To assess the risk of hospital admission for coronavirus disease 2019 (covid-19) among patient facing and non-patient facing healthcare workers and their household members.Nationwide linkage cohort study.Scotland, UK, 1 March to 6 June 2020.Healthcare workers aged 18-65 years, their households, and other members of the general population.Admission to hospital with covid-19.The cohort comprised 158 445 healthcare workers, most of them  being patient facing, and 229 905 household members. Of all hospital admissions for covid-19 in the working age population (18-65 year olds), 17.2% (360/2097) were in healthcare workers or their households. After adjustment for age, sex, ethnicity, socioeconomic deprivation, and comorbidity, the risk of admission due to covid-19 in non-patient facing healthcare workers and their households was similar to the risk in the general population  and 0.86 (0.49 to 1.51), respectively). In models adjusting for the same covariates, however, patient facing healthcare workers, compared with non-patient facing healthcare workers, were at higher risk , as were household members of patient facing healthcare workers . After sub-division of patient facing healthcare workers into those who worked in “front door,” intensive care, and non-intensive care aerosol generating settings and other, those in front door roles were at higher risk . For most patient facing healthcare workers and their households, the estimated absolute risk of hospital admission with covid-19 was less than 0.5%, but it was 1% and above in older men with comorbidity.Healthcare workers and their households contributed a sixth of covid-19 cases admitted to hospital. Although the absolute risk of admission was low overall, patient facing healthcare workers and their household members had threefold and twofold increased risks of admission with covid-19. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to spread globally, with more than 8 million cases of coronavirus disease 2019 (covid-19) and more than half a million deaths as of 10 July 2020.Healthcare workers, who have been integral to the response to covid-19, may be at increased risk of contracting SARS-CoV-2 and hence subsequently transmitting it to their household, workplace contacts, or both.Despite this, the extent of these risks is not well understood, as most studies have been in single centres and limited by small sample sizes and/or biased selection and recording of disease.Using record linkage, we evaluated the risk of admission to hospital with covid-19 among healthcare workers in patient and non-patient facing roles along with the risk in their household members. We further evaluated the risk of admission with covid-19 in patient facing healthcare workers in different clinical settings including intensive care and “front door” departments.We included healthcare workers if on 1 March 2020 (the date of the first positive reported case of covid-19 in Scotland) they were directly employed by the NHS, contracted to provide NHS general practice services in Scotland, or both. We defined healthcare workers as people providing healthcare services, whether they did so directly  or indirectly .We used the CHI number to create a cohort linking these data on healthcare workers to multiple Scotland-wide databases (supplementary figure A). These included datasets containing individual level clinical information for virology testing for SARS-CoV-2, general hospital admission data, community prescribing, critical care admissions, and the national register for deaths (appendix 1).We also used the CHI database to identify all individuals who were not themselves healthcare workers but shared a household with a healthcare worker. We assigned people to the same household if the address  on the CHI database was identical for both; fuzzy matching was not allowed. These household members were then also linked to the Scotland-wide datasets to construct a household member specific cohort (supplementary figure A). The healthcare worker cohort was restricted to the working age population (18-65 years), but the household member cohort included all ages.Finally, we appended selected variables from the healthcare worker and household member data to an existing Scottish case-control study, REACT-COVID-19.We restricted outcomes to the time period from 1 March to 6 June 2020. The primary outcome was admission to hospital with covid-19, defined as the first positive test for SARS-CoV-2 in hospital and/or the individual being admitted within 28 days of testing positive. Secondary outcomes reported were admission to intensive care and death occurring within 28 days of first testing positive. We included tests irrespective of whether they were done for screening or clinical purposes. We chose hospital admission as the primary outcome because milder disease not requiring admission is likely to be subject to ascertainment bias , and because admission with covid-19 is a clinically significant event.We defined occupational roles for all healthcare workers by using the SWISS/GPCD databases. We categorised broad roles into patient facing, non-patient facing, or undetermined. We defined roles on the basis of formal job titles for nursing staff, allied health professionals, and support staff and specialty for medical staff. Selected nursing staff were additionally assigned on the basis of their working location . We deliberately made these definitions narrow, assigning around a fifth of healthcare workers to “undetermined” (appendix 3). We did this to avoid non-differential misclassification bias. We assigned household members to the role of the associated member of staff . Where a household included more than more than one healthcare worker, we applied the highest risk designation.We further divided patient facing roles into the following settings: “front door” , intensive care, non-intensive care but still exposed to aerosol generating procedures , and “other.” We made these designations before database linkage . During the course of the pandemic, guidance on infection prevention and control was updated. The key changes that took place, including the release dates, are summarised in appendix 4.Occupation related covariates obtained from the healthcare worker database were seniority grade, occupation , length of service, immigration status, and full/part time working status. We obtained age, sex, and fifth of the Scottish Index of Multiple Deprivation (SIMD), an area based measure of socioeconomic deprivation,A small proportion of people in the SWISS database who failed to meet the criteria for inclusion may have done so because of missing data (appendix 2). However, among those selected, no data were missing for the variables included in the regression model, other than part time status  and ethnicity. For ethnicity, missingness was caused when the ONOMAP algorithm failed to assign an ethnic group. As this was rare (1.22%), we used simple imputation to assign an ethnicity based on the most common ethnicity in the household or, where this was missing, for all members of a household in Scotland.We plotted the cumulative incidence of admission to hospital with covid-19 for healthcare workers, household members, and working age adults in the general population who were not healthcare workers or their household members. We obtained the denominator for the last group by subtracting the healthcare worker and household cohorts from the 2019 mid-year estimates. In the healthcare worker and household cohorts, we modelled hospital admission with covid-19 by using Cox regression, calculating robust standard errors to allow for clustering due to shared household membership and stratifying on groups of health board areas to allow for differences in baseline hazard. We chose these strata a priori on the basis of data for the general Scottish population. We treated age as a continuous covariate. To avoid residual confounding due to any non-linearity in the association between age and the (log) hazard rate, we fitted age by using a penalised spline function.In the case-control study, we did conditional logistic regression. As REACT-COVID-19 used incidence density sampling,https://github.com/ChronicDiseaseEpi/hcw/.We have also provided a separate prespecified statistical analysis plan. We used R version 3.6.1 for analyses. The analytical code is available at No patients were involved in setting the research question or the outcome measures, nor were they involved in developing plans for design of the study. No patients were asked to advise on interpretation or writing up of results.The cohort comprised 158 445 healthcare workers and 229 905 household members. Most healthcare workers , but only 88 274 (38.4%) household members, were women. More than half of healthcare workers  were patient facing, with 32 615 (20.6%) classified as non-patient facing and 35 097 (22.2%) as undetermined . Most paWe estimated the total Scottish population to be 5 463 300, with the working age population (18-65 years) estimated at 3 452 592 (supplementary figure B). Across the entire Scottish population, 6346 hospital admissions with covid-19 occurred . This meThe risk of admission to hospital with covid-19 was 0.20% (181/90 733), 0.07% (23/32 615), and 0.11% (39/35 097) in patient facing, non-patient facing, and undetermined healthcare workers . With thCompared with non-patient facing healthcare workers, after adjustment for age, sex, socioeconomic status, ethnicity, and comorbidity, patient facing workers were at a higher risk of hospital admission  .Compared with the general population, the risk among non-patient facing healthcare workers was not increased, including after adjustment for age, sex, socioeconomic deprivation, and comorbidity  . HealthcIn the cumulative incidence plots , the risIn further exploratory analysis, we evaluated the risk of hospital admission with covid-19 within occupational roles in healthcare workers (using nursing and midwifery staff as the referent). Absolute risk across occupational roles ranged from 0.07% in administrative staff to 0.20% in nursing and midwifery staff (supplementary table C). Given the small number of admissions within some occupational groups, drawing strong conclusions as to whether any specific occupational role carried a higher adjusted risk of admission was difficult. The confidence intervals were wide, and all risk estimates crossed the null.The risk of admission to hospital with covid-19 was 0.07% (89/136 563), 0.04% (20/44 812), and 0.07% (32/48 530) in household members of patient facing, non-patient facing, and undetermined healthcare workers . The oveAssociations seen among household members were similar, albeit attenuated, to those seen among healthcare workers. In models adjusting for age and sex, compared with household members of non-patient facing healthcare workers, those in households of patient facing healthcare workers had a higher risk of hospital admission . We also saw this association after adjusting for age, sex, ethnicity, socioeconomic deprivation, and comorbidity . Those in households of non-patient facing healthcare workers had a similar risk to that seen in the general population  (v 227 (13.1%)). Household members were more similar to the general population.Among hospital admissions with covid-19, compared with the general population, healthcare workers were similar in terms of age and comorbidity . HoweverIn nearly 160 000 healthcare workers and 250 000 household members of healthcare workers, we found that admission to hospital with covid-19 was uncommon, with an overall risk of less than 0.5% during the covid-19 pandemic period (1 March 2020 to 6 June 2020). Compared with other adults of working age, however, this risk was higher. Accounting for age, sex, and other confounders, patient facing healthcare workers and members of their households were, respectively, threefold and twofold more likely to be admitted to hospital. Healthcare workers and their households accounted for one in six of all admissions with covid-19 in the working age population (18-65 years).Across both the general and the healthcare worker populations, the absolute risk of hospital admissions remains relatively small, ranging from 0.06% to 0.20%. This low absolute risk reflects the fact that risk from covid-19 is strongly related to age and that most adults  included our analyses were aged between 18 and 65 years. Nevertheless, within healthcare workers who were admitted to hospital, a non-trivial proportion resulted in admission to critical care or