Incidence and risk factors for and the effect of a program to reduce the incidence of surgical site infection after cardiac surgery.

BACKGROUND: Surgical site infection (SSI) after cardiac surgery (CS) is a serious complication that increases hospital length of stay (LOS), has a substantial financial impact, and increases mortality. The study described here was done to evaluate the effect of a program to reduce SSI after CS. METHODS: In January 2007, a multi-disciplinary CS infection-prevention team developed guidelines and implemented bundled tactics for reducing SSI. Data for all patients who underwent CS from 2006-2008 were used to determine whether there was: 1) A difference in the incidence of SSI in white patients and those belonging to minority groups; 2) a reduction in SSI after intervention; and 3) a statistically significant difference in the incidence of SSI in the third quarter of each year as compared with the other quarters of the year. RESULTS: Of 3,418 patients who underwent CS; 1,125 (32.9%) were members of minority groups and 2,293 (67.1%) were white. Eighty (2.3%) patients developed SSI. There was no significant difference in the incidence of SSI in non-Hispanic white patients and all others (2.1% vs. 2.8%, p=0. 42). The incidence of SSI decreased significantly from 2006 (3.0%) to 2007 (2.5%) and 2008 (1.4%), (p=0.03). Surgical site infection occurred more often in the third quarter of each of the years of the study than in other quarters of each year (3.3 vs. 2.0%, p=0.038). CONCLUSIONS: Implementation of a program to reduce SSI after CS was associated with a lower incidence of SSI across all racial and ethnic groups and over time, but was not associated with a lower incidence of SSI in the third quarter of each year than in the other quarters.

Gender differences in risk of bloodstream and surgical site infections.

BACKGROUND: Identifying patients most at risk for hospital- and community-associated infections is one essential strategy for preventing infections. OBJECTIVE: To investigate whether rates of community- and healthcare-associated bloodstream and surgical site infections varied by patient gender in a large cohort after controlling for a wide variety of possible confounders. DESIGN: Retrospective cohort study. PARTICIPANTS: All patients discharged from January 1, 2006 through December 31, 2008 (133,756 adult discharges and 66,592 pediatric discharges) from a 650-bed tertiary care hospital, a 220-bed community hospital, and a 280-bed pediatric acute care hospital within a large, academic medical center in New York, NY. MAIN MEASURES: Data were collected retrospectively from various electronic sources shared by the hospitals and linked using patients' unique medical record numbers. Infections were identified using previously validated computerized algorithms. KEY RESULTS: Odds of community-associated bloodstream infections, healthcare-associated bloodstream infections, and surgical site infections were significantly lower for women than for men after controlling for present-on-admission patient characteristics and events during the hospital stay [odds ratios (95 % confidence intervals) were 0.85 (0.77-0.93), 0.82 (0.74-0.91), and 0.78 (0.68-0.91), respectively]. Gender differences were greatest for older adolescents (12-17 years) and adults 18-49 years and least for young children (<12 years) and older adults (≥ 70 years). CONCLUSIONS: In this cohort, men were at higher risk for bloodstream and surgical site infections, possibly due to differences in propensity for skin colonization or other anatomical differences.

Frequency of patient contact with health care personnel and visitors: implications for infection prevention.

BACKGROUND: Contact with health care workers may be an important means of infection transmission between patients, yet little is known about patterns of patient contact with staff and visitors in hospitals. In a cross-sectional study, the frequency, type, and duration of contacts made by health care workers, other hospital staff, and visitors to patients in acute care settings were documented. METHODS: Patients were observed in seven units of three academic hospitals, with recording of each occurrence of someone's entry into the patient's room. The health care worker's role, the duration of the visit, and the highest level of patient contact made were noted. Staff were also surveyed to determine their perception of how many patients per hour they come into contact with, how long they spend with patients, and the level of patient contact that occurs. FINDINGS: Hourly room entries ranged from 0 to 28 per patient (median, 5.5), and patients received visits from 0 to 18 different persons per hour (median, 3.5). Nurses made the most visits (45%), followed by personal visitors (23%), medical staff (17%), nonclinical staff (7%), and other clinical staff (4%). Visits lasted 1 to 124 minutes (median, 3 minutes for all groups). Persons entering patients' rooms touched nothing inside the room, only the environment, the patient's intact skin, or the patient's blood/body fluids 22%, 33%, 27%, and 18% of the time, respectively. Medical staff estimated visiting an average of 2.8 different patients per hour (range, 0.5-7.0), and nursing staff estimated visiting an average of 4.5 different patients per hour (range, 0.5-18.0). CONCLUSIONS: Examining patterns of patient contact may improve understanding of transmission dynamics in hospitals. New transmission models should consider the roles of health care workers beyond patients' assigned nurses and physicians.

Comparison of two computer algorithms to identify surgical site infections.

