Interrater reliability of surveillance for ventilator-associated events and pneumonia

OBJECTIVETo compare interrater reliabilities for ventilator-associated event (VAE) surveillance, traditional ventilator-associated pneumonia (VAP) surveillance, and clinical diagnosis of VAP by intensivists.DESIGNA retrospective study nested within a prospective multicenter quality improvement study.SETTINGIntensive care units (ICUs) within 5 hospitals of the Centers for Disease Control and Prevention Epicenters.PATIENTSPatients who underwent mechanical ventilation.METHODSWe selected 150 charts for review, including all VAEs and traditionally defined VAPs identified during the primary study and randomly selected charts of patients without VAEs or VAPs. Each chart was independently reviewed by 2 research assistants (RAs) for VAEs, 2 hospital infection preventionists (IPs) for traditionally defined VAP, and 2 intensivists for any episodes of pulmonary deterioration. We calculated interrater agreement using κ estimates.RESULTSThe 150 selected episodes spanned 2,500 ventilator days. In total, 93–96 VAEs were identified by RAs; 31–49 VAPs were identified by IPs, and 29–35 VAPs were diagnosed by intensivists. Interrater reliability between RAs for VAEs was high (κ, 0.71; 95% CI, 0.59–0.81). Agreement between IPs using traditional VAP criteria was slight (κ, 0.12; 95% CI, −0.05–0.29). Agreement between intensivists was slight regarding episodes of pulmonary deterioration (κ 0.22; 95% CI, 0.05–0.39) and was fair regarding whether episodes of deterioration were attributable to clinically defined VAP (κ, 0.34; 95% CI, 0.17–0.51). The clinical correlation between VAE surveillance and intensivists’ clinical assessments was poor.CONCLUSIONSProspective surveillance using VAE criteria is more reliable than traditional VAP surveillance and clinical VAP diagnosis; the correlation between VAEs and clinically recognized pulmonary deterioration is poor.Infect Control Hosp Epidemiol 2017;38:172–178</jats:sec

Which comorbid conditions should we be analyzing as risk factors for healthcare-associated infections?

OBJECTIVETo determine which comorbid conditions are considered causally related to central-line associated bloodstream infection (CLABSI) and surgical-site infection (SSI) based on expert consensus.DESIGNUsing the Delphi method, we administered an iterative, 2-round survey to 9 infectious disease and infection control experts from the United States.METHODSBased on our selection of components from the Charlson and Elixhauser comorbidity indices, 35 different comorbid conditions were rated from 1 (not at all related) to 5 (strongly related) by each expert separately for CLABSI and SSI, based on perceived relatedness to the outcome. To assign expert consensus on causal relatedness for each comorbid condition, all 3 of the following criteria had to be met at the end of the second round: (1) a majority (&gt;50%) of experts rating the condition at 3 (somewhat related) or higher, (2) interquartile range (IQR)≤1, and (3) standard deviation (SD)≤1.RESULTSFrom round 1 to round 2, the IQR and SD, respectively, decreased for ratings of 21 of 35 (60%) and 33 of 35 (94%) comorbid conditions for CLABSI, and for 17 of 35 (49%) and 32 of 35 (91%) comorbid conditions for SSI, suggesting improvement in consensus among this group of experts. At the end of round 2, 13 of 35 (37%) and 17 of 35 (49%) comorbid conditions were perceived as causally related to CLABSI and SSI, respectively.CONCLUSIONSOur results have produced a list of comorbid conditions that should be analyzed as risk factors for and further explored for risk adjustment of CLABSI and SSI.Infect Control Hosp Epidemiol 2017;38:449–454</jats:sec

The effect of adding comorbidities to current centers for disease control and prevention central-line–associated bloodstream infection risk-adjustment methodology