death. Among admitted healthcare workers, one in eight were admitted into critical care and six (2.5%) died; in admitted household members, one in five were admitted to critical care and 18 (12.9%) died. Therefore, as well as having implications for the transmission of covid-19,We report the risk of covid-19 in nearly 250 000 household members of healthcare workers. Previous evidence on the risk of covid-19 to household members of healthcare workers is sparse,Several studies have reported an increased risk of covid-19 infection and high prevalence of SARS-CoV-2 in healthcare workers, especially in front line workers.In response to emerging evidence and international guidance, the NHS in Scotland introduced several changes to infection prevention and control guidance during the course of the pandemic.In moderate risk settings, where patients may have only suspected, or even unsuspected, covid-19, the use of more resource intensive and burdensome personal protective equipment of the kind deployed in high risk settings is very challenging.Several limitations need to be considered. Firstly, given the small number of deaths in the healthcare worker population, we were unable to estimate the risk of covid-19 related mortality compared with the general population. The Office for National Statistics (ONS) in England did not find increased covid-19 mortality among healthcare workers.As the northern hemisphere enters winter and non-pharmacological measures in populations are relaxed, governments, healthcare managers, and occupational health specialists need to consider how best to protect healthcare workers in the event of a resurgent pandemic. This is necessary to protect the healthcare workers and their families,Several systematic reviews and reports have summarised studies of covid-19 infections in healthcare workersMost studies have been small, based in single centres, and cross sectional in nature and used methods highly susceptible to bias or restricted their populations to physicians and nursesStudies evaluating the risk of covid-19 infection in household members of healthcare workers are lackingHealthcare workers and their households contributed a sixth of hospital admissions with covid-19 among working age adultsHealthcare workers in patient facing roles—especially those in “front door” roles—are, along with their households, at higher risk of admission with covid-19Importantly, those in non-patient facing roles had similar risks to the general population


In December 2019, pneumonia cases of unknown origin were reported in Wuhan City, Hubei Province, China. Identified as the coronavirus disease (COVID-19), the number of cases grew rapidly by human-to-human transmission in Wuhan. Social media, especially Sina Weibo , has become an important platform for the public to obtain information and seek help.This study aims to analyze the characteristics of suspected or laboratory-confirmed COVID-19 patients who asked for help on Sina Weibo.We conducted data mining on Sina Weibo and extracted the data of 485 patients who presented with clinical symptoms and imaging descriptions of suspected or laboratory-confirmed cases of COVID-19. In total, 9878 posts seeking help on Sina Weibo from February 3 to 20, 2020 were analyzed. We used a descriptive research methodology to describe the distribution and other epidemiological characteristics of patients with suspected or laboratory-confirmed SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. The distance between patients’ home and the nearest designated hospital was calculated using the geographic information system ArcGIS.All patients included in this study who sought help on Sina Weibo lived in Wuhan, with a median age of 63.0 years (IQR 55.0-71.0). Fever  was the most common symptom. Ground-glass opacity  was the most common pattern on chest computed tomography; 39.67% (167/421) of families had suspected and/or laboratory-confirmed family members; 36.58% (154/421) of families had 1 or 2 suspected and/or laboratory-confirmed members; and 70.52% (232/329) of patients needed to rely on their relatives for help. The median time from illness onset to real-time reverse transcription-polymerase chain reaction (RT-PCR) testing was 8 days (IQR 5.0-10.0), and the median time from illness onset to online help was 10 days (IQR 6.0-12.0). Of 481 patients, 32.22% (n=155) lived more than 3 kilometers away from the nearest designated hospital.Our findings show that patients seeking help on Sina Weibo lived in Wuhan and most were elderly. Most patients had fever symptoms, and ground-glass opacities were noted in chest computed tomography. The onset of the disease was characterized by family clustering and most families lived far from the designated hospital. Therefore, we recommend the following: (1) the most stringent centralized medical observation measures should be taken to avoid transmission in family clusters; and (2) social media can help these patients get early attention during Wuhan’s lockdown. These findings can help the government and the health department identify high-risk patients and accelerate emergency responses following public demands for help. In December 2019, pneumonia cases of unknown origin were reported in Wuhan City, Hubei Province, China. The illness was identified and officially named as coronavirus disease 2019 (COVID-19), which is caused by a novel viral strain called severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)  and reseSince the COVID-19 outbreak, social media, especially Sina Weibo, has become an important platform for the public to obtain epidemic-related information quickly and effectively. According to the official outbreak data released by Sina Weibo on February 26, 2020, 51.2 million users cumulatively posted 350 million pieces of epidemic-related content. Online readership of epidemic-related topics reached 754.5 billion. Sina Weibo established a communication channel that allowed the government to effectively listen and respond to public opinion quickly. Here, by collecting data from Sina Weibo from February 3 to 20, 2020, we aim to analyze the characteristics of suspected or laboratory-confirmed patients with the SARS-CoV-2 infection.In this study, we describe the characteristics of suspected or laboratory-confirmed patients with the SARS-CoV-2 infection, the distribution of patients throughout Wuhan, and the relationship between helpers  and patients. Social media was used to obtain timely access to public demand so that the government and the health department could identify high-risk patients and take measures to help these patients.Sina Weibo launched a platform to provide online help channels for patients infected with SARS-CoV-2. From February 3 to 20, 2020, we obtained 9878 posts by using the keyword 肺炎患者求助 (COVID-19 pneumonia patients seeking help) from Sina Weibo through its application programming interface (API). Python (Python Software Foundation) was used to implement a rule-based screening and classification method on the PyCharm platform. We used the collected posts as a training set, including related posts and unrelated posts. Based on the post-for-help rules formulated by Sina Weibo, we considered the post text, as well as keywords pertaining to name, age, home address, time of illness, and description of illness as a related post; otherwise, it was deemed an irrelevant post. We excluded 6922 irrelevant posts that only described opinions and feelings about help seeking related to COVID-19 and initially collected 2956 related posts that contained mentions of clinical symptoms and/or imaging descriptions. Then, we manually screened out and excluded posts. We excluded 1679 reposted posts, 556 posts with a significant amount of missing valid clinical data, 195 nonpneumonia patient posts, and 41 patient posts with non-Wuhan home addresses. Finally, we selected 485 patient posts that presented clinical symptoms and imaging descriptions  and 2. TWe collected clinical symptoms, chest computed tomography (CT) findings , days from illness onset to online help, days from illness onset to RT-PCR testing, RT-PCR test results, the relationship between helpers and patients, and home address details from Sina Weibo’s records. We performed a study on the clinical characteristics of suspected or laboratory-confirmed patients with the SARS-CoV-2 infection seeking help on Sina Weibo. Suspected cases were identified as having fever or respiratory symptoms such as shortness of breath, cough, productive sputum, or chest pain. A laboratory-confirmed case with SARS-CoV-2 infection was defined as a positive result to high throughput sequencing or real-time reverse transcription-polymerase chain reaction (RT-PCR) assay of throat swabs and sputum .Person-to-person transmission of COVID-19 in hospital and family settings has been increasing -29. FamiOur research also found that the number of patients in the Wuchang, Jiang'an, Qiaokou, Hongshan, and Hanyang districts was greater than in other districts. In total, 32.22% (155/481) of patients lived more than 3 kilometers away from their nearest designated hospital. According to Baidu maps, adults can walk 4 kilometers in 1 hour. Considering that the patients in this study were older and their health condition may have slowed them down even more, we estimate that patients could walk 3 kilometers in a 1-hour period. Hence, this indicates that a patient would need to walk more than 1 hour to see a doctor since public transportation was suspended at the time. This may be one of the reasons why patients wanted to be admitted to a hospital. In addition, on February 5th, the Wuhan municipal health commission designated 28 hospitals for the treatment of laboratory-confirmed patients with the SARS-CoV-2 infection. The empty bed rate of hospitals within the city was only 3.6%. Thus, patients could not be hospitalized for the various reasons above. This also reflected an insufficiency of medical resources during the initial outbreak .We also explored the relationship between helpers and patients. Judging from the content of Sina Weibo posts asking for help, “Mom” and “Dad” were high-frequency words; 70.52% (232/329) of helpers were the patients' relatives, indicating that the publishers of the help information were mostly the children of the elderly. Unfamiliarity with new technology may have hindered elderly people from seeking assistance from the outside world.With the rapid and effective dissemination of help information, since February 5th, the People's Daily has launched an all-media operation to provide online help channels for patients with the SARS-CoV-2 infection. The government implemented a policy to maximize hospital admissions, which led to a rapid decrease in the number of people seeking help on Sina Weibo on February 6th and remained at low levels since February 8th, indicating that the needs of the public had been met. This also means that it is important to establish new and effective communication mechanisms for the dissemination of important factual information in a timely manner. Through this epidemic, we can see that medical resources are insufficiently allocated. There are substantial regional disparities in health care resource availability and accessibility in China . The rapOur study has some limitations. First, given that our data was collected from a social media platform, the description of patients’ symptoms and laboratory information were likely to be incomplete. Second, the urgent timeline for data extraction and the subjective judgment of the collectors might undermine the data quality to a certain extent. Finally, we learned that most of these patients have been admitted to the hospital with government help and many patients remain in the hospital, so we did not compare the 28-day rate for the composite endpoint.In summary, our study found that the distance between patients and hospitals and the closure of public transportation further increased the difficulty of hospitalization for the elderly. We recommend the application of centralized medical observations to avoid the spread of COVID-19 through family clusters. In a public health emergency, making full use of available social media platforms can establish effective, factual communication channels and shorten admission times, helping patients get early attention during the Wuhan lockdown. These findings can help the government and health departments pay attention to the elderly population during the outbreak and accelerate emergency responses following public demands for help.


