To identify clinical signs and symptoms (ie, “terms”) that accurately predict laboratory-confirmed influenza cases and thereafter generate and evaluate various influenza-like illness (ILI) case definitions for detecting influenza. A secondary objective explored whether surveillance of data beyond the chief complaint improves the accuracy of predicting influenza.

Retrospective, cross-sectional study.

Large urban academic medical center hospital.

A total of 1,581 emergency department (ED) patients who received a nasopharyngeal swab followed by rRT-PCR testing between August 30, 2009, and January 2, 2010, and between November 28, 2010, and March 26, 2011.

An electronic surveillance system (GUARDIAN) scanned the entire electronic medical record (EMR) and identified cases containing 29 clinical terms relevant to influenza. Analyses were conducted using logistic regressions, diagnostic odds ratio (DOR), sensitivity, and specificity.

The best predictive model for identifying influenza for all ages consisted of cough (DOR=5.87), fever (DOR=4.49), rhinorrhea (DOR=1.98), and myalgias (DOR=1.44). The 3 best case definitions that included combinations of some or all of these 4 symptoms had comparable performance (ie, sensitivity=89%–92% and specificity=38%–44%). For children <5 years of age, the addition of rhinorrhea to the fever and cough case definition achieved a better balance between sensitivity (85%) and specificity (47%). For the fever and cough ILI case definition, using the entire EMR, GUARDIAN identified 37.1% more influenza cases than it did using only the chief complaint data.

A simplified case definition of fever and cough may be suitable for implementation for all ages, while inclusion of rhinorrhea may further improve influenza detection for the 0–4-year-old age group. Finally, ILI surveillance based on the entire EMR is recommended.

Infect Control Hosp Epidemiol 2015;00(0): 1–8

To develop a method for calculating the number of healthcare-associated infections (HAIs) that must be prevented to reach a HAI reduction goal and identifying and prioritizing healthcare facilities where the largest reductions can be achieved.

Acute care hospitals that report HAI data to the Centers for Disease Control and Prevention’s National Healthcare Safety Network.

The cumulative attributable difference (CAD) is calculated by subtracting a numerical prevention target from an observed number of HAIs. The prevention target is the product of the predicted number of HAIs and a standardized infection ratio goal, which represents a HAI reduction goal. The CAD is a numeric value that if positive is the number of infections to prevent to reach the HAI reduction goal. We calculated the CAD for catheter-associated urinary tract infections for each of the 3,639 hospitals that reported such data to National Healthcare Safety Network in 2013 and ranked the hospitals by their CAD values in descending order.

Of 1,578 hospitals with positive CAD values, preventing 10,040 catheter-associated urinary tract infections at 293 hospitals (19%) with the highest CAD would enable achievement of the national 25% catheter-associated urinary tract infection reduction goal.

The CAD is a new metric that facilitates ranking of facilities, and locations within facilities, to prioritize HAI prevention efforts where the greatest impact can be achieved toward a HAI reduction goal.

Infect. Control Hosp. Epidemiol. 2015;36(12):1379–1384

Influenza A (H1N1) pdm09 became the predominant circulating strain in the United States during the 2013–2014 influenza season. Little is known about the epidemiology of severe influenza during this season.

A retrospective cohort study of severely ill patients with influenza infection in intensive care units in 33 US hospitals from September 1, 2013, through April 1, 2014, was conducted to determine risk factors for mortality present on intensive care unit admission and to describe patient characteristics, spectrum of disease, management, and outcomes.

A total of 444 adults and 63 children were admitted to an intensive care unit in a study hospital; 93 adults (20.9%) and 4 children (6.3%) died. By logistic regression analysis, the following factors were significantly associated with mortality among adult patients: older age (>65 years, odds ratio, 3.1 [95% CI, 1.4–6.9], P=.006 and 50–64 years, 2.5 [1.3–4.9], P=.007; reference age 18–49 years), male sex (1.9 [1.1–3.3], P=.031), history of malignant tumor with chemotherapy administered within the prior 6 months (12.1 [3.9–37.0], P<.001), and a higher Sequential Organ Failure Assessment score (for each increase by 1 in score, 1.3 [1.2–1.4], P<.001).

Risk factors for death among US patients with severe influenza during the 2013–2014 season, when influenza A (H1N1) pdm09 was the predominant circulating strain type, shifted in the first postpandemic season in which it predominated toward those of a more typical epidemic influenza season.

