Bacterial co-pathogens are commonly identified in viral respiratory infections and are important causes of morbidity and mortality. The prevalence of bacterial infection in patients infected with ...SARS-CoV-2 is not well understood.
To determine the prevalence of bacterial co-infection (at presentation) and secondary infection (after presentation) in patients with COVID-19.
We performed a systematic search of MEDLINE, OVID Epub and EMBASE databases for English language literature from 2019 to April 16, 2020. Studies were included if they (a) evaluated patients with confirmed COVID-19 and (b) reported the prevalence of acute bacterial infection.
Data were extracted by a single reviewer and cross-checked by a second reviewer. The main outcome was the proportion of COVID-19 patients with an acute bacterial infection. Any bacteria detected from non-respiratory-tract or non-bloodstream sources were excluded. Of 1308 studies screened, 24 were eligible and included in the rapid review representing 3338 patients with COVID-19 evaluated for acute bacterial infection. In the meta-analysis, bacterial co-infection (estimated on presentation) was identified in 3.5% of patients (95%CI 0.4–6.7%) and secondary bacterial infection in 14.3% of patients (95%CI 9.6–18.9%). The overall proportion of COVID-19 patients with bacterial infection was 6.9% (95%CI 4.3–9.5%). Bacterial infection was more common in critically ill patients (8.1%, 95%CI 2.3–13.8%). The majority of patients with COVID-19 received antibiotics (71.9%, 95%CI 56.1 to 87.7%).
Bacterial co-infection is relatively infrequent in hospitalized patients with COVID-19. The majority of these patients may not require empirical antibacterial treatment.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The proportion of patients infected with SARS-CoV-2 that are prescribed antibiotics is uncertain, and may contribute to patient harm and global antibiotic resistance.
The aim was to estimate the ...prevalence and associated factors of antibiotic prescribing in patients with COVID-19.
We searched MEDLINE, OVID Epub and EMBASE for published literature on human subjects in English up to June 9 2020.
We included randomized controlled trials; cohort studies; case series with ≥10 patients; and experimental or observational design that evaluated antibiotic prescribing.
The study participants were patients with laboratory-confirmed SARS-CoV-2 infection, across all healthcare settings (hospital and community) and age groups (paediatric and adult).
The main outcome of interest was proportion of COVID-19 patients prescribed an antibiotic, stratified by geographical region, severity of illness and age. We pooled proportion data using random effects meta-analysis.
We screened 7469 studies, from which 154 were included in the final analysis. Antibiotic data were available from 30 623 patients. The prevalence of antibiotic prescribing was 74.6% (95% CI 68.3–80.0%). On univariable meta-regression, antibiotic prescribing was lower in children (prescribing prevalence odds ratio (OR) 0.10, 95% CI 0.03–0.33) compared with adults. Antibiotic prescribing was higher with increasing patient age (OR 1.45 per 10 year increase, 95% CI 1.18–1.77) and higher with increasing proportion of patients requiring mechanical ventilation (OR 1.33 per 10% increase, 95% CI 1.15–1.54). Estimated bacterial co-infection was 8.6% (95% CI 4.7–15.2%) from 31 studies.
Three-quarters of patients with COVID-19 receive antibiotics, prescribing is significantly higher than the estimated prevalence of bacterial co-infection. Unnecessary antibiotic use is likely to be high in patients with COVID-19.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Berry et al highlight the critical need for timely, accurate and accessible epidemiologic data for the ongoing coronavirus disease 2019 (COVID-19) outbreak to inform public health response efforts. ...Individual-level case data are particularly valuable during outbreaks but are seldom made available in real time. Interactive data visualizations are also needed to contextualize this information and provide a pan-Canadian summary. These tools engage not only the public health and research communities but also the general public whose cooperation is essential to effectuate response efforts.
The prevalence of bacterial infection in patients with COVID-19 is low, however, empiric antibiotic use is high. Risk stratification may be needed to minimize unnecessary empiric antibiotic use.
To ...identify risk factors and microbiology associated with respiratory and bloodstream bacterial infection in patients with COVID-19.
We searched MEDLINE, OVID Epub and EMBASE for published literature up to 5 February 2021.
Studies including at least 50 patients with COVID-19 in any healthcare setting.
We used a validated ten-item risk of bias tool for disease prevalence. The main outcome of interest was the proportion of COVID-19 patients with bloodstream and/or respiratory bacterial co-infection and secondary infection. We performed meta-regression to identify study population factors associated with bacterial infection including healthcare setting, age, comorbidities and COVID-19 medication.
Out of 33 345 studies screened, 171 were included in the final analysis. Bacterial infection data were available from 171 262 patients. The prevalence of co-infection was 5.1% (95% CI 3.6–7.1%) and secondary infection was 13.1% (95% CI 9.8–17.2%). There was a higher odds of bacterial infection in studies with a higher proportion of patients in the intensive care unit (ICU) (adjusted OR 18.8, 95% CI 6.5–54.8). Female sex was associated with a lower odds of secondary infection (adjusted OR 0.73, 95% CI 0.55–0.97) but not co-infection (adjusted OR 1.05, 95% CI 0.80–1.37). The most common organisms isolated included Staphylococcus aureus, coagulase-negative staphylococci and Klebsiella species.
