Surgical site infection surveillance

Journal of Hospital Infection (2000) 45: 173–184 doi:10.1053/jhin.2000.0736, available online at http://www.idealibrary.com on

REVIEW

Surgical site infection surveillance E.T. M. Smyth and A. M. Emmerson* Infection Control, Department of Bacteriology,The Royal Hospitals NHS Trust, Belfast BT12 6BA, UK; *Department of Microbiology and Infectious Diseases and the PHLS, University Hospital, Queen’s Medical Centre, Nottingham NG7 2UH, UK

Summary: Surgical site infection (SSI) is the third most commonly reported nosocomial infection and accounts for 14–16% of all nosocomial infections among hospital inpatients. A successful SSI surveillance programme includes standardized definitions of infection, effective surveillance methods and stratification of the SSI rates according to risk factors associated with the development of SSI. Surveillance with feedback of information to surgeons and other relevant staff has been shown to be an important element in the overall strategy to reduce the numbers of SSIs. This paper examines the essential components of a SSI surveillance system including surveillance methods, data collection and handling, analysis and presentation of results to clinical staff. © 2000 The Hospital Infection Society

Keywords: Surgical site infection; surveillance; nosocomial infection.

Introduction The cost of nosocomial infections in England in 1986 has been estimated at £111 million or 950 000 lost bed-days.1 A UK study of the excess hospital costs attributable to nosocomial infection in surgical patients reported a mean extra cost per patient of £1041 and an increased length of hospital stay of 8.2 days.2 Equivalent data for the cost of nosocomial infections in the United States for 1992 have been reported as $4.5 billion, with $1.6 billion (35.6%) allocated as the cost of surgical site infections (SSIs).3 The morbidity, mortality and the cost to health services of surgical site infections is huge. In the United States alone there are an estimated 27 million surgical procedures performed each year with almost one-third of patients over the age of sixty-five years.4 Surgical site infections are the third most frequently reported nosocomial infection, accounting for 14–16% of all nosocomial infections among hospital inpatients.5 During Received 24 October 1999; manuscript accepted 13 January 2000. Author for correspondence: Dr E. T. M. Smyth, Infection Control, Department of Bacteriology, Kelvin Building, NHS Trust, Belfast BT12 6BA. Fax: ]44 (0)28 90311416; E-mail: [email protected]

0195–6701/00/030173]12 $35.00

1986–1996, hospitals conducting SSI surveillance employing the Center for Disease Control’s (CDC’s) National Nosocomial Infections Surveillance (NNIS) System reported 15 523 SSIs following 593 344 surgical procedures.6 Among surgical patients, SSIs accounted for 38% of nosocomial infections and were the commonest nosocomial infection encountered.6 Two-thirds of SSIs were confined to the surgical incision and one third involved organs or spaces accessed during the surgical procedure.3 Seventy-seven percent of patients who died with an SSI were reported as having the infection causally related to death.6 The average SSI prolongs the hospital stay by 7.3 days,3 and costs range from $31523 to $75007 per infection. In the case of spinal fusion associated SSIs, the average cost may be greater than $100 000 or as much as five times the cost of the procedure itself.7 In addition, patients with an SSI incur 4.6 extra ambulatory care visits compared with patients without an SSI.8 These data highlight the enormous problem facing health services today and indicate the importance of attempting to decrease the incidence of SSIs. In the landmark project, the Study of the Efficacy of Nosocomial Infection Control (SENIC) investigators showed that an adequately designed © 2000 The Hospital Infection Society

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and funded SSI surveillance programme could be expected to decrease the overall rates by 32%.9 However, this reduction only occurred in a small percentage (8%) of the hospitals recruited.9 In addition, many workers have shown that feedback of appropriate data to surgeons has been an indispensable component of strategies to reduce SSI rates.9–14 The mechanisms by which this occurs have never been adequately defined and remain speculative. The Hawthorne effect (improvements based on positive feedback) has been frequently invoked but some authors have disputed this.15 However, not all workers have shown a reduction in SSI rates after continuous surveillance.16 The elements essential for a successful programme of prevention of SSIs include intensive surveillance and infection control activities and regular feedback of SSI rates to surgeons.17 Despite the apparent effectiveness in lowering SSI rates when surgeons receive feedback, however, there has been no consensus on which surveillance methods are best for collecting data on SSIs.18 In addition, there is no evidence to support an argument that highfrequency reporting (e.g., monthly) yields better infection control than lower frequency reporting (e.g., quarterly or semi-annually).19 In devising an SSI surveillance programme, consideration needs to be given to the definitions of infection, data collection, handling and analysis, presentation of results and which patients are to be included.20,21 Despite all the efforts to design and execute the ideal SSI surveillance programme, however, many questions remain to be answered. Surveillance components Objectives and priorities There should be clear and unambiguous objectives and priorities regarding an SSI surveillance programme.20 Obviously the major aim is to reduce the rate of SSI, thereby reducing patient morbidity and mortality.22 This should also result in financial savings for the hospital.23 Depending on infection control personnel available, and the willingness of clinical staff to become involved, surveillance will have to be tailored to the art of the possible rather than risk the integrity of the total surveillance strategy by being over ambitious. Surveillance initiatives require total staff involvement lest they become something that the infection control team undertakes but is of little relevance to the clinical areas

E.T. M. Smyth and A. M. Emmerson

concerned. It is imperative that ownership of the surveillance programme be established.

Definitions One of the most important aspects of SSI surveillance is the definition of infection.20 It is crucial that a surveillance programme uses standardized definitions otherwise inaccurate and misleading results may be obtained and reported. In addition, to compare data over time it is essential that the definitions should remain unchanged so that baseline SSI rates may be established, patients’ risk of developing SSI stratified, results of interventions analysed and the possibility of interhospital comparisons considered.24 Some scoring systems such as ASEPSIS and the Southampton Wound Assessment Scale have been devised specifically to grade surgical wounds, enabling an objective assessment of SSI to be made.25,26 Nevertheless these methods are time consuming and require additional staff in order that the quality of collected data is not compromised.27,28 Many other definitions exist.29–32 However, the most widely used definition of SSI is that employed by the CDC’s NNIS System.33 The previous CDC definitions published in 198834 considered surgical wound infection (SWI) related to the skin incision only whereas the current definition now classifies SSIs into incisional or organ/space and has also introduced the change in terminology from SWI to SSI.33 Incisional SSIs are further subdivided into superficial incisional SSIs (involving only skin and subcutaneous tissue) and deep incisional SSIs (involving deeper soft tissues). Organ/space SSIs involve any part of the anatomy, other than incised body wall layers, that was opened or manipulated during a surgical procedure. It is important to employ objective criteria when defining SSIs as failure to do so may lead to errors when reporting SSI rates.35,36 In a survey of 297 USA hospitals, 78.1% used CDC definitions.37 The CDC NNIS definitions of SSIs are de facto a national standard in the United States and have been adopted by many workers around the world. Standardization of definitions will become increasingly important as SSI rates are interpreted as a measure of quality of patient care. Certainly the agreement of a European standard definition of SSI would be widely welcomed and the adoption of the CDC NNIS definitions should be considered.

