Race and ethnicity as risk factors for colonization and infection with key bacterial pathogens: a scoping review.

Background: Racial and ethnic disparities in infectious disease burden have been reported in the USA and globally, most recently for COVID-19. It remains unclear whether such disparities also exist for priority bacterial pathogens that are increasingly antibiotic-resistant. We conducted a scoping review to summarize published studies that report on colonization or community-acquired infection with pathogens among different races and ethnicities. Methods: We conducted an electronic literature search of MEDLINE®, Daily, Global Health, Embase, Cochrane Central, and Web of Science from inception to January 2022 for eligible observational studies. Abstracts and full-text publications were screened in duplicate for studies that reported data for race or ethnicity for at least one of the pathogens of interest. Results: Fifty-four observational studies in 59 publications met our inclusion criteria. Studies reported results for Enterobacterales, Enterococcus faecium, Escherichia coli, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Staphylococcus aureus, and were conducted in Australia, Brazil, Israel, New Zealand, and USA. USA studies most often examined Black and Hispanic minority groups with studies regularly reporting a higher risk of these pathogens in Black persons and mixed results for Hispanic persons. Ethnic minority groups (e.g. Bedouins in Israel, Aboriginals in Australia) were often reported to be at a higher risk in other countries. Conclusion: Sufficient evidence was identified in this scoping review justifying future systematic reviews and meta-analyses evaluating the relationship between community-acquired pathogens and race and ethnicity. However, we noted that only a fraction of studies reported data stratified by race and ethnicity, highlighting a substantial gap in the literature.

Socioeconomic status and the risk for colonization or infection with priority bacterial pathogens: a global evidence map.

Low socioeconomic status (SES) is thought to exacerbate risks for bacterial infections, but global evidence for this relationship has not been synthesized. We systematically reviewed the literature for studies describing participants' SES and their risk of colonization or community-acquired infection with priority bacterial pathogens. Fifty studies from 14 countries reported outcomes by participants' education, healthcare access, income, residential crowding, SES deprivation score, urbanicity, or sanitation access. Low educational attainment, lower than average income levels, lack of healthcare access, residential crowding, and high deprivation were generally associated with higher risks of colonization or infection. There is limited research on these outcomes in low- and middle-income countries (LMICs) and conflicting findings regarding the effects of urbanicity. Only a fraction of studies investigating pathogen colonization and infection reported data stratified by participants' SES. Future studies should report stratified data to improve understanding of the complex interplay between SES and health, especially in LMICs.

Erratum: 17157 Racial/ethnic disparities in antibiotic-resistant infections: Knowledge gaps and opportunities for educational interventions - ERRATUM.

[This corrects the article DOI: 10.1017/cts.2021.601.].

Postictal generalized electroencephalographic suppression following electroconvulsive therapy: Temporal characteristics and impact of anesthetic regimen.

OBJECTIVE: Postictal generalized electroencephalographic suppression (PGES) has been defined as electroencephalographic (EEG) activity of less than 10 microvolts following a generalized seizure. PGES is associated with an increased risk of sudden unexplained death in epilepsy, as well as treatment efficacy of electroconvulsive therapy (ECT). We investigated the impact of anesthetic on PGES expression and temporal characteristics. METHODS: We recorded postictal EEG in 50 ECT sessions in 11 patients with treatment resistant depression (ClinicalTrials.gov NCT02761330). For each participant, repeated sessions included either ketamine or etomidate general anesthesia during ECT. An automated algorithm was employed to detect PGES within 5 minutes after seizure termination. RESULTS: PGES was detected in 31/50 recordings, with intermittent epochs recurring up to five minutes after seizure termination. PGES total duration was greater following ketamine than etomidate anesthesia (p = 0.04). PGES expression declined loglinearly as a function of time (r = -0.89, p < 10-4). EEG amplitude during PGES did not vary linearly with time. CONCLUSIONS: PGES can occur intermittently for several minutes following seizure termination. Anesthetic effects should be considered when correlating PGES duration to clinical outcomes. SIGNIFICANCE: Prolonged EEG monitoring several minutes following seizure termination may be necessary to fully evaluate the presence and total duration of PGES.

Protocol for the Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography (P-DROWS-E) study: a prospective observational study of delirium in elderly cardiac surgical patients.

INTRODUCTION: Delirium is a potentially preventable disorder characterised by acute disturbances in attention and cognition with fluctuating severity. Postoperative delirium is associated with prolonged intensive care unit and hospital stay, cognitive decline and mortality. The development of biomarkers for tracking delirium could potentially aid in the early detection, mitigation and assessment of response to interventions. Because sleep disruption has been posited as a contributor to the development of this syndrome, expression of abnormal electroencephalography (EEG) patterns during sleep and wakefulness may be informative. Here we hypothesise that abnormal EEG patterns of sleep and wakefulness may serve as predictive and diagnostic markers for postoperative delirium. Such abnormal EEG patterns would mechanistically link disrupted thalamocortical connectivity to this important clinical syndrome. METHODS AND ANALYSIS: P-DROWS-E (Prognosticating Delirium Recovery Outcomes Using Wakefulness and Sleep Electroencephalography) is a 220-patient prospective observational study. Patient eligibility criteria include those who are English-speaking, age 60 years or older and undergoing elective cardiac surgery requiring cardiopulmonary bypass. EEG acquisition will occur 1-2 nights preoperatively, intraoperatively, and up to 7 days postoperatively. Concurrent with EEG recordings, two times per day postoperative Confusion Assessment Method (CAM) evaluations will quantify the presence and severity of delirium. EEG slow wave activity, sleep spindle density and peak frequency of the posterior dominant rhythm will be quantified. Linear mixed-effects models will be used to evaluate the relationships between delirium severity/duration and EEG measures as a function of time. ETHICS AND DISSEMINATION: P-DROWS-E is approved by the ethics board at Washington University in St. Louis. Recruitment began in October 2018. Dissemination plans include presentations at scientific conferences, scientific publications and mass media. TRIAL REGISTRATION NUMBER: NCT03291626.

Voltage-based automated detection of postictal generalized electroencephalographic suppression: Algorithm development and validation.

OBJECTIVE: Postictal generalized electroencephalographic suppression (PGES) is a pattern of low-voltage scalp electroencephalographic (EEG) activity following termination of generalized seizures. PGES has been associated with both sudden unexplained death in patients with epilepsy and therapeutic efficacy of electroconvulsive therapy (ECT). Automated detection of PGES epochs may aid in reliable quantification of this phenomenon. METHODS: We developed a voltage-based algorithm for detecting PGES. This algorithm applies existing criteria to simulate expert epileptologist readings. Validation relied on postictal EEG recording from patients undergoing ECT (NCT02761330), assessing concordance among the algorithm and four clinical epileptologists. RESULTS: We observed low-to-moderate concordance among epileptologist ratings of PGES. Despite this, the algorithm displayed high discriminability in comparison to individual epileptologists (C-statistic range: 0.86-0.92). The algorithm displayed high discrimination (C-statistic: 0.91) and substantial peak agreement (Cohen's Kappa: 0.65) in comparison to a consensus of clinical ratings. Interrater agreement between the algorithm and individual epileptologists was on par with that among expert epileptologists. CONCLUSIONS: An automated voltage-based algorithm can be used to detect PGES following ECT, with discriminability nearing that of experts. SIGNIFICANCE: Algorithmic detection may support clinical readings of PGES and improve precision when correlating this marker with clinical outcomes following generalized seizures.