While the odds of respiratory and bloodstream bacterial infection are low in patients with COVID-19, meta-regression revealed potential risk factors for infection, including ICU setting and mechanical ventilation. The risk for secondary infection is substantially greater than the risk for co-infection in patients with COVID-19. Understanding predictors of co-infection and secondary infection may help to support improved antibiotic stewardship in patients with COVID-19.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
COVID-19 and antimicrobial resistance (AMR) are two intersecting global public health crises.
We aimed to describe the impact of the COVID-19 pandemic on AMR across health care settings.
A search was ...conducted in December 2021 in WHO COVID-19 Research Database with forward citation searching up to June 2022.
Studies evaluating the impact of COVID-19 on AMR in any population were included and influencing factors were extracted. Reporting of enhanced infection prevention and control and/or antimicrobial stewardship programs was noted.
Pooling was done separately for Gram-negative and Gram-positive organisms. Random-effects meta-analysis was performed.
Of 6036 studies screened, 28 were included and 23 provided sufficient data for meta-analysis. The majority of studies focused on hospital settings (n = 25, 89%). The COVID-19 pandemic was not associated with a change in the incidence density (incidence rate ratio 0.99, 95% CI: 0.67–1.47) or proportion (risk ratio 0.91, 95% CI: 0.55–1.49) of methicillin-resistant Staphylococcus aureus or vancomycin-resistant enterococci cases. A non-statistically significant increase was noted for resistant Gram-negative organisms (i.e. extended-spectrum beta-lactamase, carbapenem-resistant Enterobacterales, carbapenem or multi-drug resistant or carbapenem-resistant Pseudomonas aeruginosa or Acinetobacter baumannii, incidence rate ratio 1.64, 95% CI: 0.92–2.92; risk ratio 1.08, 95% CI: 0.91–1.29). The absence of reported enhanced infection prevention and control and/or antimicrobial stewardship programs initiatives was associated with an increase in gram-negative AMR (risk ratio 1.11, 95% CI: 1.03–1.20). However, a test for subgroup differences showed no statistically significant difference between the presence and absence of these initiatives (p 0.40).
The COVID-19 pandemic may have hastened the emergence and transmission of AMR, particularly for Gram-negative organisms in hospital settings. But there is considerable heterogeneity in both the AMR metrics used and the rate of resistance reported across studies. These findings reinforce the need for strengthened infection prevention, antimicrobial stewardship, and AMR surveillance in the context of the COVID-19 pandemic.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Obstructive sleep apnea (OSA) is associated with abnormal cerebral perfusion at wakefulness, but whether these anomalies evolve over time is unknown. Here, we examined longitudinal changes in ...regional cerebral blood flow (rCBF) distribution in late middle‐aged and older adults with treated or untreated OSA. Twelve controls (64.8 ± 8.0 years) and 23 participants with newly diagnosed OSA (67.8 ± 6.2 years) were evaluated with polysomnography and cerebral 99mTc‐HMPAO single‐photon emission computed tomography during wakeful rest. OSA participants were referred to a sleep apnea clinic and 13 of them decided to start continuous positive airway pressure (CPAP). Participants were tested again after 18 months. Voxel‐based analysis and extracted relative rCBF values were used to assess longitudinal changes. Untreated OSA participants showed decreased relative rCBF in the left hippocampus and the right parahippocampal gyrus over time, while treated participants showed trends for increased relative rCBF in the left hippocampus and the right parahippocampal gyrus. No changes were found over time in controls. Untreated OSA is associated with worsening relative rCBF in specific brain areas over time, while treated OSA shows the opposite. Considering that OSA possibly accelerates cognitive decline in older adults, CPAP treatment could help reduce risk for cognitive impairment.
Untreated OSA is associated with worsening relative rCBF in specific brain areas over time, while treated OSA shows the opposite. Considering that OSA possibly accelerates cognitive decline in older adults, CPAP treatment could help reduce risk for cognitive impairment.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Abstract Introduction Recent literature proposes that amyloid-β and phosphorylated tau (p-tau) synergism accelerates biomarker abnormalities in controls. Yet, it remains to be answered whether this ...synergism is the driving force behind Alzheimer disease (AD) dementia. Methods We stratified 314 mild cognitive impairment individuals using 18 Fflorbetapir positron emission tomography amyloid-β imaging and cerebrospinal fluid p-tau. Regression and voxel-based logistic regression models with interaction terms evaluated 2-year changes in cognition and clinical status as a function of baseline biomarkers. Results We found that the synergism between 18 Fflorbetapir and p-tau, rather than their additive effects, was associated with the cognitive decline and progression to AD. Furthermore, voxel-based analysis revealed that temporal and inferior parietal were the regions where the synergism determined an increased likelihood of developing AD. Discussion Together, the present results support that progression to AD dementia is driven by the synergistic rather than a mere additive effect between amyloid-β and p-tau proteins.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
The link between brain amyloid-β (Aβ), metabolism, and dementia symptoms remains a pressing question in Alzheimer's disease. Here, using positron emission tomography (
Fflorbetapir tracer for Aβ and
...FFDG tracer for glucose metabolism) with a novel analytical framework, we found that Aβ aggregation within the brain's default mode network leads to regional hypometabolism in distant but functionally connected brain regions. Moreover, we found that an interaction between this hypometabolism with overlapping Aβ aggregation is associated with subsequent cognitive decline. These results were also observed in transgenic Aβ rats that do not form neurofibrillary tangles, which support these findings as an independent mechanism of cognitive deterioration. These results suggest a model in which distant Aβ induces regional metabolic vulnerability, whereas the interaction between local Aβ with a vulnerable environment drives the clinical progression of dementia.
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