Infect. Control Hosp. Epidemiol. 2015;36(11):1251–1260

To evaluate the impact and burden of the new National Healthcare Safety Network surveillance definition, mucosal barrier injury laboratory-confirmed bloodstream infection (MBI-LCBI), in hematology, oncology, and stem cell transplant populations.

Retrospective cohort study.

Two hematology, oncology, and stem cell transplant units at a large academic medical center.

Central line–associated bloodstream infections (CLABSIs) identified during a 14-month period were reviewed and classified as MBI-LCBI or non-MBI-LCBI (MBI-LCBI criteria not met). During this period, interventions to improve central line maintenance were implemented. Characteristics of patients with MBI-LCBI and non-MBI-LCBI were compared. Total CLABSI, MBI-LCBI, and non-MBI-LCBI rates were compared between baseline and postintervention phases of the study period.

Among 66 total CLABSI cases, 47 (71%) met MBI-LCBI criteria. Patients with MBI-LCBI and non-MBI-LCBI were similar in regard to most clinical and demographic characteristics. Between the baseline and postintervention study periods, the overall CLABSI rate decreased from 3.37 to 3.21 infections per 1,000 line-days (incidence rate ratio, 0.95; 4.7% reduction, P=.84), the MBI-LCBI rate increased from 2.08 to 2.61 infections per 1,000 line-days (incidence rate ratio, 1.25; 25.3% increase, P=.44), and the non-MBI-LCBI rate decreased from 1.29 to 0.60 infections per 1,000 line-days (incidence rate ratio, 0.47; 53.3% reduction, P=.12).

Most CLABSIs identified among hematology, oncology, and stem cell transplant patients met MBI-LCBI criteria, and CLABSI prevention efforts did not reduce these infections. Further review of the MBI-LCBI definition and impact is necessary to direct future definition changes and reporting mandates.

Infect Control Hosp Epidemiol 2014;00(0): 1–6

To develop an automated method for ventilator-associated condition (VAC) surveillance and to compare its accuracy and efficiency with manual VAC surveillance

The intensive care units (ICUs) of 4 hospitals

This study was conducted at Detroit Medical Center, a tertiary care center in metropolitan Detroit. A total of 128 ICU beds in 4 acute care hospitals were included during the study period from August to October 2013. The automated VAC algorithm was implemented and utilized for 1 month by all study hospitals. Simultaneous manual VAC surveillance was conducted by 2 infection preventionists and 1 infection control fellow who were blinded to each another’s findings and to the automated VAC algorithm results. The VACs identified by the 2 surveillance processes were compared.

During the study period, 110 patients from all the included hospitals were mechanically ventilated and were evaluated for VAC for a total of 992 mechanical ventilation days. The automated VAC algorithm identified 39 VACs with sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 100%. In comparison, the combined efforts of the IPs and the infection control fellow detected 58.9% of VACs, with 59% sensitivity, 99% specificity, 91% PPV, and 92% NPV. Moreover, the automated VAC algorithm was extremely efficient, requiring only 1 minute to detect VACs over a 1-month period, compared to 60.7 minutes using manual surveillance.

The automated VAC algorithm is efficient and accurate and is ready to be used routinely for VAC surveillance. Furthermore, its implementation can optimize the sensitivity and specificity of VAC identification.

Infect. Control Hosp. Epidemiol. 2015;36(9):999–1003

The 2008 Centers for Medicare & Medicaid Services hospital-acquired conditions policy limited additional payment for conditions deemed reasonably preventable.

To examine whether this policy was associated with decreases in billing rates for 2 targeted conditions, vascular catheter-associated infections (VCAI) and catheter-associated urinary tract infections (CAUTI).

Adult Medicare patients admitted to 569 acute care hospitals in California, Massachusetts, or New York and subject to the policy.

We used an interrupted times series design to assess whether the hospital-acquired conditions policy was associated with changes in billing rates for VCAI and CAUTI.