New-onset atrial fibrillation (AF) in patients with chronic obstructive pulmonary disease (COPD) is associated with an accelerated decline in lung function, and a significant increase in mortality rate. A deeper understanding of the risk factors for new-onset AF during COPD will provide insights into the relationship between COPD and AF and guide clinical practice. This systematic review and meta-analysis is designed to identify risk factors for new-onset AF in patients with COPD, and to formulate recommendations for preventing AF in COPD patients that will assist clinical decision making.PubMed, Embase, Web of Science and Cochrane Library databases were searched for studies, which reported the results of potential risk factors for new-onset AF in COPD patients.Twenty studies involving 8,072,043 participants were included. Fifty factors were examined as potential risk factors for new-onset AF during COPD. Risk factors were grouped according to demographics, comorbid conditions, and COPD- and cardiovascular-related factors. In quantitative analysis, cardiovascular- and demographic-related factors with a greater than 50% increase in the odds of new-onset AF included age (over 65 years and over 75 years), acute care encounter, coronary artery disease, heart failure and congestive heart failure. Only one factor is related to the reduction of odds by more than 33.3%, which is black race (vs white). In qualitative analysis, the comparison of the risk factors was conducted between COPD-associated AF and non-COPD-associated AF. Cardiovascular-related factors for non-COPD-associated AF were also considered as risk factors for new-onset AF during COPD; however, the influence tended to be stronger during COPD. In addition, comorbid factors identified in non-COPD-associated AF were not associated with an increased risk of AF during COPD.New-onset AF in COPD has significant demographic characteristics. Older age (over 65 years), males and white race are at higher risk of developing AF. COPD patients with a history of cardiovascular disease should be carefully monitored for new-onset of AF, and appropriate preventive measures should be implemented. Even patients with mild COPD are at high risk of new-onset AF. This study shows that risk factors for new-onset AF during COPD are mainly those associated with the cardiovascular-related event and are not synonymous with comorbid factors for non-COPD-associated AF. The pathogenesis of COPD-associated AF may be predominantly related to the cardiac dysfunction caused by the chronic duration of COPD, which increases the risk of cardiovascular-related factors and further increases the risk of AF during COPD.CRD42019137758. In addition, the risk of AF related hospitalization was 1.3 times higher in 60% ≤ FEV1 <80% of predicted and 1.8-times higher in FEV1 <60% of predicted compared with FEV1 ≥ 80% of predicted (Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of morbidity and mortality worldwide , and is redicted .COPD is one of the main risk factors for AF. For patients with COPD, the high incidence of AF is combined with an alarming mortality rate. The incidence of AF in severe COPD patients is approximately four times higher than that in non-COPD patients . The in-Comprehensive integration of risk factors for AF in patients with COPD is important in bridging the knowledge gaps and providing information for the application of COPD guidelines. Therefore, this systematic review and meta-analysis was designed to identify risk factors for new-onset AF in patients with COPD, and to formulate recommendations for limiting AF in COPD patients that will assist clinical decision making.All methods of this systematic reviews and meta-analyses followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines . DetailsThe review authors searched for medical literature before December 2019. The search was conducted in four electronic databases including the Cochrane Library, PubMed, Embase, Web of Science (WOS), and the reference lists from review articles. The search strategy used a combination of MeSH, Emtree and text word, conducted with the following keywords: COPD or chronic obstructive pulmonary disease and AF or atrial fibrillation. The review articles with relevant topic were screened through keyword search, and their reference lists were further searched for eligible studies. The details of search strategies can be found in the This meta-analysis included studies that met the following inclusion criteria: 1.Adult patients diagnosed with COPD (18 years of age or older). COPD was diagnosed according to the latest reference standards during the study, such as the GOLD criteria. 2.The studies reported the results of identified risk factors for new-onset AF in COPD patients. The adjusted odds ratio (OR), SE, and 95% confidence intervals (CI) for each candidate risk factor are reported as appropriate. 3.No publication date, status or language restrictions were applied.Exclusion criteria: 1.Secondary study, editorials, and animal experiments were excluded. 2.P values showed inconsistency were excluded.Studies that reported ORs with 95% CIs and  3.Studies that included only unadjusted OR and studies that involved new-onset AF in patients only after cardiothoracic surgery were excluded.Two authors  independently reviewed the studies to be included in light of the titles, abstracts and keywords. If a study was found relevant to this topic, the full text would be further evaluated by at least two reviewers to assess whether it fulfills the selection criteria. In case of disagreement between the reviewers, the third reviewer (J Liu) would be consulted to resolve the disagreement. An inter-rater reliability analysis was conducted to assess the consistency of the study selection between two independent review authors. A study diagram was prepared for this selection to demonstrate the entire process of literature research and the selection of studies.The data was independently extracted by two review authors (Q Huang and H Xiong), and the resulting differences were resolved by a third reviewer (J Liu). The extracted data included: (1) citation information, participant characteristics, study types, and study settings; (2) definitions of COPD and AF; and (3) the adjusted OR, SE, and 95% CI for each candidate risk factor. Note that none of the studies from which data was extracted include more than one model where multiple variables were included, and any included study using empirical model building strategies  may exclude variables from the adjusted models. If the data were insufficient, an email was sent to the authors to request the data. Note that all included studies provide complete information on the main outcomes.Two review authors (J Liu and J Lu) independently applied the guidelines of the PRISMA statement to evaluate each involved study . For obsQ test (substantive heterogeneity was indicated by P < 0.05) and the I2 test (substantive heterogeneity was indicated by I2 >50%). If substantive heterogeneity existed, sensitivity analysis was performed to analyze the potential sources of the heterogeneity. Egger’s test was used for assessing the publication bias in case that there are over 10 included studies . Considering the play of chance and some genuine variation in risk-factor effects, the pooled effect size was calculated by the random-effects method in this study. The heterogeneity of eligible studies was assessed by the Cochrane  studies . The α vRisk factors were grouped by factor type , and stratified by effect size, with specific consideration for factors associated with a greater than 50% increase or 33.3% decrease in odds of new-onset AF (the inverse of a 50% increase in odds corresponds to a 33.3% decrease in odds).P < 0.05. The 95% CI and P value of potential risk factors involved in this study showed consistency.95% CIs were used to estimate the precision of the ORs. A wide CI indicates lower OR precision, whereas a narrow CI indicates higher OR precision. If a 95% CI spans 1.0, the increased or decreased odds does not reach statistical significance. Usually 95% CI excludes null value means Quantitative meta-analysis examined candidate risk factors identified in at least two studies . The priAll included risk factors (pooled and un-pooled) were reported in at least one study. To assess whether increased risk factors for community-associated AF also increased the risk of COPD-associated AF, we compared the risk factors of new-onset AF in COPD with those of community-associated AF previously identified in This study is a meta-analysis using data from previously published studies, hence patients and the general public were not involved in this study.κ, 0.80). Of the 20 included studies, 13 studies used retrospective cohort designs , coronary artery disease (CAD), heart failure (HF) and congestive heart failure (CHF). Of these, six factors were associated with a greater than 50% increase in odds: age (over 65 years and over 75 years), acute care encounter, CAD, HF and CHF.Among all risk factors, the 95% CI and Two pooled factors were associated with decreased odds of new-onset AF in the meta-analysis: black ethnicity (compared with white), and peripheral vascular disease (PVD). Only one factor is related to the reduction of odds by more than 33.3%, which is black race (vs white). Six factors were not associated with new-onset AF after the pooled analysis: hypertension, hyperlipidemia, diabetes mellitus (DM), hepatic failure, long-acting beta-agonist use and theophylline use.I2 was greater than 50% in nine pooled risk factors, seven of which were included the study by Heterogeneity analysis showed that I2 decreased from 57% to 0% and the OR decreased from 1.13 (95% CI [1.12–1.15]) to 1.09 (95% CI [1.06–1.12]) for acute respiratory failure, I2 decreased from 98% to 63% and the OR increased from 1.41 (95% CI [1.19–1.69]) to 1.55 (95% CI [1.29–1.85]) for MI and I2 decreased from 94% to 35% and the OR increased from 2.57 (95% CI [1.01–6.49]) to 3.88 (95% CI [2.43–6.21]) for CAD.Sensitivity analysis of acute respiratory failure, MI and CAD showed that the high heterogeneity was derived from the study by P value with consistency. To qualitatively compare the risk factors of COPD-associated AF and community-associated AF risk factors, we included 25 additional factors that were evaluated in only one study. Eleven un-pooled factors were associated with a greater than 50% increase in odds of new-onset AF, including lower health-related quality of life . Low HRQoL is defined as AQ20 summary score ≥ 14), chronic kidney disease (CKD), vascular disease, thyroid diseases, worsening MRC dyspnea score, history of cardiac arrest, valvular heart disease, history of cardiogenic shock, cardiomyopathy, left ventricular hypertrophy, and obstructive sleep apnea (OSA). No factor was associated with a greater than 33.3% decrease in the odds. Eight factors were not associated with new-onset AF in terms of odds, including cancer, neurologic dysfunction, hematologic dysfunction, metabolic dysfunction, prior beta-blocker use, tiotropium use, betamimetics and ipratropium use and venous thromboembolism.All of the un-pooled ORs showed the 95% CI and Of the 11 community-associated factors identified by In this, study, we conducted a systematic review and meta-analysis to characterize and evaluate risk factors for new-onset AF in COPD patients. Fifty potential risk factors were grouped by factor type. Quantitative analysis demonstrated that risk factors for new-onset AF in COPD patients are mainly those associated with cardiovascular-related events and demographic factors . Although COPD-related factors can also trigger new-onset AF in COPD patients, the risk effect is moderate. In this study, we further explored the differences between the risk factors for COPD-associated AF and the identified risk factors for non-COPD-associated AF. Although cardiovascular-related factors for non-COPD-associated AF are also considered to be risk factors for new-onset AF in COPD patients, the influences are stronger in COPD patients. Interestingly, comorbid factors  identified in non-COPD-associated AF are not associated with an increased risk of new-onset AF in COPD patients. This highlights the importance of distinguishing risk factors for AF in patients with COPD and the potential need for different preventive interventions.New-onset AF in COPD patients has significant demographic characteristics. Older age (over 65 years and over 75 years), male and whites are at higher risk of morbidity. Among all the demographic factors, older age is the dominant factor . Both COPD and AF have age distribution characteristics, and the prevalence increases with age. In the study conducted by Méndez-Bailón et al., they found that the prevalence in AF patients with COPD also showed a increasing tendency with age. Their results indicate that the overall prevalence of COPD was approximately 17% in AF patients, increasing to more than 40% in individuals aged >75 years. . This inThe most commonly identified risk factors for new-onset AF in COPD patients are associated with cardiovascular events. Although these factors are also considered to be risk factors