Hostname: page-component-8448b6f56d-jr42d Total loading time: 0 Render date: 2024-04-24T00:51:07.144Z Has data issue: false hasContentIssue false
  • English
  • Français

Automatic Detection of Patients with Nosocomial Infection by a Computer-Based Surveillance System: A Validation Study in a General Hospital

Published online by Cambridge University Press:  21 June 2016

L. Pokorny ,
A. Rovira ,
M. Martín-Baranera ,
C. Gimeno ,
C. Alonso-Tarrés  and
J. Vilarasau
L. Pokorny
Affiliation:
Preventive Medicine Department, School of Medicine, Autonomous University of Barcelona, Hospital General de L'Hospitalet, Consorci Sanitari Integral, L'Hospitalet de Llobregat, Barcelona, Spain Physiotherapy Department, Universidade do Extremo Sul Catarinense, Brazil
A. Rovira
Affiliation:
Intensive Care Unit, Hospital General de L'Hospitalet, Consorci Sanitari Integral, L'Hospitalet de Llobregat, Barcelona, Spain
M. Martín-Baranera*
Affiliation:
Preventive Medicine Department, School of Medicine, Autonomous University of Barcelona, Hospital General de L'Hospitalet, Consorci Sanitari Integral, L'Hospitalet de Llobregat, Barcelona, Spain Unit of Clinical Epidemiology, Hospital General de L'Hospitalet, Consorci Sanitari Integral, L'Hospitalet de Llobregat, Barcelona, Spain
C. Gimeno
Affiliation:
Medical Documentation Unit, Hospital General de L'Hospitalet, Consorci Sanitari Integral, L'Hospitalet de Llobregat, Barcelona, Spain
C. Alonso-Tarrés
Affiliation:
Microbiology Unit, Hospital General de L'Hospitalet, Consorci Sanitari Integral, L'Hospitalet de Llobregat, Barcelona, Spain
J. Vilarasau
Affiliation:
Quality Department, Hospital General de L'Hospitalet, Consorci Sanitari Integral, L'Hospitalet de Llobregat, Barcelona, Spain
*
Unit of Clinical Epidemiology, Hospital General de L'Hospitalet, Consorci Sanitari Integral, Av. Josep Molins 29-41. E-08906 L'Hospitalet de Llobregat, Barcelona, Spain (Montse.Martin@sanitatintegral.org)
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective.

To validate an automated system for the detection of patients with nosocomial infection (NI) in an intensive care unit (ICU).

Design.

Retrospective analysis of data from the hospital information system. We applied 3 different NI suspicion criteria (positive microbiology reports, antibiotic administration, and diagnosis of clinical infection) and compared the results to those of a prospective NI incidence study done in the ICU during the same period (1999-2002).

Setting.

A 250-bed general hospital in Barcelona, Spain.

Patients.

From April 15, 1999, through June 30, 2002, 1380 patients were admitted to the ICU. Of these, 1043 had an ICU stay of more than 24 hours and were included in the study.

Results.

At least one NI suspicion criterion was present for 242 patients (23.2%); 2 criteria were present for 184 patients (17.6%); and all 3 criteria were present for 112 (11.7%). Comparison of hospital information system data to the results of the prospective study indicated that the combination of 2 criteria demonstrated the most satisfactory sensitivity (94.3%; 95% confidence interval [CI], 79.5%-99.0%) and specificity (83.6%; 95% CI, 76.8%-88.9%). The positive predictive value was 55.9% (95% CI, 42.5%-68.6%); the negative predictive value was 98.5% (95% CI, 94.2%-99.7%). The system could assign a site of infection for 90.4% of the NIs detected.

Conclusion.