Before the policy, billing rates for VCAI and CAUTI were increasing (prepolicy odds ratio per quarter for VCAI, 1.17 [95% CI, 1.11–1.23]; for CAUTI, 1.19 [1.16–1.23]). The policy was associated with an immediate drop in billing rates for VCAI and CAUTI (odds ratio for change at policy implementation for VCAI, 0.75 [95% CI, 0.69–0.81]; for CAUTI, 0.87 [0.79–0.96]). In the postpolicy period, we observed a decreasing trend in the billing rate for VCAI and a leveling-off in the billing rate for CAUTI (postpolicy odds ratio per quarter for VCAI, 0.98 [95% CI, 0.97–0.99]; for CAUTI, 0.99 [0.97–1.00]).

The Centers for Medicare & Medicaid Services hospital-acquired conditions policy appears to have been associated with immediate reductions in billing rates for VCAI and CAUTI, followed by a slight decreasing trend or leveling-off in rates. These billing rates, however, may not correlate with changes in clinically meaningful patient outcomes and may reflect changes in coding practices.

Infect. Control Hosp. Epidemiol. 2015;36(8):871–877

To determine whether reuse of insulin pens among multiple patients resulted in transmission of bloodborne pathogens (BBP).

Retrospective cohort study.

Two Veterans Affairs medical centers.

Veterans who received insulin via insulin pens from 2010 to 2013.

Patients were identified through electronic health records, notified of possible exposure, and serotested for human immunodeficiency virus, hepatitis C virus (HCV), and hepatitis B virus. Newly discovered case patients were assessed in relation to potential proximate patients to determine viral strain relatedness by HCV envelope (env) gene sequencing.

Of 1,791 hospitalized veterans who received insulin via insulin pen, 1,155 were tested for at least 1 viral infection after exposure. Of these, 67 patients were newly diagnosed with 1 or more viral BBPs. For human immunodeficiency virus and hepatitis B virus no additional strain testing of case or proximate patients was possible; 8 HCV cases and 45 proximates (40 unique patients; 5 patients were positive for 2 genotypes) were identified as needing strain testing. Only 3 cases and their 19 proximates had samples available for further testing. None of the 26 remaining proximate patients had blood available for further testing. Median genetic distance between the HCV env sequences of those available for additional testing ranged from 14% to 24%, indicating nonrelatedness.

Our investigation revealed that exposure to insulin pen reuse did not result in HCV transmission among patients who had viral genetic analysis performed. Analysis for any additional potential transmission of blood-borne pathogens was limited by the available samples.

Infect Control Hosp Epidemiol 2015;36(10):1121–1129

Hospital Ebola preparation is underway in the United States and other countries; however, the best approach and resources involved are unknown.

To examine costs and challenges associated with hospital Ebola preparation by means of a survey of Society for Healthcare Epidemiology of America (SHEA) members.

Electronic survey of infection prevention experts.

A total of 257 members completed the survey (221 US, 36 international) representing institutions in 41 US states, the District of Columbia, and 18 countries. The 221 US respondents represented 158 (43.1%) of 367 major medical centers that have SHEA members and included 21 (60%) of 35 institutions recently defined by the US Centers for Disease Control and Prevention as Ebola virus disease treatment centers. From October 13 through October 19, 2014, Ebola consumed 80% of hospital epidemiology time and only 30% of routine infection prevention activities were completed. Routine care was delayed in 27% of hospitals evaluating patients for Ebola.

Convenience sample of SHEA members with a moderate response rate.

Hospital Ebola preparations required extraordinary resources, which were diverted from routine infection prevention activities. Patients being evaluated for Ebola faced delays and potential limitations in management of other diseases that are more common in travelers returning from West Africa.

Infect Control Hosp Epidemiol 2015;00(0): 1–5

Multidrug-resistant organisms (MDROs) are an increasing burden among healthcare facilities. We assessed facility-level perceived importance of and responses to various MDROs.

A pilot survey to assess staffing, knowledge, and the perceived importance of and response to various multidrug resistant organisms (MDROs)

Acute care and long-term healthcare facilities

In 2012, a survey was distributed to infection preventionists at ~300 healthcare facilities. Pathogens assessed were Clostridium difficile, carbapenem-resistant Enterobacteriaceae (CRE), carbapenem-resistant Acinetobacter, methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus, multidrug-resistant (defined as bacterial resistance to ≥3 antibiotic classes) Pseudomonas, and extended-spectrum β-lactamase-producing Escherichia coli.

