Eur. J. Epidemiol. 0392-2990 November 1991, p. 641-648
EUROPEAN JOURNAL
Vol. 7, No. 6
OF
EPIDEMIOLOGY
SURGICAL INFECTIONS SURVEILLANCE:RESULTS OF A SIXMONTH INCIDENCE STUDYIN TWO ITALIANHOSPITALS M.L. MORO*, SOMMELLA L.**, GIALLI M.***, TAVANTI L.***, CIOLLI L. °, MASINI R. °°, CAPACCIOLI L. °°°, TORRIOLI R. °°°, TRESALTI E. *.1 *Laboratorio di Epidemiologia e Biostatistica, Istituto Superiore di Sanitd, Rome, Italy. **Direzione Sanitaria, Policlinico Universitario A. Gemelli, Largo Gemelli, 8- 00168 Rome. ***Direzione Sanitaria Ospedale Civile, Arezzo. °Istituto di Clinica Ortopedica, Policlinico Universitario A. Gemelli, Rome. °°Istituto di Semeiotica Chirurgica, Policlinico Universitario A. Gemelli, Rome. °°°Chirurgia Generale, Ospedale Civile, Arezzo.
Key words: Surgical wound infections - Surveillance - Computerized system. In a six-month incidence study of surgical wound infections (SWI) in two Italian hospitals, 1,019 surgical patients, in three general surgery wards, and 433 surgical patients in one orthopedics ward were studied. For the SWI surveillance, the DANOP-DATA system was used: this microcomputer program was developed by Danish authors and tested in a European multicenter study coordinated by the World Health Organization in 1989. Two Italian hospitals participated in the multicenter study. The overall infection rate was 1.2 per 100 operations in orthopedics and 4.9/100 in general surgery. The risk of infection increased with age (RR = 2.06; 95% CL = 1.203.53), wound class (RR = 3.38; 95% CL = 1.97-5.8), length ofpre-operative stay (RR = 2.71; 95% CL = 1.54-4.74), and duration of operation (RR = 2.59; 95% CL = 1.48-4.54). The infection rates ranged from 3.7 to 7.3/100 among the three general surgery wards; this variability by ward was only partially explained by differences in the age distribution of in-patients, wound class, duration of operation and length ofpre-operative stay. When all these risk factors were simultaneously taken into account using a logistic regression model, the odds ratio, comparing one of the three general surgical wards with the other two, was still 2.29 (95% CL = 1.23-4.26). The observed variability can be attributed to differences, among the participating wards, in the case-mix of patients treated and/or to differences in the quality of infection control programs implemented.
INTRODUCTION
Postoperative wound infections are common complications of surgical procedures: they account for nearly 25-40% of hospital-acquired infections (10,13). The overall incidence of surgical wound infections 1 Corresponding Author
(SWI) varied, in different studies, from 4.7 to 17.0 percent (6,17,19,21); this was due to differences in the study populations (surgical service mix, type of procedures) and in the effectiveness of infection control programs. In 1973, Cruse and Foord advocated ongoing surveillance of postoperative wound infections as a way of reducing infection rates, because it would increase surgeons' awareness (6). Several later studies
641
Moro M.L. et aL
Eur. J. Epidemiol.
have suggested a drop in surgical wound infections incidence after surveillance was introduced (9,14,21), including the Study on the Efficacy of Nosocomial Infection Control (SENIC), which found a lower incidence of SWI in hospitals with an extensive surveillance system when compared with hospitals with less extensive surveillance systems or with no surveillance at all (11). Based on these results, several institutions and professional associations have recommended the surveillance of SWI, particularly the analysis and feed-back of surgeon-specific SWI rates (2,5). Timely feed-back, to surgeons, of collected data is the key to a successful surveillance system; in order to aid continuous recording and timely analysis of infections, general guidelines were set up and a microcomputer program (the DANOP-DATA system) was developed by Danish authors (15). In 1989, the World Health Organization coordinated a European multicenter study on SWI infections, using the D A N O P - D A T A system: two Italian hospitals participated in this study. Results from the two Italian hospitals are presented in this paper, and key problems in the choice of a data set for the surveillance of SWI are discussed. MATERIALS AND METHODS
Study population Four surgical wards were enrolled in the study, on a voluntary basis: two general surgical wards of an 880bed acute general hospital (Ospedale Civile, Mezzo) and one general surgery ward and one orthopedics ward of a 1,750-bed university teaching hospital (Policlinico Universitario A. Gemelli, Rome). In each ward, the study was carried out for a sixmonth period in 1989. During these periods, all hospitalized patients undergoing surgical operations in the four wards were included in the study, with the exception of patients undergoing transurethral operations, anal operations, closed reductions of dislocations and endoscopies. Data collection For each operated patient, information concerning date of birth, hospital, ward, admission, operation date, incision and closure time, codes of operation, elective/acute surgery, wound class, perioperative antimicrobial prophylaxis, and course were recorded. For patients with infections, the date of observed infection, presence of pus, and type and localization of infection were recorded. Microbiological results were not recorded. All wounds were classified by the surgeon responsible for the operation, in accordance with the National Research Council classification scheme (1). After surgery, patients were followed-up by the responsible surgeon in each ward or by the infection control nurse, who regularly visited the study wards in