BACKGROUND: Surgical site infections (SSIs), the second most common healthcare-associated infections, increase hospital stay and healthcare costs significantly. Traditional surveillance of SSIs is labor-intensive. Mandatory reporting and new non-payment policies for some SSIs increase the need for efficient and standardized surveillance methods. Computer algorithms using administrative, clinical, and laboratory data collected routinely have shown promise for complementing traditional surveillance. METHODS: Two computer algorithms were created to identify SSIs in inpatient admissions to an urban, academic tertiary-care hospital in 2007 using the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes (Rule A) and laboratory culture data (Rule B). We calculated the number of SSIs identified by each rule and both rules combined and the percent agreement between the rules. In a subset analysis, the results of the rules were compared with those of traditional surveillance in patients who had undergone coronary artery bypass graft surgery (CABG). RESULTS: Of the 28,956 index hospital admissions, 5,918 patients (20.4%) had at least one major surgical procedure. Among those and readmissions within 30 days, the ICD-9-CM-only rule identified 235 SSIs, the culture-only rule identified 287 SSIs; combined, the rules identified 426 SSIs, of which 96 were identified by both rules. Positive and negative agreement between the rules was 36.8% and 97.1%, respectively, with a kappa of 0.34 (95% confidence interval [CI] 0.27-0.41). In the subset analysis of patients who underwent CABG, of the 22 SSIs identified by traditional surveillance, Rule A identified 19 (86.4%) and Rule B identified 13 (59.1%) cases. Positive and negative agreement between Rules A and B within these "positive controls" was 81.3% and 50.0% with a kappa of 0.37 (95% CI 0.04-0.70). CONCLUSION: Differences in the rates of SSI identified by computer algorithms depend on sources and inherent biases in electronic data. Different algorithms may be appropriate, depending on the purpose of case identification. Further research on the reliability and validity of these algorithms and the impact of changes in reimbursement on clinician practices and electronic reporting is suggested.

A comparison of methods to detect urinary tract infections using electronic data.

BACKGROUND: The use of electronic medical records to identify common health care-associated infections (HAIs), including pneumonia, surgical site infections, bloodstream infections, and urinary tract infections (UTIs), has been proposed to help perform HAI surveillance and guide infection prevention efforts. Increased attention on HAIs has led to public health reporting requirements and a focus on quality improvement activities around HAIs. Traditional surveillance to detect HAIs and focus prevention efforts is labor intensive, and computer algorithms could be useful to screen electronic data and provide actionable information. METHODS: Seven computer-based decision rules to identify UTIs were compared in a sample of 33,834 admissions to an urban academic health center. These decision rules included combinations of laboratory data, patient clinical data, and administrative data (for example, International Statistical Classification of Diseases and Related Health Problems, Ninth Revision [ICD-9] codes). RESULTS: Of 33,834 hospital admissions, 3,870 UTIs were identified by at least one of the decision rules. The use of ICD-9 codes alone identified 2,614 UTIs. Laboratory-based definitions identified 2,773 infections, but when the presence of fever was included, only 1,125 UTIs were identified. The estimated sensitivity of ICD-9 codes was 55.6% (95% confidence interval [CI], 52.5%-58.5%) when compared with a culture- and symptom-based definition. Of the UTIs identified by ICD-9 codes, 167/1,125 (14.8%) also met two urine-culture decision rules. DISCUSSION: Use of the example of UTI identification shows how different algorithms may be appropriate, depending on the goal of case identification. Electronic surveillance methods may be beneficial for mandatory reporting, process improvement, and economic analysis.

Community-associated methicillin-resistant Staphylococcus aureus prevalence: how common is it? A methodological comparison of prevalence ascertainment.

BACKGROUND: Community-associated methicillin-resistant Staphylococcus aureus (CA-MRSA) infections are becoming increasingly prevalent. There is geographic variation in their reported prevalence across the United States; however, studies reporting on CA-MRSA prevalence also demonstrate great variability in their case-finding methodology. We conducted a study to see how three different methods to ascertain CA-MRSA prevalence would lead to different estimates. METHODS: Different methods were used to identify cases of CA-MRSA colonization and/or infection in New York City. Method 1: retrospective review of clinical and surveillance cultures identified through a hospital computer database. Method 2: prospective collection of surveillance cultures in the same hospital's emergency department. Method 3: prospective collection of surveillance cultures in a community setting. RESULTS: Differing values for CA-MRSA prevalence resulted depending on the method and denominator used. All nares cultures as the denominator led to prevalence estimates of 0.3%-0.6%; all S. aureus as the denominator led to rates of 1.2%-5%; all MRSA as the denominator led to estimates of 5.5%-50%. CONCLUSIONS: A comparison of three methods revealed that variability in case-finding methodologies can lead to different prevalence estimates. Key factors to consider when comparing CA-MRSA rates include the definition of CA-MRSA, choice of denominator, and method and setting of sample collection.

EpiPortal: an electronic decision support system for infection control.

A comprehensive, electronic hospital epidemiology decision support system serves diverse users but its primary user is the infection control professional (ICP). Utilizing off-the-shelf components and accepted standards enables the system to be open, vendor-independent and ICP-controlled. Its development can flexibly respond to the evolving nature of infection control practice.

Automated real-time detection and notification of positive infection cases.

Infection control in the healthcare setting is an essential component for patient safety and quality of care. To assist with daily infection control functions, we have implemented an alert in the Vigilens Health Monitor (a clinical decision support system at our institution) for real-time detection and notification of positive infection cases in both inpatient and outpatient settings.