Risk Factors for Postoperative Infection RICHARD A. GARIBALDI, M.D., DEBORAH GUSHING,
R.N.,
M.P.H., TRUDY LERER, M.s.,
During a 4-year period, we collected prospective epidemiologic data and intraoperative wound cultures from 1,852 surgery patients at a university-afliliated community hospital in order to identify the critical risk factors for postoperative wound infections and study the impact of perioperative antibiotics on the bacteriology of infected wounds. Stepwise logistic regression analysis revealed four risk factors that were independent of each other and highly predictive for subsequent wound infection. These were the surgical wound class, American Society of Anesthesiologists physical status grouping, duration of surgery, and results of intraoperative cultures. Addition of other variables to our model did not increase the predicted probability of infection. Even though patients with positive intraoperative cultures had an increased rate of infection, this information had limited clinical utility. The predictive value of a positive culture was low (32%), false-positive rate was high (82%), and concordance with isolates from infected wounds was low (41% when both cultures were positive). Patients who had received perioperative antibiotics and who developed infections were frequently infected with organisms that were resistant to the perioperative drug regimen, compared with patients who had not received antibiotics. A better understanding of the variables that affect the epidemiology and pathogenesis of postoperative wound infection will enable us to make more valid comparisons of rates among hospitals, help us to develop more effective infection control strategies
From the University of Connecticut Health Center, Farmington, Connecticut. This study was funded by a grant from the National Institutes of Health, National Institute of Surgery and Infectious Diseases Al19046. Reprint requests should be addressed to Richard A. Garibaldi, M.D., Department of Medicine, University of Connecticut Health Center, Farmington, Connecticut 06030.
3B-158s
September
16,
1991
The American
Journal
of Medicine
and provide ments.
Farmington,
Connecticut
us with more effective
treat-
W
e collected prospective information on more than 1,800 operations at a university-afhliated community hospital in order to analyze the relative contributions of specific risk factors in predisposing to postoperative wound infections. It was our purpose to identify the three or four major independent variables associated with infection that might enable clinicians and clinical researchers to stratify patients into risk groups on the basis of their expected likelihood of wound infection. In addition, we wanted to examine the pathophysiologic association between bacteria isolated from the wound during surgery and the subsequent development of infection, including the impact of perioperative antibiotics on the bacterial flora of these infections. METHODS Clinical Protocol
A total of 1,852 patients were enrolled in our protocol over a 4-year period from January 1982 to January 1986. The study population included patients undergoing operations with skin incisions greater than 6 cm in length. Each patient was identified prior to surgery and standard epidemiologic and clinical data were recorded, including receipt of antibiotics (Table I). Information regarding the number of diagnoses recorded in the chart was not included. During the operative procedure, a semiquantitative culture was collected from subcutaneous fat in the superficial wound margin just prior to closing. The culture technique utilized a 5 p,rn Millipore filter in a holder that was pressed on the wound surface for 5 seconds and subsequently placed directly on trypticase-soy agar with 5% sheep blood for culture [ll. Cultures were considered to be positive if greater than 30 cfu of bacteria were identified; most of the positive cultures had colony counts that were too numerous to count. Information regarding the operative procedure was also collected at the time of surgery. Postoperatively, each patient was followed three times weekly for the duration of their hospitalization to identify infections; information was collected on all patients for at least 2 weeks after surgery, includVolume
91 (suppl
3B)
CONFERENCE
ing the first office visit if the patient had been discharged home prior to that time. Infections were diagnosed by the observation of cellulitis, the presence of pus or purulent exudate or by written acknowledgement of a wound infection by an attending physician. The nurse who followed patients postoperatively had no knowledge of the patient’s intraoperative culture results. Statistical Analysis