for non-COPD-related AF, the risk of cardiovascular events in patients with COPD is often amplified compared to that in non-COPD patients. As a chronic disease, COPD is independently associated with cardiovascular risk . The advIn the chronic process, patients with chronic COPD have a higher risk of cardiac abnormalities  than non-COPD patients. These organic cardiopathies contribute to the occurrence of AF. The results of this study demonstrate a potential association between cardiovascular-related history and new-onset AF in patients with COPD. However, in clinical practice, clinicians often ignore the history of heart disease in patients with COPD . Attenti1 <80% of predicted compared with FEV1 ≥ 80%.Although the risk effect of COPD-related factors is not as strong as cardiovascular and demographic factors, the impact on COPD-associated AF remains substantial. Moreover, we found that slight decreases in the pulmonary function of COPD patients were associated with increased risk of AF; therefore, this situation requires careful monitoring and prevention. As a remedy for NMV failure, IMV usually indicates a further deterioration of pulmonary function in COPD patients. We found that not only IMV, but also NMV, risk triggering AF. This finding is consistent with the study by In addition, we found that COPD-related factors have the potential to trigger AF . In accordance with our findings, previous studies have shown that new-onset AF in COPD patients can be caused by COPD-related factors, such as the inflammatory state of COPD and the use of beta-agonists. In animal models, streptococcus-induced pneumonia has been shown to cause myocardial micro-abscesses, which can develop into fibrotic lesions prone to arrhythmia formation . The forComorbid conditions are major factors that can be used to predict the risk of community-associated AF. However, our findings suggest that the risk effects of comorbid conditions are low compared with the effects of cardiovascular-related or COPD-related factors. This may reflect the greater severity of the disease  in COPD patients or fewer competing risk factors, resulting in a lower proportion of comorbid factors attributed to new-onset AF in patients with COPD . CompareThis study of risk factors contributed to our understanding of the pathogenesis of AF in COPD patients. AF is thought to occur in two steps that involve the production of arrhythmogenic substrates , followeI2 decreased from 57% to 0%), MI (I2 decreased from 98% to 63%) and CAD (I2 decreased from 94% to 35%). The analysis of heterogeneity is limited due to the insufficient number of studies reporting risk factors. In addition, other potential sources of heterogeneity in our analysis of the results of baseline characteristics are as follows: (1) The designs of the included studies were heterogeneous. Compared with meta-analyses including randomized controlled trials, those including observational studies have greater potential bias. (2) The studies included patients with diseases defined using different strategies. Although all strategies are reasonable, different criteria may lead to variations in clinicians’ decision-making, which may increase the heterogeneity of population selection.There are several limitations in the current systematic review and meta-analysis. Firstly, there was substantial heterogeneity in the results of some risk factors. In order to respond to this limitation, we then performed a sensitivity analysis to explore the potential sources of heterogeneity. We identified the study by Secondly, interpretation of the observed ORs depends on all necessary confounders being included, without any colliders or intermediate variables. However, the adjusted ORs reported in the included studies cannot eliminate residual confounding caused by unknown, missed and inaccurate confounding factors, and there is a lack of discussion about the causality of the variables. Therefore, limited by the existing methods, it is difficult to analyze the causal associations of 50 variables without additional original research. Furthermore, we also suggest more original research to use the causal modelling approaches such as directed acyclic graphs to distinguish between confounders, colliders, and intermediate variables when considering causal risk factors. Thirdly, due to different research purposes, the included studies only reported the results of some potential risk factors related to the research purpose, while other variables were only mentioned as an adjusted variable, and did not report the adjusted ORs and 95% CIs. Thus, the number of risk factors included in the meta-analysis was less than the number of adjusted variables included in the adjusted models. This suggests that more original studies are needed to report more comprehensive results for further analysis and evaluation of specific risk factors. In addition, because no risk factor was reported in 10 or more original studies; Egger’s test and funnel plots were not included in our analysis. Further high-quality original studies can also help to limit the potential publication bias.This systematic review and meta-analysis reveals the demographic characteristics of patients with new-onset AF in COPD patients. Older age (over 65 years and over 75 years), males and whites are at higher risk of developing AF. The dominant factors are cardiovascular , and may be amplified in the context of COPD. COPD patients with a history of cardiovascular disease should be carefully monitored for new-onset AF, and appropriate preventive measure should be implemented. Even for patients with mild COPD, clinicians should not relax their vigilance in monitoring patients for new-onset AF. The pathogenesis of AF in COPD patients may be primarily related to cardiac dysfunction caused by the chronic duration of COPD, which increases the risk effect of cardiovascular-related factors and further increases the risk of AF in patients with COPD. This highlights the importance of identifying risk factors for predicting COPD-associated AF and the potential need for different preventive interventions.10.7717/peerj.10376/supp-1Supplemental Information 1Click here for additional data file.10.7717/peerj.10376/supp-2Supplemental Information 2Click here for additional data file.10.7717/peerj.10376/supp-3Supplemental Information 3Click here for additional data file.


Tobacco smoking is one of the biggest and avoidable public health threats in the world, especially in low- and middle-income countries. India represents a highly complex public health environment due to the large number of smokers and complexities arising from tobacco use in different forms, including a variety of smokeless tobacco (SLT) products. Electronic cigarettes, an alternative nicotine delivery system with significantly less harmful emissions than smoke, could be an option for those who are unable to achieve smoking abstinence using other available means. This study, which we believe is the first of this kind in India, was conducted to obtain data on the characteristics and tobacco use profile of e-cigarette users (vapers) in India.An interview-based survey was performed in the 8 largest metropolitan cities in India using a convenience sampling approach involving a total of 3000 subjects. Inclusion criteria were being a current e-cigarette user and aged 18 years or more. Interviewers were asked to continue recruitment until a total sample of 375 was reached in each target city.A total of 3000 vapers  participated to the study. The majority (80%) were first exposed to nicotine via tobacco smoking, SLT use, or both. Most of the subjects (79%) believed that e-cigarettes were less harmful than smoking. The vast majority of smokers (71.3%) reported smoking cessation (30.0%) or reduction in consumption (41.3%) with the help of e-cigarettes. Similar changes were observed in SLT users. Participants reported minimal side effects and some health benefits after e-cigarette use initiation.Indian vapers who participated to this study were predominantly smokers and SLT users before e-cigarette use initiation, with the majority subsequently quitting or reducing tobacco use. Minimal side effects of e-cigarette were experienced, while some health benefits were also reported. Therefore, India represents a complex public health challenge ) and categorical variables reported as number (percentage). Non parametric tests (McNemar test) were used to compare smoking and SLT use prevalence between the time before e-cigarette use initiation and the time of the survey while smoking consumption was compared between these two time-points using Wilcoxon signed rank test. Binary logistic regression analysis was performed to examine correlates of having quit smoking. The dependent variable was being a former (rather than a current) smoker, and independent variables were age, gender, education level, smoking duration and consumption, FTCD, e-cigarette use duration, frequency of e-cigarette use, liquid consumption, nicotine concentration used, number of different flavors used (one vs. two or more), type of flavors used (tobacco/mint/menthol only vs. at least one non-tobacco/mint/menthol flavor), and perceptions of e-cigarette harmfulness . All data were analyzed using the SPSS ver. 22.0 software.n = 375) from each of the 8 metropolitan cities. The demographics characteristics and history of tobacco use before e-cigarette use initiation of the study participants are presented in Table n = 6) reported not smoking tobacco.A total of 3000 respondents were recruited in equal numbers (P < 0.001); 30% of those who reported smoking tobacco before e-cigarette use initiation had quit after e-cigarette use initiation (former smokers). Daily tobacco cigarette consumption was reduced among those who continued to smoke from 8 (5–10) to 5 (4–8) cigarettes per day (Wilcoxon signed rank test P < 0.001). Current SLT use was reported by 17.9% of participants, higher than before e-cigarette use initiation (McNemar test P < 0.001). However, 71.0% of current SLT users reported also smoking tobacco, compared to 98.5% before e-cigarette use initiation (McNemar test P < 0.001).Current tobacco use patterns of study participants are presented in Table The e-cigarette use patterns of the study population are presented in Table Table Approximately, one third of participants reported at least one adverse symptom that they attributed to e-cigarette use Table . The comParticipants’ perception of harm about e-cigarettes is presented in Fig. The results of the logistic regression analysis are presented in Table To the best of our knowledge, this is the first study to examine the characteristics of vapers in India. Participants were mostly smokers and SLT users, in agreement with other studies of adult populations showing that e-cigarette use is predominantly observed among current or former smokers , 26, 27.A substantial proportion of the study population experienced benefits in terms of reduction or quitting smoking. A similar proportion reported smoking reduction and cessation in the Eurobarometer study, although that was a population-representative sample . FrequenLess than 35% of participants experienced some self-reported side effects after e-cigarette use initiation. The most common were cough and dry mouth and throat. These have been observed in previous studies too , 37, 38 India has the second lowest quit rate among GATS-2 countries surveyed at the end of 2017 . DespiteA limitation of this study is the convenience sampling design, which cannot ensure that the profile of the vapers examined represent the average Indian consumer. Still, this remains the first study examining patterns of e-cigarette use in India. Additionally, the tobacco use status of participants was self-reported and not objectively verified, although this is common for large surveys. Data on tobacco use patterns and dependence before e-cigarette use initiation were accessed retrospectively, and there is a possibility for recall bias. Finally, despite participants responding on whether e-cigarettes helped them quit or reduce tobacco use, this is a cross-sectional study, and causal relationships cannot be confirmed. Future prospective or longitudinal studies are needed to confirm the suggested result.In conclusion, and considering the above-mentioned limitations, Indian e-cigarette users who participated in the study were predominantly smokers and SLT users, with a significant proportion of them managing to quit or reduce their tobacco consumption. Minimal side effects were experienced, while some health benefits were also reported. Our findings highlight the potential of e-cigarettes to be an additional option for tobacco control. This may be especially relevant for countries such as India, which have (a) high tobacco-related health burden, (b) complex tobacco landscape, and (c) inadequate infrastructure and resources for offering tobacco cessation to help smokers and SLT users quit. While the opportunity for India seems to have been missed due to the implemented ban, public health authorities should encourage additional research and consider suitable modifications in the regulatory framework if findings support such a need.