Surgical site infection surveillance

Surveillance methods A number of methods for the surveillance of nosocomial infections have been developed and their sensitivity and specificity have been assessed. These include chart review11,38,39, Kardex (nursing care plan) review40,41, laboratory-based ward surveillance42,43, laboratory-based telephone surveillance42, ward liaison surveillance42,44,45, treatment chart surveillance40,42, temperature chart surveillance40,42, treatment and temperature chart surveillance40,42, risk factor surveillance39,40,42,46, antimicrobial use40,47–49 and microbiology reports.40,47,50,51 Chart review per se would appear to be an unreliable means of detecting SSIs.15 However, the daily inspection of postoperative wounds coupled with daily hospital chart review was considered by Olson and Lee11 to be the most sensitive and exact method of performing SSI surveillance, although these methods are very time consuming and difficult to implement. Nevertheless, Lee15 reported that an effective strategy was to regard all surgical and clinic nurses as ‘nurse epidemiologist extenders’ (or ‘surgical link nurses’) who, after appropriate training, would be able to recognize clinically suspicious wounds and report these to the nurse epidemiologist [or infection control nurse (ICN)]. There is evidence to support the integrity of data collected by persons who do not examine the surgical wounds directly.18 A study compared data collected by an infection control practitioner (ICP) who reviewed patients’ charts and interviewed relevant clinical personnel regarding the patient’s progress (i.e., indirect methods) with that performed by a hospital epidemiologist (the ‘gold standard’ observer) who, in addition to reviewing the patients’ charts, examined the surgical wounds.18 The ICPs returned a sensitivity of 83.8% (95% CI: 75.7–91.9%) and a specificity of 99.8% (95% CI: 99–100%) compared with the ‘gold standard’ observer.18 The amount of data obtained from patient chart review is variable and is regulated by the completeness of the medical record and experience of the reviewer.52 This can lead to reduced reliability of medical record review due to such factors as staff turnover, heavy workload and other conflicting priorities.53 Most infection control teams (ICTs) will be limited by resources and will have to adapt to local circumstances when attempting to set up surveillance protocols based on the above methodologies. Review of the Kardex has been employed to highlight risk factors such as fever or antibiotic

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use, which in turn led to a systematic review of the patient’s clinical record.40,41 A saving of 15–19 h of the ICP’s time was recorded by employing this method.40 However, this approach will depend on the quality and accuracy of the Kardex data which may decrease the overall attractiveness of this method. The use of microbiology reports per se would not appear to be useful in the recognition of SSIs and some authors advocate that no diagnosis of wound infection should be made without a direct wound examination.15 Nevertheless, they may serve as an important backup detection method. A positive microbiology culture does not necessarily indicate a SSI (e.g., colonization vs. infection) but may signal suspicion regarding a suspect surgical wound. Sensitivity of microbiology reports as a case finding method for nosocomial infection surveillance range from 33% to 84%47, however, some indirect methods of case-finding have been used successfully. Due to the fact that conventional methods for SSI surveillance are resource intensive, alternative methods have been developed which, while designed to operate with reduced resources, are recommended by their proponents as valid substitutes for traditional surveillance methods. One of these is the use of postoperative antibiotic exposure as a marker for the presence of infection.30,52 Interestingly, postoperative antibiotic exposure is not a component of the NNIS definition for SSI.33 Yokoe and Platt52 advocate the analysis of the timing and duration of postoperative antibiotic exposure as a tool for identifying, amongst other events, classically defined SSIs. However, nosocomial infection surveillance relying on patient antibiotic exposure has low sensitivity.40,47 There are drawbacks to the use of antibiotic data alone to detect SSIs.22,54 Many surgical patients are given antibiotics for infections other than SSIs; some may be on preoperative antibiotics or prolonged prophylaxis. In addition, surgical drainage may be all that is required for the treatment of an SSI hence this would be missed on antibiotic criteria alone. Another potential drawback would be that post-discharge surveillance would be difficult if not impossible. Computer systems have been described which monitor nosocomial infections. Using an extensive database, coupled with a sophisticated knowledge base and logic rules, Evans et al.55 showed that computer surveillance was more sensitive and efficient than conventional surveillance. The drawback of this method is that it requires a degree of information technology (IT) that is beyond the implementation

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of most hospitals. Depending on the surveillance methodology employed the infection control personnel must choose the means by which they will collect the relevant surveillance data. This may involve interaction with clinical staff, patient chart review, Kardex review, wound examination, operation notes, antibiotic data and microbiology data. Inpatient SSI surveillance Surgical site infections have been identified by employing direct observation or by traditional infection control (indirect) methods. Surveillance has involved direct observation of the surgical site by the surgeon, trained nurse, or infection control personnel.10–12,18,38 Indirect detection of SSIs has been achieved by infection control personnel through a review of laboratory reports, patient records and discussions with primary care providers.11,12,14,18,40,49,56 The surgical literature suggests that direct observation of surgical sites is the most accurate method of detecting SSIs, nevertheless sensitivity data are lacking.6 However, Cardo et al.18, focussing on indirect methods, did address this area. Much of the SSI data reported in the infection control literature has been generated by indirect case-finding methods6,40,49,50,57,58 but some studies of direct methods have been conducted.6,18,59 Some studies have used both methods of detection.6,14,18,30,60 Indirect SSI surveillance can be performed utilizing a number of data sources (Table I). The optimum frequency of case-finding, employing direct or indirect methods, is unknown and may vary from daily to O3 times per week until the patient’s discharge from hospital.6 The NNIS system collects patient data that will allow it to analyse patient risk factors and develop strategies for stratifying risk for the development of SSIs (Table II). Some of the data may be available in electronic format and may be downloaded into a surveillance database. However, for many it may be a case of manual data collection and computer entry.62–64 Employing scanning technology, questionnaires can be automatically processed, removing the data entry bottleneck and facilitating surveillance initiatives that would be otherwise impossible.65–67 Day patient SSI surveillance and post-discharge SSI surveillance Between 12% and 84% of SSIs are detected after patients are discharged from hospital.6 Studies have

