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Use of Medicare Claims to Rank Hospitals by Surgical Site Infection Risk following Coronary Artery Bypass Graft Surgery

Published online by Cambridge University Press:  02 January 2015

Susan S. Huang ,
Hilary Placzek ,
James Livingston ,
Allen Ma ,
Fallon Onufrak ,
Julie Lankiewicz ,
Ken Kleinman ,
Dale Bratzler ,
Margaret A. Olsen  and
Rosie Lyles
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Susan S. Huang*
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
Hilary Placzek
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
James Livingston
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Allen Ma
Affiliation:
Oklahoma Foundation for Medical Quality, Oklahoma City, Oklahoma
Fallon Onufrak
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Julie Lankiewicz
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Ken Kleinman
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Dale Bratzler
Affiliation:
Oklahoma Foundation for Medical Quality, Oklahoma City, Oklahoma
Margaret A. Olsen
Affiliation:
Division of Infectious Diseases, Washington University School of Medicine, St. Louis, Missouri
Rosie Lyles
Affiliation:
Department of Medicine, Cook County Hospital, Chicago, Illinois
Yosef Khan
Affiliation:
Department of Infection Control, Ohio State University School of Medicine, Columbus, Ohio
Paula Wright
Affiliation:
Department of Infection Control, Massachusetts General Hospital, Boston, Massachusetts
Deborah S. Yokoe
Affiliation:
Channing Laboratory, Department of Medicine and Department of Infection Control, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
Victoria J. Fraser
Affiliation:
Division of Infectious Diseases, Washington University School of Medicine, St. Louis, Missouri
Robert A. Weinstein
Affiliation:
Department of Medicine, Cook County Hospital, Chicago, Illinois
Kurt Stevenson
Affiliation:
Department of Infection Control, Ohio State University School of Medicine, Columbus, Ohio
David Hooper
Affiliation:
Department of Infection Control, Massachusetts General Hospital, Boston, Massachusetts
Johanna Vostok
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts
Rupak Datta
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts Division of Infectious Diseases and Health Policy Research Institute, University of California Irvine School of Medicine, Irvine, California
Wato Nsa
Affiliation:
Oklahoma Foundation for Medical Quality, Oklahoma City, Oklahoma
Richard Platt
Affiliation:
Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts Channing Laboratory, Department of Medicine and Department of Infection Control, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
*
University of California Irvine School of Medicine, Division of Infectious Diseases, 101 City Drive South, City Tower, Suite 400, ZC 4081, Orange, CA 92868-3217 (sshuang@uci.edu)
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Abstract

Objective.

To evaluate whether longitudinal insurer claims data allow reliable identification of elevated hospital surgical site infection (SSI) rates.

Design.

We conducted a retrospective cohort study of Medicare beneficiaries who underwent coronary artery bypass grafting (CABG) in US hospitals performing at least 80 procedures in 2005. Hospitals were assigned to deciles by using case mix–adjusted probabilities of having an SSI-related inpatient or outpatient claim code within 60 days of surgery. We then reviewed medical records of randomly selected patients to assess whether chart-confirmed SSI risk was higher in hospitals in the worst deciles compared with the best deciles.

Participants.

Fee-for-service Medicare beneficiaries who underwent CABG in these hospitals in 2005.

Results.

We evaluated 114,673 patients who underwent CABG in 671 hospitals. In the best decile, 7.8% (958/12,307) of patients had an SSI-related code, compared with 24.8% (2,747/11,068) in the worst decile (P<.001). Medical record review confirmed SSI in 40% (388/980) of those with SSI-related codes. In the best decile, the chart-confirmed annual SSI rate was 3.2%, compared with 9.4% in the worst decile, with an adjusted odds ratio of SSI of 2.7 (confidence interval, 2.2–3.3; P<.001) for CABG performed in a worst-decile hospital compared with a best-decile hospital.

Conclusions.

Claims data can identify groups of hospitals with unusually high or low post-CABG SSI rates. Assessment of claims is more reproducible and efficient than current surveillance methods. This example of secondary use of routinely recorded electronic health information to assess quality of care can identify hospitals that may benefit from prevention programs.

Type
Original Article
Information
Infection Control & Hospital Epidemiology , Volume 32 , Issue 8 , August 2011 , pp. 775 - 783
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2011