order to detect infected patients. Infections occurring after discharge were not actively surveyed. Infections were diagnosed according to definitions and criteria suggested by CDC (4). Data were recorded by the surgeon on a special registration form and entered into a computer either by the surgeon himself or by an infection control nurse. At the end of the six-month period, results of the surveillance were thoroughly discussed with the surgeons. Statistical analysis Data were entered in an IBM PC PSII 50, using the DANOP-DATA system, and analyzed using the SPSS statistical package (20). For computing infection rates by type of operation in patients who underwent more than one operation, the principal operation, as recorded by the surgeon, was considered. Dichotomous variables were analyzed using the chi-square test; continuous variables for more than two groups were compared by analysis of variance and F-test. Multiple logistic regression was used in order to estimate if the risk of infection, adjusted for age, wound class, duration of operation, and length of preoperative stay, was significantly different in the three general surgical wards. For this purpose, data were entered in an IBM 3270 and analyzed with the PLR program of the BMDP statistical package (7). Infection risks in two general surgical wards from one of the two hospitals were not significantly different, after adjustment for the other risk variables in the logistic regression model: thus, these two wards were compared in the final model with the third one, which showed higher infection rates. RESULTS Infection rates and risk factors Overall, 1,452 patients were included in the study: 433 in orthopedics (29.8%) and 1,019 in general surgery (70.2%). 74 patients (5.1%) underwent incidental surgery (more than one operation performed at the same time). 55 patients (3.8%) developed a surgical infection: 40 (72.7%) developed a superficial infection, 14 (25.5%) a deep infection, and one (1.8%) developed both superficial and deep infections. Infection rates were higher for patients who underwent incidental surgery: 13 infections out of 74 patients (17.6%), compared with 42 infections out of 1,378 (3.0%) patients who underwent a single operation (p = 0.00001; chi-square test). Table 1 shows the infection rates by specialty and class of wound: infection rates are higher for general surgery and, as expected, for contaminated and dirty wounds. Risk of infection varies with type of operation (Table 2): abdominal and thoracic operations are associated with higher infection rates. Older age groups, longer preoperative stays, and longer duration
642
Vol. 7, 1991
Surgical wound infections surveillance
T A B L E 1. - Infection rates b y class o f wound*. ORTHOPEDICS
GENERAL SURGERY
N. of operations
% Infection Rates total/deep
N. of operations
% Infection Rates total/deep
Clean Clean - Contaminated Contaminated Dirty
284 138 6 5
0.4/0.0 2.9/0.0 0.0 0.0
413 438 153 15
2.7/0.5 4.3/0.5 10.5/5.9 26.7/6.7
Total
433
1.2/0.0
1019
4.9/1.4
* only first - listed (principal) operations are included
T A B L E 2. - Infection rates b y type o f principal operation. ORTHOPEDICS Operation* Open reduction of fracture with internal fixation Arthroplasty Amputations Other operations on muscle and bones
Total
GENERAL SURGERY
Number
% Infection Rates total/deep
Operation
Number % Infection Rates total/deep
93 68 4
1.1/0.0 4.4/0.0 --
Tyroidectomy Lungs, pleura, chest wall Mastectomy
268
0.4/0.0
Other operations on mamma Explorative laparotomy Inguinal hernia Other hernia Stomach Appendectomy Small intestine Colon resection Rectum resection Enterostomic Gastro - entero - anastoses Cholecystectomy Other biliary tract Pancreas Spleen Other abdominal Urinary tract and male genitalia Female genitalia Skin and subcutaneous tissue Miscellaneous
433
68 43 64 23 18 141 11 41 162 20 36 12 13 5 154 22 6 19 25 62 17 25 32 1019
* the coding file of the DANO - DATA system included only four categories for orthopedic operations 643
-16.3/4.7 3.1/0.0
-2.8/0.7 9.1/0.0 -4.3/0.6 10.0/5.0 11.1/2.8 16.7/8.3 15.4/0.0 20.0/0.0 4.5/0.6 9.1/0.0 33.3/16.7 15,8/10.5 8.0/4.0 1.6/0.0 -3. l/0.0
Moro M.L. et al.
of operations are surgical infection operations is not with an increased
Eur. J. Epidemiol.
Variability of infection risk among wards
associated with an increased risk of (Table 3). By contrast, the urgency of associated, in the study population, risk of infection.