Values for all variables considered in the analysis were grouped into clinically meaningful discrete categories. Associations between each potential risk factor and postoperative wound infection were first evaluated using contingency table analysis and chi-square statistics or Fisher’s exact probability test. Odds ratios (OR) and corresponding 95% confidence intervals (CI) were computed for all operative procedures [2]. A multiple logistic regression model was used to identify variables that were significantly associated with the occurrence of postoperative wound infection while simultaneously controlling for the effects of other potentially confounding risk factors. Employing a step-up procedure, a variable was entered into the model if its addition resulted in a significant improvement of fit (p < 0.05) 131. After determining the variables that were most predictive of infection in a statistically significant sense, other variables that were believed to have clinical relevance were forced into the model to analyze their additional contributions to the infection risk. Predicted probabilities of the risk of infection for different subgroups of patients were calculated from estimates of the logistic coefficient obtained for different models. Study Population
The 1,852 patients were comprised of 60% females and 40% males. Most (69%) were between the ages of 31 and 70 years; 18% were greater than 70 years of age. The majority (79%) were on the general surgery service; 19% were on the gynecology service; less than 2% were from the other surgical specialties. The groupings of cases by surgical wound class included 42% clean surgeries, 54% clean-contaminated, 3% contaminated, and 1% dirty. The two most common procedures that were included in the study were cholecystectomies (31% of surgeries) and hernia repairs (26%). More than 98% of surgeries were elective. Most patients (83%) were hospitalized for 3 days or less prior to surgery. Most patients were without serious underlying medical problems at the time of surgery; 34% were in the American Society of Anesthesiologists (ASA) physical status class I, 33% were in class II, 31% were in class III, and 2% were in classes IV or September
16,
1991
ON NOSOCOMIAL
INFECTIONS
/ GARIBALDI
ET AL
TABLE I PossibleRisk Factorsfor PostoperativeWound Infections HostFactors
SurgeryFactors
Severity of illness ASA physical status classification lmmunocompromising diseases Diabetes mellitus Estimated prognosis Nutritional status Serum albumin Weight Presence of other infections Duration of preoperative stay
Emergency/elective Hair removal technique Service Surgeon Site of suraerv Procedure-or procedures lntraoperative culture Perioperative antibiotics &raaion of surgery Packs Primary or secondary closure Drapes Irrigation Glove punctures
V 141. Fewer than 7% were diabetics; of these, less than half used insulin. Forty-eight percent of all cases received perioperative antibiotics. This included 32% of patients undergoing clean surgeries; many of these were hernia repairs in which Marlex mesh, a synthetic tissue reinforcement, was implanted and for which prophylactic antibiotics were routinely prescribed. Cephalosporin antibiotics, either alone or in combination, were prescribed in more than 80% of cases in which prophylactic antibiotics were given. RESULTS
Overall, 120 postoperative wound infections were identified, for a rate of 6.5%. Specific rates and ORs for host factors and procedure-related risk-factors are presented in Table II. Associations previously identified in the literature as factors that predisposed to postoperative wound infection were corroborated in our study [5-121. There was a direct association between the occurrence of wound infections and high ASA classification, lengthier preoperative hospitalizations, and the presence of an infection at another site at the time of operation. The most significant procedure-associated risk factors were the surgical wound class, identification of positive intraoperative wound cultures, duration of surgery, and occurrence of glove punctures. Surgical incisions involving lower abdominal sites also were associated with higher rates of infection compared with upper abdominal sites (OR, 2.0; CI, 1.2-3.11, as were emergency procedures (OR, 7.6; CI, 3.2-18.2); however, the number of emergency procedures included in the study was small. Stepwise logistic regression analysis identified four variables that were independent of each other and highly predictive for wound infection. These were the surgical wound class, ASA physical status grouping, duration of the procedure, and results of intraoperative cultures (Table III>. Using this
The American
Journal
of Medicine
Volume
91 (suppl3B)
3B-159s
CONFERENCE ON NOSOCOMIAL INFECTIONS / GARIBALDI ET AL
TABLE II
TABLE Ill
Rekionships Between Risk Factorsand PostoperativeWound Infection
Stepwise Logistic RegressionAnalysisof the Association Between Risk Factorsand Wound Infection
No.