A total of 74 unique facilities responded, including 44 skilled nursing facilities (SNFs) and 30 acute care facilities (ACFs). While ACFs consistently isolated patients with active infections or colonization due to these MDROs, SNFs had more variable responses. SNFs had more multi-occupancy rooms and reported less specialized training in infection control and prevention than did ACFs. Of all facilities with multi-occupancy rooms, 86% employed a cohorting practice for patients, compared with 50% of those without multi-occupancy rooms; 20% of ACFs and 7% of SNFs cohorted staff while caring for patients with the same MDRO. MRSA and C. difficile were identified as important pathogens in ACFs and SNFs, while CRE importance was unknown or was considered important in <50% of SNFs.

We identified stark differences in human resources, knowledge, policy, and practice between ACFs and SNFs. For regional control of emerging MDROs like CRE, there is an opportunity for public health officials to provide targeted education and interventions. Education campaigns must account for differences in audience resources and baseline knowledge.

Infect Control Hosp Epidemiol 2015;00(0): 1–6

To determine whether there is a difference in antibody titers and functionality after receipt of the influenza vaccine for obese versus nonobese healthcare workers (HCW).

Prospective observational study.

Tertiary medical center.

Healthcare workers.

Baseline influenza antibody titers for obese and nonobese HCW were recorded during the hospital’s 2011 annual influenza vaccination day and follow-up antibody titers were measured 4 weeks later. Antibodies were measured using the hemagglutination inhibition assay and functionality was measured using the micro-neutralization method.

Of 200 initial HCWs, 190 completed the study (97 obese and 93 nonobese). Seroprotection after immunization was not significantly different for nonobese compared with obese HCW for each strain (influenza A [H1N1], 99% and 99%; influenza A [H3N2], 100% and 99%; and influenza B, 67% and 71%, respectively)

All geometric mean titers measured by micro-neutralization showed statistically significant increases in activity. In comparison, there was no difference in the 4-fold increase in H1N1 or B titers. There was a significant difference in the 4-fold increase of H3N2 titers between the nonobese and obese HCWs (82/93 [88%] vs 64/97 [66%], P=.003)

In an ad hoc analysis we found that obese HCWs had a statistically greater number of 4-fold decreases in titers with H1N1 and H3N2.

There was no significant difference in protection from influenza between obese and nonobese HCWs after immunization.

Infect Control Hosp Epidemiol 2014;00(0): 1–5

To evaluate behavioral-based interventions to improve hand hygiene (HH) among healthcare workers (HCWs) at a Thai tertiary care center.

A quasi-experimental study was performed in 6 intensive care units with computer-generated allocation. Baseline demographic characteristics, self-reported stage of HH behavioral commitment, and observed HH adherence were examined from January 1, 2012, through December 31, 2012 (preintervention), and from January 1, 2013, through December 31, 2013 (postintervention). Self-reported HH was categorized by the stages construct from the Transtheoretical Model of Health Behavior Change. The intensive care unit group randomization was to either standard-of-care HH education every 3 months (S1), intensified HH interventions (S2), or intensified HH interventions plus increased availability of alcohol-based handrub throughout the unit (S3).

Among125 HCWs from 6 intensive care units (42 in S1, 41 in S2, 42 in S3) there were 1,936 total HH observations; most HCWs (100 [ 80%]) were nurses or nurse assistants. Compared with preintervention, overall postintervention HH adherence improved in HCWs assigned to S2 (65% vs 85%; P=.02) and S3 (66% vs 95%; P=.005) but not S1 (68% vs 71%; P=.84). Improvement in HH adherence was demonstrated among HCWs who reported lower stages of HH commitment in S2 (21% vs 84%; P<.001) and S3 (24% vs 89%; P<.001) and in HCWs who self-reported higher stages of commitment in S3 (78% vs 96%; P<.001).

HCW HH programs may benefit from stage-based tailored strategies to promote sustained HH adherence.

Infect Control Hosp Epidemiol 2015;00(0): 1–5

Hospitals in the National Healthcare Safety Network began reporting laboratory-identified (LabID) Clostridium difficile infection (CDI) events in January 2013. Our study quantified the differences between the LabID and traditional surveillance methods.

Cohort study.

A cohort of 29 community hospitals in the southeastern United States.

