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To evaluate the National Health Safety Network (NHSN) hospital-onset Clostridioides difficile infection (HO-CDI) standardized infection ratio (SIR) risk adjustment for general acute-care hospitals with large numbers of intensive care unit (ICU), oncology unit, and hematopoietic cell transplant (HCT) patients.
Retrospective cohort study.
Eight tertiary-care referral general hospitals in California.
We used FY 2016 data and the published 2015 rebaseline NHSN HO-CDI SIR. We compared facility-wide inpatient HO-CDI events and SIRs, with and without ICU data, oncology and/or HCT unit data, and ICU bed adjustment.
For these hospitals, the median unmodified HO-CDI SIR was 1.24 (interquartile range [IQR], 1.15–1.34); 7 hospitals qualified for the highest ICU bed adjustment; 1 hospital received the second highest ICU bed adjustment; and all had oncology-HCT units with no additional adjustment per the NHSN. Removal of ICU data and the ICU bed adjustment decreased HO-CDI events (median, −25%; IQR, −20% to −29%) but increased the SIR at all hospitals (median, 104%; IQR, 90%–105%). Removal of oncology-HCT unit data decreased HO-CDI events (median, −15%; IQR, −14% to −21%) and decreased the SIR at all hospitals (median, −8%; IQR, −4% to −11%).
For tertiary-care referral hospitals with specialized ICUs and a large number of ICU beds, the ICU bed adjustor functions as a global adjustment in the SIR calculation, accounting for the increased complexity of patients in ICUs and non-ICUs at these facilities. However, the SIR decrease with removal of oncology and HCT unit data, even with the ICU bed adjustment, suggests that an additional adjustment should be considered for oncology and HCT units within general hospitals, perhaps similar to what is done for ICU beds in the current SIR.
To determine whether the Society for Healthcare Epidemiology of America (SHEA) and the Infectious Diseases Society of America (IDSA) Clostridioides difficile infection (CDI) severity criteria adequately predicts poor outcomes.
Retrospective validation study.
Setting and participants:
Patients with CDI in the Veterans’ Affairs Health System from January 1, 2006, to December 31, 2016.
For the 2010 criteria, patients with leukocytosis or a serum creatinine (SCr) value ≥1.5 times the baseline were classified as severe. For the 2018 criteria, patients with leukocytosis or a SCr value ≥1.5 mg/dL were classified as severe. Poor outcomes were defined as hospital or intensive care admission within 7 days of diagnosis, colectomy within 14 days, or 30-day all-cause mortality; they were modeled as a function of the 2010 and 2018 criteria separately using logistic regression.
We analyzed data from 86,112 episodes of CDI. Severity was unclassifiable in a large proportion of episodes diagnosed in subacute care (2010, 58.8%; 2018, 49.2%). Sensitivity ranged from 0.48 for subacute care using 2010 criteria to 0.73 for acute care using 2018 criteria. Areas under the curve were poor and similar (0.60 for subacute care and 0.57 for acute care) for both versions, but negative predictive values were >0.80.
Model performances across care settings and criteria versions were generally poor but had reasonably high negative predictive value. Many patients in the subacute-care setting, an increasing fraction of CDI cases, could not be classified. More work is needed to develop criteria to identify patients at risk of poor outcomes.
Laboratory identification of carbapenem-resistant Enterobacteriaceae (CRE) is a key step in controlling its spread. Our survey showed that most Veterans Affairs laboratories follow VA guidelines for initial CRE identification, whereas 55.0% use PCR to confirm carbapenemase production. Most respondents were knowledgeable about CRE guidelines. Barriers included staffing, training, and financial resources.
Although most hospitals report very high levels of hand hygiene compliance (HHC), the accuracy of these overtly observed rates is questionable due to the Hawthorne effect and other sources of bias. In the study, we aimed (1) to compare HHC rates estimated using the standard audit method of overt observation by a known observer and a new audit method that employed a rapid (<15 minutes) “secret shopper” method and (2) to pilot test a novel feedback tool.
Quality improvement project using a quasi-experimental stepped-wedge design.
This study was conducted in 5 acute-care hospitals (17 wards, 5 intensive care units) in the Midwestern United States.
