Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-22T23:02:35.293Z Has data issue: false hasContentIssue false
  • English
  • Français

Accuracy of Hospital Discharge Coding Data for the Surveillance of Drain-Related Meningitis

Published online by Cambridge University Press:  02 January 2015

Maaike S. M. van Mourik ,
Annet Troelstra ,
Karel G. M. Moons  and
Marc J. M. Bonten
Maaike S. M. van Mourik*
Affiliation:
Department of Medical Microbiology, University Medical Center Utrecht, Utrecht, The Netherlands
Annet Troelstra
Affiliation:
Department of Medical Microbiology, University Medical Center Utrecht, Utrecht, The Netherlands
Karel G. M. Moons
Affiliation:
Julius Center for Health Science and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
Marc J. M. Bonten
Affiliation:
Department of Medical Microbiology, University Medical Center Utrecht, Utrecht, The Netherlands Julius Center for Health Science and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
*
Department of Medical Microbiology, HP G04.614, PO Box 85500, 3508 GA Utrecht, Netherlands ([email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Surveillance of healthcare-associated infections is labor intensive and complex. Discharge coding is an accessible source of information that may support detection of cases. For drain-related meningitis, however, discharge coding data had low sensitivity (32%) and positive predictive value (35%) and could neither replace nor improve existing complex surveillance systems.

Type
Concise Communication
Information
Infection Control & Hospital Epidemiology , Volume 34 , Issue 4 , April 2013 , pp. 433 - 436
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2013

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.Rosenthal, MB. Nonpayment for performance? Medicare's new reimbursement rule. N Engl J Med 2007;357(16):1573–1575.Google Scholar
2.Gaynes, R, Richards, C, Edwards, J, et al.Feeding back surveillance data to prevent hospital-acquired infections. Emerg Infect Dis 2001;7(2):295–298.Google Scholar
3.Klompas, M, Yokoe, DS. Automated surveillance of health care-associated infections. Clin Infect Dis 2009;48(9);1268–1275.CrossRefGoogle ScholarPubMed
4.van Mourik, MS, Moons, KG, van Solinge, WW, et al.Automated detection of healthcare associated infections: external validation and updating of a model for surveillance of drain-related meningitis. PLoS ONE 2012;7(12):e51509.Google Scholar
5.Calderwood, MS, Ma, A, Khan, YM, et al.Use of medicare diagnosis and procedure codes to improve detection of surgical site infections following hip arthroplasty, knee arthroplasty, and vascular surgery. Infect Control Hosp Epidemiol 2012;33(1):40–49.Google Scholar
6.Jhung, MA, Banerjee, SN. Administrative coding data and health care-associated infections. Clin Infect Dis 2009;49(6):949–955.CrossRefGoogle ScholarPubMed
7.Lozier, AP, Sciacca, RR, Romagnoli, MF, Connolly, ES Jr. Ventriculostomy-related infections: a critical review of the literature. Neurosurgery 2002;51(1):170–181.CrossRefGoogle ScholarPubMed
8.Centers for Disease Control and Prevention. International Classification of Diseases, Ninth Revision, Clinical Modification, Sixth Edition. http://www.cdc.gov/nchs/icd/icd9cm.htm#ftp. October 1, 2010. Accessed September 5, 2011.Google Scholar
9.Moro, ML, Morsillo, F. Can hospital discharge diagnoses be used for surveillance of surgical-site infections? J Hosp Infect 2004;56(3):239–241.Google Scholar
10.Curtis, M, Graves, N, Birrell, F, et al.A comparison of competing methods for the detection of surgical-site infections in patients undergoing total arthroplasty of the knee, partial and total arthroplasty of hip and femoral or similar vascular bypass. J Hosp Infect 2004;57(3):189–193.Google Scholar