O v e r a l l i n f e c t i o n rates r a n g e d , in t h e t h r e e g e n e r a l s u r g e r y w a r d s i n c l u d e d in t h e study, f r o m 3.7 to 7 . 3 / 1 0 0 o p e r a t i o n s ( T a b l e 4). T h e o b s e r v e d d i f f e r e n c e s c a n b e
TABLE 3. - Risk factors for surgical wound infections ¢. OPERATIONS
% INFECTION RATES
1 - 14 15 - 44 45 - 64 > 65
124 262 332 297
0.8 2.7 5.7 7.7
2.06 (1.20 - 3.53)'
Clean, clean - contaminated contaminated, dirty
851 168
3.5 11.9
3.38 (1.97 - 5.80)
1 2 - 32 4-7 >=8
556 158 142 163
3.2 3.8 6.3 10.4
2.71 (1.54 - 4.74)"
DURATION OF OPERATION (minutes)
1 - 60 61 - 120 > = 121
513 337 169
2.7 5.6 10.1
2.59 (1.48 - 4.54)"'
OPERATION
Elective Urgent
833 186
4.9 4.8
RISK FACTOR AGE in years*
WOUND CLASS PRE - OPERATIVE HOSPITAL STAY (in days)
RR (95% CL)
1.02 (0.50 - 2.06)
¢ only patients in the three general surgical wards are considered * for four operations, age was missing ' >65 v s < = 6 4 y e a r s " >=8 vs = 8 days
1.65
(0.86 - 3.16)
DURATION OF OPERATION > 120 minutes
1.59
(0.81 - 3.11)
* interval scale
WARD 1 % of operations 0
5
10 15 20 25
WARD 2 % of operations 0
4
8
12 16 20
WARD 3 % of operations 0 5 10 15 20 25 30 35
Appendectomy Cholecystectomy Inguinal hernia
~!~i~i~!ii~i~ii~i~ii~i~i~ii~i~i~i~i~!~i~i~i~i!!!i!!!!~ii!iii!i~iiii~ii~i~i~ii~i~i~i~i~i~ii~!~!!ii~!ii~i|
.:.:.:.:.:.:.~.:.:.:.:.:.:.:.:.:.~:.:.:.:.:.:.:.y.:.:+:.:.:.:.~.:.~.:.:~:~:.:~:~:.7~:.:.:.;.~.:.~.:.:.:.:.:.:.~:~:.;
ilililiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii
Thyroidectomy Mastectomy
iiiililiiiiiiiiiiiiiiliiiii!iiiiiiiiiiiii
--4
Urinary tract Lungs, pleura
iiiiiiiiililii!il
Gastrectomy Colon resection Other abdominal
Figure. 1. - Distribution of the overall ten most frequent operations in the three general surgery wards
DISCUSSION
Several institutions have e m p h a s i z e d the importance of SWI surveillance. A number of studies have demonstrated a reduction in infection rates
following the introduction of surveillance systems; however, the available data that supports these findings is not entirely convincing, since in most of these studies there was no concomitant prospective control (24). Outcome surveillance is useful in increasing surgeons'
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Moro M.L. et al.
Eur. J. Epidemiol.
awareness, in routinely evaluating the quality of care provided and in allowing timely interventions. In order to realize these goals, standardized guidelines for recording and defining infections are needed, along with simple tools for the analysis and presentation of data. The DANOP-DATA system provided the surgeons with a simple tool used to follow up surgical infections. Variables to be recorded were kept at a minimum and the simplicity of the system enhanced the surgeons' cooperation in the study. After surgery, data concerning operations were easily collected by the surgeon and entered, shortly afterwards, into the computer. Infection data were recorded on a second form and linked to each individual procedure through a unique number (age and code of operation). The value of a simple data collection system is clear, yet there are drawbacks. This paper presents results that may be useful in discussing the potential problems of using SWI rates as an indicator of quality of care. The principal aim of outcome surveillance is to routinely point out infections amenable to intervention through improvement in the quality of care. Though the overall infection rates recorded in the three general surgery wards were in the range of other studies' results (10,21), the infection rate in ward 1 was double that of ward 3 (OR = 2.29; 95% CL = 1.23-4.26). In order to attribute the observed difference to variations in quality of care, other factors which can have an important influence on nosocomial infections should be ruled out: for example, variations in patient case-mix among wards (type of operations and severity of underlying diseases) and variations iri recognition of infection (16). Data on the contamination class and type of operation were included in the DANOP-DATA system: the infection risk in our patients was significantly higher for contaminated and dirty wounds (OR = 3.37; 95% CL = 1.74-6.5); however, adjustment for this risk factor did not explain the observed variation by ward. Table 2 points Out a variation in infection rates by operation type, ranging from 0% to 33% even for operations in the same wound class; Figure 1 highlights differences in the distribution of operations by ward. Several authors have suggested that priority should be given to monitoring single operation types, rather than overall infection rates (22,26), but this kind of comparison is hindered by the small number of patients per type of operation. In addition to the type of operation by ward, infection rates can be i n f l u e n c e d by clinical characteristics of patients. Patient' s age was the only information available on patient risks: however, the increased infection risk observed in one of the three wards was not explained by differences in the patients' distribution by age. Severity o f illness, which includes both severity of underlying disease and presence of comorbidities, is not taken :into account in the DANOP-DATA system, even though it can.substantially affect infection risk (8,12).