Sites recruited a hand hygiene observer from outside the acute-care units to rapidly and covertly observe entry and exit HHC during the study period, October 2016–September 2017. After 3 months of observations, sites received a monthly feedback tool that communicated HHC information from the new audit method.
The absolute difference in HHC estimates between the standard and new audit methods was ~30%. No significant differences in HHC were detected between the baseline and feedback phases (OR, 0.92; 95% CI, 0.84–1.01), but the standard audit method had significantly higher estimates than the new audit method (OR, 9.83; 95% CI, 8.82–10.95).
HHC estimates obtained using the new audit method were substantially lower than estimates obtained using the standard audit method, suggesting that the rapid, secret-shopper method is less subject to bias. Providing feedback using HHC from the new audit method did not seem to impact HHC behaviors.
To examine variation in antibiotic coverage and detection of resistant pathogens in community-onset pneumonia.
A total of 128 hospitals in the Veterans Affairs health system.
Hospitalizations with a principal diagnosis of pneumonia from 2009 through 2010.
We examined proportions of hospitalizations with empiric antibiotic coverage for methicillin-resistant Staphylococcus aureus (MRSA) and Pseudomonas aeruginosa (PAER) and with initial detection in blood or respiratory cultures. We compared lowest- versus highest-decile hospitals, and we estimated adjusted probabilities (AP) for patient- and hospital-level factors predicting coverage and detection using hierarchical regression modeling.
Among 38,473 hospitalizations, empiric coverage varied widely across hospitals (MRSA lowest vs highest, 8.2% vs 42.0%; PAER lowest vs highest, 13.9% vs 44.4%). Detection rates also varied (MRSA lowest vs highest, 0.5% vs 3.6%; PAER lowest vs highest, 0.6% vs 3.7%). Whereas coverage was greatest among patients with recent hospitalizations (AP for anti-MRSA, 54%; AP for anti-PAER, 59%) and long-term care (AP for anti-MRSA, 60%; AP for anti-PAER, 66%), detection was greatest in patients with a previous history of a positive culture (AP for MRSA, 7.9%; AP for PAER, 11.9%) and in hospitals with a high prevalence of the organism in pneumonia (AP for MRSA, 3.9%; AP for PAER, 3.2%). Low hospital complexity and rural setting were strong negative predictors of coverage but not of detection.
Hospitals demonstrated widespread variation in both coverage and detection of MRSA and PAER, but probability of coverage correlated poorly with probability of detection. Factors associated with empiric coverage (eg, healthcare exposure) were different from those associated with detection (eg, microbiology history). Providing microbiology data during empiric antibiotic decision making could better align coverage to risk for resistant pathogens and could promote more judicious use of broad-spectrum antibiotics.
The purpose of this study was to quantify the effect of multidrug-resistant (MDR) gram-negative bacteria and methicillin-resistant Staphylococcus aureus (MRSA) healthcare-associated infections (HAIs) on mortality following infection, regardless of patient location.
We conducted a retrospective cohort study of patients with an inpatient admission in the US Department of Veterans Affairs (VA) system between October 1, 2007, and November 30, 2010. We constructed multivariate log-binomial regressions to assess the impact of a positive culture on mortality in the 30- and 90-day periods following the first positive culture, using a propensity-score–matched subsample.
Patients identified with positive cultures due to MDR Acinetobacter (n=218), MDR Pseudomonas aeruginosa (n=1,026), and MDR Enterobacteriaceae (n=3,498) were propensity-score matched to 14,591 patients without positive cultures due to these organisms. In addition, 3,471 patients with positive cultures due to MRSA were propensity-score matched to 12,499 patients without positive MRSA cultures. Multidrug-resistant gram-negative bacteria were associated with a significantly elevated risk of mortality both for invasive (RR, 2.32; 95% CI, 1.85–2.92) and noninvasive cultures (RR, 1.33; 95% CI, 1.22–1.44) during the 30-day period. Similarly, patients with MRSA HAIs (RR, 2.77; 95% CI, 2.39–3.21) and colonizations (RR, 1.32; 95% CI, 1.22–1.50) had an increased risk of death at 30 days.
