Hostname: page-component-76fb5796d-vvkck Total loading time: 0 Render date: 2024-04-26T05:59:06.240Z Has data issue: false hasContentIssue false
  • English
  • Français

Admission-Specific Chronic Disease Scores as Alternative Predictors of Surgical Site Infection for Patients Undergoing Coronary Artery Bypass Graft Surgery

Published online by Cambridge University Press:  21 June 2016

Ruth Batista ,
Keith Kaye  and
Deborah S. Yokoe
Ruth Batista
Affiliation:
Disciplina de Doenças Infecciosas e Parasitárias, Universidade Federal de São Paulo, São Paulo, Brazil
Keith Kaye
Affiliation:
Department of Medicine, Duke University Medical Center, Durham, North Carolina
Deborah S. Yokoe*
Affiliation:
Channing Laboratory, Brigham and Women's Hospital, Boston, Massachusetts Harvard Medical School, Boston, Massachusetts
*
181 Longwood Avenue, Channing Laboratory, Boston, MA 02115, (deborah.yokoe@channing.harvard.edu)
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective.

To evaluate the admission chronic disease score (ACDS) and a variant of the ACDS as predictors of surgical site infection (SSI) for study participants who underwent coronary artery bypass graft (CABG) surgery.

Design.

Retrospective case-control study.

Setting.

A 750-bed academic medical center.

Participants.

All participants with an SSI that was identified through hospital-based surveillance (defined as case patients) and a random sample of participants without SSI following CABG surgery (defined as control subjects) between July 1, 1999, and June 30, 2001.

Results.

An ACDS based on medications ordered on the day of hospital admission was determined for 264 study participants admitted prior to the day of the surgical procedure. A preadmission chronic disease score (PACDS) based on outpatient medications was calculated for 281 participants, using the record of preadmission medications in the patient's discharge summary. The ACDS and PACDS were significantly higher for case patients, compared with control subjects (P = .03 and P = .05, respectively). American Society of Anesthesiologists (ASA) score and the standard National Nosocomial Infection Surveillance system (NNIS) risk index were not significant predictors of SSI. In logistic regression models, only the ACDS (odds ratio, 1.02 per 100 ACDS points), the PACDS (odds ratio, 1.02 per 100 PACDS points), the highest PACDS quintile (odds ratio, 2.89 [compared with lowest quintile]), and a modified NNIS-PACDS score of 2 (odds ratio, 3.5 [compared with a score of 0]) were significant predictors of SSI.

Conclusions.

Because preoperative medications are likely to reflect comorbidities that influence the risk of SSI, medication-based scoring systems such as the ACDS and PACDS may allow for better risk stratification than the standard NNIS risk index, particularly for patient populations with relatively homogenous wound classification and ASA score distributions.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 27 , Issue 8 , August 2006 , pp. 802 - 808
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. 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.Google Scholar
2. Kirkland, KB, Briggs, JP, Trivette, SL, Wilkinson, WE, Sexton, DJ. The impact of surgical site infections in the 1990s: attributable mortality, excess length of hospitalization, and extra costs. Infect Control Hosp Epidemiol 1999; 20:725–730.CrossRefGoogle ScholarPubMed
3. Gaynes, RP, Horan, TC. Surveillance of nosocomial infections. In: Mayhall, CG, ed. Hospital Epidemiology and Infection Control. Baltimore: Williams & Wilkins; 1996:1017–1031.Google Scholar
4. O'Leary, DS. The Joint Commission looks to the future. JAMA 1987; 258:951–952.Google Scholar
5. Mangram, AJ, Horan, TC, Pearson, ML, Silver, LS, Jarvis, WR. The Hospital Infection Control Practices Advisory Committee. Guideline for prevention of surgical site infection. Infect Control Hosp Epidemiol 1999; 20:247–280.CrossRefGoogle ScholarPubMed
6. Cullen, DJ, Apolone, G, Greenfield, S, Guadagnoli, E, Cleary, P. ASA Physicial Status and age predict morbidity after three surgical procedures. Ann Surg 1994; 220:3–9.Google Scholar
7. Vacanti, CJ, VanHouten, RJ, Hill, RC. A statistical analysis of the relationship of physical status to postoperative mortality in 68,388 cases. Anesth Analg 1970; 49:564–566.Google Scholar
8. Garibaldi, RA, Cushin, D, Lere, T. Risk factors for postoperative infection. Am J Med 1991; 91:158S–163S.Google Scholar
9. House of delegates of the American Society of Anesthesiologists. New classification of physical status. Anesthesiology 1963; 24:111.Google Scholar
10. Owens, WD, Felts, JA, Spitznagel, EL. ASA physical status classification: a study of consistency of ratings. Anesthesiology 1978; 49:239–243.Google Scholar
11. Salemi, C, Anderson, D, Flores, D. American Society of Anesthesiology scoring discrepancies affecting the National Nosocomial Infection Surveillance System surgical-site infection risk index rates. Infect Control Hosp Epidemiol 1997; 18:246–247.Google Scholar
12. Roy, M, Herwaldt, LA, Embrey, R, Kuhns, K, Wenzel, RP, Perl, TM. Does the Centers for Disease Control's NNIS system risk index stratify patients undergoing cardiothoracic operations by their risk of surgical site infection? Infect Control Hosp Epidemiol 2000; 21:186–190.Google Scholar
13. Clark, DO, Von Korff, M, Saunders, K, Baluch, WM, Simon, GE. A chronic disease score with empirically derived weights. Med Care 1995; 33:783–795.Google Scholar
14. Von Korff, M, Wagner, EH, Saunders, K. A chronic disease score from automated pharmacy data. J Clin Epidemiol 1992; 45:197–203.Google Scholar
15. Kaye, KS, Sands, K, Donahue, JG, Chan, KA, Fishman, P, Platt, R. Preoperative drug dispensing as predictor of surgical site infection. Emerg Infect Dis 2001; 7:57–65.CrossRefGoogle ScholarPubMed
16. Yokoe, DS, Noskin, GA, Cunningham, SM, et al. Enhanced identification of postoperative infections among inpatients. Emerg Infect Dis 2004; 10:1924–1930.Google Scholar
17. Goodman, LA, Kruskal, WH. Measures of association for cross classifications. J Am Stat Assoc 1954; 49:732–764.Google Scholar
18. Haley, RW, Culver, DH, Morgan, WM, White, JW, Emori, TG, Hooton, TM. Identifying patients at high risk of surgical wound infection: a simple multivariate index of patient susceptibility and wound contamination. Am J Epidemiol 1985; 121:206–215.Google Scholar
19. Russo, PL, Spelman, DW. A new surgical site infection risk index using risk factors identified by multivariate analysis for patients undergoing coronary artery bypass graft surgery. Infect Control Hosp Epidemiol 2002; 23:372–376.Google Scholar