POI No. (%)
Hostfactor ASA class a. I b. II c. Ill d. IV,V Preoperative stay (days) a. 53 b. 4-7 c. 8-14 d. 215 Presence of other infection Yes No
631 611 578 32
20 18 66 6
(3.2) (4.6) (11.4) (18.8)
4.2 (2.8-6.4) Cc t d/a t b)t
1,538 157 111 46
64 22 18 16
(4.2) (14.0) (16.2) (34.8)
5.0 (3.4-7.3) (b t c t d/aH
84 1,768
13 (15.5) 107 (6.1)
788 1,009 38 17
21 81 11 10
No
276 1,576
38 (13.8) 82 (5.2)
2.9 (1.9-4.4)
D;yti;;fOoperation b. c. d. GITF
8 34 39 39
4.6 (3.1-6.8) (c t d/a t b)
(2.6) (8.0) (28) (41.2)
2.8 (1.5-5.3)
3.2 (2.0-5.2) (b/aH 22.6 (11.3-45.2) (c t d/a)t
(min) 283 995 423 151
61-120 121-180 >180 puncture
602 1,210
No
(2.8) (3.4) (9.0) (25.8)
69 (11.5) 48 (4.0)
3.1 (2.1-4.6)
onfidenceintervals. roupings of risk factors used to calculate odds ratio.
model, we determined the predicted probability of infection for specific subgroups of patients (Table IV). As variables were sequentially entered into the calculation for patients in each surgical wound class, the predicted probability of infection increased. The unadjusted predicted probability of infection for clean operations was 2.2%; for clean surgeries with long durations with patients in high ASA classes, the predicted probability was 8.3%. For clean-contaminated operations, the predicted probability increased from 8.5% to 20% when the two other variables were included; for contaminated and dirty operations, the predicted probability increased from 28% to 41%. After including the ASA class and the duration of the procedure in the model, the addition of a positive intraoperative wound culture further increased the predicted probability of infection in all surgical wound classes, up to 17% in high-risk clean operations, 37% in high-risk clean-contaminated operations, and 61% in high-risk contaminated and dirty operations. The only other variable that increased the predicted probability of infection in this model was the inclusion of a preoperative stay of >3 days for 3B-160s
September
16, 1991
Wound class Duration of surgery lntraoperative contamination ASA group
OddsRatio (Cl)* 2.7 3.0 3.0 2.4
(1.9-4.6) (1.6-3.6) (2.0-4.6) (1.8-4.0)
Improvement p Valuet
R.N.,
M.P.H., TRUDY LERER, M.s.,
During a 4-year period, we collected prospective epidemiologic data and intraoperative wound cultures from 1,852 surgery patients at a university-afliliated community hospital in order to identify the critical risk factors for postoperative wound infections and study the impact of perioperative antibiotics on the bacteriology of infected wounds. Stepwise logistic regression analysis revealed four risk factors that were independent of each other and highly predictive for subsequent wound infection. These were the surgical wound class, American Society of Anesthesiologists physical status grouping, duration of surgery, and results of intraoperative cultures. Addition of other variables to our model did not increase the predicted probability of infection. Even though patients with positive intraoperative cultures had an increased rate of infection, this information had limited clinical utility. The predictive value of a positive culture was low (32%), false-positive rate was high (82%), and concordance with isolates from infected wounds was low (41% when both cultures were positive). Patients who had received perioperative antibiotics and who developed infections were frequently infected with organisms that were resistant to the perioperative drug regimen, compared with patients who had not received antibiotics. A better understanding of the variables that affect the epidemiology and pathogenesis of postoperative wound infection will enable us to make more valid comparisons of rates among hospitals, help us to develop more effective infection control strategies
From the University of Connecticut Health Center, Farmington, Connecticut. This study was funded by a grant from the National Institutes of Health, National Institute of Surgery and Infectious Diseases Al19046. Reprint requests should be addressed to Richard A. Garibaldi, M.D., Department of Medicine, University of Connecticut Health Center, Farmington, Connecticut 06030.
3B-158s
September
16,
1991
The American
Journal
of Medicine
and provide ments.