E.T. M. Smyth and A. M. Emmerson

Table I

Sources of data for indirect detection of SSIs

Sources of data for indirect detection of SSIs Patients’ medical records including surgical procedure notes Microbiology and other pathology reports Radiology reports Pharmacy data (antimicrobials) Inpatient data (re-admissions/post-discharge follow-up) Outpatient data (post-discharge follow-up) Primary care data (post-discharge follow-up in the community)

Table II

NNIS surgical patients core variables6

NNIS core data for SSI surveillance Operation date NNIS operative procedure category61 Surgeon identifier Patient identifier Age and sex Duration of operation Wound class Use of general anaesthesia ASA class Emergency Trauma Multiple procedures Endoscopic approach Discharge date

shown that most SSIs become evident within 21 days postoperatively.6 Therefore, dependence on inpatient surveillance alone underestimates the true rate of SSI. This has been observed for coronary artery by-pass graft procedures.6 Difficulties arise when comparing SSI rates that may, or may not, include post-discharge surveillance. This problem affects not just the classical surgical inpatient but an increasing number of patients who are treated in day procedure units. In order to have valid comparisons of post-discharge SSI rates over time the surveillance methodology, including SSI definitions, must not alter. It has been recommended that NNIS SSI definitions be used without modification in the outpatient setting.6 Financial restraints in the health service continue to encourage more day surgery and promote shorter hospital stays for those patients requiring inpatient treatment. Healthcare clinicians and analysts estimate that 75% of all operations in the United States will be performed in outpatient settings by the year 2000.68 Both direct and indirect surveillance methods may be used. Nevertheless there is a major problem

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of follow-up in the community. Some workers have employed outpatient clinic review69,70, surgeon questionnaires69,71,72, patient telephone questionnaires72 and questionnaires mailed to patients.73 A difficulty with most of these methods is that consistent adherence to agreed definitions of SSI will be difficult if not impossible. In fact Seaman and Lammers74 found that patients had an inability to diagnose their own wound infections. They reported that patients correctly identified their infections in only 11 cases whereas medical examiners diagnosed infection in 21 wounds, and called into question the validity of data obtained using patient-returned questionnaires or telephone surveys.74 Sands et al.8 adopted an automated screening approach for the detection of post-discharge SSIs. Utilizing the extensive automated records of a health maintenance organization they documented 132 SSIs from a total of 5572 non-obstetric procedures. Of these, 84% occurred after hospital discharge and 63% were managed outside the hospital facility. They concluded that most SSIs occur after discharge and are not detectable by conventional surveillance. An Australian study found that less than one-third of SSIs were diagnosed before discharge.75 Clearly cognizance needs to be taken of the contribution to overall SSI rates made by post-discharge SSIs. The potential advantages and disadvantages of surveillance methodologies for the detection of SSIs in the outpatient setting are summarized in Table III. There is a great need for prospective studies comparing different methods of

postdischarge surveillance and national guidelines are urgently required.76 Risk factors for surgical site infection Whether or not an SSI develops after a surgical procedure depends on the interaction between the host, the microbes and the operation-environment related factors.77,78 Some of these are tabulated in Table IV. Surgical technique is important.78 Care exercised in tissue handling, removal of devitalized tissue, haemostasis and wound closure without tension are paramount.78,86 The surgeon may be an important immune modulator for the patient.87 Local and systemic host defences can be enhanced or suppressed by the surgeon.88 Inappropriate timing ([2 hours before operation) of antimicrobial prophylaxis has been shown to be a risk factor for the development of SSIs.89,90 Hypothermia has recently been proposed as delaying healing and predisposing patients to SSIs.91 In addition, alcohol abuse has been postulated as a risk factor for SSI.92 A number of scoring systems have been used to stratify the severity of illness in surgical patients, e.g., APACHE II, POSSUM, the Sepsis score of Elebute and Stoner; but none of these are specific for SSI.93 Nearly all the studies that have employed multivariate analysis to select the most significant risk factors for SSI have found three categories of independent variables to be most predictive of infection risk: a marker for host susceptibility (e.g., ASA class94,95 or number of discharge diagnoses57);

Table III Potential advantages and disadvantages of surveillance methods for detection of SSIs7 Method

Potential advantages

Potential disadvantages

Routine direct wound examination by trained professionals Outpatient chart review by trained professionals Surgeon-reporting: Self-initiated By survey (mail)

High sensitivity and specificity

Labour intensive

Acceptable sensitivity and specificity

Labour intensive, suboptimal documentation

High specificity, resource efficient Acceptable specificity, relatively resource efficient

Poor sensitivity Suboptimal sensitivity

Relatively resource efficient

Unreliable sensitivity and specificity Labour intensive, unreliable sensitivity and specificity Unreliable sensitivity and specificity when used in isolation

Patient reporting: By mail By telephone Microbiology data

Good public relations Relatively resource efficient, may ‘flag’ potential SSIs

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Table IV

Risk factors associated with SSI79

Host-related risk factors

Procedure-related risk factors

Definite Age78,80 Obesity78,81 Disease severity57 ASA Score58 Nasal carriage of Staphylococcus aureus82 Remote infection81,83 Duration of preoperative hospitalization10,81 Likely Malnutrition and low serum albumin78 Diabetes mellitus60, 78

Definite Preoperative hair removal84 Type of procedure57 Antibiotic prophylaxis85 Duration of surgery10,58,80,81

Possible Malignancy Immunosuppressive therapy Breast size in women

Table V

Likely Multiple procedures Tissue trauma Foreign material Blood transfusion Possible Preoperative showers Emergency surgery Drains