Variations among individual wards or in the same ward over a period of time can depend on differences in patient case-mix, and if adequate methods to adjust for severity of illness are lacking, comparisons can be misleading. The mean length of postoperative hospitalization was similar in the three wards; hence, it can be assumed that the probability of detecting hospital infection in the three study populations was the same. The proportion of surgical infections occuring after discharge is, in fact, directly associated to the mean length of hospital stay, as pointed out by several authors (3,23): variations among wards in the length of stay can influence the observed infection rates if post-discharge infections are not actively surveyed. The aforementioned issues point out that the need for a simple monitoring system should be balanced with the risk of overlooking relevant information, necessary to interpret the collected data. Severity of illness data and hospital practice data (such as length of stay) are essential for comparisons. The D A N O P - D A T A system has been developed in Northern European countries and some of the choices in defining the minimum data base set are strictly dependent on specific characteristics of the health system organizations in those countries; thus, some modifications are needed when the same system is applied in different countries. For example, microbiological data were not included in the program, based on the assumption that other hospital information systems (i.e. laboratory data) could be used to collect data on the pathogens most frequently responsible for SWI. In most of the Italian hospitals a computerized data base of microbiological results is not available; hence, this information, if not collected during surveillance, is lost, hindering the continuous assessment of likely sources of infections. Another problem we faced using this system was the lack of more detailed information concerning surgical prophylaxis: the DANOP-DATA system allows for the recording of perioperative surgical chemoprophylaxis courses only (e.g. all the chemoprophylaxis courses, started more than two hours before the intervention or soon after the intervention, were not included). However, several Italian studies pointed out an inappropriate use of antibiotics in surgery (both for timing and length of prophylaxis) (18,25): the consistent variation, observed among the three wards in the proportion of patients receiving chemoprophylaxis, can be simply attributed to variations in the compliance with recommended standards for the administration of chemoprophylaxis. Recording surgical wound infections can be more easily accomplished through a standardized system, and a personal computer program can assist in the analysis and timely feed-back of results. However, it is necessary to collect essential information to allow for comparisons and to adjust an established minimum data base to local needs.
646
Surgical wound infections surveillance
Vol. 7, 1991 Aknowledgments The authors aknowledge Susanna Lana for manuscript preparation.
11. Haley R. W., Culver D.H., White J. W., Morgan T.M., Emori G.B., Munn V.P., Hooton T.M. (1985): The efficacy of infection surveillance and control programs in preventing nosocomial infections in U.S. hospitals. Am J Epidemiol, 121:182-205.
REFERENCES
12. Hooton T.M., Haley R.W., Culver D.H., White J.W., Meade Morgan W. (1981): The Joint Associations of Multiple Risk Factors with the Occurrence of Hospital Infection. Am J Med, 70: 947-959
1. American College of Surgeons, Committee on Control of Surgical Infections (1984): Manual on control of infections in surgical patients. 2nd ed. J.B. Lippincott, Philadelphia. . American College of Surgeons, Committee on Control of Surgical Infections of the Committee on Pre- and Postoperative Care. Altheimer W.A., Burke J.F., Pruitt B.A., Sandusky W.R. (eds). (1984): Manual on Control of Infections in Surgical Patients. JB Lippincott Company, Philadelphia. . Brown R.B., Bradley S., Opitz E., Cipriani D., Pieczarka R., Sands M. (1987): Surgical wound infections documented after hospital discharge. Am J Infect Control, 15: 54-58. .
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13. Horan T.C, White J. W., Jarvis W.R., Emori T.G., Culver D.H., Munn V.P., Thornsberry C., Olson D.R., Hughes J.M. (1985): Nosocomial Infection Surveillance, 1984. MMWR, 35 (Suppl. 1): 17-29. 14. Kaiser A.B. (1982): Effective and Creative Surveillance and Reporting of surgical wound infections. Infect Control, 3 (1): 41-43. 15. Kjaeldgaard P., Cordtz T., Sejeberg D., Kjaersgaard E., Sillemann M.P., La Cour Andersen J., Jepsen O.B. (1989): The DANOPDATA system: A low-cost personal computer base program for monitoring of wound infections in surgical ward. J Hosp Infect, 13: 273-279. 16. Larson E., Oram L.F., Hedrick E. (1988): Nosocomial Infection Rates as an Indicator of Quality. Medical Care, 26 (7): 676-684.
Centers for Disease Control. (1981): Outline for Surveillance and Control of N o s o c o m i a l infections. A p p e n d i x II: Guidelines for determining presence and classification of infection. Centers for Disease Control, U.S. Department of Health, Atlanta.
17. Leigh D.A. (1981): An eight year study of postoperative wound infection in two district hospitals. J Hosp Infect, 2: 207-217.
Centers for Disease Control. (1985): Gtfidelines for the prevention of surgical wound infections. Centers for Disease Control, US Department of Health, Atlanta.
18. Mozzillo N., Greco D., Pescini A. (1988): Chemoprophylaxis in Surgical Wards: results of a national survey in Italy. European J Epidemiol, 4 (3): 343-348.
Cruse P., Foord R. (1980): The epidemiology of wound infections. Surg Clin North Am, 60: 27-40.
