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To describe the epidemiology of patients with nonintestinal carbapenem-resistant Enterobacterales (CRE) colonization and to compare clinical outcomes of these patients to those with CRE infection.
A secondary analysis of Consortium on Resistance Against Carbapenems in Klebsiella and other Enterobacteriaceae 2 (CRACKLE-2), a prospective observational cohort.
A total of 49 US short-term acute-care hospitals.
Patients hospitalized with CRE isolated from clinical cultures, April, 30, 2016, through August 31, 2017.
We described characteristics of patients in CRACKLE-2 with nonintestinal CRE colonization and assessed the impact of site of colonization on clinical outcomes. We then compared outcomes of patients defined as having nonintestinal CRE colonization to all those defined as having infection. The primary outcome was a desirability of outcome ranking (DOOR) at 30 days. Secondary outcomes were 30-day mortality and 90-day readmission.
Of 547 patients with nonintestinal CRE colonization, 275 (50%) were from the urinary tract, 201 (37%) were from the respiratory tract, and 71 (13%) were from a wound. Patients with urinary tract colonization were more likely to have a more desirable clinical outcome at 30 days than those with respiratory tract colonization, with a DOOR probability of better outcome of 61% (95% confidence interval [CI], 53%–71%). When compared to 255 patients with CRE infection, patients with CRE colonization had a similar overall clinical outcome, as well as 30-day mortality and 90-day readmission rates when analyzed in aggregate or by culture site. Sensitivity analyses demonstrated similar results using different definitions of infection.
Patients with nonintestinal CRE colonization had outcomes similar to those with CRE infection. Clinical outcomes may be influenced more by culture site than classification as “colonized” or “infected.”
Background: The gold standard for diagnosis of COVID-19 has been SARS-CoV-2 detection by reverse-transcriptase-quantitative polymerase chain reaction (RT-qPCR), which provides a semiquantitative indicator of viral load (cycle threshold, Ct). Our research group previously described how African American race and poverty were associated with an increased likelihood of hospitalization due to COVID-19. We sought to characterize the relationship between Ct values and clinical outcomes while controlling for sociodemographic factors. Methods: We conducted a cross-sectional study of SARS-CoV-2–positive patients admitted to Froedtert Health between March 16 and June 1, 2020. Ct values were obtained by direct interrogation of either cobas SARS-CoV-2 or Cepheid Xpert Xpress platforms. Patient demographics, comorbidities, symptoms at admission, health insurance, and hospital course were collected using electronic medical records. A proxy for socioeconomic disadvantage, area-deprivation index (ADI), was assigned using ZIP codes. Multivariate models were performed to assess associations between Ct values and clinical outcomes while controlling for ADI, race, and type of insurance. Results: Overall, 302 patients were included. The mean age was 60.89 years (SD, 18.2); 161 (53%) were men, 177 (58%) were African Americans; and 156 (51%) had Medicaid or were uninsured. Of the 302 inpatients, 158 (52%) required admission to the ICU, 199 (65.9%) were discharged to home, 49 (16.2%) were discharged to a nursing home, and 54 (17.9%) died. Lower Ct values (higher viral load) were associated with Medicaid or lack of insurance (coefficient, −2.88, 95% confidence interval [CI], −4.96 to −0.79, P = .007) and age >60 years old (coefficient, −2.98, 95% CI −4.87 to −1.08, P = .002). Contrary to what was expected, higher CT values (lower viral load) were associated with higher ADI scores (coefficient, 2.62, 95% CI, 0.52–4.85; P = .017). However, when patients were stratified into low, medium, and high ADI, those with Medicaid or no insurance had the lowest mean Ct values (23.3, 25.9, and 27.6, respectively) compared to Medicare or other insurance (Figure 1). Body mass index (odds ratio [OR], 1.04; 95% CI, 1.02–1.07; P = .001) and male sex (OR, 2.15; 95% CI, 1.28–3.60; P = .004) were independently associated with ICU admission. Every increase of a CT point (OR, 0.90; 95% CI, 0.85–0.95; p <0.001) and age >60 years old (OR 2.62, 95% CI; 1.14-6.04; p=0.023) was associated with death. Conclusions: In this cross-sectional study of adults tested for COVID-19 in a large midwestern academic health system, lower Ct values were independently associated with poverty and age >60 years old.
