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Nosocomial Infection in an Intensive-Care Unit Identification of Risk Factors

Published online by Cambridge University Press:  02 January 2015

Rafael Fernandez-Crehuet ,
Carmen Diaz-Molina ,
Jokin de Irala ,
Diego Martinez-Concha ,
Inmaculada Salcedo-Leal  and
Josefa Masa-Calles
Rafael Fernandez-Crehuet*
Affiliation:
Department of Preventive Medicine, Reina Sofia University Hospital, Cdrdoba, Spain
Carmen Diaz-Molina
Affiliation:
Department of Preventive Medicine, Reina Sofia University Hospital, Cdrdoba, Spain
Jokin de Irala
Affiliation:
Preventive Medicine and Preventive Medicine and Public Health, Faculty of Medicine, University of Cdrdoba, Cdrdoba, Spain
Diego Martinez-Concha
Affiliation:
Department of Preventive Medicine, Reina Sofia University Hospital, Cdrdoba, Spain
Inmaculada Salcedo-Leal
Affiliation:
Department of Preventive Medicine, Reina Sofia University Hospital, Cdrdoba, Spain
Josefa Masa-Calles
Affiliation:
Department of Preventive Medicine, Reina Sofia University Hospital, Cdrdoba, Spain
*
Servicio de Medicina Preventiva y Salud Pública, Hospital Universitario Reina Sofia, Avenida Menández Pida; s/n 14004, Córdoba, Spain
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Abstract

Objective:

To identify risk factors predictive of nosocomial infection in an intensive-care unit (ICU) and to identify patients with a higher risk of nosocomial infection using a predictive model of nosocomial infection in our ICU.

Design:

Prospective study; daily concurrent surveillance of intensive-care-unit patients.

Setting/Patients:

All patients admitted for at least 24 hours to the ICU of a tertiary-level hospital from February to November 1994 were followed daily.

Methods:

Variables measuring extrinsic and intrinsic risk factors for nosocomial infection were collected on each patient during their ICU stay, and the Cox Proportional Hazards multivariable technique was used to identify the variables significantly associated with infection.

Results:

The population studied consisted of 944 patients. The main risk factors identified were intrinsic; the significant extrinsic risk ofactors identified were head of the bed in a horizontal (<30°) position (this variable presented the highest increase of the infection hazard ratio) and the use of sedative medication. Patients presenting the highest risk scores using the predictive model are those with the highest risk of nosocomial infection.

Conclusion:

The important preventive measures derived from our results are that underlying conditions suffered by the patient at the ICU admission should be corrected promptly, the depression of the patient's level of consciousness with sedatives should be monitored carefully, and the horizontal position of the head of the bed should be avoided totally. Patients with a high risk of infection can be the target of special preventive measures.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 18 , Issue 12 , December 1997 , pp. 825 - 830
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1997

