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To increase reliability of the algorithm used in our fully automated electronic surveillance system by adding rules to better identify bloodstream infections secondary to other hospital-acquired infections.
Intensive care unit (ICU) patients with positive blood cultures were reviewed. Central line–associated bloodstream infection (CLABSI) determinations were based on 2 sources: routine surveillance by infection preventionists, and fully automated surveillance. Discrepancies between the 2 sources were evaluated to determine root causes. Secondary infection sites were identified in most discrepant cases. New rules to identify secondary sites were added to the algorithm and applied to this ICU population and a non-ICU population. Sensitivity, specificity, predictive values, and kappa were calculated for the new models.
Of 643 positive ICU blood cultures reviewed, 68 (10.6%) were identified as central line–associated bloodstream infections by fully automated electronic surveillance, whereas 38 (5.9%) were confirmed by routine surveillance. New rules were tested to identify organisms as central line–associated bloodstream infections if they did not meet one, or a combination of, the following: (I) matching organisms (by genus and species) cultured from any other site; (II) any organisms cultured from sterile site; (III) any organisms cultured from skin/wound; (IV) any organisms cultured from respiratory tract. The best-fit model included new rules I and II when applied to positive blood cultures in an ICU population. However, they didn’t improve performance of the algorithm when applied to positive blood cultures in a non-ICU population.
Electronic surveillance system algorithms may need adjustment for specific populations.
Infect. Control Hosp. Epidemiol. 2015;36(12):1396–1400
Central line–associated bloodstream infection (BSI) rates are a key quality metric for comparing hospital quality and safety. Traditional BSI surveillance may be limited by interrater variability. We assessed whether a computer-automated method of central line–associated BSI detection can improve the validity of surveillance.
Retrospective cohort study.
Eight medical and surgical intensive care units (ICUs) in 4 academic medical centers.
Traditional surveillance (by hospital staff) and computer algorithm surveillance were each compared against a retrospective audit review using a random sample of blood culture episodes during the period 2004–2007 from which an organism was recovered. Episode-level agreement with audit review was measured with κ statistics, and differences were assessed using the test of equal κ coefficients. Linear regression was used to assess the relationship between surveillance performance (κ) and surveillance-reported BSI rates (BSIs per 1,000 central line–days).
We evaluated 664 blood culture episodes. Agreement with audit review was significantly lower for traditional surveillance (κ [95% confidence interval (CI)] = 0.44 [0.37–0.51]) than computer algorithm surveillance (κ [95% CI] [0.52–0.64]; P = .001). Agreement between traditional surveillance and audit review was heterogeneous across ICUs (P = .001); furthermore, traditional surveillance performed worse among ICUs reporting lower (better) BSI rates (P = .001). In contrast, computer algorithm performance was consistent across ICUs and across the range of computer-reported central line–associated BSI rates.
Compared with traditional surveillance of bloodstream infections, computer automated surveillance improves accuracy and reliability, making interfacility performance comparisons more valid.
Infect Control Hosp Epidemiol 2014;35(12):1483–1490
Manual surveillance for central line-associated bloodstream infections (CLABSIs) by infection prevention practitioners is time-consuming and often limited to intensive care units (ICUs). An automated surveillance system using existing databases with patient-level variables and microbiology data was investigated.
Patients with a positive blood culture in 4 non-ICU wards at Barnes-Jewish Hospital between July 1, 2005, and December 31, 2006, were evaluated. CLABSI determination for these patients was made via 2 sources; a manual chart review and an automated review from electronically available data. Agreement between these 2 sources was used to develop the best-fit electronic algorithm that used a set of rules to identify a CLABSI. Sensitivity, specificity, predictive values, and Pearson's correlation were calculated for the various rule sets, using manual chart review as the reference standard.
