enhanced methods for inpatient surgical site infection surveillance following hysterectomy, colorectal, and vascular procedures. In: Program and Abstracts of the 19th Annual Scientific Meeting of the Society for Healthcare Epidemiology of America (SHEA); March 18–22, 2009; San Diego, CA. Abstract 494.Google Scholar
18. 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;36:155–164.CrossRefGoogle ScholarPubMed
19. West, J, Khan, Y, Murray, DM, Stevenson, KB. Assessing specific secondary ICD-9-CM codes as potential predictors of surgical site infections. Am J Infect Control 2010;38:701–705.Google Scholar
20. Yokoe, DS, Avery, TR, Huang, SS. Surgical site infection surveillance following total hip and knee arthroplasty using California administrative data. In: Program and Abstracts of the 21st Annual Scientific Meeting of the Society for Healthcare Epidemiology of America (SHEA); April 1–4, 2011; Dallas. Abstract 619.Google Scholar
21. Healthcare Cost and Utilization Project (HCUP). HCUPnet. Rockville, MD: Agency for Healthcare Research and Quality, 2011. http://hcupnet.ahrq.gov.Google Scholar
22. Charlson, ME, Pompei, P, Ales, KL, MacKenzie, CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987;40:373–383.Google Scholar
23. Schneeweiss, S, Seeger, JD, Maclure, M, Wang, PS, Avorn, J, Glynn, RJ. Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data. Am J Epidemiol 2001;154:854–864.CrossRefGoogle ScholarPubMed
24. Schneeweiss, S, Wang, PS, Avorn, J, Glynn, RJ. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res 2003;38:1103–1120.CrossRefGoogle ScholarPubMed