Automated Methods for Surveillance of Surgical Site Infections
Automated Methods for Surveillance of Surgical Site Infections
Automated data, especially from pharmacy and administrative claims, are available for much of the U.S. population and might substantially improve both inpatient and postdischarge surveillance for surgical site infections complicating selected procedures, while reducing the resources required. Potential improvements include better sensitivity, less susceptibility to interobserver variation, more uniform availability of data, more precise estimates of infection rates, and better adjustment for patients' coexisting illness.
The Centers for Disease Control and Prevention (CDC) recommends routine surveillance for surgical site infections; accrediting agencies such as the Joint Commission for Accreditation of Healthcare Organizations require it. Surveillance identifies clusters of infection, establishes baseline risks for infection, provides comparisons between institutions or surgical specialties, identifies risk factors, and permits evaluation of control measures. Achieving these goals requires health-care systems to have access to different information types (Table 1).
An ideal surveillance system should have several attributes, including meaningful definitions of infection, consistent interpretation of classification criteria, applicability to procedures performed in both inpatient and ambulatory facilities, ability to detect events after discharge, sufficient precision to distinguish small absolute differences in attack rates, ability to adjust for different distribution of severity of illness across populations, and reasonable cost. Most current systems lack at least one of these attributes; for example, the system recommended by CDC's Hospital Infection Control Practices Advisory Committee (HICPAC) is excellent for clinical decision-making, but some elements are difficult to apply for surveillance purposes. Information required to apply some of its criteria may not be available for all cases; for example, the criterion of recovery of microbial growth from a normally sterile site may be affected by variation in obtaining specimens for culture. Some elements of CDC's National Nosocomial Infections (NNIS) System definition require substantial judgment or interpretation. An example is determining whether purulent drainage is present: An attending physician's diagnosis is sufficient, although the way physicians record or confirm their diagnoses may differ. For these reasons, case ascertainment is affected by considerable interobserver variability.
Although most surgical site infections become manifest after the patient is discharged from the hospital, there is no accepted method for detecting them. The most widely described method of conducting postdischarge surveillance is questionnaire reporting by surgeons. This method has been shown to have poor sensitivity (15%) and positive predictive value (28%), even when surgeons are compliant in returning the questionnaires. Moreover, a questionnaire-based surveillance system requires substantial resources. Reporting by patients via questionnaires also has poor sensitivity (28%) because many patients do not return questionnaires mailed to them a month after surgery. Telephone questionnaires have been used effectively but are too resource intensive for routine use.
Many procedures must be monitored to allow confident conclusions that relatively small differences in observed attack rates do not reflect chance variations. Identifying these small differences, understanding their cause, and undertaking quality improvement programs to reduce their occurrence would have large consequences when applied to the >45 million surgical procedures performed annually in the United States. Reducing the overall infection rate by a quarter of a percent would prevent >100,000 infections per year. For coronary artery bypass surgery alone, a one percentage point decrease in the risk for infection would prevent >3,500 infections per year in the United States. Because of the need to observe large numbers of procedures, conducting surveillance for the entire surgical population is desirable. However, to conserve scarce resources, some programs survey only a fraction of their procedures or rotate surveillance among different procedure types.
Determining whether relatively small differences in infection rates result from differences in care rather than in patients' susceptibility to infection requires robust risk-adjustment methods that can take into account different case-mixes in different institutions. Available methods do not have optimal resolution and depend in part on the Anesthesia Society of America (ASA) score. The ASA score, a subjective assessment of the patient's overall health status, may reflect interobserver variability that can adversely affect stratification of risk for surgical infection.
Automated methods to augment current surveillance methods should improve the quality of surveillance for surgical site infections and reduce the resources required. To achieve these goals, surveillance should be based on the growing body of data that health-care systems, including hospitals, physicians' offices, health maintenance organizations (HMOs), and insurance companies, routinely collect during care delivery. Many types of automated data are now or will soon become widely available, including information about patients, surgical procedures, and patients' postoperative courses (Table 2). Three ways to use these data to support surveillance programs are inpatient surveillance, postdischarge surveillance, and case-mix adjustment.
