Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-09T08:48:31.722Z Has data issue: false hasContentIssue false
  • English
  • Français

Electronic Surveillance of Antibiotic Exposure and Coded Discharge Diagnoses as Indicators of Postoperative Infection and Other Quality Assurance Measures

Published online by Cambridge University Press:  21 June 2016

Lisa R. Hirschhorn ,
Judith S. Currier  and
Richard Platt
Lisa R. Hirschhorn*
Affiliation:
Channing Laboratory the Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts
Judith S. Currier
Affiliation:
Channing Laboratory the Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts
Richard Platt
Affiliation:
Channing Laboratory the Department of Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts
*
Boston City Hospital, 818 Harrison Ave., Boston, MA 0.2115
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives:

To assess postoperative exposure to parenteral antibiotics and coded discharge diagnoses of infection as markers of nosocomial infection, postoperative morbidity, and potentially inappropriate antibiotic use after cesarean section.

Design:

Retrospective cohort study to compare automated markers with the criterion of record review.

Setting:

Tertiary care hospital.

Patients:

Women admitted to a large teaching hospital after April 15, 1987, and discharged before October 1, 1989, who underwent a nonrepeat, nonelective cesarean section and had received prophylaxis with a cephalosporin.

Methods:

Antibiotic exposure and discharge diagnosis codes were obtained from a large electronic hospital data base. A sample of charts was reviewed to determine the presence of infection, other postoperative complications, and postoperative antibiotic exposure.

Results:

A total of 2,197 women who had undergone a nonrepeat nonelective cesarean section were included in the study cohort. These women were assigned to 6 subgroups based on postoperative antibiotic exposure status and discharge codes suggesting endometritis, other postoperative infection, or no infection. Review of 457 records indicated that the overall infection rate was 9%. Eight percent of all the patients had a coded diagnosis for infection, and 16% received some parenteral antibiotics after the first postoperative day. Exposure to at least 2 days of parenteral postoperative antibiotics was the best marker by which to discriminate between infected and uninfected patients, with a sensitivity of 81 %, a specificity of 95%, and a positive predictive value of 61% for detecting infection. The corresponding figures for coded diagnoses for infection had rates of 65%, 97%, and 74%, respectively. The combination of discharge codes and exposure to parenteral postoperative antibiotics resulted in a more accurate but less sensitive marker for nosocomial infections, with a positive predictive value of 94% and a sensitivity of 59%. The groups with discordant parenteral postoperative antibiotics exposure and discharge codes for infection were enriched for errors in coding, noninfectious morbidity, and unexplained antibiotic use. Less than 1% of the entire cohort had ≥ 2 days of parenteral postoperative antibiotics without any reason apparent in the medical record.

Conclusions:

Parenteral postoperative antibiotic exposure determined from automated pharmacy records correlated with the results of the more labor-intensive manual review of medical records for the identification of nosocomial infection. In addition, information on antibiotic exposure combined with coded discharge diagnoses provided a rapid screen to identify subgroups of patients with higher rates of infectious and noninfectious morbidity, unexplained antibiotic use, and errors in discharge coding. Information derived from electronic data bases created for administrative purposes may be useful as a marker for infectious complications, inappropriate antibiotic prescribing, and other issues related to total quality hospital monitoring.

Type
Original Articles
Information
Infection Control & Hospital Epidemiology , Volume 14 , Issue 1 , January 1993 , pp. 21 - 28
Copyright
Copyright © The Society for Healthcare Epidemiology of America 1993 

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. Classen, DC, Burke, JP, Pestotnik, SL, Evans, RS, Stevens, LE. Surveillance for quality assessment: IV. Surveillance using a hospital information system. Infect Control Hosp Epidemiol. 1991;12:239–244.CrossRefGoogle ScholarPubMed
2. Plan, R, Stryker, WS, Komaroff, AL. Pharmacoepidemiology in hospitals using automated data systems. Am J Prev Med. 1988;4:39–47.Google Scholar
3. Packer, C. More hospitals automate their pharmacies. Hospitals. 1989;63:77.Google ScholarPubMed
4. Bleich, HL, Beckley, RF, Horowitz, GL, et al. Clinical computing in a teaching hospital. N Engl J Med. 1985;312:756–764.CrossRefGoogle ScholarPubMed
5. Wenzel, RP, Osterman, CA Hunting, KJ, Gwaltney, JM. Hospital-acquired infections. I. Surveillance in a university hospital. Am] Epidemiol. 1976;103:251–260.10.1093/oxfordjournals.aje.a112223CrossRefGoogle ScholarPubMed
6. Brinsko, V, Chmel, H. Antibiotic monitoring as a surveillance tool for nosocomial infections./ Hosp Infect. 1984;S5A:95–99.10.1016/0195-6701(84)90038-0CrossRefGoogle Scholar
7. Broderick, A, Mori, M, Nettle, MD, Streed, BA, Wenzel, RP. Nosocomial infections: validation of surveillance and computer modeling to identify patients at risk. Am J Epidemiol. 1990;131:734–742.CrossRefGoogle ScholarPubMed
8. Iezzoni, LI, Burnside, S, Sickles, L, Moskowitz, MA, Sawitz, E, Levine, PA. Coding of acute myocardial infarction. Clinical and policy implications. Ann Intern Med. 1988;109:745–751.10.7326/0003-4819-109-9-745CrossRefGoogle ScholarPubMed
9. Lloyd, SS, Rissing, JP. Physician and coding errors in patients records. JAMA. 1985:254:1330–1336.CrossRefGoogle Scholar
10. The I&national Classification of Diseases, Ninth Revision, Clinical Modification. 2nd ed. Ann Arbor, Mi: Commission on Professional and Hospital Activities; 1990.Google Scholar
11. Snedecor, GW, Cochran, WG. Statistical Methods. 8th ed. Ames, Ia: Iowa State University Press; 1991.Google Scholar
12. Garner, JS, Jatvis, WR, Emori, TG, Horan, TC, Hughes, JM. CDC definitions for nosocomial infections, 1988. Am/Infect Control. 1988;16:128–140.Google ScholarPubMed
13. Weinstein, MC, Feinstein, HV. Clinical Decision Analysis. Philadelphia, Pa: W.B. Saunders Company; 1980.Google Scholar
14. Rosner, B. Fundamentals of Biostatistics. 3rd ed. Boston, Ma: PWS-Kent Publishing Company; 1990.Google Scholar
15. Armitage, I? Statistical Methods in Medical Research. 2nd ed. Oxford: Blackwell; 1973.Google Scholar
16. Evans, RS, Larsen, RA, Burke, JP, et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA. 1986;256:1007–1011.CrossRefGoogle ScholarPubMed