Hostname: page-component-848d4c4894-ttngx Total loading time: 0 Render date: 2024-05-09T01:15:12.487Z Has data issue: false hasContentIssue false
  • English
  • Français

Predictive Ability of Positive Clinical Culture Results and International Classification of Diseases, Ninth Revision, to Identify and Classify Noninvasive Staphylococcus aureus Infections: A Validation Study

Published online by Cambridge University Press:  02 January 2015

LaRee A. Tracy ,
Jon P. Furuno ,
Anthony D. Harris ,
Mary Singer ,
Patricia Langenberg  and
Mary-Claire Roghmann
LaRee A. Tracy*
Affiliation:
Department of Epidemiology and Preventive Medicine, University of Maryland, Baltimore
Jon P. Furuno
Affiliation:
Department of Epidemiology and Preventive Medicine, University of Maryland, Baltimore
Anthony D. Harris
Affiliation:
Department of Epidemiology and Preventive Medicine, University of Maryland, Baltimore
Mary Singer
Affiliation:
Center for Drug Evaluation and Research, US Food and Drug Administration, Silver Spring, Maryland
Patricia Langenberg
Affiliation:
Department of Epidemiology and Preventive Medicine, University of Maryland, Baltimore
Mary-Claire Roghmann
Affiliation:
Department of Epidemiology and Preventive Medicine, University of Maryland, Baltimore Medical Care Clinical Center, Veterans Affairs Maryland Health Care System, Baltimore
*
University of Maryland, Baltimore, Baltimore, MD (ltracy@epi.umaryland.edu)
Get access Rights & Permissions [Opens in a new window]

Extract

Objective.

To develop and validate an algorithm to identify and classify noninvasive infections due to Staphylococcus aureus by using positive clinical culture results and administrative data.

Design.

Retrospective cohort study.

Setting.

Veterans Affairs Maryland Health Care System.

Methods.

Data were collected retrospectively on all S. aureus clinical culture results from samples obtained from nonsterile body sites during October 1998 through September 2008 and associated administrative claims records. An algorithm was developed to identify noninvasive infections on the basis of a unique S. aureus-positive culture result from a nonsterile site sample with a matching International Classification of Diseases, Ninth Revision (ICD-9-CM), code for infection at time of sampling. Medical records of a subset of cases were reviewed to find the proportion of true noninvasive infections (cases that met the Centers for Disease Control and Prevention National Healthcare Safety Network [NHSN] definition of infection). Positive predictive value (PPV) and negative predictive value (NPV) were calculated for all infections and according to body site of infection.

Results.

We identified 4,621 unique S. aureus-positive culture results, of which 2,816 (60.9%) results met our algorithm definition of noninvasive S. aureus infection and 1,805 (39.1%) results lacked a matching ICD-9-CM code. Among 96 cases that met our algorithm criteria for noninvasive S. aureus infection, 76 also met the NHSN criteria (PPV, 79.2% [95% confidence interval, 70.0%–86.1%]). Among 98 cases that failed to meet the algorithm criteria, 80 did not meet the NHSN criteria (NPV, 81.6% [95% confidence interval, 72.8%–88.0%]). The PPV of all culture results was 55.4%. The algorithm was most predictive for skin and soft-tissue infections and bone and joint infections.

Conclusion.

When culture-based surveillance methods are used, the addition of administrative ICD-9-CM codes for infection can increase the PPV of true noninvasive S. aureus infection over the use of positive culture results alone.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 31 , Issue 7 , July 2010 , pp. 694 - 700
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2010