Chromatin is folded into successive layers to organize linear DNA. Genes within the same topologically associating domains (TADs) demonstrate similar expression and histone-modification profiles, and boundaries separating different domains have important roles in reinforcing the stability of these features. Indeed, domain disruptions in human cancers can lead to misregulation of gene expression. However, the frequency of domain disruptions in human cancers remains unclear. Here, as part of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), which aggregated whole-genome sequencing data from 2,658 cancers across 38 tumor types, we analyzed 288,457 somatic structural variations (SVs) to understand the distributions and effects of SVs across TADs. Notably, SVs can lead to the fusion of discrete TADs, and complex rearrangements markedly change chromatin folding maps in the cancer genomes. Notably, only 14% of the boundary deletions resulted in a change in expression in nearby genes of more than twofold. A pan-cancer genomic analysis reports the effects of structural variations on chromatin domains (TADs). Most TAD disruptions do not result in appreciable changes in expression of nearby genes. Chromosomes are organized into chromosome territories2. Inside chromosome territories, certain regions of the chromatin are attached to the nuclear periphery and form repressive nuclear lamin-associated domains (LADs)3. Recent chromosome conformation studies have revealed that mammalian chromosomes are structured into largely tissue-invariant TADs in which the DNA interactions are more frequent within a given domain than with regions in other domains5. TADs are considered to represent functional domains because a given TAD encompasses the regulatory elements for the genes inside the same domain7. Therefore, the integrity of the domain structures is important for the proper regulation of genes12. The disruption of domain boundaries can result in ectopic interactions between neighboring domains and affect the regulation of nearby genes9. Regulatory landscapes are an important part of human malignancies, and studies have shown that the ‘hijacking’ of enhancers can lead to overexpression of oncogenes ) in medulloblastoma13 or proto-oncogene MECOM activation due to an inversion between TADs in acute myeloid leukemia cells, which facilitates tumor formation14. Several other studies have reported the deregulation of chromatin folding structures in different cancer types16. Hence, genomic rearrangements can have a significant role in the reshuffling of TAD structures that results in altered gene regulation. Despite these recent examples of SVs that result in altered local enhancer–promoter landscapes, the frequency of such regulatory architecture rearrangements in cancer genomes remains unclear. Similarly, whether there are loci affected by potential changes in regulatory structure outside of those currently reported in the literature is unknown. To address these questions, we comprehensively characterized the effects of different SVs on TADs and gene-expression patterns observed in various tumor types to expand understanding of the link between chromatin folding and genomic rearrangements in cancer genomes.Genome organization inside the nucleus is hierarchically organized17. In this pan-cancer analysis, we sought to generate a common set of boundaries observed in different cell types. We used high-resolution chromosome conformation (Hi-C) datasets from five human cell lines that represent three distinct embryonic germ layers 17 to identify TAD boundaries in different cell types , for each bin, for the average interactions with the nearby loci for a 2-Mb genomic window. Boundaries are determined as regions with local insulation minima along the diagonal of the Hi-C matrix18. As a result, a number of boundaries, which ranged from 3,926 to 4,690, were found for different cell types. We next investigated whether our TAD boundary calls were consistent with the previously reported boundaries and showed attributes of TAD boundaries. To test this, we compared available boundary regions for IMR90 cells that were identified using a directionality-based approach (with a bin size of 40 kb)4. Our IMR90 boundary calls were highly overlapping (>84%) with published boundaries  overlap  between TAD boundaries among all profiled cell types. The median distance between the common boundaries was approximately 750 kb, consistent with the reported median TAD size in human cells19  and a breast cancer cell line MCF7 (ref. 20)  percentage of boundaries was conserved between normal and malignant cells. We next examined the enrichment of CCCTC-binding factor (CTCF)-binding and DNase I hypersensitivity sites, as well as active transcription start sites and heterochromatic regions around boundaries from various cell types that have previously been profiled by the Encyclopedia of DNA Elements (ENCODE) consortium19 and the Roadmap Epigenome project21. We observed that CTCF-binding sites and active promoter marks were enriched, whereas the heterochromatin state was depleted at the boundaries. In addition, TAD signal levels were the lowest at the boundaries compared with flanking sites  and shorter than 2 Mb in genomic length (short-range SVs). The majority of deletions, inversions and duplications could be categorized as short-range; however, complex events tended to be longer in length . In contrast, we observed a depletion  in BA-deletions, whereas BA-inversions and BA-complex events occurred at expected levels (P > 0.05) compared with the shuffled TAD boundaries  data from 2,658 cancers across 38 tumor types generated by the ICGC and TCGA projects. These sequencing data were re-analyzed with standardized, high-accuracy pipelines to align to the human genome (reference build hs37d5) and identify germline variants and somatically acquired mutations, as described in the lead paper of the PCAWG Consortiumies Fig. . OverallP < 0.02) to be affected by known deletion and duplication polymorphisms derived from genomes of healthy human populations27  across all cancer types. The median SV length of a given cancer type was not strongly correlated with the observed distributions (r2 = 0.03–0.45)  and gene density (r2 = 0.7–0.85) on a given chromosome , low/quiescent (705), repressed (481), low-active (764) and active (365) domains   Fig. . Further39. Indeed, genes located on the repressed side of deletions were significantly upregulated  difference between genes that were located on more or less transcriptionally active domains after BA-deletions  compared with the rest of the patients with melanoma , which were previously profiled by WGS. For the majority of the BA-SVs detected by the WGS data (>90%), we were able to observe a change in the folding pattern in Hi-C contact maps of the respective cell line ; we therefore analyzed whether the BA-SVs observed in our cancer cell lines exhibited similar neo-TAD formation. We grouped bins on the basis of their location compared with the SV breakpoints and the nearest TAD boundary. If bins were between the SV breakpoints and the nearest TAD boundary, we classified these interactions as intra-TAD/SV and if bins were not constrained by the nearest boundary, we classified these interactions as inter-TAD/SV  across the entire chromosome 15, which was reported by WGS and spectral karyotypingnds Fig. . Similarnds Fig. . In addidow Fig. , wherease 5 Fig. . We coule 5 Fig. . Given te 5 Fig. , studyin43. These low expression changes may be reminiscent of mutations, in which there is a subset of chromatin-scale events that may be more likely to have functional effects (drivers) among a backdrop of considerable passenger events. Although we compared expression patterns of tumors in this study, cancer genomes may have other alterations that could affect the observed gene expression patterns, including copy-number alterations, dysregulation of transcription factors, chromatin regulators or cis-regulatory elements44. Therefore, the availability of histology-specific matched control samples coupled with WGS and chromatin organization datasets will augment our understanding of the functions of SV in genome folding and transcriptional dysregulation in cancers and contribute to our ability to discern signal from noise in appropriate contexts.We explored the distributions of somatic SVs in a variety of tumor types and their potential roles in the disruption of chromatin folding and gene regulation. We found that certain boundaries are affected in a cancer-specific manner, which was likely due to the distribution of cancer-specific driver genes. Additionally, we observed a difference between the disruptions between different SV types; deletions tended to occur within TADs and LADs, whereas duplications tended to span TADs and generally occurred within inter-LAD regions. These results suggest that mechanistic differences may underlie the generation of different types of SV. For example, genome organization may influence partner selection during genomic rearrangements, as suggested by the distribution of different SV types in the genome to varying degrees. Disruption of folding domains could result in aberrant interactions between flanking domains and potentially contribute to the re-shaping of gene expression around the affected regions. Notably, we did not observe a strong association between global changes in gene expression after the disruption of each TAD, and only 14% of overall cases resulted in upregulation of more than twofold, which is consistent with the findings of recent studiesGSE63525). Intra-chromosomal 25-kb-resolution raw observed, MAPQGE30-filtered values were normalized by dividing by the multiplication of Knight and Ruiz normalization scores for two contacting loci. We calculated the TAD signal by moving a window across the Hi-C matrix diagonal, the sum of the interaction for a given bin of up to 2-Mb flanking regions and log2 of the observed bin to the mean of interaction values within the given 2-Mb window. To identify TAD boundaries, we used an approach that is based on insulation score calculation18, and called TAD boundaries for each chromosome of each cell line with the following parameters: ‘-is 1000000 -ids 200000 -im mean -bmoe 1 -nt 0.1 --v’.Chromatin conformation assay (Hi-C) data for cell lines of GM12878, HUVEC, IMR90, HMEC, NHEK and K562 were downloaded from GEO  that occurred within two Hi-C bins or 50 kb in genomic range. The same bootstrapping method (described above) was applied to calculate the significance of the overlap between common boundaries with TAD boundaries from the cancer cell lines K562 and MCF7.http://compbio.mit.edu/epilogos/). We calculated the ratio of a TAD genomic space covered by each chromatin state, divided by the length of the TAD, and generated a normalized matrix in which columns are TADs and rows are each chromatin state, which have been extensively studied by the Roadmap Epigenome Consortia21. We applied hierarchical clustering to rows to identify similar chromatin states and k-means clustering to columns to group TADs that contain similar epigenetic modifications. We performed k-means clustering with k = 2–8 clusters and decided on k = 5 clusters as previous chromatin studies19 have used 5 distinct epigenetically modified chromosomal domains and k = 5 corresponded to better visually discernible domains. To determine how our TAD clustering correlate with gene expression in cancer-free and cancerous tissues, we downloaded normalized gene expression values for 2,663 different cancer-free samples from the GTEx Portal36 (v.1.6) and used normalized gene-expression values for ICGC cancer samples. We plotted the median expression of the genes in GTEx and ICGC samples, located in each domain