19. National Research Council Division of Medical Science, Ad Hoc Committee of the Committee of Trauma. (1964): Postoperative wound infections: the influence of ultraviolet irradiation of the operating room and various other factors. Ann Surg, 160 (2): 9-192.
Department of Biomathematics, University of California (1985): BMDP Statistical Software. University of California, Los Angeles.
Ehrenkranz N.J. (1981): Surgical Wound Infection Occurrence in Clean Operations. Risk Stratification for Interhospital Comparisons. Am J Med, 70: 909914. Gil-Egea M.J., Pi-Sunyer M.T., Verdaguer A., Sanz F., Sitges-Serra A., Torre Eleizegui L. (1987): Surgical wound infection: Prospective study of 4,468 Clean wounds. Infect Control, 8 (7): 277280.
10. Haley R.W., Hooton T.M., Culver D.H., Stanley R.C., Emori T.G., Hardison C.D., Quade D., Schachtman R.H., Schaberg D.R., Shah B.V., Schatz G.D. (1981): Nosocomial infections in U.S. hospitals, 1975-1976. Estimated frequency by selected characteristics of patients. Am J Med, 70: 947-959.
20. Norusis M.J. (1986): SPSS/PC for the IBM PC/XT/AT. SPSS Inc., Chicago. 21. Olson M., O'Connor M., Schwartz M.L. (1984): Surgical wound infections. A 5-year prospective study of 20,193 wounds at the Minneapolis VA Medical Center. Ann Surg, 199 (3): 253-259. 22. Pelle H., Jepsen O.B., Larsen S. 0., Christensen F., Dreisler A., Jorgensen P.J., Kirstein A., Kjoller M., Lange A., Laursen K., Nickelsen C.A.N., Osler M., Rasmussen H. (1986): Wound infection after Caesarean Section. Infect Control, 7:456-461. 23. Reimer K., Gleed C., Nicolle L.E. (1987): The impact of post-discharge infection on surgical wound infection rates. Infect Control, 8 (6): 237240. 647
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24. Scheckler W.E. (1988): Surgeon-Specific Wound Infection Rates - A Potentially Dangerous and Misleading Strategy. Infect Control and Hosp Epidemiol, 9 (4): 145-146. 25. Scroccaro G., Falconi M., Martini N. (1987): S.I.P.A.C.: Studio italiano sulla profilassi
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antibiotica in Chirurgia. Giornale Italiano di Farmacia Clinica, 1 (2): 68-74. 26. Simchen E., Wax Y., Pevsner B. (1988): The Israeli Study of Surgical Infections (ISSI): II. Initial Comparison Among Hospitals With Special Focus on Hemia Operations. Infect Control, 9: 241-249.
EUROPEAN JOURNAL
Vol. 7, No. 6
OF
EPIDEMIOLOGY
SURGICAL INFECTIONS SURVEILLANCE:RESULTS OF A SIXMONTH INCIDENCE STUDYIN TWO ITALIANHOSPITALS M.L. MORO*, SOMMELLA L.**, GIALLI M.***, TAVANTI L.***, CIOLLI L. °, MASINI R. °°, CAPACCIOLI L. °°°, TORRIOLI R. °°°, TRESALTI E. *.1 *Laboratorio di Epidemiologia e Biostatistica, Istituto Superiore di Sanitd, Rome, Italy. **Direzione Sanitaria, Policlinico Universitario A. Gemelli, Largo Gemelli, 8- 00168 Rome. ***Direzione Sanitaria Ospedale Civile, Arezzo. °Istituto di Clinica Ortopedica, Policlinico Universitario A. Gemelli, Rome. °°Istituto di Semeiotica Chirurgica, Policlinico Universitario A. Gemelli, Rome. °°°Chirurgia Generale, Ospedale Civile, Arezzo.
Key words: Surgical wound infections - Surveillance - Computerized system. In a six-month incidence study of surgical wound infections (SWI) in two Italian hospitals, 1,019 surgical patients, in three general surgery wards, and 433 surgical patients in one orthopedics ward were studied. For the SWI surveillance, the DANOP-DATA system was used: this microcomputer program was developed by Danish authors and tested in a European multicenter study coordinated by the World Health Organization in 1989. Two Italian hospitals participated in the multicenter study. The overall infection rate was 1.2 per 100 operations in orthopedics and 4.9/100 in general surgery. The risk of infection increased with age (RR = 2.06; 95% CL = 1.203.53), wound class (RR = 3.38; 95% CL = 1.97-5.8), length ofpre-operative stay (RR = 2.71; 95% CL = 1.54-4.74), and duration of operation (RR = 2.59; 95% CL = 1.48-4.54). The infection rates ranged from 3.7 to 7.3/100 among the three general surgery wards; this variability by ward was only partially explained by differences in the age distribution of in-patients, wound class, duration of operation and length ofpre-operative stay. When all these risk factors were simultaneously taken into account using a logistic regression model, the odds ratio, comparing one of the three general surgical wards with the other two, was still 2.29 (95% CL = 1.23-4.26). The observed variability can be attributed to differences, among the participating wards, in the case-mix of patients treated and/or to differences in the quality of infection control programs implemented.