Farmington,
Connecticut
us with more effective
treat-
W
e collected prospective information on more than 1,800 operations at a university-afhliated community hospital in order to analyze the relative contributions of specific risk factors in predisposing to postoperative wound infections. It was our purpose to identify the three or four major independent variables associated with infection that might enable clinicians and clinical researchers to stratify patients into risk groups on the basis of their expected likelihood of wound infection. In addition, we wanted to examine the pathophysiologic association between bacteria isolated from the wound during surgery and the subsequent development of infection, including the impact of perioperative antibiotics on the bacterial flora of these infections. METHODS Clinical Protocol
A total of 1,852 patients were enrolled in our protocol over a 4-year period from January 1982 to January 1986. The study population included patients undergoing operations with skin incisions greater than 6 cm in length. Each patient was identified prior to surgery and standard epidemiologic and clinical data were recorded, including receipt of antibiotics (Table I). Information regarding the number of diagnoses recorded in the chart was not included. During the operative procedure, a semiquantitative culture was collected from subcutaneous fat in the superficial wound margin just prior to closing. The culture technique utilized a 5 p,rn Millipore filter in a holder that was pressed on the wound surface for 5 seconds and subsequently placed directly on trypticase-soy agar with 5% sheep blood for culture [ll. Cultures were considered to be positive if greater than 30 cfu of bacteria were identified; most of the positive cultures had colony counts that were too numerous to count. Information regarding the operative procedure was also collected at the time of surgery. Postoperatively, each patient was followed three times weekly for the duration of their hospitalization to identify infections; information was collected on all patients for at least 2 weeks after surgery, includVolume
91 (suppl
3B)
CONFERENCE
ing the first office visit if the patient had been discharged home prior to that time. Infections were diagnosed by the observation of cellulitis, the presence of pus or purulent exudate or by written acknowledgement of a wound infection by an attending physician. The nurse who followed patients postoperatively had no knowledge of the patient’s intraoperative culture results. Statistical Analysis
Values for all variables considered in the analysis were grouped into clinically meaningful discrete categories. Associations between each potential risk factor and postoperative wound infection were first evaluated using contingency table analysis and chi-square statistics or Fisher’s exact probability test. Odds ratios (OR) and corresponding 95% confidence intervals (CI) were computed for all operative procedures [2]. A multiple logistic regression model was used to identify variables that were significantly associated with the occurrence of postoperative wound infection while simultaneously controlling for the effects of other potentially confounding risk factors. Employing a step-up procedure, a variable was entered into the model if its addition resulted in a significant improvement of fit (p < 0.05) 131. After determining the variables that were most predictive of infection in a statistically significant sense, other variables that were believed to have clinical relevance were forced into the model to analyze their additional contributions to the infection risk. Predicted probabilities of the risk of infection for different subgroups of patients were calculated from estimates of the logistic coefficient obtained for different models. Study Population
The 1,852 patients were comprised of 60% females and 40% males. Most (69%) were between the ages of 31 and 70 years; 18% were greater than 70 years of age. The majority (79%) were on the general surgery service; 19% were on the gynecology service; less than 2% were from the other surgical specialties. The groupings of cases by surgical wound class included 42% clean surgeries, 54% clean-contaminated, 3% contaminated, and 1% dirty. The two most common procedures that were included in the study were cholecystectomies (31% of surgeries) and hernia repairs (26%). More than 98% of surgeries were elective. Most patients (83%) were hospitalized for 3 days or less prior to surgery. Most patients were without serious underlying medical problems at the time of surgery; 34% were in the American Society of Anesthesiologists (ASA) physical status class I, 33% were in class II, 31% were in class III, and 2% were in classes IV or September
16,
1991
ON NOSOCOMIAL
INFECTIONS
/ GARIBALDI
ET AL