NNIS Categories of variables as predictors of SSI risk58, 97

Category

Variable

NNIS risk index criteria for presence of a risk factor*

Intrinsic degree of microbial contamination of the surgical site Duration of an operation

Wound class, i.e., clean, clean-contaminated, contaminated or dirty96 Time, in hours, of the duration of the surgical procedure from skin incision to skin closure58

Contaminated or dirty If present, scores one point

Makers for host susceptibility

American Society of Anesthesiologists (ASA) Physical Status Classification94

Length of operation [T hours where T is the approximate 75th percentile of the duration of the surgical procedure T is surgical procedure-specific If present, scores one point ASA score of 3, 4 or 5 If present, scores one point

* Risk index is obtained by summing the scores of the individual variables. Ranges from 0 to 3.

an estimation of the occurrence of intraoperative wound contamination (e.g., surgical wound class96); and a measure of the duration of the surgery.60,97 Because of the effect of these risk factors all analyses of SSI rates should be stratified by these variables.97 The surgical wound classification96 has been used as a risk index10,11,58 and has been suggested as the most important factor in the stratification of SSI rates.98 However, both the SENIC Project and the NNIS System have improved on this simple risk index. In the NNIS system three categories of variables are used as predictors of SSI risk (Table V). Presence of a risk factor is scored by awarding a single point for each of the three categories listed in Table V. The actual NNIS risk index is arrived at

by summing the scores for each of the three individual categories, hence the index ranges from 0 (no risk factors present) to 3 (all risk factors present). The ASA Physical Status Classification (preoperative assessment score)94,95,99 has been modified100 since its incorporation into the NNIS risk index.58 Doubts have been expressed as to the scientific precision of the classifications99 and this has led to reported discrepancy rates of 50–59% in the application of the classification.101,102 However, the CDC have not altered the NNIS risk index to include the new ASA classification.6 The NNIS risk index is not suitable for all surgical procedures. For example, Caesarean section, craniotomy and ventricular shunt procedures are not

Surgical site infection surveillance

Table VI

SENIC SSI risk index57

Variable

SENIC risk index criteria for presence of a risk factor*

Wound class, i.e., clean, clean-contaminated, contaminated or dirty Type of operation

Contaminated or dirty infection. If present, scores 1 point

Duration of an operation Discharge diagnosis

Abdominal operation If present, scores 1 point Operation lasting longer than 2 hours. If present, scores 1 point Patient having P3 discharge diagnoses If present, scores 1 point

* Risk index is obtained by summing the scores of the individual variables. Ranges from 0 to 4.

adequately stratified by the NNIS SSI risk index.103 Research needs to be directed towards procedurespecific risk factors where it has been shown that current risk indices are unsatisfactory.104 The SENIC study collected potential variables that might have been candidates for incorporation into a SSI risk index and subjected them to logistic regression.57 Four of these variables were found to be independently associated with SSI risk (Table VI). Summing the scores (risk present\1, absent\0) of the variables produced the SSI risk index. The SENIC risk index predicted the SSI risk twice as well as the wound classification method alone.6 In a recent study the NNIS risk index appeared to have a better ability than the SENIC index for discriminating and predicting the risk of acquiring a SSI,105 although others have arrived at different conclusions.106 There is evidence to suggest that many of the classic risk factors for SSI are not determinants for post-discharge SSI; patients with post-discharge SSI would appear to be more similar to those without development of any infection.107 More studies are required to clarify the predictors of post-discharge SSI. Comparison of SSI rates For various reasons there is great pressure to introduce surveillance methods to facilitate comparisons between surgeons, surgical procedures and hospitals. This is becoming popular within health services and has led to arguments for and against hospital league tables for nosocomial infections.108 International comparisons of infection surveillance results have been published and this has led to calls for more harmonization between surveillance networks.109 The danger is that unless a surveillance method

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stratifies the patients for the presence or absence of SSI risk factors then valid comparisons are not possible.25 Indeed, misleading comparisons may result in untold damage to individuals and hospitals alike.24 Stratification of SSI rates should be carried out not only for hospitals but also for surgeon-specific rates. However, this may limit the ability to calculate the latter for hospitals that have low case volumes or a biased case mix because of unique referral patterns.110 Recommendations for SSI surveillance have been produced by a consensus group consisting of members of the Society for Healthcare Epidemiology of America (SHEA), the Association for Practitioners in Infection Control (APIC), the Center for Disease Control (CDC), and the Surgical Infection Society (SIS).97 In addition, other groups have provided guidance on SSI surveillance.21 Recently, the Hospital Infection Control Practices Advisory Committee in the United States produced guidelines on the prevention of SSIs.6 Recommendations for SSI surveillance from this publication are presented in Table VII. Presentation of SSI data – the use of statistical process control (SPC) charts in the monitoring of SSI rates Shewhart, working at Bell Laboratories, is credited with introducing SPC theory to industry.111 Statistical process control theory has been used for more than 70 years in industry to monitor variation in complex processes.19 The underlying principle of SPC theory is that all processes (components of care leading to SSIs) vary inherently and the end results (SSIs) can be described in statistical terms.111 Two kinds of variation are postulated by SPC. Common cause or natural variation results from intrinsic variations in a process that cannot be controlled precisely.111–113 On the other hand, special cause or unnatural variation, due to influences outside the process, results in acute deviation from the norm, e.g., outbreaks of infection etc.111,112 This variation produces a different pattern from the underlying common cause variation on a SPC chart. Reducing process variation will usually improve quality but the two kinds of variation require different corrective strategies with the special cause variation always having first priority.19 Surgical site infections are amenable to analysis by SPC P chart methodology.111 Control charts for attributes are applicable to situations when it is necessary to control a distinct quality attribute such as the SSI

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Table VII Recommendations for SSI surveillance6 Recommendations Definitions

Use CDC definitions without modification for identifying SSI among surgical inpatients and outpatients. For patient case-finding, use direct prospective observation, indirect prospective detection, or a combination of both direct and indirect methods for the duration of the patient’s hospitalization. Assign the surgical wound classification upon completion of an operation. A surgical team member should make the assignment. For each patient undergoing an operation chosen for surveillance, record those variables shown to be associated with increased SSI risk (e.g., surgical wound class, ASA class and duration of operation). Periodically calculate operation-specific SSI rates stratified by variables shown to be associated with increased SSI risk (e.g., NNIS risk index). Report appropriately stratified, operation-specific rates to surgical team members.The optimum frequency and format for such rate computations will be determined by stratified case-load sizes (denominators) and the objectives of local, continuous quality improvement initiatives. No recommendation to make available to the infection control committee coded surgeon-specific data. An unresolved issue.