Automated data, especially from pharmacy and administrative claims, are available for much of the U.S. population and might substantially improve both inpatient and postdischarge surveillance for surgical site infections complicating selected procedures, while reducing the resources required. Potential improvements include better sensitivity, less susceptibility to interobserver variation, more uniform availability of data, more precise estimates of infection rates, and better adjustment for patients' coexisting illness.
The Centers for Disease Control and Prevention (CDC) recommends routine surveillance for surgical site infections; accrediting agencies such as the Joint Commission for Accreditation of Healthcare Organizations require it. Surveillance identifies clusters of infection, establishes baseline risks for infection, provides comparisons between institutions or surgical specialties, identifies risk factors, and permits evaluation of control measures. Achieving these goals requires health-care systems to have access to different information types (Table 1).
An ideal surveillance system should have several attributes, including meaningful definitions of infection, consistent interpretation of classification criteria, applicability to procedures performed in both inpatient and ambulatory facilities, ability to detect events after discharge, sufficient precision to distinguish small absolute differences in attack rates, ability to adjust for different distribution of severity of illness across populations, and reasonable cost. Most current systems lack at least one of these attributes; for example, the system recommended by CDC's Hospital Infection Control Practices Advisory Committee (HICPAC) is excellent for clinical decision-making, but some elements are difficult to apply for surveillance purposes. Information required to apply some of its criteria may not be available for all cases; for example, the criterion of recovery of microbial growth from a normally sterile site may be affected by variation in obtaining specimens for culture. Some elements of CDC's National Nosocomial Infections (NNIS) System definition require substantial judgment or interpretation. An example is determining whether purulent drainage is present: An attending physician's diagnosis is sufficient, although the way physicians record or confirm their diagnoses may differ. For these reasons, case ascertainment is affected by considerable interobserver variability.
Although most surgical site infections become manifest after the patient is discharged from the hospital, there is no accepted method for detecting them. The most widely described method of conducting postdischarge surveillance is questionnaire reporting by surgeons. This method has been shown to have poor sensitivity (15%) and positive predictive value (28%), even when surgeons are compliant in returning the questionnaires. Moreover, a questionnaire-based surveillance system requires substantial resources. Reporting by patients via questionnaires also has poor sensitivity (28%) because many patients do not return questionnaires mailed to them a month after surgery. Telephone questionnaires have been used effectively but are too resource intensive for routine use.
Many procedures must be monitored to allow confident conclusions that relatively small differences in observed attack rates do not reflect chance variations. Identifying these small differences, understanding their cause, and undertaking quality improvement programs to reduce their occurrence would have large consequences when applied to the >45 million surgical procedures performed annually in the United States. Reducing the overall infection rate by a quarter of a percent would prevent >100,000 infections per year. For coronary artery bypass surgery alone, a one percentage point decrease in the risk for infection would prevent >3,500 infections per year in the United States. Because of the need to observe large numbers of procedures, conducting surveillance for the entire surgical population is desirable. However, to conserve scarce resources, some programs survey only a fraction of their procedures or rotate surveillance among different procedure types.
Determining whether relatively small differences in infection rates result from differences in care rather than in patients' susceptibility to infection requires robust risk-adjustment methods that can take into account different case-mixes in different institutions. Available methods do not have optimal resolution and depend in part on the Anesthesia Society of America (ASA) score. The ASA score, a subjective assessment of the patient's overall health status, may reflect interobserver variability that can adversely affect stratification of risk for surgical infection.
Automated methods to augment current surveillance methods should improve the quality of surveillance for surgical site infections and reduce the resources required. To achieve these goals, surveillance should be based on the growing body of data that health-care systems, including hospitals, physicians' offices, health maintenance organizations (HMOs), and insurance companies, routinely collect during care delivery. Many types of automated data are now or will soon become widely available, including information about patients, surgical procedures, and patients' postoperative courses (Table 2). Three ways to use these data to support surveillance programs are inpatient surveillance, postdischarge surveillance, and case-mix adjustment.
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