Hostname: page-component-8448b6f56d-t5pn6 Total loading time: 0 Render date: 2024-04-19T09:35:04.163Z Has data issue: false hasContentIssue false
  • English
  • Français

Electronic Algorithmic Prediction of Central Vascular Catheter Use

Published online by Cambridge University Press:  02 January 2015

Bala Hota ,
Brian Harting ,
Robert A. Weinstein ,
Rosie D. Lyles ,
Susan C. Bleasdale ,
William Trick  and
Centers for Disease Control and Prevention Epicenters
Bala Hota*
Affiliation:
John H. Stroger, Jr, Hospital of Cook County, Illinois Rush University Medical Centers, Illinois
Brian Harting
Affiliation:
Rush University Medical Centers, Illinois
Robert A. Weinstein
Affiliation:
John H. Stroger, Jr, Hospital of Cook County, Illinois Rush University Medical Centers, Illinois
Rosie D. Lyles
Affiliation:
John H. Stroger, Jr, Hospital of Cook County, Illinois
Susan C. Bleasdale
Affiliation:
Chicago, and Northwest Suburban Medical Associates, Arlington Heights, Illinois
William Trick
Affiliation:
John H. Stroger, Jr, Hospital of Cook County, Illinois
*
1900 S Polk, Room 1248, Chicago, IL, 60612 (bhota@rush.edu)
Get access Rights & Permissions [Opens in a new window]

Extract

Objective.

To develop prediction algorithms for the presence of a central vascular catheter in hospitalized patients with use of data present in an electronic health record. Such algorithms could be used for measurement of device utilization rates and for clinical decision support rules.

Design.

Criterion standard.

Setting.

John H. Stroger, Jr, Hospital of Cook County, a 464-bed public hospital in Chicago, Illinois.

Participants.

Patients admitted to the medical intensive care unit from May 31, 2005 through June 26, 2006 (derivation data set, May 31, 2005-September 28, 2005; validation data set, September 29, 2005-June 28, 2006).

Methods.

Covariates were collected from the electronic medical record for each patient; the outcome variable was presence of a central vascular device. Multivariate models were developed using the derivation set and the generalized estimating equation. Three models, each with increasing database requirements, were validated using the validation set. Device utilization ratios and performance characteristics were calculated.

Results.

Although Charlson score and duration of intensive care unit stay were significant predictors in all models, factors that indicated use or presence of a central line were also important. Device utilization rates derived from the algorithmic models were as accurate as those obtained using manual sampling.

Conclusions.