Case finding

Stratification for SSI risk

Operation-specific SSI rates

Proportion (percentages)

Surgeon-specific SSI rates

Special cause variation

Jan Apr Jul Oct Jan Apr Jul Oct Jan Apr Jul Oct 1995

1996

1997

Figure 1 Statistical process control P chart for SSIs from 7759 surgical procedures 1995–1997. (…), upper and lower control limits; (- - -), upper and lower warning limits; (- . - . -), mean SSI 0.028.

rate.111 The vertical or Y-axis in the SPC P chart is scaled in terms of proportions (percentages). Data are analysed using SSI/100 surgical procedures, with ^2 standard deviations (σ) representing upper and lower warning limits (UWL and LWL), respectively,

and ^3σ representing upper and lower control limits (UCL and LCL), respectively, above and below the mean. An example of an SPC P chart (including a special cause variation) is shown in Figure 1. Statistical process control P charts have proved helpful in monitoring SSI trends in hospitals.111,114 This technique would facilitate the monitoring of the spectrum of procedures within a surgical unit. Application of NNIS criteria for SSI surveillance including the risk index in the construction of SPC P charts would allow for comparison with NNIS peer-group hospitals. This would negate the need for the production of hospital SSI rates league tables. Discarding benchmarking in favour of setting arbitrary SSI rate targets as performance goals has been advocated.19 Although SPC charts are extremely useful in monitoring SSI rates over time, however, they need to be constructed and interpreted with caution.115–117

Conclusion Surveillance for SSIs is a very important part of any nosocomial infection surveillance strategy. Its success depends on many interrelated factors. Paramount

Surgical site infection surveillance

among these is the ability of the infection control team to form a partnership with the surgical staff. Creating a sense of ownership of the surveillance initiative amongst the surgical staff will enhance co-operation and ensure that the best use is made of the information generated. The methods of casefinding will vary depending on the resources available. Therefore each surveillance initiative will have to be carefully assessed and priority may need to be given to surgical procedures associated with high risk of development of SSI. Improving existing SSI risk factor indices and developing new measures of risk will need to be given high priority. Given the current increasing trend towards day surgery and shorter surgical inpatient stays, surveillance methodologies will have to adapt to include some form of post-discharge surveillance. Comparative SSI data can only be obtained when there is consensus on surveillance methodology and great care and sensitivity needs to be exercised when considering the thorny issue of ‘league tables’ of infection.108 Statistical process control theory has a role to play in the surveillance of SSIs and is worthy of serious consideration. Communication of timely, accurate, risk-stratified data on SSI rates is essential if surveillance is to become an indispensable tool to surgeons.118 However, this requires the active participation of all the players involved, including the medical director and heads of the surgical departments.118 Additional support from hospital administration and scientific bodies is essential.118 Finally, although we can never eliminate SSIs, by a process of sharing information capable of influencing behaviour, we can, at last, begin to reach our realistic goal of the ‘irreducible minimum’.119

References 1. Department of Health and Social Security. Hospital infection control. Guidance on the control of infec-tion in hospitals. Prepared by the joint DHSS/PHLS Hospital Infection Working Group. HC (88) 33. London: HMSO 1988. 2. Coello R, Glenister H, Fereres J, Bartlett C, Leigh D, Sedgwick J, Cooke EM. The cost of infection in surgical patients: a case-control study. J Hosp Infect 1993; 25: 239–250. 3. Martone WJ, Jarvis WR, Culver DH, Haley RW. Incidence and nature of endemic and epidemic nosocomial infections. In: Bennett JV, Brachman PS, Eds. Hospital Infections. Boston: Little, Brown, and Company 1992; 577–596.

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4. U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics. Vital and Health Statistics, Detailed Diagnoses and Procedures, National Hospital Discharge Survey 1994, Vol. 127. Hyattsville, Maryland: DHHS Publication, 1997. 5. Emori TG, Gaynes RP. An overview of nosocomial infections, including the role of the microbiology laboratory. Clin Microbiol Rev 1993; 6: 428–442. 6. Mangram AJ, Horan TC, Pearson ML, Silver LC, Jarvis WR and the Hospital Infection Control Practices Advisory Committee. Guideline for prevention of surgical site infection, 1999. Infect Control Hosp Epidemiol 1999; 20: 247–278. 7. Manian FA. Surveillance of surgical site infections in alternative settings: Exploring the current options. Am J Infect Control 1997; 25: 102–105. 8. Sands K, Vineyard G, Platt R. Surgical site infections occurring after hospital discharge. J Infect Dis 1996; 173: 963–970. 9. 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. 10. Cruse PJ, Foord R. The epidemiology of wound infection; a 10-year prospective study of 62,939 wounds. Surg Clin North Am 1980; 60: 27–40. 11. Olsen MM, Lee JT Jr. Continuous 10-year wound infection surveillance: results, advantages, and unanswered questions. Arch Surg 1990; 125: 794–803. 12. Condon RE, Schulte WJ, Malangoni MA, AndersonTeschendorf MJ. Effectiveness of a surgical wound surveillance program. Arch Surg 1983; 118: 303–307. 13. Olson M, O’Connor M, Schwartz ML. Surgical wound infections. A 5-year prospective study of 20 193 wounds at the Minneapolis VA Medical Center. Ann Surg 1984; 199: 253–259. 14. Gil-Egea MJ, Pi-Sunyer MT, Verdaguer A, Sanz F, Sitges-Serra A, Eleizegui LT. Surgical wound infections: prospective study of 4468 clean wounds. Infect Control 1987; 8: 277–280. 15. Lee JT. Wound infection surveillance. Infect Dis Clin North Am 1992; 6: 643–656. 16. Poulsen KB, Jepsen OB. Failure to detect a general reduction of surgical wound infections in Danish hospitals. Dan Med Bull 1995; 42: 485–488. 17. Haley RW. The scientific basis for using surveillance and risk factor data to reduce nosocomial infections rates. J Hosp Infect 1995; 30: 3–14. 18. Cardo DM. Falk PS, Mayhall CG. Validation of surgical wound surveillance. Infect Control Hosp Epidemiol 1993; 14: 211–215. 19. Lee JT. Contemporary wound infection surveillance issues. New Horizons 1998; 6(Suppl): S20–S29. 20. The Infection Control Standards Working Party. Standards in infection control in hospitals. Joint publication by the Association of Medical Microbiologists. Hospital Infection Society, Infection Control Nurses Association and the Public Health Laboratory Service. HMSO 1993. 21. Department of Health and Social Security. Hospital infection control. Guidance on the control of infection

182

22.