type. Expression differences between heterochromatin and repressed domain expression with active domain expression were tested with one-tailed Mann–Whitney U-test. We also calculated the total number of genomic regions covered by each domain type. Finally, identified open and closed chromatin compartments (at a 100-kb resolution) in cancer samples using DNA methylation levels were identified as described previously37. We determined the percentage of our domain calls covered with open and closed chromatin calls from available cancer types.To cluster individual TADs (defined as genomic regions between two adjacent common boundaries) based on epigenetic modifications, we used a comprehensive epigenome-profiling dataset from various human cell types. To this end, we used an entropy-based approach (epilogos) to calculate the occurrence of each chromatin state enrichment for a given genomic region across all cell types profiled by Roadmap Epigenome Consortia  from the UCSC Genome Browser ENCODE portal. In addition, H3K9me3 and input DNA ChIP–seq alignment files (.bam) for each cell type were also downloaded. We randomly selected the same number of alignment reads for H3K9me3 and input DNA from .bam files and calculated logWe downloaded all available CTCF peak-calling results and DNase I hypersensitivity regions from the UCSC Genome Browser ENCODE portal from 80 and 115 different cell lines, respectively  via dbGaP (https://dbgap.ncbi.nlm.nih.gov/aa/wga.cgi?page=login) for access to the TCGA portion of the dataset, and to the ICGC Data Access Compliance Office  for the ICGC portion. In addition, to access somatic SNVs derived from TCGA donors, researchers will need to obtain dbGaP authorization.Somatic and germline variant calls, mutational signatures, subclonal reconstructions, transcript abundance, splice calls and other core data generated by the ICGC/TCGA PCAWG Consortium are described by the lead paperhttps://www.synapse.org/) with accession number syn7596712. The SV classification algorithm is comprehensively defined in another study23. The code for the classification algorithm is available on GitHub (https://github.com/cancerit/ClusterSV/). In brief, this algorithm clusters individual SV junctions into SV events that may involve multiple junctions. The single junction events were interpreted, as the ‘basic’ SV types . However, in many cases events involving multiple SV junctions were detected. The SV events that involved many SV junctions could not be classified into any simple SV types. Therefore, these SV events were classified as complex. We specifically focused on the events that occurred within a chromosome in this study; we therefore did not use the translocation event calls between different chromosomes. To understand the effects of SVs, we first grouped the deletions, inversions or duplications on the basis of the length of the SVs.We obtained the consensus SV calls and annotations of each variation , which can be found at Synapse  and duplications  from WGS data from 236 individuals representing 125 human populations27; and from a comprehensive review of deletions  and duplications  events from 23 different studies including 2,647 different individuals25. We noticed that the number of BA-SVs present in germline deletions and duplications was low and these events happened less than expected by chance, which was estimated using a bootstrapping method.To understand the germline BA-SV occurrences, we downloaded structural alteration calls from three different studies: deletion events  from WGS data of the 1000 Genomes projectWe next profiled short-range SVs and BA-SVs for each of the cancer studies in our ICGC dataset. To calculate the average number of SVs or BA-SVs per sample for each of the cancer studies, we divided the sum of all observed short-range SVs or BA-SVs in a given cancer type by the total number of samples in that cancer study. Observed SVs and BA-SVs across cancer studies were plotted as stacked bar charts representing deletions, inversions and duplications.15 and SVs that affected only one anchor (CTCF-binding site) of an insulated neighborhood were considered as loop-disrupting SVs.To identify the recurrently affected boundaries in each cancer study, we generated a matrix in which each column represented a sample in the cancer study and rows represented the TAD boundaries. A binary score was assigned to each row (a TAD boundary) that indicated whether that boundary was affected by BA-SV(s) in a given sample. Boundaries that were affected in more than 10% of the samples in a cancer study, are reported as recurrently affected boundaries in Supplementary Table We determined flanking domain annotations of BA-SVs, by identifying the type of the nearest domain for the break-ends of each BA-SV. This analysis resulted in a half-matrix that contained the observed frequencies of pair-wise flanking domain types. We plotted the observed values for BA-SV deletions, inversions, duplications or complex rearrangements separately. To understand the genomic distribution of domain neighborhoods, we counted the flanking domains of each TAD boundary.34 from GSE22428. For a filter, we used LAD calls from an independent study3. Genomic coordinates were converted to the hg19 assembly with the UCSC liftover tool. To calculate the significance of the observed overlaps between different SV types and constitutive LAD and constitutive inter-LADs, we used the same bootstrapping method, in which break-ends of each SV type were randomly shuffled on the same chromosome 10,000 times and z-scores were calculated between observed and expected values.To profile SVs between nuclear LADs and inter-LADs, we obtained HMM state calls from three different human cell types for constitutive LADs and constitutive inter-LADsU-tests.We identified the nearest genes to the break-ends of BA-SVs as the nearest RefSeq genes that did not overlap with the break-ends. The RefSeq gene table was downloaded from the UCSC Genome Browser in May 2016. We called genes located upstream of the 5′ end of an SV upstream genes and genes located downstream of the 3′ end of an SV downstream genes for each BA-SV. Fold changes in expression for each of the upstream and downstream genes were calculated by dividing observed normalized RPKM values in the particular sample with BA-SVs, with average normalized RPKM values in the rest of the same cancer study samples. We filtered the genes with low expression values (<0.1 FPKM), as fold changes with those genes would be seemingly high for even small fluctuations. Copy-number variations could be another confounding factor for observed gene-expression fold changes. Therefore, we obtained consensus copy-number calls for the ICGC cohort based on consensus SV results. We removed cases in which copy numbers are more than four for either the upstream or the downstream genes. In addition, we removed genes that were distal to the break-ends by more than 1 Mb. Expression differences between genes that flanked different BA-SV break-ends were tested using one-tailed Mann–Whitney https://pyvcf.readthedocs.org) to load .vcf files and pybedtools46 to perform genomic-interval analyses.We used pyvcf  and breast adenocarcinoma cancer cell line (HCC1954) were obtained from the American Type Culture Collection and the esophageal adenocarcinoma (OE33) cell line was obtained from Sigma-Aldrich. Stocks were stored in liquid nitrogen. These cell lines were authenticated by comparing SV results from previous WGS datasets from the same cancer lines.48. To identify consensus SVs for SW480 and OE33 cell lines, we ran DELLY49, Lumpy50 and BRASS51 algorithms. SV breaks-ends reported by two different callers were included in this analysis. For the SNU-C1 cell line, SV calls were obtained from Supplementary Table 2 of a previous publication41, genomic coordinates were converted to the hg19 assembly using the UCSC liftover tool. HCC1954 whole-genome data were previously analyzed by the ICGC Structural Variation subgroup and we used the consensus structural alterations for this cell line.We obtained the WGS datasets of the SW480, SNU-C1 and OE33 cell lines from previous publications17 using 2–5 million cells per experiment that were digested with the MboI restriction enzyme and analyzed in duplicate. Hi-C libraries were sequenced on a NextSeq 500 or a HiSeq 4000. Reads were aligned to the hg19 reference genome using BWA-MEM52 and PCR duplicates were removed using Picard. Hi-C interaction matrices were generated using in house pipelines, and matrices were normalized using the iterative correction method53. ATAC-seq data for the OE33 cell line were obtained from a previous study54 and H3K27ac ChIP–seq datasets for the HCC1954 and SW480 cell lines were obtained from Hon et al.55 and Rahnamoun et al.56, respectively.Hi-C was performed using the in situ Hi-C protocol as previously describedTo investigate the potential function of SVs in TAD fusions, we classified the interactions on the basis of the nearest TAD boundary. For each SV, the average interaction frequency was calculated within a 2-Mb region of the SV. This average frequency ratio was used to ‘scale’ the interactions to account for ploidy. This was done by taking the average interaction frequency over that region and dividing it by the genome-wide average (controlling for the distance between loci) over a window of identical size. Certain WGS-defined SVs do not appear to have a signal in the Hi-C data, possibly due to false-positive SV calls, and we excluded regions for which the scaling factor was less than 0.1 to remove potential false-positive calls. In addition, we truncated the default 2-Mb window if there was another SV to avoid biases introduced by complex variants.Further information on research design is available in the Any methods, additional references, Nature Research reporting summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at 10.1038/s41588-019-0564-y.Supplementary InformationSupplementary NoteReporting SummarySupplementary TablesSupplementary Tables 1–11