We found that HAIs due to gram-negative bacteria and MRSA conferred significantly elevated 30- and 90-day risks of mortality. This finding held true both for invasive cultures, which are likely to be true infections, and noninvasive infections, which are possibly colonizations.
Mathematical modeling is a valuable methodology used to study healthcare epidemiology and antimicrobial stewardship, particularly when more traditional study approaches are infeasible, unethical, costly, or time consuming. We focus on 2 of the most common types of mathematical modeling, namely compartmental modeling and agent-based modeling, which provide important advantages—such as shorter developmental timelines and opportunities for extensive experimentation—over observational and experimental approaches. We summarize these advantages and disadvantages via specific examples and highlight recent advances in the methodology. A checklist is provided to serve as a guideline in the development of mathematical models in healthcare epidemiology and antimicrobial stewardship.
Estimates of the excess length of stay (LOS) attributable to healthcare-associated infections (HAIs) in which total LOS of patients with and without HAIs are biased because of failure to account for the timing of infection. Alternate methods that appropriately treat HAI as a time-varying exposure are multistate models and cohort studies, which match regarding the time of infection. We examined the magnitude of this time-dependent bias in published studies that compared different methodological approaches.
We conducted a systematic review of the published literature to identify studies that report attributable LOS estimates using both total LOS (time-fixed) methods and either multistate models or matching patients with and without HAIs using the timing of infection.
Of the 7 studies that compared time-fixed methods to multistate models, conventional methods resulted in estimates of the LOS to HAIs that were, on average, 9.4 days longer or 238% greater than those generated using multistate models. Of the 5 studies that compared time-fixed methods to matching on timing of infection, conventional methods resulted in estimates of the LOS to HAIs that were, on average, 12.6 days longer or 139% greater than those generated by matching on timing of infection.
Our results suggest that estimates of the attributable LOS due to HAIs depend heavily on the methods used to generate those estimates. Overestimation of this effect can lead to incorrect assumptions of the likely cost savings from HAI prevention measures.
Infect. Control Hosp. Epidemiol. 2015;36(9):1089–1094
Standard estimates of the impact of Clostridium difficile infections (CDI) on inpatient lengths of stay (LOS) may overstate inpatient care costs attributable to CDI. In this study, we used multistate modeling (MSM) of CDI timing to reduce bias in estimates of excess LOS.
A retrospective cohort study of all hospitalizations at any of 120 acute care facilities within the US Department of Veterans Affairs (VA) between 2005 and 2012 was conducted. We estimated the excess LOS attributable to CDI using an MSM to address time-dependent bias. Bootstrapping was used to generate 95% confidence intervals (CI). These estimates were compared to unadjusted differences in mean LOS for hospitalizations with and without CDI.
During the study period, there were 3.96 million hospitalizations and 43,540 CDIs. A comparison of unadjusted means suggested an excess LOS of 14.0 days (19.4 vs 5.4 days). In contrast, the MSM estimated an attributable LOS of only 2.27 days (95% CI, 2.14–2.40). The excess LOS for mild-to-moderate CDI was 0.75 days (95% CI, 0.59–0.89), and for severe CDI, it was 4.11 days (95% CI, 3.90–4.32). Substantial variation across the Veteran Integrated Services Networks (VISN) was observed.
CDI significantly contributes to LOS, but the magnitude of its estimated impact is smaller when methods are used that account for the time-varying nature of infection. The greatest impact on LOS occurred among patients with severe CDI. Significant geographic variability was observed. MSM is a useful tool for obtaining more accurate estimates of the inpatient care costs of CDI.
Infect. Control Hosp. Epidemiol. 2015;36(9):1024–1030
Healthcare-associated methicillin-resistant Staphylococcus aureus (MRSA) infections are a major cause of morbidity, mortality, and cost among hospitalized patients. Little is known about their impact on post-discharge resource utilization. The purpose of this study was to estimate post-discharge healthcare costs and utilization attributable to positive MRSA cultures during a hospitalization.