23.

24.

25.

26.

27. 28.

29.

30.

31.

32.

33.

34.

35.

36.

37.

E.T. M. Smyth and A. M. Emmerson

in hospitals. Prepared by the joint DHSS/PHLS Hospital Infection Working Group. HSG (95) 10. London: HMSO 1995. Roy M-C, Perl TM. Basics of surgical-site infection surveillance. Infect Control Hosp Epidemiol 1997; 18: 659–668. Penin GB, Ehrenkranz NJ. Priorities for surveillance and cost-effective control of postoperative infection. Arch Surg 1988; 123: 1305–1308. National Nosocomial Infections Surveillance System. Nosocomial infections rates for interhospital comparison: limitations and possible solutions. A report from the National Nosocomial Infections Surveillance (NNIS) System. Infect Control Hosp Epidemiol 1991; 12: 609–621. Wilson APR, Weavill C, Burridge J, Kelsey MC. The use of the wound scoring method ‘ASEPSIS’ in postoperative wound surveillance. J Hosp Infect 1990; 16: 297–309. Bailey IS, Karran SE, Toyn K, Brough P, Ranaboldo C, Karran SJ. Community surveillance of complications after hernia surgery. BMJ 1992; 304: 469–471. Wilson APR. Surveillance of wound infections. J Hosp Infect 1995; 29: 81–86. Wilson APR, Helder N, Theminimulle SK, Scott GM. Comparison of wound scoring methods for use in audit. J Hosp Infect 1998; 39: 119–126. Steering Group of the Second National Prevalence Survey. National prevalence survey of hospital acquired infections: definitions. J Hosp Infect 1993; 24: 69–76. Simchen E, Wax Y, Pevsner B et al. The Israeli Study of Surgical Infections (ISSI): I. Methods for developing a standardized surveillance system for a multicenter study of surgical infections. Infect Control Hosp Epidemiol 1988; 9: 232–240. Peel ALG, Taylor EW. Proposed definitions for the audit of postoperative infection: a discussion paper. Ann Roy Coll Surg Engl 1991; 73: 385–388. Crowe MJ, Crooke EM. Review of case definitions for nosocomial infections – towards a consensus. J Hosp Infect 1998; 39: 3–11. Horan TC, Gaynes RP, Martone WJ, Jarvis WR, Emori TG. CDC definitions of nosocomial surgical site infections, 1992: a modification of CDC definitions of surgical wound infections. Infect Control Hosp Epidemiol 1992; 13: 606–608. Garner JS, Jarvis WR, Emori TG, Horan TC, Hughes JM. CDC definitions for nosocomial infections. Am J Infect Control 1988; 16: 128–140. Taylor G, McKenzie M, Kirkland T, Wiens R. Effect of surgeon’s diagnosis on surgical wound infection rates. Am J Infect Control 1990; 18: 295–299. Ehrenkranz NJ, Richter EI, Phillips PM, Shultz JM. An apparent excess of operative site infections: analyses to evaluate false-positive diagnoses. Infect Control Hosp Epidemiol 1995; 16: 712–716. Larson E, Horan T, Cooper B, Kotilainen H, Landry S, Terry B. Study of the definitions of nosocomial infections (SDNI). Research Committee of the Association for Practitioners in Infection Control. Am J Infect Control 1991; 19: 259–267.

38. Haley RW, Schaberg DR, McClish DK et al. The accuracy of retrospective chart review in measuring nosocomial infection rates. Am J Epidemiol 1980; 111: 516–533. 39. Lima NL, Pereira CRB, Souza IC et al. Selective surveillance for nosocomial infections in a Brazilian Hospital. Infect Control Hosp Epidemiol 1993; 14: 197–202. 40. Wenzel RP, Osterman CA, Hunting KJ, Gwaltney JM Jr. Hospital acquired infections, I: surveillance in a university hospital. Am J Epidemiol 1976; 103: 251–260. 41. Yung WH, Seto WH, Pritchett CJ. The use of routine wound swabs and Kardex review for the surveillance of surgical wound infections. J Infect 1991; 23: 161–167. 42. Glenister HM, Taylor LJ, Bartlett CLR, Cooke EM, Sedgwick JA, Mackintosh CA. An evaluation of surveillance methods for detecting infections in hospital inpatients. J Hosp Infect 1993; 23: 229–242. 43. Gross PA, Beaugard A, Van Antwerpen C. Surveillance for nosocomial infections: can the sources of data be reduced? Infect Control 1980; 1: 233–236. 44. Edwards LD, Levin S, Lepper MH. A comprehensive surveillance system of infections and antimicrobials used at Presbyterian – St. Luke’s Hospital, Chicago. Am J Public Health 1972; 62: 1053–1055. 45. Scheckler WE, Peterson PJ. Nosocomial infections in 15 rural Wisconsin hospitals–results and conclusions from 6 months of comprehensive surveillance. Infect Control 1986; 7: 397–402. 46. Sharbaugh RJ. An evaluation of the efficiency of a hospital infection control program. Am J Infect Control 1981; 9: 35–42. 47. Perl TM. Surveillance, reporting and the use of computers. In: Wenzel RP, Ed. Prevention and Control of Nosocomial Infections, 3rd edn. Baltimore, MD: Williams & Wilkins; 1993: 139–176. 48. Broderick A, Mori M, Nettleman MD, Streed SA, Wenzel RP. Nosocomial infections: validation of surveillance and computer modeling to identify at risk. Am J Epidemiol 1990; 131: 734–742. 49. Hirschhorn L, Currier J, Platt R. Electronic surveillance of antibiotic exposure and coded discharge diagnoses as indicators of postoperative infection and other quality assurance measures. Infect Control Hosp Epidemiol 1993; 14: 21–28. 50. Laxson LB, Blaser WJ, Parkhurst SM. Surveillance for the detection of nosocomial infections and the potential for nosocomial outbreaks. I. Microbiology culture surveillance is an effective method of detecting nosocomial infections. Am J Infect Control 1984; 12: 318–324. 51. Hambreaeus A, Malmborg A-S. Surveillance of Hospital infections: at the bedside or at the bacteriological laboratory? Scand J Infect Dis 1997; 9: 289–292. 52. Yokoe DS, Platt R. Surveillance for surgical site infections: the uses or antibiotic exposure. Infect Control Hosp Epidemiol 1994; 15: 717–723. 53. Birnbaum D, King L. Disadvantages of infection surveillance by medical record chart review. Am J Infect Control 1981; 9: 15–17.