INTRODUCTION
Postoperative wound infections are common complications of surgical procedures: they account for nearly 25-40% of hospital-acquired infections (10,13). The overall incidence of surgical wound infections 1 Corresponding Author
(SWI) varied, in different studies, from 4.7 to 17.0 percent (6,17,19,21); this was due to differences in the study populations (surgical service mix, type of procedures) and in the effectiveness of infection control programs. In 1973, Cruse and Foord advocated ongoing surveillance of postoperative wound infections as a way of reducing infection rates, because it would increase surgeons' awareness (6). Several later studies
641
Moro M.L. et aL
Eur. J. Epidemiol.
have suggested a drop in surgical wound infections incidence after surveillance was introduced (9,14,21), including the Study on the Efficacy of Nosocomial Infection Control (SENIC), which found a lower incidence of SWI in hospitals with an extensive surveillance system when compared with hospitals with less extensive surveillance systems or with no surveillance at all (11). Based on these results, several institutions and professional associations have recommended the surveillance of SWI, particularly the analysis and feed-back of surgeon-specific SWI rates (2,5). Timely feed-back, to surgeons, of collected data is the key to a successful surveillance system; in order to aid continuous recording and timely analysis of infections, general guidelines were set up and a microcomputer program (the DANOP-DATA system) was developed by Danish authors (15). In 1989, the World Health Organization coordinated a European multicenter study on SWI infections, using the D A N O P - D A T A system: two Italian hospitals participated in this study. Results from the two Italian hospitals are presented in this paper, and key problems in the choice of a data set for the surveillance of SWI are discussed. MATERIALS AND METHODS
Study population Four surgical wards were enrolled in the study, on a voluntary basis: two general surgical wards of an 880bed acute general hospital (Ospedale Civile, Mezzo) and one general surgery ward and one orthopedics ward of a 1,750-bed university teaching hospital (Policlinico Universitario A. Gemelli, Rome). In each ward, the study was carried out for a sixmonth period in 1989. During these periods, all hospitalized patients undergoing surgical operations in the four wards were included in the study, with the exception of patients undergoing transurethral operations, anal operations, closed reductions of dislocations and endoscopies. Data collection For each operated patient, information concerning date of birth, hospital, ward, admission, operation date, incision and closure time, codes of operation, elective/acute surgery, wound class, perioperative antimicrobial prophylaxis, and course were recorded. For patients with infections, the date of observed infection, presence of pus, and type and localization of infection were recorded. Microbiological results were not recorded. All wounds were classified by the surgeon responsible for the operation, in accordance with the National Research Council classification scheme (1). After surgery, patients were followed-up by the responsible surgeon in each ward or by the infection control nurse, who regularly visited the study wards in
order to detect infected patients. Infections occurring after discharge were not actively surveyed. Infections were diagnosed according to definitions and criteria suggested by CDC (4). Data were recorded by the surgeon on a special registration form and entered into a computer either by the surgeon himself or by an infection control nurse. At the end of the six-month period, results of the surveillance were thoroughly discussed with the surgeons. Statistical analysis Data were entered in an IBM PC PSII 50, using the DANOP-DATA system, and analyzed using the SPSS statistical package (20). For computing infection rates by type of operation in patients who underwent more than one operation, the principal operation, as recorded by the surgeon, was considered. Dichotomous variables were analyzed using the chi-square test; continuous variables for more than two groups were compared by analysis of variance and F-test. Multiple logistic regression was used in order to estimate if the risk of infection, adjusted for age, wound class, duration of operation, and length of preoperative stay, was significantly different in the three general surgical wards. For this purpose, data were entered in an IBM 3270 and analyzed with the PLR program of the BMDP statistical package (7). Infection risks in two general surgical wards from one of the two hospitals were not significantly different, after adjustment for the other risk variables in the logistic regression model: thus, these two wards were compared in the final model with the third one, which showed higher infection rates. RESULTS Infection rates and risk factors Overall, 1,452 patients were included in the study: 433 in orthopedics (29.8%) and 1,019 in general surgery (70.2%). 