During the study period, 391 positive blood cultures from 331 patients were evaluated. Eighty-five (22%) of these were confirmed to be CLABSI by manual chart review. The best-fit model included presence of a catheter, blood culture positive for known pathogen or blood culture with a common skin contaminant confirmed by a second positive culture and the presence of fever, and no positive cultures with the same organism from another sterile site. The best-performing rule set had an overall sensitivity of 95.2%, specificity of 97.5%, positive predictive value of 90%, and negative predictive value of 99.2% compared with intensive manual surveillance.
Although CLABSIs were slightly overpredicted by electronic surveillance compared with manual chart review, the method offers the possibility of performing acceptably good surveillance in areas where resources do not allow for traditional manual surveillance.
To develop and validate a risk prediction model that could identify patients at high risk for Clostridium difficile infection (CDI) before they develop disease.
Design and Setting.
Retrospective cohort study in a tertiary care medical center.
Patients admitted to the hospital for at least 48 hours during the calendar year 2003.
Data were collected electronically from the hospital's Medical Informatics database and analyzed with logistic regression to determine variables that best predicted patients' risk for development of CDI. Model discrimination and calibration were calculated. The model was bootstrapped 500 times to validate the predictive accuracy. A receiver operating characteristic curve was calculated to evaluate potential risk cutoffs.
A total of 35,350 admitted patients, including 329 with CDI, were studied. Variables in the risk prediction model were age, CDI pressure, times admitted to hospital in the previous 60 days, modified Acute Physiology Score, days of treatment with high-risk antibiotics, whether albumin level was low, admission to an intensive care unit, and receipt of laxatives, gastric acid suppressors, or antimotility drugs. The calibration and discrimination of the model were very good to excellent (C index, 0.88; Brier score, 0.009).
The CDI risk prediction model performed well. Further study is needed to determine whether it could be used in a clinical setting to prevent CDI-associated outcomes and reduce costs.
Healthcare-associated infections are likely to be caused by drug-resistant and possibly mixed organisms and to be treated with inappropriate antibiotics. Because prompt appropriate treatment is associated with better outcomes, we studied the epidemiology of healthcare-associated complicated skin and skin-structure infections (cSSSIs).
Persons hospitalized with cSSSI and a positive culture result.
We conducted a single-center retrospective cohort study from April 2006 through December 2007. We differentiated healthcare-associated from community-acquired cSSSIs by at least 1 of the following risk factors: (1) recent hospitalization, (2) recent antibiotics, (3) hemodialysis, and (4) transfer from a nursing home. Inappropriate treatment was defined as no antimicrobial therapy with activity against the offending pathogen(s) within 24 hours after collection of a culture specimen. Mixed infections were those caused by both a gram-positive and a gram-negative organism.
Among 717 hospitalized patients with cSSSI, 527 (73.5%) had healthcare-associated cSSSI. Gram-negative organisms were more common (relative risk, 1.24 [95% confidence interval, 1.14–1.35) and inappropriate treatment trended toward being more common (odds ratio, 1.29 [95% confidence interval, 0.85–1.95]) in healthcare-associated cSSSI than in community-acquired cSSSI. Mixed cSSSIs occurred in 10.6% of patients with healthcare-associated cSSSI and 6.3% of those with community-acquired cSSSI (P = .082) and were more likely to be treated inappropriately than to be nonmixed infections (odds ratio, 2.42 [95% confidence interval, 1.43–4.10]). Both median length of hospital stay (6.2 vs 2.9 days; P < .001) and mortality rate (6.6% vs 1.1%; P = .003) were significantly higher for healthcare-associated cSSSI than community-acquired cSSSI.
Healthcare-associated cSSSIs are common and are likely to be caused by gram-negative organisms. Mixed infections carry a <2-fold greater risk of inappropriate treatment. Healthcare-associated cSSSIs are associated with increased mortality and prolonged length of hospital stay, compared with community-acquired cSSSIs.
Healthcare-associated, community-acquired bacteremia is a subcategory of community-acquired bacteremia distinguished by recent exposure of the patient to the healthcare system before hospital admission. Our objective was to apply this category to a prospective cohort of hospitalized patients with gram-negative bacteremia to determine differences in the epidemiological characteristics, treatment, and outcome of community-acquired bacteremia; healthcare-associated, community-acquired bacteremia; and hospital-acquired bacteremia.