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References

1.Klevens, RM, Edwards, JR, Richards, CL Jret al.Estimating health care–associated infections and deaths in U.S. hospitals, 2002. Public Health Rep 2007;122(2):160–166.Google Scholar
2.Horan, TC, Culver, DH, Gaynes, RP, Jarvis, WR, Edwards, JR, Reid, CR. Nosocomial infections in surgical patients in the United States, January 1986–June 1992: National Nosocomial Infections Surveillance (NNIS) System. Infect Control Hosp Epidemiol 1993;14(2):73–80.Google Scholar
3.Jarvis, WR. Benchmarking for prevention: the Centers for Disease Control and Prevention's National Nosocomial Infections Surveillance (NNIS) system experience. Infection 2003;31(suppl 2):44–48.Google ScholarPubMed
4.Haley, RW, Culver, DH, White, JW, et al.The efficacy of infection surveillance and control programs in preventing nosocomial infections in US hospitals. Am J Epidemiol 1985;121(2):182–205.Google Scholar
5.Medicare Quality Improvement Community. Surgical care improvement project. http://www.qualitynet.org/dcs/ContentServer?cid=1192804535739&pagename=QnetPublic%2FPage%2FQnetTier3&c=Page. Accessed June 8, 2011.Google Scholar
6.Joint Commission. Performance measurement initiatives: Surgical Care Improvement Project core measure set. http://jointcommission.org/surgical_care_improvement_project/. Accessed June 8, 2011.Google Scholar
7.Centers for Disease Control and Prevention. National Healthcare Safety Network: surgical site infection (SSI) event. http://www.cdc.gov/nhsn/PDFs/pscManual/9pscSSIcurrent.pdf. Accessed October 22, 2010.Google Scholar
8.Institute for Healthcare Improvement. Saving Lives Web and action program: preventing surgical site infections. http://www.ihi.org/IHI/Programs/AudioAndWebPrograms/WebAction_SSI.htm. Accessed October 22, 2010.Google Scholar
9.Bratzler, DW, Hunt, DR. The surgical infection prevention and surgical care improvement projects: national initiatives to improve outcomes for patients having surgery. Clin Infect Dis 2006;43:322–330.Google Scholar
10.Avato, JL, Lai, KK. Impact of postdischarge surveillance on surgical-site infection rates for coronary artery bypass procedures. Infect Control Hosp Epidemiol 2002;23(7):364–367.Google Scholar
11.Sands, K, Vineyard, G, Platt, R. Surgical site infections occurring after hospital discharge. J Infect Dis 1996;173(4):963–970.Google Scholar
12.Petherick, ES, Dalton, JE, Moore, PJ, Cullum, N. Methods for identifying surgical wound infection after discharge from hospital: a systematic review. BMC Infect Dis 2006;6:170.Google Scholar
13.Tanner, J, Khan, D, Aplin, C, Ball, J, Thomas, M, Bankart, J. Post-discharge surveillance to identify colorectal surgical site infection rates and related costs. J Hosp Infect 2009;72(3):243–250.CrossRefGoogle ScholarPubMed
14.Holbrook, KF, Nottebart, VF, Hameed, SR, Platt, R. Automated postdischarge surveillance for postpartum and neonatal nosocomial infections. Am J Med 1991;91:125S–130S.CrossRefGoogle ScholarPubMed
15.Fanning, C, Johnston, BI, Macdonald, S, LeFort-Jost, S, Dockerty, E. Postdischarge surgical site infection surveillance. Can J Infect Control 1995;10(3):75–79.Google Scholar
16.Taylor, EW, Duffy, K, Lee, K, et al.Telephone call contact for post-discharge surveillance of surgical site infections: a pilot, methodological study. J Hosp Infect 2003;55(1):8–13.Google Scholar
17.Platt, R. Toward better benchmarking. Infect Control Hosp Epidemiol 2005;26:433–434.CrossRefGoogle ScholarPubMed
18.Platt, R, Yokoe, DS, Sands, KE; and the CDC Eastern Massachusetts Prevention Epicenter Investigators. Automated methods for surveillance of surgical site infections. Emerg Infect Dis 2001;7(2):212–216.Google Scholar
19.Anderson, DJ, Chen, LF, Sexton, DJ, Kaye, KS. Complex surgical site infections and the devilish details of risk adjustment: important implications for public reporting. Infect Control Hosp Epidemiol 2008;29(10):941–946.CrossRefGoogle ScholarPubMed
20.Giles, KA, Hamdan, AD, Pomposelli, FB, Wyers, MC, Siracuse, JJ, Schermerhorn, ML. Body mass index: surgical site infections and mortality after lower extremity bypass from the National Surgical Quality Improvement Program 2005–2007. Ann Vase Surg 2009;24(1):48.Google Scholar
21.Schneeweiss, S, Seeger, JD, Maclure, M, Wang, PS, Avorn, J, Glynn, RJ. Performance of comorbidity scores to control for confounding in epidemiologic studies. Am J Epidemiol 2001;154(9):854–864.Google Scholar
22.Schneeweiss, S, Wang, PS, Avorn, J, Glynn, RJ. Improved comorbidity adjustment for predicting mortality in Medicare populations. Health Serv Res 2003;38(4):1103–1120.Google Scholar
23.Platt, 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
24.Huang, SS, Livingston, JM, Rawson, NS, Schmaltz, S, Platt, R. Developing algorithms for healthcare insurers to systematically monitor surgical site infection rates. BMC Med Res Methodol 2007;7:20.CrossRefGoogle ScholarPubMed
25.Centers for Disease Control and Prevention. Prevention Epicenters Program. http://www.cdc.gov/HAI/epiCenters/what-R-epicenters.html. Accessed June 8, 2011.Google Scholar
26.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.CrossRefGoogle ScholarPubMed
27.Breslow, NE, Clayton, DG. Approximate inference in generalized linear mixed models. J Am Stat Assoc 1993;88(421):9–25.Google Scholar
28.Yokoe, DS, Noskin, GA, Cunningham, SM, et al.Enhanced identification of postoperative infections among inpatients. Emerg Infect Dis 2004;10(11):1924–1930.Google Scholar
29.Centers for Medicare and Medicaid Services. CMS proposes definition of meaningful use of certified electronic health records (EHR) technology. http://www.cms.gov/apps/media/press/factsheet.asp?Counter=3564. Accessed June 27, 2010.Google Scholar