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.Johnson, JK, Khoie, T, Shurland, S, Kreisel K, Siine, OC, Roghmann, MC. Skin and soft tissue infections caused by methicillin-resistant Staphylococcus aureus USA300 clone. Emerg Infect Dis 2007;13(8): 1195–1200.Google Scholar
2.Pallin, DJ, Egan, DJ, Pelletier, AJ, Espinola, JA, Hooper, DC, Camargo, CA Jr. Increased US emergency department visits for skin and soft tissue infections, and changes in antibiotic choices, during the emergence of community-associated methicillin-resistant Staphylococcus aureus. Ann Emerg Med 2008;51(3): 291–298.CrossRefGoogle ScholarPubMed
3.Moran, GJ, Krishnadasan, A, Gorwitz, RJ, et al. Methicillin-resistant S. aureus infections among patients in the emergency department. N Engl J Med 2006;355(7): 666–674.Google Scholar
4.King, MD, Humphrey, BJ, Wang, YF, Kourbatova, EV, Ray, SM, Blumberg, HM. Emergence of community-acquired methicillin-resistant Staphylococcus aureus USA 300 clone as the predominant cause of skin and soft-tissue infections. Ann Intern Med 2006;144(5): 309–317.CrossRefGoogle ScholarPubMed
5.Klevens, RM, Morrison, MA, Nadle, J, et al. Invasive methicillin-resistant Staphylococcus aureus infections in the United States. JAMA 2007;298(15): 1763–1771.Google Scholar
6.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 20O8;36(5):309–332.Google Scholar
7.Cohen, J. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit. Psychol Bull 1968;70(4): 213–220.CrossRefGoogle ScholarPubMed
8.Arkin, CF, Wachtel, MS. How many patients are necessary to assess test performance? JAMA 1990;263(2): 275–278.Google Scholar
9.Li, J, Fine, J. On sample size for sensitivity and specificity in prospective diagnostic accuracy studies. Stat Med 2004;23(16): 2537–2550.Google Scholar
10.Skull, SA, Andrews, RM, Byrnes, GB, et al. ICD-10 codes are a valid tool for identification of pneumonia in hospitalized patients aged ≥ 65 years. Epidemiol Infect 2008;136(2): 232–240.CrossRefGoogle Scholar
11.Aronsky, D, Haug, PJ, Lagor, C, Dean, NC. Accuracy of administrative data for identifying patients with pneumonia. Am J Med Qual 2005;20(6): 319–328.Google Scholar
12.Sherman, ER, Heydon, KH, St John, KH, et al. Administrative data fail to accurately identify cases of healthcare-associated infection. Infect Control Hosp Epidemiol 2006;27(4): 332–337.Google Scholar
13.Platt, R, Yokoe, DS, Sands, KE. Automated methods for surveillance of surgical site infections. Emerg Infect Dis 2001;7(2): 212–216.Google Scholar
14.Yokoe, DS, Classen, D. Improving patient safety through infection control: a new healthcare imperative. Infect Control Hosp Epidemiol 2008;29(suppl 1):S3–S11.Google Scholar
15.Klein, E, Smith, DL, Laxminarayan, R. Hospitalizations and deaths caused by methicillin-resistant Staphylococcus aureus, United States, 1999-2005. Emerg Infect Dis 2007;13(12): 1840–1846.CrossRefGoogle ScholarPubMed
16.Kuehnert M, J, Hill, HA, Kupronis, BA, Tokars, JI, Solomon, SL, Jernigan, DB. Methicillin-resistant-Staphyiococcus aureus hospitalizations, United States. Emerg Infect Dis 2005;11(6):868–872.Google Scholar
17.Noskin, GA, Rubin, RJ, Schentag, JJ, et al. National trends in Staphylococcus aureus infection rates: impact on economic burden and mortality over a 6-year period (1998-2003). Clin Infect Dis 2007;45(9): 1132–1140.Google Scholar
18.Noskin, GA, Rubin, RJ, Schentag, JJ, et al. The burden of Staphylococcus aureus infections on hospitals in the United States: an analysis of the 2000 and 2001 Nationwide Inpatient Sample Database. Arch Intern Med 2005;165(15): 1756–1761.CrossRefGoogle ScholarPubMed
19.McCaig, LF, McDonald, LC, Mandai, S, Jernigan, DB. Staphylococcus aureus-associated skin and soft tissue infections in ambulatory care. Emerg Infect Dis 2006;12(11): 1715–1723.Google Scholar