Early mobilization of adults receiving intensive care improves health outcomes, yet little is known about mobilization practices in paediatric intensive care units (PICUs). We aimed to determine the prevalence of and factors associated with physical rehabilitation in PICUs across Europe.A 2-day, cross-sectional, multicentre point prevalence study was conducted in May and November 2018. The primary outcome was the prevalence of physical therapy (PT)- or occupational therapy (OT)-provided mobility. Clinical data and data on patient mobility, potential mobility safety events, and mobilization barriers were prospectively collected in patients admitted for ≥72 h.n = 47, 10%), oversedation , and medical contraindication . Potential safety events occurred in 6% of all documented mobilization events.Data of 456 children admitted to one of 38 participating PICUs from 15 European countries were collected (456 patient days); 70% were under 3 years of age. The point prevalence of PT- and/or OT-provided mobility activities was 39% (179/456) (95% CI 34.7–43.9%) during the patient days, with significant differences between European regions. Nurses were involved in 72% (924/1283) of the mobility events; in the remaining 28%, PT/OT, physicians, family members, or other professionals were involved. Of the factors studied, family presence was most strongly positively associated with out-of-bed mobilization . Invasive mechanical ventilation with an endotracheal tube was negatively associated with out-of-bed mobility . Patients were completely immobile on 25% (115/456) of patient days. Barriers to mobilization were reported on 38% of patient days. The most common reported patient-related barriers were cardiovascular instability (Therapists are infrequently consulted for mobilization of critically ill children in European PICUs. This study highlights the need for a systematic and interdisciplinary mobilization approach for critically ill children. The paediatric intensive care unit is a stressful environment for critically ill children. To provide safety, comfort, and hemodynamic stability, children are often sedated and considered too sick to be mobilized . HoweverEarly mobilization of patients in adult ICUs has proven feasible and safe with favourable clinical outcomes including decreased delirium incidence and duration of ICU stay, less sedatives consumption, and improved muscle strength and functional status , 10. EarA recent point prevalence study of physical rehabilitation in United States PICUs  demonstrated that younger children and those with higher baseline function were less likely to receive therapist-provided mobility . In viewhttps://park.web.jhu.edu). Institutional review board or clinical governance approval was obtained at all sites with waiver of informed consent. For comparability across studies internationally, we utilized the same methodology as the original PARK-PICU study in the United States of America (USA)  and PCPC 4 vs. PCPC1: 2.24 [95% CI 1.14–4.40]). Therapist-provided mobility was also associated with having a CVC in place  and with family presence during one of the study days . In contrast, fewer therapist-provided mobility episodes were provided to children with urinary catheter in place  , a nurse alone provided the mobilization event; in 17% of cases, a nurse together with family; in 6% of cases, a nurse together with a PT or OT; and in 16% of cases, family alone , notably not in mechanically ventilated patients . On the other 341 patient days, 1283 total mobility events occurred, with a median of 3 (IQR 3–5) per patient. In most cases . Non-mechanically ventilated patients were significantly more likely than mechanically ventilated patients to achieve out-of-bed mobility . The most common out-of-bed activity for all patients was being held by a parent or a nurse (n = 519), with bed-to-chair transfer without standing (n = 120), and mat play (n = 51).Out-of-bed mobility was achieved as the highest level of mobility for PICU patients on 248 study days, i.e. an overall prevalence of 46% (95% CI 41.0–50.3%). Seventy-eight percent of the children below 3 years of age received out-of-bed mobility compared to 22% of patients 3 years of age and older . The most common barriers were cardiovascular instability , oversedation , and medical contraindication .At least one barrier to mobilization was reported for 177 of 456 patients (39%), with two or more barriers reported for 87 of these 177 patients . The most commonly reported potential safety events were decreased oxygen saturation , change in heart rate , and change in blood pressure . An endotracheal tube dislocation occurred only once. There were no cardiac arrests or falls reported.Staff reported potential safety events in 74 (6%) of 1286 mobility events in 43 patient days (9%). These were more commonly among mechanically ventilated compared to non-ventilated patients  was low relative to the large number of mobility events and similar to that reported in paediatric (4%) and adult (3%) studies . Most ofThere are several important similarities and differences with the USA-PARK-PICU findings , a retroOur study highlights the crucial role of nurses in mobilization of critically ill children. Nurses are a constant presence at the bedside, so it is not surprising that they provided the majority of mobilization events. In both the USA-PARK-PICU study and USA ARDS-Net point prevalence study in adults, nurses facilitated two thirds of mobility events , 18. PICPresence of family was very strongly associated with increased out-of-bed mobility in our study. While the USA-PARK-PICU study observed that family presence was positively associated with mobility in children under 3, our European data magnifies this association powerfully. It is likely these family members provided assistance to facilitation success with the mobilization procedure. Parents indeed have a uniquely supportive role during physiotherapy that clinicians cannot provide , 33 whicProfessional and organizational issues such as time constraints, lack of resources to implement early mobilization, and factors related with invasive devices and patient characteristics could be barriers to early rehabilitation across all ICU populations . Our stuOur study has several limitations. First, only PICUs with sufficient research staffing or interest in early mobilization may have participated, potentially biasing the results to overestimate mobilization practices. Still, this risk is low because only 16% of the participating PICUs had a formal protocol. Second, mobility assessments were unblinded, which may have led to greater mobility delivery on the study days. Efforts had been made to limit knowledge of the study, and the observed relatively low prevalence makes it unlikely that single-day escalation of mobility efforts has biased the overall estimates. Third, we were not able to report whether a patient was medically able to be mobilized or get out of bed. Fourth, combining PT- and OT-provided mobility as the primary outcome may not recognize their unique contributions. But, due to small numbers of OT-provided mobility, we were not able to provide discipline-specific mobility associations. Further, in lack of knowing the proportion of PTs/OTs who work exclusively in the PICU, we could not analyse the difference in therapist-provided mobility between those who work exclusively in the PICU and those who not. Finally, data of non-participating PICUs were not available, thereby limiting the generalizability of the results. Although a remarkable number of 38 PICUs from 16 European countries was involved in this study, the number of participating PICUs per country differed greatly, with the strongest representation of PICUs from Northern Europe. In view of the regional differences, the results of this study should be interpreted with caution. Further, France, one of the biggest European countries with many PICUs, was not represented in this study. There may be additional cultural differences in PICU practices across Europe, and the study findings cannot be considered a true representation of all PICUs in Europe.In this point prevalence study, 25% of critically ill children across Europe were completely immobile. When mobilization occurred, the most common activity was children being held by family or nurses. Nurses are the most frequent providers of mobilization and therapists are less frequently consulted, bringing to light differences between European regions and highlighting the need for a systematic, interprofessional approach to mobilization across PICUs. Removing modifiable barriers such as oversedation and lack of medical order, combined with facilitation of parental involvement, will be important to increase mobilization and rehabilitation in European PICUs, especially in ventilated children. Finally, the short- and long-term impacts of early mobilization and rehabilitation programmes should be evaluated in critically ill children to determine best practice for paediatric critical care.Additional file 1. Mobilisation practices in critically ill children: A European point prevalence study (EU PARK-PICU), contain the following information: 1 Additional description of data analysis. 2 eTables. 3 eFigures.


The combined application of linear amplification-mediated PCR (LAM-PCR) protocols with next-generation sequencing (NGS) has had a large impact on our understanding of retroviral pathogenesis. Previously, considerable effort has been expended to optimize NGS methods to explore the genome-wide distribution of proviral integration sites and the clonal architecture of clinically important retroviruses like human T-cell leukemia virus type-1 (HTLV-1). Once sequencing data are generated, the application of rigorous bioinformatics analysis is central to the biological interpretation of the data. To better exploit the potential information available through these methods, we developed an optimized bioinformatics pipeline to analyze NGS clonality datasets. We found that short-read aligners, specifically designed to manage NGS datasets, provide increased speed, significantly reducing processing time and decreasing the computational burden. This is achieved while also accounting for sequencing base quality. We demonstrate the utility of an additional trimming step in the workflow, which adjusts for the number of reads supporting each insertion site. In addition, we developed a recall procedure to reduce bias associated with proviral integration within low complexity regions of the genome, providing a more accurate estimation of clone abundance. Finally, we recommend the application of a “clean-and-recover” step to clonality datasets generated from large cohorts and longitudinal studies. In summary, we report an optimized bioinformatics workflow for NGS clonality analysis and describe a new set of steps to guide the computational process. We demonstrate that the application of this protocol to the analysis of HTLV-1 and bovine leukemia virus (BLV) clonality datasets improves the quality of data processing and provides a more accurate definition of the clonal landscape in infected individuals. The optimized workflow and analysis recommendations can be implemented in the majority of bioinformatics pipelines developed to analyze LAM-PCR-based NGS clonality datasets. A hallmark of all retroviral infections and an essential step in the life cycle is the proviral DNA integration into the host genome . More thvia Taq polymerase extension from LTR-specific primers that extend into the host DNA from both the 5' and 3' LTRs. Biotinylated dTTPs are also incorporated during this extension step. Y-shaped adapters containing UMIs are ligated to the A overhangs, and streptavidin bead-mediated selection is carried out, followed by PCR amplification. Removal of PCR duplicates combining UMI, shear site, and integration site information ensures accurate estimation of clone abundance. Overall, our approach comprises several modifications compared to the previously reported protocols. The modified method assesses both the 5' and 3' LTR flanking sequences, increases the sensitivity, and significantly reduces the cost, making it a viable technique for application in the clinic. In a longitudinal study of adult T-cell leukemia (ATL) patients, we demonstrated that the optimized NGS mapping protocol applied to HTLV-1 outperformed other currently available methods, enabling the detection of patients refractory to first-line therapy and providing a better estimation of response to therapy  and BLV-infected animals (asymptomatic and tumor stages) described in Clonality libraries were prepared according to https://github.com/GIGA-AnimalGenomics-BLV/Public/tree/master/PIC.A detailed step-by-step description of the proviral integration site calling pipeline is available on github: Bowtie2 v2.2.9 index (bowtie2-build) and BLAST v2.6 (makeblastdb-dbtype nucl) databases were constructed from the BLV_YR2 3'LTR  or HTLV_ATK  sequence. Ten million reads containing either the BLV or HTLV-1 3'LTR sequence were randomly selected from our NGS clonality sequencing dataset. Datasets were downsampled to different sets of various read numbers with seqtk.n = 516) or HTLV-1-infected individuals (n = 187) containing the appropriate linker sequence were mapped to their respective index with bowtie2 -L 14 -N 1 -U prior to or after trimming. Trimming was performed in two steps. First, the read section expected to contain the viral LTR was isolated with fastx_trimmer. Next, cutadapt -n 2 -m 10 was used