BACKGROUNDRisk adjustment is needed to fairly compare central-line–associated bloodstream infection (CLABSI) rates between hospitals. Until 2017, the Centers for Disease Control and Prevention (CDC) methodology adjusted CLABSI rates only by type of intensive care unit (ICU). The 2017 CDC models also adjust for hospital size and medical school affiliation. We hypothesized that risk adjustment would be improved by including patient demographics and comorbidities from electronically available hospital discharge codes.METHODSUsing a cohort design across 22 hospitals, we analyzed data from ICU patients admitted between January 2012 and December 2013. Demographics and International Classification of Diseases, Ninth Edition, Clinical Modification (ICD-9-CM) discharge codes were obtained for each patient, and CLABSIs were identified by trained infection preventionists. Models adjusting only for ICU type and for ICU type plus patient case mix were built and compared using discrimination and standardized infection ratio (SIR). Hospitals were ranked by SIR for each model to examine and compare the changes in rank.RESULTSOverall, 85,849 ICU patients were analyzed and 162 (0.2%) developed CLABSI. The significant variables added to the ICU model were coagulopathy, paralysis, renal failure, malnutrition, and age. The C statistics were 0.55 (95% CI, 0.51–0.59) for the ICU-type model and 0.64 (95% CI, 0.60–0.69) for the ICU-type plus patient case-mix model. When the hospitals were ranked by adjusted SIRs, 10 hospitals (45%) changed rank when comorbidity was added to the ICU-type model.CONCLUSIONSOur risk-adjustment model for CLABSI using electronically available comorbidities demonstrated better discrimination than did the CDC model. The CDC should strongly consider comorbidity-based risk adjustment to more accurately compare CLABSI rates across hospitals.Infect Control Hosp Epidemiol 2017;38:1019–1024</jats:sec

Control of Vancomycin-Resistant Enterococcus in Health Care Facilities in a Region

Background In late 1996, vancomycin-resistant enterococci were first detected in the Siouxland region of Iowa, Nebraska, and South Dakota. A task force was created, and in 1997 the assistance of the Centers for Disease Control and Prevention was sought in assessing the prevalence of vancomycin-resistant enterococci in the region’s facilities and implementing recommendations for screening, infection control, and education at all 32 health care facilities in the region. Methods The infection-control intervention was evaluated in October 1998 and October 1999. We performed point-prevalence surveys, conducted a case– control study of gastrointestinal colonization with vancomycin-resistant enterococci, and compared infection-control practices and screening policies for vancomycin-resistant enterococci at the acute care and long-term care facilities in the Siouxland region. Results Perianal-swab samples were obtained from 1954 of 2196 eligible patients (89 percent) in 1998 and 1820 of 2049 eligible patients (89 percent) in 1999. The overall prevalence of vancomycin-resistant enterococci at 30 facilities that participated in all three years of the study decreased from 2.2 percent in 1997 to 1.4 percent in 1998 and to 0.5 percent in 1999 (P Conclusions An active infection-control intervention, which includes the obtaining of surveillance cultures and the isolation of infected patients, can reduce or eliminate the transmission of vancomycinresistant enterococci in the health care facilities of a region. (N Engl J Med 2001;344:1427-33.

Significant regional differences in antibiotic use across 576 US hospitals and 11 701 326 adult admissions, 2016-2017

BACKGROUND: Quantifying the amount and diversity of antibiotic use in United States hospitals assists antibiotic stewardship efforts but is hampered by limited national surveillance. Our study aimed to address this knowledge gap by examining adult antibiotic use across 576 hospitals and nearly 12 million encounters in 2016-2017. METHODS: We conducted a retrospective study of patients aged ≥ 18 years discharged from hospitals in the Premier Healthcare Database between 1 January 2016 and 31 December 2017. Using daily antibiotic charge data, we mapped antibiotics to mutually exclusive classes and to spectrum of activity categories. We evaluated relationships between facility and case-mix characteristics and antibiotic use in negative binomial regression models. RESULTS: The study included 11 701 326 admissions, totaling 64 064 632 patient-days, across 576 hospitals. Overall, patients received antibiotics in 65% of hospitalizations, at a crude rate of 870 days of therapy (DOT) per 1000 patient-days. By class, use was highest among β-lactam/β-lactamase inhibitor combinations, third- and fourth-generation cephalosporins, and glycopeptides. Teaching hospitals averaged lower rates of total antibiotic use than nonteaching hospitals (834 vs 957 DOT per 1000 patient-days; P \u3c .001). In adjusted models, teaching hospitals remained associated with lower use of third- and fourth-generation cephalosporins and antipseudomonal agents (adjusted incidence rate ratio [95% confidence interval], 0.92 [.86-.97] and 0.91 [.85-.98], respectively). Significant regional differences in total and class-specific antibiotic use also persisted in adjusted models. CONCLUSIONS: Adult inpatient antibiotic use remains high, driven predominantly by broad-spectrum agents. Better understanding reasons for interhospital usage differences, including by region and teaching status, may inform efforts to reduce inappropriate antibiotic prescribing