A period of 6 months (January 1, 2013, to June 30, 2013) of prospectively collected data using both LabID and traditional surveillance definitions were analyzed. CDI events with mismatched surveillance categories between LabID and traditional definitions were identified and characterized further. Hospital-onset CDI (HO-CDI) rates for the entire cohort of hospitals were calculated using each method, then hospital-specific HO-CDI rates and standardized infection ratios (SIRs) were calculated. Hospital rankings based on each CDI surveillance measure were compared.

A total of 1,252 incident LabID CDI events were identified during 708,551 patient-days; 286 (23%) mismatched CDI events were detected. The overall HO-CDI rate was 6.0 vs 4.4 per 10,000 patient-days for LabID and traditional surveillance, respectively (P<.001); of 29 hospitals, 25 (86%) detected a higher CDI rate using LabID compared with the traditional method. Hospital rank in the cohort differed greatly between surveillance measures. A rank change of at least 5 places occurred in 9 of 28 hospitals (32%) between LabID and traditional CDI surveillance methods, and for SIR.

LabID surveillance resulted in a higher hospital-onset CDI incidence rate than did traditional surveillance. Hospital-specific rankings varied based on the HO-CDI surveillance measure used. A clear understanding of differences in CDI surveillance measures is important when interpreting national and local CDI data.

Infect Control Hosp Epidemiol 2014;00(0): 1–7

The effectiveness of practice bundles on reducing ventilator-associated pneumonia (VAP) has been questioned.

To implement a comprehensive program that included a real-time bundle compliance dashboard to improve compliance and reduce ventilator-associated complications.

Before-and-after quasi-experimental study with interrupted time-series analysis.

Academic medical center.

In 2007 a comprehensive institutional ventilator bundle program was developed. To assess bundle compliance and stimulate instant course correction of noncompliant parameters, a real-time computerized dashboard was developed. Program impact in 6 adult intensive care units (ICUs) was assessed. Bundle compliance was noted as an overall cumulative bundle adherence assessment, reflecting the percentage of time all elements were concurrently in compliance for all patients.

The VAP rate in all ICUs combined decreased from 19.5 to 9.2 VAPs per 1,000 ventilator-days following program implementation (P<.001). Bundle compliance significantly increased (Z100 score of 23% in August 2007 to 83% in June 2011 [P<.001]). The implementation resulted in a significant monthly decrease in the overall ICU VAP rate of 3.28/1,000 ventilator-days (95% CI, 2.64–3.92/1,000 ventilator-days). Following the intervention, the VAP rate decreased significantly at a rate of 0.20/1,000 ventilator-days per month (95% CI, 0.14–0.30/1,000 ventilator-days per month). Among all adult ICUs combined, improved bundle compliance was moderately correlated with monthly VAP rate reductions (Pearson correlation coefficient, −0.32).

A prevention program using a real-time bundle adherence dashboard was associated with significant sustained decreases in VAP rates and an increase in bundle compliance among adult ICU patients.

Infect. Control Hosp. Epidemiol. 2015;36(11):1261–1267

Incidence of catheter-associated urinary tract infection (CAUTI) is a quality benchmark. To streamline conventional detection methods, an electronic surveillance system augmented with natural language processing (NLP), which gathers data recorded in clinical notes without manual review, was implemented for real-time surveillance.

To assess the utility of this algorithm for identifying indwelling urinary catheter days and CAUTI.

Large, urban tertiary care Veterans Affairs hospital.

All patients admitted to the acute care units and the intensive care unit from March 1, 2013, through November 30, 2013, were included. Standard surveillance, which includes electronic and manual data extraction, was compared with the NLP-augmented algorithm.

The NLP-augmented algorithm identified 27% more indwelling urinary catheter days in the acute care units and 28% fewer indwelling urinary catheter days in the intensive care unit. The algorithm flagged 24 CAUTI versus 20 CAUTI by standard surveillance methods; the CAUTI identified were overlapping but not the same. The overall positive predictive value was 54.2%, and overall sensitivity was 65% (90.9% in the acute care units but 33% in the intensive care unit). Dissimilarities in the operating characteristics of the algorithm between types of unit were due to differences in documentation practice. Development and implementation of the algorithm required substantial upfront effort of clinicians and programmers to determine current language patterns.

The NLP algorithm was most useful for identifying simple clinical variables. Algorithm operating characteristics were specific to local documentation practices. The algorithm did not perform as well as standard surveillance methods.

Infect. Control Hosp. Epidemiol. 2015;36(9):1004–1010