Surgical site infection surveillance

54. Lee JT. Wound infection surveillance. Infect Control Hosp Epidemiol 1995; 16: 326–327. 55. Evans R, Larsen R, Burke J et al. Computer surveillance of hospital-acquired infections and antibiotic use. JAMA 1986; 256: 1007–1011. 56. Cruse P. Wound infection surveillance. Rev Infect Dis 1981; 4: 734–737. 57. 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. 58. Culver DH, Horan TC, Gaynes RP et al. Surgical wound infection rates by wound class, operative procedure, and patient risk index. National Nosocomial Infections Surveillance System. Am J Med 1991; 91(Suppl 3B): 152S–157S. 59. Barber GR, Miransky J, Brown AE et al. Direct observations of surgical wound infections at a comprehensive cancer center. Arch Surg 1995; 130: 1042–1047. 60. Ehrenkranz NJ. Surgical wound infection occurrence in clean operations: risk stratification for inter hospital comparisons. Am J Med 1981; 70: 909–914. 61. Horan TC, Emori TG. Definitions of key terms used in the NNIS system. Am J Infect Control 1997; 25: 112–116. 62. Kjaeldgaard P, Cordtz T, Sejberg D. et al. The DANOP-DATA system: A low-cost personal computer based program for monitoring of wound infections in surgical ward. J Hosp Infect 1989; 13: 273– 279. 63. Mertens R, Jans B, Kurz X. A computerized nationwide network for nosocomial infection surveillance in Belgium. Infect Control Hosp Epidemiol 1994; 15: 171–179. 64. Smyth ETM. Data handling systems for hospital epidemiology. Curr Opin Infect Dis 1997; 10: 292– 295. 65. Smyth ETM, McIlvenny G. Hospital acquired infection surveillance: removing the manual data entry bottleneck. Br J Healthcare Computing & Info Manage 1997; 14: 31–34. 66. Smyth ETM, McIlvenny G, Barr JG, Dickson LM, Thompson IT. Automated entry of hospital infection surveillance data. Infect Control Hosp Epidemiol 1997; 18: 486–491. 67. Smyth ETM, Emmerson AM. Survey of infection in hospitals: use of an automated data entry system. J Hosp Infect 1996; 34: 87–97. 68. Hecht AD. Creating greater efficiency in ambulatory surgery. J Clin Anesth 1995; 7: 581–584. 69. Goulbourne IA, Ruckley CV. Operations for hernia and varicose veins in a day-bed unit. Br Med J 1979; 2: 712–714. 70. Weigelt JA, Dryer D, Haley RW. The necessity and efficiency of wound surveillance after discharge. Arch Surg 1992; 127: 77–82. 71. Manian FA, Meyer L. Adjunctive use of monthly physician questionnaires for surveillance of surgical site infections after hospital discharge and in ambulatory surgical patients: report of a seven year experience. Am J Infect Control 1997; 25: 390–394. 72. Manian FA, Meyer L. Comparison of patient telephone survey with traditional surveillance and

183

73.

74.

75.

76.

77. 78.

79.

80.

81.

82.

83.

84.

85.

86.

87.

88.

monthly physician questionnaires in monitoring surgical wound infections. Infect Control Hosp Epidemiol 1993; 14: 216–218. Fanning C, Johnston BL, MacDonald S, LeFortJost S, Dockerty E. Postdischarge surgical site infection surveillance. Can J Infect Control 1995; 10: 75–79. Seaman M, Lammers R, Inability of patients to selfdiagnose wound infections. J Emerg Med 1991; 9: 215–219. Mitchell DH, Swift G, Gilbert GL. Surgical would infection surveillance: the importance of infections that develop after hospital discharge. Aust NZ Surg 1999; 69: 117–120. Holtz TH, Wenzel RP. Postdischarge surveillance for nosocomial wound infection: A brief review and commentary. Am J Infect Control 1992; 20: 206–213. Lauwers S, de Smet F. Surgical site infections. Acta Clinica Belgica 1998; 53: 303–310. Emmerson M. A microbiologists view of factors contributing to infection. New Horizons 1998; 6(Suppl): S3–S10. Kluytmans JA. Surgical infections including burns. In: Wenzel RP, Ed. Prevention and Control of Nosocomial Infections, 3rd edn. Baltimore: Williams & Wilkins, 1997; 841–865. Bremmelgaard A, Raahave D, Beier-Holgersen R, Pedersen JV, Andersen S, Sørensen AI. Computeraided surveillance of surgical infections and identification of risk factors. J Hosp Infect 1989; 13: 1–18. National Academy of Sciences, National Research Council. Postoperative wound infections: the influence of ultraviolet irradiation of the operating room and of various other risk factors. Ann Surg 1964; 160(Suppl 2): 1–132. Kluytmans JAJW, Mouton JW, VandenBergh MFQ et al. Reduction of surgical-site infections in cardiothoracic surgery by elimination of nasal carriage of Staphylococcus aureus. Infect Control Hosp Epidemiol 1996; 17: 780–785. Simchen E, Rozin R, Wax Y. The Israeli study of surgical infections of drains and the risk of wound infection in operations for hernia. Surg Gynaecol Obstet 1990; 170: 331–337. Mishriki SF, Law DJW, Jeffery PJ. Factors affecting the incidence of postoperative wound infection. J Hosp Infect 1990; 16: 223–230. Dellinger EP, Gross PA, Barrett TL et al. Consensus paper: quality standard for antimicrobial prophylaxis in Surgical Procedures. Infect Control Hosp Epidemiol 1994; 15: 182–188. Altemeier WA, Burke JF, Pruitt BA, Sandsky WR, Eds. Manual on Control of Infection in Surgical Patients, 2nd edn. Philadelphia: Lippincott, 1984: 28. Holzheimer RG, Haupt W, Thiede A, Schwarzkopf A. The challenge of postoperative infections: does the surgeon make a difference? Infect Control Hosp Epidemiol 1997; 18: 449–456. Windsor ACJ, Klava AA, Somers SS, Guillou PJ, Reynolds JV. Manipulation of local and systemic host defence in the prevention of perioperative sepsis. Br J Surg 1995; 82: 1460–1467.