to remove residual sequences, which arise from read overlap (see github for details). Custom R scripts were used to compare the outputs.Bowtie2 indexes were constructed with BLV LTRs or HTLV-1 LTRs as separate chromosomes . Reads from BLV-infected sheep ]. Trimmed reads from 184 HTLV-1 infected samples with at least 300 reads after removal of PCR duplicates were mapped with bowtie2 -very-sensitive -N 1 to the combined human genome (hg19) with the HTLV-1 proviral genome as an additional chromosome . Using the R package PIC, the position and abundance of integration sites were first called with high-confidence reads mapped in proper pairs . For each integration site, the RECALL abundance was calculated from the set of reads located in a window spanning 600 bp up‐ and downstream of the high-confidence integration site. Multi-mapped reads attributed to two distinct integration sites were assigned using clone abundance as weight factor. Like with high-confidence reads, PCR duplicates were removed based on the integration site, shear site, and random tag information.n = 553, unpublished data). From this dataset 377,710 unique integration sites were obtained. Integration sites found in only one animal were retained, while those detected in >1 animal were further explored. Their relative distribution across all serial samples of each individual was assessed by computing the Shannon entropy (r) and highest abundance across all animals and all samples (Sa). Integration sites with a Sr or Sa <0.85 were attributed to the animal with the highest recurrence (if Sr <0.85) or the highest abundance (if Sa <0.85) of that particular integration site. Integration sites that did not meet any of these criteria were flagged as “uncertain” and excluded, while the others were retrieved. This category contains integration sites found at a similar occurrence in different animals and at a low abundance in each of them. A detailed description of the protocol is available on github https://github.com/GIGA-AnimalGenomics-BLV/Public/tree/master/PIC/R. The corresponding function is called tagContamination.Integration sites  were acquired from longitudinal samples collected from 30 BLV-infected sheep over >5 years . The presence of mismatches increases the level of uncertainty in the alignment, which is reflected in the mapping quality score (MAPQ). Reads with a low MAPQ have a greater chance of being filtered out to avoid retaining misalignments. The majority of these mismatches result from the presence of sequencing adapters and viral sequences, which can be eliminated from reads prior to mapping them to a reference genome . We usedn = 45,739).Low-complexity sequences are frequent in eukaryotic genomes, and regions that contain such repetitive elements are difficult to resolve with short reads. Integration sites falling within low-complexity regions will often be attributed inaccurate clone abundances due to the poor mapping quality of sequencing reads supporting these sites. A representative example is ATL 2, a primary ATL classified as an acute leukemia subtype, which consists of a single dominant T-cell clone as revealed by the presence of a clonal T-cell receptor gamma gene rearrangement . SurprisWe applied our high-throughput protocol to several hundred samples collected from HTLV-1‐ and BLV-infected individuals and after processing, the data obtained 35,758 and 302,740 unique proviral integration sites, respectively. This revealed recurrent integration sites across samples likely caused by incorrectly assigned reads as the chance of finding an identical insertion site in two independent individuals is extremely low. Possible causes are cross-contamination during library preparation, bioinformatics issues in the demultiplexing process or index hopping and carryover during or between sequencing runs, which have been described as the main source of misassignment when sample and library preparation are processed in rigorously controlled conditions. Addressing this concern is particularly critical in longitudinal studies, which basically aim at examining a cohort of infected individuals (HTLV-1 or BLV in our studies) as well as multiple serial samples from the same individual. Cross-contamination will result in the inaccurate identification of identical integration sites across distinct samples.a, “abundance”). Second, the maximum number of occurrences of this given integration site in each individual . For both parameters, we compute the Shannon entropy (a or Sr <0.85), we assign each integration site to one of the following classes: abundance, occurrence, or uncertain. The “abundance” and “occurrence” categories define integration sites that can be attributed to a specific individual based on a higher proportion of reads identified in one individual  hosted by the European Bioinformatics Institute (EMBL-EBI) through study accession number PRJEB19394, Retroviral Integration Database (RID).The studies involving human participants were reviewed and approved by the ethics committee CPP Ile de France II, CNIL: number 1692254 and registration number 000001072. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by University of Saskatchewan Animal Care Committee following the Canada Council on Animal Care Guidelines, Protocol #19940212.MA: prepared the libraries. KD and MA: developed the NGS protocol. NR and VH: were involved in data collection and developed the bioinformatics pipeline. NR, VH, KD, SK, JW, AB, MG, and AV: contributed to data analysis and interpretation. AM and OH: provided patient samples. PG and NA: collected animal samples. AV: supervised the research. NR, VH, and AV: wrote the manuscript. All authors contributed to the revision of the report and gave their final approval for submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


To the Editor—Healthcare workers (HCWs) are at high risk of COVID-19 because of prolonged close contact with SARS-CoV-2–infected patients. Personal protective equipment (PPE), particularly respirators for care of patients undergoing aerosol-generating procedures (AGPs), is fundamental in protecting HCWs. However, global PPE shortages have occurred during this crisis, forcing healthcare institutions to consider alternative PPE management approaches with regard to appropriate utilization and conservation. At our institution, we limited PPE distribution from central supply to avoid misuse and to maintain visibility on usage and needs. This approach led to delays in obtaining PPE by bedside teams. Furthermore, frequent changes in public health guidance and hospital PPE approaches resulted in HCW confusion and apprehension. To address these issues, we implemented several strategies to optimize PPE utilization and education. The most valuable was a PPE stewardship initiative, which allowed us to simultaneously reserve PPE, assure HCW safety, provide real-time education and guidance regarding optimal infection control practices and PPE use, and ensure timely PPE availability. Here we present our successful PPE stewardship initiative at the Ann & Robert H. Lurie Children’s Hospital of Chicago.2 to strategically use respirators for the patients at highest risk  team, who instated daily bedside COVID-19 rounds on all suspected and confirmed COVID-19 inpatients. IP&C provided patient-specific recommendations regarding isolation status and PPE utilization, the recommendations for which frequently changed based on new science and evolving public health guidance. Rounds were conducted as a multidisciplinary team that included IP&C, respiratory therapists, bedside and quality nurses, physicians, and unit leadership. These rounds allowed for discussion and dissemination of current PPE guidance and fostered discussions regarding risk mitigation practices. The benefits were reciprocal—clinicians provided patient-related information to clarify the level of PPE required, and in return, IP&C provided PPE feedback and education regarding PPE needed, how to don and doff, handling N95s for reuse/extended use, and identifying additional PPE preservation opportunities. Decisions regarding PPE use, isolation status, and room type  were made with consideration of anticipated AGPs and respiratory interventions. We differentiated high-risk and low-risk AGPsAs the pandemic evolved in Chicago, the volume of suspected and confirmed COVID-19 cases increased, and the need to extend our PPE stewardship initiative, particularly to overnight teams, became evident. Thus, nursing leadership launched a team to extend and further support IP&C and PPE stewardship efforts. This new team, the “PPE Spotters,” was composed of bedside and quality nurses and respiratory therapists with the following goals: (1) to work closely with supply chain team to monitor and optimize PPE utilization, supply, and distribution; (2) to provide “just-in-time” training related to PPE donning/doffing and PPE reuse; and (3) to provide general support to hospital staff for COVID-19–related questions. The team was available 24/7 through a dedicated mobile phone to provide resources such as PPE, testing supplies, and educational videos and handouts. They performed bedside rounds with the care teams for suspected and confirmed COVID-19 inpatients to answer PPE-related questions, to deliver and evaluate bedside PPE supply, to collect unused PPE for return to central supply, and to ensure PPE utilization aligned with patient-specific care plans. The PPE Spotters had a profound impact on supply chain management by consolidating demand for the entire hospital to a single stocking location, the PPE Spotter cart. This approach allowed supply chain and central supply to anticipate demand and avoid periodic and unpredictable depletion of critical supplies such as respirators and respiratory viral specimen collections kits. In addition to their partnership with inpatient caregivers, the PPE spotters provided support to the emergency department, the transport team, anesthesia services, and procedural services.We measured the number of patient days that confirmed or suspected patients with COVID-19 remained under isolation, in addition to numbers of N95 masks distributed to hospital units from central supply during the 4-week period before and the 8-week period after the PPE Spotter team was formed. Before the PPE Spotter team was implemented, the central supply service distributed 18.2 masks per COVID-19 isolation patient-day  and after the PPE Spotter team was implemented, the central supply service distributed 3.0 masks per COVID-19 isolation patient-day . Thus, our PPE stewardship efforts successfully limited N95 misuse as community COVID-19 burden increased, despite rising inpatient volumes of confirmed COVID-19 and enacting universal COVID-19 admission testing, which required ~8–24 hours of COVID-19 isolation for all inpatients pending test results.Following the intervention, IP&C met with hospital units and departments to discuss successes and areas for improvement in our ongoing hospital COVID-19 response. A consistent theme was that the PPE Spotter team was very well received and fulfilled the intended educational and PPE preservation goals. Stakeholders identified IP&C and the PPE Spotter team as an excellent multidisciplinary resource, particularly during periods of evolving PPE guidance and supply chain insecurity. An additional benefit of this team was further expanding the pool of hospital staff to advocate for HCW safety and IP&C principles. We are working to determine how we can continue a similar model as COVID-19 activity decreases to maintain a readily available resource to bedside healthcare providers. In summary, our stewardship initiatives for PPE were successful in minimizing PPE misuse, and they provided a conduit for real-time education to frontline providers.