74 patients (5.1%) underwent incidental surgery (more than one operation performed at the same time). 55 patients (3.8%) developed a surgical infection: 40 (72.7%) developed a superficial infection, 14 (25.5%) a deep infection, and one (1.8%) developed both superficial and deep infections. Infection rates were higher for patients who underwent incidental surgery: 13 infections out of 74 patients (17.6%), compared with 42 infections out of 1,378 (3.0%) patients who underwent a single operation (p = 0.00001; chi-square test). Table 1 shows the infection rates by specialty and class of wound: infection rates are higher for general surgery and, as expected, for contaminated and dirty wounds. Risk of infection varies with type of operation (Table 2): abdominal and thoracic operations are associated with higher infection rates. Older age groups, longer preoperative stays, and longer duration
642
Vol. 7, 1991
Surgical wound infections surveillance
T A B L E 1. - Infection rates b y class o f wound*. ORTHOPEDICS
GENERAL SURGERY
N. of operations
% Infection Rates total/deep
N. of operations
% Infection Rates total/deep
Clean Clean - Contaminated Contaminated Dirty
284 138 6 5
0.4/0.0 2.9/0.0 0.0 0.0
413 438 153 15
2.7/0.5 4.3/0.5 10.5/5.9 26.7/6.7
Total
433
1.2/0.0
1019
4.9/1.4
* only first - listed (principal) operations are included
T A B L E 2. - Infection rates b y type o f principal operation. ORTHOPEDICS Operation* Open reduction of fracture with internal fixation Arthroplasty Amputations Other operations on muscle and bones
Total
GENERAL SURGERY
Number
% Infection Rates total/deep
Operation
Number % Infection Rates total/deep
93 68 4
1.1/0.0 4.4/0.0 --
Tyroidectomy Lungs, pleura, chest wall Mastectomy
268
0.4/0.0
Other operations on mamma Explorative laparotomy Inguinal hernia Other hernia Stomach Appendectomy Small intestine Colon resection Rectum resection Enterostomic Gastro - entero - anastoses Cholecystectomy Other biliary tract Pancreas Spleen Other abdominal Urinary tract and male genitalia Female genitalia Skin and subcutaneous tissue Miscellaneous
433
68 43 64 23 18 141 11 41 162 20 36 12 13 5 154 22 6 19 25 62 17 25 32 1019
* the coding file of the DANO - DATA system included only four categories for orthopedic operations 643
-16.3/4.7 3.1/0.0
-2.8/0.7 9.1/0.0 -4.3/0.6 10.0/5.0 11.1/2.8 16.7/8.3 15.4/0.0 20.0/0.0 4.5/0.6 9.1/0.0 33.3/16.7 15,8/10.5 8.0/4.0 1.6/0.0 -3. l/0.0
Moro M.L. et al.
of operations are surgical infection operations is not with an increased
Eur. J. Epidemiol.
Variability of infection risk among wards
associated with an increased risk of (Table 3). By contrast, the urgency of associated, in the study population, risk of infection.
O v e r a l l i n f e c t i o n rates r a n g e d , in t h e t h r e e g e n e r a l s u r g e r y w a r d s i n c l u d e d in t h e study, f r o m 3.7 to 7 . 3 / 1 0 0 o p e r a t i o n s ( T a b l e 4). T h e o b s e r v e d d i f f e r e n c e s c a n b e
TABLE 3. - Risk factors for surgical wound infections ¢. OPERATIONS
% INFECTION RATES
1 - 14 15 - 44 45 - 64 > 65
124 262 332 297
0.8 2.7 5.7 7.7
2.06 (1.20 - 3.53)'
Clean, clean - contaminated contaminated, dirty
851 168
3.5 11.9
3.38 (1.97 - 5.80)
1 2 - 32 4-7 >=8
556 158 142 163
3.2 3.8 6.3 10.4
2.71 (1.54 - 4.74)"
DURATION OF OPERATION (minutes)
1 - 60 61 - 120 > = 121
513 337 169
2.7 5.6 10.1
2.59 (1.48 - 4.54)"'
OPERATION
Elective Urgent
833 186
4.9 4.8
RISK FACTOR AGE in years*
WOUND CLASS PRE - OPERATIVE HOSPITAL STAY (in days)
RR (95% CL)
1.02 (0.50 - 2.06)
¢ only patients in the three general surgical wards are considered * for four operations, age was missing ' >65 v s < = 6 4 y e a r s " >=8 vs = 8 days
1.65
(0.86 - 3.16)
DURATION OF OPERATION > 120 minutes
1.59
(0.81 - 3.11)
* interval scale
WARD 1 % of operations 0
5
10 15 20 25
WARD 2 % of operations 0
4
8
12 16 20
WARD 3 % of operations 0 5 10 15 20 25 30 35
Appendectomy Cholecystectomy Inguinal hernia
~!~i~i~!ii~i~ii~i~ii~i~i~ii~i~i~i~i~!~i~i~i~i!!!i!!!!~ii!iii!i~iiii~ii~i~i~ii~i~i~i~i~i~ii~!~!!ii~!ii~i|
.:.:.:.:.:.:.~.:.:.:.:.:.:.:.:.:.~:.:.:.:.:.:.:.y.:.:+:.:.:.:.~.:.~.:.:~:~:.:~:~:.7~:.:.:.;.~.:.~.:.:.:.:.:.:.~:~:.;
ilililiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii
Thyroidectomy Mastectomy
iiiililiiiiiiiiiiiiiiliiiii!iiiiiiiiiiiii
--4
Urinary tract Lungs, pleura
iiiiiiiiililii!il
Gastrectomy Colon resection Other abdominal
Figure. 1. - Distribution of the overall ten most frequent operations in the three general surgery wards
DISCUSSION
Several institutions have e m p h a s i z e d the importance of SWI surveillance. A number of studies have demonstrated a reduction in infection rates
following the introduction of surveillance systems; however, the available data that supports these findings is not entirely convincing, since in most of these studies there was no concomitant prospective control (24). Outcome surveillance is useful in increasing surgeons'
645
Moro M.L. et al.