A 6-month prospective cohort study.
A 1,250-bed tertiary care hospital.
Adults hospitalized with gram-negative bacteremia.
Among 250 patients, 160 (64.0%) had bacteremia within 48 hours after admission; 132 (82.5%) of these were considered to have healthcare-associated, community-acquired bacteremia, according to previously published criteria. For patients with healthcare-associated, community-acquired bacteremia, compared with patients with community-acquired bacteremia, malignancies (59 [44.7%] of 132 patients vs 3 [10.7%] of 28 patients; P = .001), open wounds at admission (42 [31.8%] vs 3 [10.7%]; P = .02), and intravascular catheter-related infections (26 [19.7%] vs 0; P = .009) were more frequent and Escherichia coli as a causative agent was less frequent (16 [57.1%] vs 33 [25.0%]; P = .001). There was no difference between these 2 groups in inadequate empirical antibiotic treatment (36 [27.3%] vs 6 [21.4%]; P = .5) and hospital mortality (18 [13.6%] vs 2 [[7.1%]; P = .5). Compared with 90 patients with hospital-acquired bacteremia, patients with healthcare-associated, community-acquired bacteremia had a higher Charlson score (odds ratio [OR], 1.31 [95% confidence interval (CI), 1.14–1.49]) but were less likely to have lymphoma (OR, 0.07 [95% CI, 0.01–0.51]), neutropenia (OR, 0.21 [95% CI, 0.07–0.61]), a removable foreign body (OR, 0.08 [95% CI, 0.03–0.20]), or Klebsiella pneumoniae infection (OR, 0.26 [95% CI, 0.11–0.62]).
Many cases of gram-negative bacteremia that occurred in hospitalized patients were healthcare associated. The patients differed in some aspects from patients with community-acquired bacteremia and from those with hospital-acquired bacteremia, but not in mortality.
To develop and evaluate computer algorithms with high negative predictive values that augment traditional surveillance for central line–associated bloodstream infection (CLABSI).
Barnes-Jewish Hospital, a 1,250-bed tertiary care academic hospital in Saint Louis, Missouri.
We evaluated all adult patients in intensive care units who had blood samples collected during the period from July 1, 2005, to June 30,2006, that were positive for a recognized pathogen on culture. Each isolate recovered from culture was evaluated using the definitions for nosocomial CLABSI provided by the National Healthcare Safety Network of the Centers for Disease Control and Prevention. Using manual surveillance by infection prevention specialists as the gold standard, we assessed the ability of various combinations of dichotomous rules to determine whether an isolate was associated with a CLABSI. Sensitivity, specificity, and predictive values were calculated.
Infection prevention specialists identified 67 cases of CLABSI associated with 771 isolates recovered from blood samples. The algorithms excluded approximately 40%-62% of the isolates from consideration as possible causes of CLABSI. The simplest algorithm, with 2 dichotomous rules (ie, the collection of blood samples more than 48 hours after admission and the presence of a central venous catheter within 48 hours before collection of blood samples), had the highest negative predictive value (99.4%) and the lowest specificity (44.2%) for CLABSI. Augmentation of this algorithm with rules for common skin contaminants confirmed by another positive blood culture result yielded in a negative predictive value of 99.2% and a specificity of 68.0%.
An automated approach to surveillance for CLABSI that is characterized by a high negative predictive value can accurately identify and exclude positive culture results not representing CLABSI from further manual surveillance.
The incidence of community-associated, healthcare-associated, and hospital-acquired sterile-site infections due to methicillin-re-sistant Staphylococcus aureus (MRSA) isolates and the susceptibility of the isolates to non-β-lactam antibiotics were evaluated for 549 hospitalized patients during a 3-year period. The incidence of community-associated MRSA infection increased significantly. The annual percentage of MRSA isolates from cases of healthcare-associated and hospital-acquired infection that were susceptible to 3 or more non-β-lactam antibiotics increased significantly.
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