TABLE I PossibleRisk Factorsfor PostoperativeWound Infections HostFactors
SurgeryFactors
Severity of illness ASA physical status classification lmmunocompromising diseases Diabetes mellitus Estimated prognosis Nutritional status Serum albumin Weight Presence of other infections Duration of preoperative stay
Emergency/elective Hair removal technique Service Surgeon Site of suraerv Procedure-or procedures lntraoperative culture Perioperative antibiotics &raaion of surgery Packs Primary or secondary closure Drapes Irrigation Glove punctures
V 141. Fewer than 7% were diabetics; of these, less than half used insulin. Forty-eight percent of all cases received perioperative antibiotics. This included 32% of patients undergoing clean surgeries; many of these were hernia repairs in which Marlex mesh, a synthetic tissue reinforcement, was implanted and for which prophylactic antibiotics were routinely prescribed. Cephalosporin antibiotics, either alone or in combination, were prescribed in more than 80% of cases in which prophylactic antibiotics were given. RESULTS
Overall, 120 postoperative wound infections were identified, for a rate of 6.5%. Specific rates and ORs for host factors and procedure-related risk-factors are presented in Table II. Associations previously identified in the literature as factors that predisposed to postoperative wound infection were corroborated in our study [5-121. There was a direct association between the occurrence of wound infections and high ASA classification, lengthier preoperative hospitalizations, and the presence of an infection at another site at the time of operation. The most significant procedure-associated risk factors were the surgical wound class, identification of positive intraoperative wound cultures, duration of surgery, and occurrence of glove punctures. Surgical incisions involving lower abdominal sites also were associated with higher rates of infection compared with upper abdominal sites (OR, 2.0; CI, 1.2-3.11, as were emergency procedures (OR, 7.6; CI, 3.2-18.2); however, the number of emergency procedures included in the study was small. Stepwise logistic regression analysis identified four variables that were independent of each other and highly predictive for wound infection. These were the surgical wound class, ASA physical status grouping, duration of the procedure, and results of intraoperative cultures (Table III>. Using this
The American
Journal
of Medicine
Volume
91 (suppl3B)
3B-159s
CONFERENCE ON NOSOCOMIAL INFECTIONS / GARIBALDI ET AL
TABLE II
TABLE Ill
Rekionships Between Risk Factorsand PostoperativeWound Infection
Stepwise Logistic RegressionAnalysisof the Association Between Risk Factorsand Wound Infection
No.
POI No. (%)
Hostfactor ASA class a. I b. II c. Ill d. IV,V Preoperative stay (days) a. 53 b. 4-7 c. 8-14 d. 215 Presence of other infection Yes No
631 611 578 32
20 18 66 6
(3.2) (4.6) (11.4) (18.8)
4.2 (2.8-6.4) Cc t d/a t b)t
1,538 157 111 46
64 22 18 16
(4.2) (14.0) (16.2) (34.8)
5.0 (3.4-7.3) (b t c t d/aH
84 1,768
13 (15.5) 107 (6.1)
788 1,009 38 17
21 81 11 10
No
276 1,576
38 (13.8) 82 (5.2)
2.9 (1.9-4.4)
D;yti;;fOoperation b. c. d. GITF
8 34 39 39
4.6 (3.1-6.8) (c t d/a t b)
(2.6) (8.0) (28) (41.2)
2.8 (1.5-5.3)
3.2 (2.0-5.2) (b/aH 22.6 (11.3-45.2) (c t d/a)t
(min) 283 995 423 151
61-120 121-180 >180 puncture
602 1,210
No
(2.8) (3.4) (9.0) (25.8)
69 (11.5) 48 (4.0)
3.1 (2.1-4.6)
onfidenceintervals. roupings of risk factors used to calculate odds ratio.
model, we determined the predicted probability of infection for specific subgroups of patients (Table IV). As variables were sequentially entered into the calculation for patients in each surgical wound class, the predicted probability of infection increased. The unadjusted predicted probability of infection for clean operations was 2.2%; for clean surgeries with long durations with patients in high ASA classes, the predicted probability was 8.3%. For clean-contaminated operations, the predicted probability increased from 8.5% to 20% when the two other variables were included; for contaminated and dirty operations, the predicted probability increased from 28% to 41%. After including the ASA class and the duration of the procedure in the model, the addition of a positive intraoperative wound culture further increased the predicted probability of infection in all surgical wound classes, up to 17% in high-risk clean operations, 37% in high-risk clean-contaminated operations, and 61% in high-risk contaminated and dirty operations. The only other variable that increased the predicted probability of infection in this model was the inclusion of a preoperative stay of >3 days for 3B-160s
September
16, 1991
Wound class Duration of surgery lntraoperative contamination ASA group
OddsRatio (Cl)* 2.7 3.0 3.0 2.4
(1.9-4.6) (1.6-3.6) (2.0-4.6) (1.8-4.0)
Improvement p Valuet