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References

1. Emmerson, AM. The epidemiology of infections in intensive care units. Intensive Care Med 1990;16(suppl 3):197S–200S.CrossRefGoogle ScholarPubMed
2. 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;121:182–205.CrossRefGoogle ScholarPubMed
3. Ayliffe, GAJ. Nosocomial infection. The irreducible minimum. Infect Control 1986;7(suppl):92S–95S.Google ScholarPubMed
4. Craven, DE, Kunches, LM, Lichtenberg, DA, et al. Nosocomial infections and fatality in medical and surgical intensive care unit patients. Arch Intern Med 1988;148:1161–1168.CrossRefGoogle ScholarPubMed
5. Jarvis, WR, Edwards, JR, Culver, DH, et al. Nosocomial infection rates in adult and pediatric intensive care units in the United States. Am J Med 1991;91 (suppl 3B):185S–91S.CrossRefGoogle ScholarPubMed
6. Joshi, N, Russell Localio, A, Hamory, BH. A predictive risk index for nosocomial pneumonia in the intensive care unit. Am J Med 1992:93:135–142.CrossRefGoogle ScholarPubMed
7. Gamer, JS, Jarvis, WR, Emori, TG, Horan, TC, Hughes, JM. CDC definitions for nosocomial infections, 1988. Am J Infect Control 1988;16:128–140.Google Scholar
8. Knaus, WA Wagner, DP, Draper, EA, Zimmerman, JE. APACHE II: a severity of disease classification system. Crit Care Med 1985;13:818–829.CrossRefGoogle ScholarPubMed
9. Instituto Nacional de la Salud. Clasificación Internacional de Enfermedades. 9th revision. Modificación Clínica. Vol 1. Madrid, Spain: Ministerio de Sanidad y Consumo. INSALUD; 1988:1–879.Google Scholar
10. Bueno Cavanillas, A, Rodriguez-Contreras, R, Delgado Rodriguez, M, et al. Preoperative stay as a risk factor for noso-cornial infection. Eur J Epidemiol 1991;7:670–676.CrossRefGoogle ScholarPubMed
11. Bueno Cavanillas, A, Ramos Cuadra, MA y Blanco Gonzalez, JI. El huésped, factores endógenos o intrínsecos. En: Gálvez Vargas, R, Delgado Rodriguez, M y Guillén Solvas, JF, eds. Infección Hospitalana. Granada, Spain: Universidad de Granada; 1993;83–97.Google Scholar
12. Knaus, WA, Harrell, FE, Fisher, CJ, et al. The clinical evaluation of new drugs for sepsis: a prospective study design based on survival analysis. JAMA 1993;270:1233–1241.CrossRefGoogle ScholarPubMed
13. Vqaue ed. Prevalencia de las infecciones nosocomiales en los hospitales espanoles: EPINE 1990-1994. Madrid, Spain: Sociedad Espafiola de Higiene y Medicina Preventiva Hospitalarias; 1995.Google Scholar
14. Lee, ET. Statistical Methods for Survival Data Analysis. 2nd ed. New York, NY: John Wiley & Sons; 1992.Google Scholar
15. Hosmer, DW, Lemeshow, SA. Applied Logistic Regression. New York, NY: John Wiley; 1989.Google Scholar
16. Celis, R, Torres, A, Gatell, JM, Almela, M, Rodriguez-Roisin, R, Agusti-Vidal, A. Nosocomial pneumonia: a multivariate analysis of risk and prognosis. Chest 1988;93:318–324.CrossRefGoogle ScholarPubMed
17. Harkness, GA, Bentley, DW, Roghmann, KJ. Risk factors for nos-cornial pneumonia in the elderly. Am J Med 1990;89:457–463.CrossRefGoogle ScholarPubMed
18. Hanson, LC, Weber, DJ, Rutala, WA. Risk factors for nosocomial pneumonia in the elderly. Am J Med 1992;92:161–166.CrossRefGoogle ScholarPubMed
19. Moro, ML, Vigano, EF, Cozzi Lepri, A, the Central Venous Catheter-Related Infections Study Group. Risk factors for central venous catheter-related infections in surgical and intensive care units. Infect Control Hosp Epidemiol 1994;15:253–264.CrossRefGoogle ScholarPubMed
20. Fridkin, SK Pear, SM, Williamson, TH, Galgiani, JN, Jarvis, WR. The role of understaffing in central venous catheter-associated bloodstream infections. Infect Control Hosp Epidemiol 1996;17:150–158.Google ScholarPubMed
21. Torres, A, Aznar, R, Gatell, JM, et al. Incidence, risk, and prognosis factors of nosocomial pneumonia in mechanically ventilated patients. Am Rev Respir Dis 1990;142:523–528.CrossRefGoogle ScholarPubMed
22. Myers, MH, Hankey, BF, Mantel, N. A logistic-exponential model for use with response-time data involving regression variables. Biometrics 1973;29:257–269.CrossRefGoogle Scholar
23. Elandt-Johnson, RC. Time Dependent Logistic Models in Follow-up Studies and Clinical Trials: I, Binary Data. Institute of Statistic Mimeo Series No. 1309. Chapel Hill, NC: University of North Carolina; 1980.Google Scholar
24. Friedman, SR, Des Jarlais, DC. The harm reduction approach and risk factors for human immunodeficiency virus (HIV) sero-conversion in injecting drug users, Amsterdam. Am J Epidemiol 1993;138:768–769. Letter.CrossRefGoogle Scholar
25. Green, MS. Symons, MI. A comparison of the logistic risk function and the proportional hazards model in prospective epidemiological studies. Journal of Chronic Diseases 1983;36:715–723.CrossRefGoogle Scholar
26. Bueno Cavanillas, A, Rodriguez-Contreras, R, Lopez Luque, A, Delgado Rodriguez, M, Galvez Vargas, R Usefulness of severity indices in intensive care medicine as a predictor of nosocomial infection risk. Intensive Cure Med 1991;17:336–339.CrossRefGoogle ScholarPubMed