Eur. J. Epidemiol.
awareness, in routinely evaluating the quality of care provided and in allowing timely interventions. In order to realize these goals, standardized guidelines for recording and defining infections are needed, along with simple tools for the analysis and presentation of data. The DANOP-DATA system provided the surgeons with a simple tool used to follow up surgical infections. Variables to be recorded were kept at a minimum and the simplicity of the system enhanced the surgeons' cooperation in the study. After surgery, data concerning operations were easily collected by the surgeon and entered, shortly afterwards, into the computer. Infection data were recorded on a second form and linked to each individual procedure through a unique number (age and code of operation). The value of a simple data collection system is clear, yet there are drawbacks. This paper presents results that may be useful in discussing the potential problems of using SWI rates as an indicator of quality of care. The principal aim of outcome surveillance is to routinely point out infections amenable to intervention through improvement in the quality of care. Though the overall infection rates recorded in the three general surgery wards were in the range of other studies' results (10,21), the infection rate in ward 1 was double that of ward 3 (OR = 2.29; 95% CL = 1.23-4.26). In order to attribute the observed difference to variations in quality of care, other factors which can have an important influence on nosocomial infections should be ruled out: for example, variations in patient case-mix among wards (type of operations and severity of underlying diseases) and variations iri recognition of infection (16). Data on the contamination class and type of operation were included in the DANOP-DATA system: the infection risk in our patients was significantly higher for contaminated and dirty wounds (OR = 3.37; 95% CL = 1.74-6.5); however, adjustment for this risk factor did not explain the observed variation by ward. Table 2 points Out a variation in infection rates by operation type, ranging from 0% to 33% even for operations in the same wound class; Figure 1 highlights differences in the distribution of operations by ward. Several authors have suggested that priority should be given to monitoring single operation types, rather than overall infection rates (22,26), but this kind of comparison is hindered by the small number of patients per type of operation. In addition to the type of operation by ward, infection rates can be i n f l u e n c e d by clinical characteristics of patients. Patient' s age was the only information available on patient risks: however, the increased infection risk observed in one of the three wards was not explained by differences in the patients' distribution by age. Severity o f illness, which includes both severity of underlying disease and presence of comorbidities, is not taken :into account in the DANOP-DATA system, even though it can.substantially affect infection risk (8,12).
Variations among individual wards or in the same ward over a period of time can depend on differences in patient case-mix, and if adequate methods to adjust for severity of illness are lacking, comparisons can be misleading. The mean length of postoperative hospitalization was similar in the three wards; hence, it can be assumed that the probability of detecting hospital infection in the three study populations was the same. The proportion of surgical infections occuring after discharge is, in fact, directly associated to the mean length of hospital stay, as pointed out by several authors (3,23): variations among wards in the length of stay can influence the observed infection rates if post-discharge infections are not actively surveyed. The aforementioned issues point out that the need for a simple monitoring system should be balanced with the risk of overlooking relevant information, necessary to interpret the collected data. Severity of illness data and hospital practice data (such as length of stay) are essential for comparisons. The D A N O P - D A T A system has been developed in Northern European countries and some of the choices in defining the minimum data base set are strictly dependent on specific characteristics of the health system organizations in those countries; thus, some modifications are needed when the same system is applied in different countries. For example, microbiological data were not included in the program, based on the assumption that other hospital information systems (i.e. laboratory data) could be used to collect data on the pathogens most frequently responsible for SWI. In most of the Italian hospitals a computerized data base of microbiological results is not available; hence, this information, if not collected during surveillance, is lost, hindering the continuous assessment of likely sources of infections. Another problem we faced using this system was the lack of more detailed information concerning surgical prophylaxis: the DANOP-DATA system allows for the recording of perioperative surgical chemoprophylaxis courses only (e.g. all the chemoprophylaxis courses, started more than two hours before the intervention or soon after the intervention, were not included). However, several Italian studies pointed out an inappropriate use of antibiotics in surgery (both for timing and length of prophylaxis) (18,25): the consistent variation, observed among the three wards in the proportion of patients receiving chemoprophylaxis, can be simply attributed to variations in the compliance with recommended standards for the administration of chemoprophylaxis. Recording surgical wound infections can be more easily accomplished through a standardized system, and a personal computer program can assist in the analysis and timely feed-back of results. However, it is necessary to collect essential information to allow for comparisons and to adjust an established minimum data base to local needs.
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Surgical wound infections surveillance
Vol. 7, 1991 Aknowledgments The authors aknowledge Susanna Lana for manuscript preparation.
11. Haley R. W., Culver D.H., White J. W., Morgan T.M., Emori G.B., Munn V.P., Hooton T.M. (1985): The efficacy of infection surveillance and control programs in preventing nosocomial infections in U.S. hospitals. Am J Epidemiol, 121:182-205.
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