184

89. Classen DC, Evans RS, Pestotnik SL, Horn SD, Menlore RL, Burke JP. The timing of prophylactic administration of antibiotics and the risk of surgical wound infection. N Engl J Med 1992; 326: 281–286. 90. Lizán-García M, García-Caballero J, Asensio-Vegas A. Risk factors for surgical-wound infection in general surgery: a prospective study. Infect Control Hosp Epidemiol 1997; 18: 310–315. 91. Kurz A, Sessler DI, Lenhardt R and the Study of Wound Infection and Temperature Group. Perioperative normothermia to reduce the incidence of surgical wound infection and shorten hospitalization. New Engl J Med 1996; 334: 1209– 1215. 92. Rantala A, Lehtonen O-P, Niinikoski J. Alcohol abuse: a risk factor for surgical wound infections? Am J Infect Control 1997; 25: 381–386. 93. Wilson APR, Ridgway GL. Scoring systems for surgical infection. Surgical Infect 1992; 4: 6–8. 94. Dripps RD, Lamont A, Eckenhoff JE. The role of anesthesia in surgical mortality. JAMA 1961; 178: 261–266. 95. Anonymous. New classification of physical status. Anesthesiology 1963; 24: 111. 96. Simmons BP. CDC guidelines on infection control. Infect Control. 1982; 3: 187–196. 97. SHEA, APIC, CDC, SIS. Consensus paper on the surveillance of surgical wound infections. Infect Control Hosp Epidemiol 1992; 13: 599–605. 98. Wischnewski N, Kampf G, Gastmeier P et al. Nosocomial wound infections: a prevalence study and analysis of risk factors. Int Surg 1998; 83: 93–97. 99. Owens WD, Felts JA, Spitznagel EL. ASA physical status classifications: a study of consistency of ratings. Anesthesiology 1978; 49: 239–243. 100. American Society of Anesthesiologists. Relative Value Guide 1999. 101. Haynes SR, Fawler PGP. An assessment of the consistency of ASA physical status classification allocation. Anesthesia 1995; 50: 195–199. 102. 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. 103. Horan T, Gaynes R, Culver D and the National Nosocomial Infections Surveillance (NNIS) System, CDC. Development of predictive risk factors for nosocomial surgical site infections (SSI) [abstract]. Infect Control Hosp Epidemiol 1994; 15(Suppl): P46(M72). 104. Roy MC, Herwaldt LA, Embrey R, Kuhns K, Wenzel RP, Perl TM. Does the NNIS risk index (NRI) predict which patients develop wound infection (SWI) after cardiothoracic (CT) surgery? [abstract]. 34th Interscience Conference on Antimicrobial Agents and Chemotherapy 1994; Orlando, FL: 196.

E.T. M. Smyth and A. M. Emmerson

105. Delgado-Rodriguez M, Sillero-Arenas M, MedinaCuadros M, Martínez-Gallego G. Nosocomial infections in surgical patients: comparison of two measures of intrinsic patient risk. Infect Control Hosp Epidemiol 1997; 18: 19–23. 106. Haley R. Measuring the intrinsic risk of wound infection in surgical patients. Problems in General Surgery 1993; 10: 396–417. 107. Medina-Cuadros M, Sillero-Arenas M, MartinezGallego G, Delgado-Rodríguez M. Surgical wound infections diagnosed after discharge from hospital: epidemiologic differences with in-hospital infections. Am J Infect Control 1996; 24: 421–428. 108. Humphreys H, Emmerson AM. Control of hospitalacquired infection: accurate data and more resources, not league tablets. J Hosp Infect 1993; 25: 75–78. 109. Mertens R, Van den Berg JM, Veerman-Brenzikofer MLV, Kurz X, Jans B, Klazinga N. International comparison of results of infection surveillance: the Netherlands versus Belgium. Infect Control Hosp Epidemiol 1994; 15: 574–580. 110. Scheckler WE. Surgeon-specific wound infection rates – a potentially dangerous and misleading strategy. Infect Control Hosp Epidemiol 1988; 9: 145–146. 111. Sellick, JA. The use of statistical process control charts in hospital epidemiology. Infect Control Hosp Epidemiol 1993; 14: 649–656. 112. Benneyan JC. Statistical quality control methods in infection control and hospital epidemiology, part I: introduction and basic theory. Infect Control Hosp Epidemiol 1998; 19: 194–214. 113. Benneyan JC. Statistical quality control methods in infection control and hospital epidemiology, part II: chart use, statistical properties, and research issues. Infect Control Hosp Epidemiol 1998; 19: 265–283. 114. Smyth ETM, McIlvenny G, Hood JM. Thirty six months of continuous surgical site infection (SSI) surveillance: use of statistical process control (SPC) P Charts in Analysis [abstract]. The Eighth Annual Meeting of the Society for Healthcare Epidemiology of America. Meeting Program 1998; P57(M24). 115. Humble C. Caveats regarding the use of control charts. Infect Control Hosp Epidemiol 1998; 19: 865–868. 116. Benneyan JC. Caveats regarding the use of control charts. Infect Control Hosp Epidemiol 1999; 20: 526. 117. Humble C. Caveats regarding the use of control charts [Reply]. Infect Control Hosp Epidemiol 1999; 20: 527. 118. Mertens R, Ronveaux O. The role of communication in surgical wound infection surveillance. Acta Chir Belg 1996; 96: 1–2. 119. Ayliffe GA. J. Nosocomial infection – the irreducible minimum. Infect Control 1986; 7(Suppl): 92–95.