Electronically available patient claims data improve models for comparing antibiotic use across hospitals: Results from 576 US facilities

BACKGROUND: The Centers for Disease Control and Prevention (CDC) uses standardized antimicrobial administration ratios (SAARs)-that is, observed-to-predicted ratios-to compare antibiotic use across facilities. CDC models adjust for facility characteristics when predicting antibiotic use but do not include patient diagnoses and comorbidities that may also affect utilization. This study aimed to identify comorbidities causally related to appropriate antibiotic use and to compare models that include these comorbidities and other patient-level claims variables to a facility model for risk-adjusting inpatient antibiotic utilization. METHODS: The study included adults discharged from Premier Database hospitals in 2016-2017. For each admission, we extracted facility, claims, and antibiotic data. We evaluated 7 models to predict an admission\u27s antibiotic days of therapy (DOTs): a CDC facility model, models that added patient clinical constructs in varying layers of complexity, and an external validation of a published patient-variable model. We calculated hospital-specific SAARs to quantify effects on hospital rankings. Separately, we used Delphi Consensus methodology to identify Elixhauser comorbidities associated with appropriate antibiotic use. RESULTS: The study included 11 701 326 admissions across 576 hospitals. Compared to a CDC-facility model, a model that added Delphi-selected comorbidities and a bacterial infection indicator was more accurate for all antibiotic outcomes. For total antibiotic use, it was 24% more accurate (respective mean absolute errors: 3.11 vs 2.35 DOTs), resulting in 31-33% more hospitals moving into bottom or top usage quartiles postadjustment. CONCLUSIONS: Adding electronically available patient claims data to facility models consistently improved antibiotic utilization predictions and yielded substantial movement in hospitals\u27 utilization rankings

Multicenter evaluation of computer automated versus traditional surveillance of hospital-acquired bloodstream infections

Objective.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.Design.Retrospective cohort study.Setting.Eight medical and surgical intensive care units (ICUs) in 4 academic medical centers.Methods.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).Results.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.Conclusions.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</jats:sec

Computer Algorithms To Detect Bloodstream Infections

Automated bloodstream infection surveillance using electronic data is an accurate alternative to surveillance using manually collected data

Influence of Role Models and Hospital Design on the Hand Hygiene of Health-Care Workers

We assessed the effect of medical staff role models and the number of health-care worker sinks on hand-hygiene compliance before and after construction of a new hospital designed for increased access to handwashing sinks. We observed health-care worker hand hygiene in four nursing units that provided similar patient care in both the old and new hospitals: medical and surgical intensive care, hematology/oncology, and solid organ transplant units. Of 721 hand-hygiene opportunities, 304 (42%) were observed in the old hospital and 417 (58%) in the new hospital. Hand-hygiene compliance was significantly better in the old hospital (161/304; 53%) compared to the new hospital (97/417; 23.3%) (p<0.001). Health-care workers in a room with a senior (e.g., higher ranking) medical staff person or peer who did not wash hands were significantly less likely to wash their own hands (odds ratio 0.2; confidence interval 0.1 to 0.5); p<0.001). Our results suggest that health-care worker hand-hygiene compliance is influenced significantly by the behavior of other health-care workers. An increased number of hand-washing sinks, as a sole measure, did not increase hand-hygiene compliance