Background: Asymptomatic SARS-CoV-2 infections play a crucial role in viral transmission. However, they are often difficult to identify given that widespread surveillance has not been the norm. We sought to determine whether COVID-19 rates reported at the county level could predict the positivity rates for SARS-CoV-2 among asymptomatic patients tested in a large academic health system. Methods: This observational study was conducted from April 23, 2020, to December 10, 2020, at Froedtert Health (FH) system, the largest academic health system in Wisconsin. On April 23, 2020, FH implemented SARS-CoV-2 surveillance among all consecutive admissions not suspected of COVID-19, all patients scheduled for elective procedures and deliveries, and all asymptomatic patients with known exposures. Samples were processed by the FH laboratory using molecular methods (RT-PCR). To obtain the daily number of newly confirmed COVID-19 cases in Milwaukee County, we accessed the Wisconsin Department of Health Services publicly available COVID-19 database. For the purpose of this study, COVID-19 rates were defined as the percentage of positive tests among all daily tests performed at the county level, while SARS-CoV-2 positivity rates were the percentage of positive tests among all daily surveillance tests performed at FH among asymptomatic patients. The association between COVID-19 rates in Milwaukee County and asymptomatic rates at FH were assessed using an autoregressive moving average time series analysis. To examine the association between these rates, we fitted a seventh-order autoregression for the residuals based on autocorrelation function and partial autocorrelation function plots of the residuals from linear regression. Results: From April 23, 2020, to December 10, 2020, there were 2,347 new asymptomatic infections detected at FH and 75,196 new COVID-19 cases reported in Milwaukee County. Figure 1 shows the time-series plot of asymptomatic SARS-CoV-2 positivity rates at FH and Figure 2 shows COVID-19 rates in Milwaukee County. As the COVID-19 rate in Milwaukee County increased by 1 unit, the asymptomatic infection rate in FH decreased by 0.024 unit (95% CI, −0.053 to 0.004; P = .095) after accounting for autocorrelation over time. Thus, there was no association between these rates. Conclusions: The positivity rates among asymptomatic patients at a large medical center were not predicted by the positivity rate at the county level. This finding suggests that the epidemiology at a county level may be determined by pockets in the population who may not interact, and thus not affect, the positivity rates among asymptomatic patients served by a hospital system within the county.
Background: The COVID-19 pandemic has disproportionately affected nursing home residents, and emerging evidence suggests quality, location, resident demographics, and staffing levels may be related to COVID-19 incidence within facilities. We describe the distribution of COVID-19 cases in Wisconsin nursing homes from January 2020 to October 2020, the effect of rural versus urban locations on COVID-19 incidence, and the temporal changes in COVID-19 incidence. Methods: We constructed a database using the Center for Medicaid and Medicare Services’ (CMS) publicly available data. Variables obtained per facility included location, number of beds, ownership type, average census, 5-star ratings (overall, quality, health, staffing, and nurse staffing categories), number of COVID-19 cases, resident Medicaid/Medicare share, area deprivation index, and social vulnerability index. Nursing homes were divided into tertiles based on total COVID-19 cases for descriptive analysis (zero cases, 1–7 cases, >7 cases). Demographic and clinical variables were reported as frequencies, mean (standard deviation) or median (interquartile range). We compared groups using the Pearson χ2 test and the Kruskal-Wallis test. COVID-19 incidence rates were calculated by dividing the number of COVID-19 cases by monthly occupied bed days and multiplied by 10,000. Results: From January 1, 2020, to November 1, 2020, in total, 3,133 SARS-CoV-2–confirmed cases were reported among 248 (70.5%) nursing homes. Urban location (P = .027), overall 5-star rating (P = .035), number of beds (p < 0.001), and average count of residents per day (p < 0.001) were associated with a greater number of COVID-19 cases. Temporal analysis showed that the highest incidence rates of COVID-19 in NHs were observed from January to May and in October 2020 (11.36 and 30.33 cases per 10,000 occupied-bed days, respectively). Urban NHs experienced higher incidence rates until September, then incidence rates among rural facilities surged (Fig.1A). In the first half of the year, NHs with lower quality scores (1-3 stars) had a higher COVID-19 incidence rate; however, in August this trend reversed, and facilities with higher quality scores (4-5 stars) showed the highest incidence rates (Fig.1B). Fig. 2 shows a temporal depiction of the shift from urban to rural settings. Conclusions: Higher COVID-19 incidence rates during the first 5 months of the pandemic were observed in urban, larger facilities with lower 5-star rating. By the end of the year, nursing homes in rural areas and those with higher quality ratings had the highest incidence rates.
Asymptomatic SARS-CoV-2 infections are often difficult to identify because widespread surveillance has not been the norm. Using time-series analyses, we examined whether COVID-19 rates at the county level could predict positivity rates among asymptomatic patients in a large health system. Asymptomatic positivity rates at the system level and county-level COVID-19 rates were not associated.
The primary aim of this study was to assess the epidemiology of carbapenem-resistant Acinetobacter baumannii (CRAB) for 9 months following a regional outbreak with this organism. We also aimed to determine the differential positivity rate from different body sites and characterize the longitudinal changes of surveillance test results among CRAB patients.
A 607-bed tertiary-care teaching hospital in Milwaukee, Wisconsin.
Any patient admitted from postacute care facilities and any patient housed in the same inpatient unit as a positive CRAB patient.
Participants underwent CRAB surveillance cultures from tracheostomy secretions, skin, and stool from December 5, 2018, to September 6, 2019. Cultures were performed using a validated, qualitative culture method, and final bacterial identification was performed using mass spectrometry.