The hospital information system was a useful tool for retrospectively detecting patients with an NI during the ICU stay. Given its high sensitivity, it may be useful as an alert for the NI team.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 27 , Issue 5 , May 2006 , pp. 500 - 503
Copyright
Copyright © The Society for Healthcare Epidemiology of America 2006

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.Eggimann, P, Pittet, D. Infection control in the ICU. Chest 2001; 120:2059–2093.CrossRefGoogle ScholarPubMed
2.Fridkin, SK, Welbel, SF, Weinstein, RA. Magnitude and prevention of nosocomial infections in the intensive care unit. Infect Dis Clin North Am 1997;11:479–496.CrossRefGoogle ScholarPubMed
3.Fernandez-Creheut, R, Diaz-Molina, C, de Irak, J, Martinez-Concha, D, Salcedo-Leal, I, Masa-Calles, J. Nosocomial infection in an intensive-care unit: identification of risk factors. Infect Control Hosp Epidemiol 1997; 18:825–830.Google Scholar
4.Girou, E, Stephan, F, Novara, A, Safar, M, Fagon, JY. Risk factors and outcome of nosocomial infections: results of matched case-control study of ICU patients. Am J Respir Crit Care Med 1998; 157:1151–1158.CrossRefGoogle ScholarPubMed
5.Fridkin, SK. Increasing prevalence of antimicrobial resistance in intensive care units. Crit Care Med 2001;29:N64–N68.CrossRefGoogle ScholarPubMed
6.Hanberger, H, García-Rodríguez, JA, Gobernado, M, Goossens, H, Nilsson, LE, Struelens, MJ. Antibiotic susceptibility among aerobic gram-negative bacilli in intensive care units in 5 European countries. JAMA 1999;281:67–71.CrossRefGoogle ScholarPubMed
7.Garner, JS, Jarvis, WR, Emori, TG, Horan, TC, Hughes, JM. CDC definitions for nosocomial infections. In: Olmsted, RN, ed. APIC Infection Control and Applied Epidemiology: Principles and Practice. St. Louis: Mosby; 1996: A1–A20.Google Scholar
8.Kollef, MH, Sharpless, L, Vlasnik, J, Pasque, C, Murphy, D, Fraser, VJ. The impact of nosocomial infections on patient outcomes following cardiac surgery. Chest 1997;112:666–675.CrossRefGoogle ScholarPubMed
9.Rosselló-Urgell, J. Nosocomial infection surveillance and control activities in Spain under HELICS and NosoMed programs frame. J Hosp Infect 2004; 56(Suppl 2):S55–S57.CrossRefGoogle ScholarPubMed
10.Gastemeier, P, Bräuer, H, Hauer, T, Schumacher, M, Daschner, F, Rüden, H. How many nosocomial infections are missed if identification is restricted to patients with either microbiology reports or antibiotic administration? Infect Control Hosp Epidemiol 1999; 20:124–127.CrossRefGoogle Scholar
11.Platt, R, Yokoe, DS, SanK, E. Automated methods for surveillance of surgical site infections. Emerg Infect Dis 2001; 7:212–216.CrossRefGoogle ScholarPubMed
12.Haley, RW, Culver, DH, White, JW, et al. The efficacy of infection surveillance and control program in preventing nosocomial infections in US Hospitals. Am J Epidemiol 1985;121:182–205.CrossRefGoogle ScholarPubMed
13.Bouam, S, Girou, E, Brun-Buisson, C, Karadimas, H, Lepage, E. An intranet-based automated system for the surveillance of nosocomial infections: prospective validation compared with physicians' self-reports. Infect Control Hosp Epidemiol 2003;24:51–55.CrossRefGoogle ScholarPubMed
14.Glenister, HM, Taylor, LJ, Bartlett, CL, Cooke, EM, Sedgwick, JA, Mackintosh, CA. An evaluation of surveillance methods for detecting infections in hospital inpatients. J Hosp Infect 1993;23:229–242.CrossRefGoogle ScholarPubMed
15.Evans, RS, Burke, JP, Classen, DC, et al. Computerized identification of patients at high risk for hospital-acquired infection. Am J Infect Control 1992; 20:4–10.CrossRefGoogle ScholarPubMed
16.Evans, RS, Larsen, RA, Burke, JF, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA 1986;256:1007–1011.CrossRefGoogle ScholarPubMed
17.McLaws, ML, Caelli, M. Pilot testing standardized surveillance: Hospital Infection Standardised Surveillance (HISS). Am J Infect Control 2000; 28:401–405.CrossRefGoogle ScholarPubMed
18.Trick, WE, Zagorski, BM, Tokars, JI, et al. Computer algorithms to detect bloodstream infections. Emerg Infect Dis 2004;10:1612–1620.CrossRefGoogle ScholarPubMed