Automated calculation of central vascular catheter use is both feasible and accurate, providing estimates statistically similar to those obtained using manual surveillance. Prediction modeling of central vascular catheter use may enable automated surveillance of bloodstream infections and enhance important prevention interventions, such as timely removal of unnecessary central lines.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 31 , Issue 1 , January 2010 , pp. 4 - 11
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.Kohn, LT, Corrigan, JM, Donaldson, MS. To err in human: building a safer health system. Committee of quality of health care in America, Institue of Medicine report. Washington, DC: National Academy of Press; 2000.Google Scholar
2.O'Grady, NP, Alexander, M, Dellinger, EP, et al. Guidelines for the prevention of intravascular catheter-related infections. Centers for Disease Control and Prevention. MMWR Recomm Rep 2002;51:1–29.Google ScholarPubMed
3.Wenzel, RP, Edmond, MB. The impact of hospital-acquired bloodstream infections. Emerg Infect Dis 2001;7:174–177.Google Scholar
4.Gaynes, RP, Solomon, S. Improving hospital-acquired infection rates: the CDC experience, Jt Comm J Qual Improv 1996;22:457–467.Google Scholar
5.Gaynes, RP, Platt, R. Monitoring patient safety in health care: building the case for surrogate measures. Jt Comm J Qual Patient Saf 2006;32:95–101.Google Scholar
6.Pronovost, P, Needham, D, Berenholtz, S, et al. An intervention to decrease catheter-related bloodstream infections in the ICU. N Engl J Med 2006;355:2725–2732.Google Scholar
7.Reduction in central line-associated bloodstream infections among patients in intensive care units-Pennsylvania, April 2001-March 2005. MMWR Morb Mortal Wkly Rep 2005;54:1013–1016.Google Scholar
8.Marschall, J, Mermel, LA, Classen, D, et al. Strategies to prevent central line-associated bloodstream infections in acute care hospitals. Infect Control Hosp Epidemiol 2008;29(Suppl 1):S22–S30.CrossRefGoogle ScholarPubMed
9.Yokoe, DS, Mermel, LA, Anderson, DJ, et al. A compendium of strategies to prevent healthcare-associated infections in acute care hospitals. Infect Control Hosp Epidemiol 2008;29(Suppl 1):S12–S21.CrossRefGoogle ScholarPubMed
10.Panackal, AA, M'Ikanatha, NM, Tsui, FC, et al. Automatic electronic laboratory-based reporting of notifiable infectious diseases at a large health system. Emerg Infect Dis 2002;8:685–691.CrossRefGoogle Scholar
11.Effler, P, Ching-Lee, M, Bogard, A, Ieong, MC, Nekomoto, T, Jernigan, D. Statewide system of electronic notifiable disease reporting from clinical laboratories: comparing automated reporting with conventional methods. JAMA 1999;282:1845–1850.Google Scholar
12.Wright, MO, Perencevich, EN, Novak, C, Hebden, JN, Standiford, HC, Harris, AD. Preliminary assessment of an automated surveillance system for infection control. Infect Control Hosp Epidemiol 2004;25:325–332.CrossRefGoogle ScholarPubMed
13.Trick, WE, Zagorski, BM, Tokars, JI, et al. Computer algorithms to detect bloodstream infections. Emerg Infect Dis 2004;10:1612–1620.Google Scholar
14.Nosocomial infection rates for interhospital comparison: limitations and possible solutions: a report from the National Nosocomial Infections Surveillance (NNIS) System. Infect Control Hosp Epidemiol 1991;12:609–621.Google Scholar
15.Cookson, ST, Ihrig, M, O'Mara, EM, Hartstein, AI, Jarvis, WR. Use of at estimation method to derive an appropriate denominator to calculate central venous catheter-associated bloodstream infection rates. Infect Control Hosp Epidemiol 1998;19:28–31.CrossRefGoogle ScholarPubMed
16.Klevens, RM, Tokars, JI, Edwards, J, Horan, T. Sampling for collection of central line-day denominators in surveillance of healthcare-associated bloodstream infections. Infect Control Hosp Epidemiol 2006;27:338–342.CrossRefGoogle ScholarPubMed
17.Tokars, JI, Klevens, RM, Edwards, JR, Horan, TC. Measurement of the impact of risk adjustment for central line-days on interpretation of central line-associated bloodstream infection rates. Infect Control Hosp Epidemiol 2007;28:1025–1029.Google Scholar
18.Wright, SB, Huskins, WC, Dokholyan, RS, Goldmann, DA, Platt, R. Administrative databases provide inaccurate data for surveillance of long-term central venous catheter-associated infections. Infect Control Hosp Epidemiol 2003;24:946–949.Google Scholar
19.Trick, WE, Chapman, WW, Wisniewski, MF, Peterson, BJ, Solomon, SL, Weinstein, RA. Electronic interpretation of chest radiograph reports to detect central venous catheters. Infect Control Hosp Epidemiol 2003;24:950–954.Google Scholar
20.Bleasdale, SC, Trick, WE, Gonzalez, IM, Lyles, RD, Hayden, MK, Weinstein, RA. Effectiveness of Chlorhexidine bathing to reduce catheter-associated bloodstream infections in medical intensive care unit patients. Arch Intern Med 2007;167:2073–2079.Google Scholar
21.Wisniewski, MF, Kieszkowski, P, Zagorski, BM, Trick, WE, Sommers, M, Weinstein, RA. Development of a clinical data warehouse for hospital infection control. J Am Med Inform Assoc 2003;10:454–462.CrossRefGoogle ScholarPubMed
22.Deyo, RA, Cherkin, DC, Ciol, MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 1992;45:613–619.Google Scholar
23.Zogorski, B, Trick, WE, Solomon, S, Cordeil, R, Weinstein, RA. Comparison of predictive models to detect central venous catheters using data stored in the hospital information system. In: Program and abstracts o. the 12th Annual Meeting of the Society for Healthcare Epidemiology of America (Salt Lake City, UT). 2002.Google Scholar
24.National Quality Forum (NQF). MQF endorsed standards: NQF #0139. Available at: http://www.qualityforum.org/measures_list.aspx. Accessed November 6, 2009.Google Scholar
25.Tokars, JI, Richards, C, Andrus, M, et al. The changing face of surveillance for health care-associated infections. Clin Infect Dis 2004;39:1347–1352.CrossRefGoogle ScholarPubMed