In total, 682 patients were tested for CRAB, of whom 16 (2.3%) were positive. Of the 16 CRAB-positive patients, 14 (87.5%) were residents from postacute care facilities and 11 (68.8%) were African American. Among positive patients, the positivity rates by body site were 38% (6 of 16) for tracheal aspirations, 56% (9 of 16) for skin, and 82% (13 of 16) for stool.
Residents from postacute care facilities were more frequently colonized by CRAB than patients admitted from home. Stool had the highest yield for identification of CRAB.
The household setting has some of the highest coronavirus disease 2019 (COVID-19) secondary-attack rates. We compared the air contamination in hospital rooms versus households of COVID-19 patients. Inpatient air samples were only positive at 0.3 m from patients. Household air samples were positive even without a COVID-19 patient in the proximity to the air sampler.
Not all patients who acquire carbapenemase-producing Enterobacteriaceae (CPE) develop infections by these organisms; many remain only colonized. Of 54 CPE-colonized patients, 16 (30%) developed CPE infections. We identified indwelling urinary catheter exposure, exposure to intravenous colistin, and overseas transfer as variables associated with CPE infection development among colonized patients.
Long-term care facilities (LTCFs) and their populations have been greatly affected by the coronavirus disease 2019 (COVID-19) pandemic. In this review, we summarize the literature to describe the current epidemiology of COVID-19 in LTCFs, clinical presentations and outcomes in the LTCF population with COVID-19, containment interventions, and the role of healthcare workers in SARS-CoV-2 transmission in these facilities.
The association between Clostridioides difficile colonization and C. difficile infection (CDI) is unknown in solid-organ transplant (SOT) patients. We examined C. difficile colonization and healthcare-associated exposures as risk factors for development of CDI in SOT patients.
The retrospective study cohort included all consecutive SOT patients with at least 1 screening test between May 2017 and April 2018. CDI was defined as the presence of diarrhea (without laxatives), a positive C. difficile clinical test, and the use of C. difficile-directed antimicrobial therapy as ordered by managing clinicians. In addition to demographic variables, exposures to antimicrobials, immunosuppressants, and gastric acid suppressants were evaluated from the time of first screening test to the time of CDI, death, or final discharge.
Of the 348 SOT patients included in our study, 33 (9.5%) were colonized with toxigenic C. difficile. In total, 11 patients (3.2%) developed CDI. Only C. difficile colonization (odds ratio [OR], 13.52; 95% CI, 3.46–52.83; P = .0002), age (OR, 1.09; CI, 1.02–1.17; P = .0135), and hospital days (OR, 1.05; 95% CI, 1.02–1.08; P = .0017) were independently associated with CDI.
Although CDI was more frequent in C. difficile colonized SOT patients, the overall incidence of CDI was low in this cohort.
Previously, we showed that disinfection of sink drains is effective at decreasing bacterial loads. Here, we report our evaluation of the ideal frequency of sink-drain disinfection and our comparison of 2 different hydrogen peroxide disinfectants.
Describe the epidemiological and molecular characteristics of an outbreak of Klebsiella pneumoniae carbapenemase (KPC)–producing organisms and the novel use of a cohorting unit for its control.
A 566-room academic teaching facility in Milwaukee, Wisconsin.
Solid-organ transplant recipients.
Infection control bundles were used throughout the time of observation. All KPC cases were intermittently housed in a cohorting unit with dedicated nurses and nursing aids. The rooms used in the cohorting unit had anterooms where clean supplies and linens were placed. Spread of KPC-producing organisms was determined using rectal surveillance cultures on admission and weekly thereafter among all consecutive patients admitted to the involved units. KPC-positive strains underwent pulsed-field gel electrophoresis and whole-genome sequencing.
A total of 8 KPC cases (5 identified by surveillance) were identified from April 2016 to April 2017. After the index patient, 3 patients acquired KPC-producing organisms despite implementation of an infection control bundle. This prompted the use of a cohorting unit, which immediately halted transmission, and the single remaining KPC case was transferred out of the cohorting unit. However, additional KPC cases were identified within 2 months. Once the cohorting unit was reopened, no additional KPC cases occurred. The KPC-positive species identified during this outbreak included Klebsiella pneumoniae, Enterobacter cloacae complex, and Escherichia coli. blaKPC was identified on at least 2 plasmid backbones.
A complex KPC outbreak involving both clonal and plasmid-mediated dissemination was controlled using weekly surveillances and a cohorting unit.
In 2018, the Clostridium difficile LabID event methodology changed so that hospitals doing 2-step tests, nucleic acid amplification test (NAAT) plus enzyme immunofluorescence assay (EIA), had their adjustment modified to EIA-based tests, and only positive final tests (eg, EIA) were counted in the numerator. We report the immediate impact of this methodological change at 3 Milwaukee hospitals.
New studies have been published regarding the epidemiology of Clostridium difficile in topics such as asymptomatic C. difficile colonization, community-associated C. difficile infection, environmental contamination outside healthcare settings, animal colonization, and the interactions between C. difficile and the gut microbiome. In addition to summarizing these findings, this review offers a perspective on the potential impact of high-throughput sequencing and other potential techniques on the prevention of C. difficile.