Our study cohort consisted of patients with an inpatient admission lasting longer than 48 hours within the US Department of Veterans Affairs (VA) system between October 1, 2007, and November 30, 2010. Of these patients, we identified those with a positive MRSA culture from microbiology reports in the VA electronic medical record. We used propensity score matching and multivariable regression models to assess the impact of positive culture on post-discharge outpatient, inpatient, and pharmacy costs and utilization in the 365 days following discharge.
Our full cohort included 369,743 inpatients, of whom, 3,599 (1.0%) had positive MRSA cultures. Our final analysis sample included 3,592 matched patients with and without positive cultures. We found that, in the 12 months following hospital discharge, having a positive culture resulted in increases in post-discharge pharmacy costs ($776, P<.0001) and inpatient costs ($12,167, P<.0001). Likewise, having a positive culture increased the risk of a readmission (odds ratio [OR]=1.396, P<.0001), the number of prescriptions (incidence rate ratio [IRR], 1.138; P<.0001) and the number of inpatient days (IRR, 1.204; P<.0001,) but decreased the number of subsequent outpatient encounters (IRR, 0.941; P<.008).
The results of this study indicate that MRSA infections are associated with higher levels of post-discharge healthcare cost and utilization. These findings indicate that financial benefits resulting from infection prevention efforts may extend beyond the initial hospital stay.
To estimate avoidable intravenous (IV) fluoroquinolone use in Veterans Affairs (VA) hospitals.
A retrospective analysis of bar code medication administration (BCMA) data.
Acute care wards of 128 VA hospitals throughout the United States.
Data were analyzed for all medications administered on acute care wards between January 1, 2006, and December 31, 2010. Patient-days receiving therapy were expressed as fluoroquinolone-days (FD) and divided into intravenous (IV; all doses administered intravenously) and oral (PO; at least one dose administered per os) FD. We assumed IV fluoroquinolone use to be potentially avoidable on a given IV FD when there was at least 1 other medication administered via the enteral route.
Over the entire study period, 884,740 IV and 830,572 PO FD were administered. Overall, avoidable IV fluoroquinolone use accounted for 46.8% of all FD and 90.9% of IV FD. Excluding the first 2 days of all IV fluoroquinolone courses and limiting the analysis to the non-ICU setting yielded more conservative estimates of avoidable IV use: 20.9% of all FD and 45.9% of IV FD. Avoidable IV use was more common for levofloxacin and more frequent in the ICU setting. There was a moderate correlation between avoidable IV FD and total systemic antibiotic use (r = 0.32).
Unnecessary IV fluoroquinolone use seems to be common in the VA system, but important variations exist between facilities. Antibiotic stewardship programs could focus on this patient safety issue as a “low-hanging fruit” to increase awareness of appropriate antibiotic use.
Investigators and medical decision makers frequently rely on administrative databases to assess methicillin-resistant Staphylococcus aureus (MRSA) infection rates and outcomes. The validity of this approach remains unclear. We sought to assess the validity of the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) code for infection with drug-resistant microorganisms (V09) for identifying culture-proven MRSA infection.
Retrospective cohort study.
All adults admitted to 3 geographically distinct hospitals between January 1, 2001, and December 31, 2007, were assessed for presence of incident MRSA infection, defined as an MRSA-positive clinical culture obtained during the index hospitalization, and presence of the V09 ICD-9-CM code. The k statistic was calculated to measure the agreement between presence of MRSA infection and assignment of the V09 code. Sensitivities, specificities, positive predictive values, and negative predictive values were calculated.
There were 466,819 patients discharged during the study period. Of the 4,506 discharged patients (1.0%) who had the V09 code assigned, 31% had an incident MRSA infection, 20% had prior history of MRSA colonization or infection but did not have an incident MRSA infection, and 49% had no record of MRSA infection during the index hospitalization or the previous hospitalization. The V09 code identified MRSA infection with a sensitivity of 24% (range, 21%–34%) and positive predictive value of 31% (range, 22%–53%). The agreement between assignment of the V09 code and presence of MRSA infection had a k coefficient of 0.26 (95% confidence interval, 0.25–0.27).
In its current state, the ICD-9-CM code V09 is not an accurate predictor of MRSA infection and should not be used to measure rates of MRSA infection.