Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, which causes coronavirus disease 2019 (COVID-19), is spread primarily through exposure to respiratory droplets from close contact with an infected person. To inform prevention measures, we conducted a case-control study among Colorado adults to assess the risk of SARS-CoV-2 infection from community exposures.Cases were symptomatic Colorado adults (aged ≥18 years) with a positive SARS-CoV-2 test by reverse transcription-polymerase chain reaction (RT-PCR) reported to Colorado’s COVID-19 surveillance system. From March 16 to December 23, 2021, cases were randomly selected from surveillance data ≤12 days after their specimen collection date. Cases were matched on age, zip code (urban areas) or region , and specimen collection date with controls randomly selected among persons with a reported negative SARS-CoV-2 test result. Data on close contact and community exposures were obtained from surveillance and a survey administered online.The most common exposure locations among all cases and controls were place of employment, social events, or gatherings and the most frequently reported exposure relationship was co-worker or friend. Cases were more likely than controls to work outside the home (adjusted odds ratio (aOR) 1.18, 95% confidence interval (CI): 1.09–1.28) in industries and occupations related to accommodation and food services, retail sales, and construction. Cases were also more likely than controls to report contact with a non-household member with confirmed or suspected COVID-19 .Understanding the settings and activities associated with a higher risk of SARS-CoV-2 infection is essential for informing prevention measures aimed at reducing the transmission of SARS-CoV-2 and other respiratory diseases. These findings emphasize the risk of community exposure to infected persons and the need for workplace precautions in preventing ongoing transmission. The coronavirus disease 2019 (COVID-19) pandemic has had a profound impact on the health and wellbeing of people and economies around the globe. Early in the pandemic, many countries implemented stay-at-home orders, restaurant closures, international travel restrictions, and other public health interventions to reduce the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes COVID-19 disease. SARS-CoV-2 is spread by respiratory or aerosolized droplets through close contact with symptomatic and asymptomatic persons, resulting in rapid transmission in the community. Close contact with a person with confirmed or suspected COVID-19 is a dominant and well-documented source of spread [In Colorado, a study of persons with laboratory-confirmed COVID-19 in March 2020 reported that the most common activities two weeks prior to symptom onset included attending gatherings of >10 persons, travelling domestically, working in a healthcare setting, visiting a healthcare setting not as a health care worker, and using public transportation .Studies characterizing how community transmission occurred are needed to better target control measures and public health interventions to mitigate the spread of SARS-CoV-2 and other respiratory infections. Here, we present findings from a case-control study that assessed close contact and community exposures associated with SARS-CoV-2 infection among Colorado adults during 2021.Cases were symptomatic Colorado adults (aged ≥18 years) with SARS-CoV-2 infection confirmed by reverse transcription-polymerase chain reaction (RT-PCR). From March 16 to December 23, 2021, cases were randomly sampled ≤12 days after their specimen collection date from among those with a completed case investigation interview in Colorado’s COVID-19 surveillance system. Controls were randomly selected from persons with a RT-PCR-confirmed negative SARS-CoV-2 test result reported to Colorado’s Electronic Laboratory Reporting (ELR) system. Cases were individually matched to up to 20 controls on age (±10 years), zip code (urban areas) or region , and specimen collection date (3–7-day period corresponding to sample selection).An online survey developed in Research Electronic Data Capture (REDCap) collected data from cases and controls about travel , contact with anyone with confirmed or suspected COVID-19, and community activities in the 14 days before illness onset (cases) or specimen collection (controls). Community activities included going in-person to a bar or club; church, religious, or spiritual gathering; grocery or retail shopping; gym or fitness center; healthcare setting; public transportation; restaurant, café, or coffee shop; dining at a restaurant indoors; salon, spa, or barber; social event or gathering; sports or sporting events. Data on COVID-19-related symptoms, working or volunteering outside the home (including occupation and industry type), and demographic characteristics  were available from Colorado’s COVID-19 surveillance system for cases; these fields were included in the online survey for controls.Both cases and controls were sent a text message inviting them to complete the online survey; during March-April 2021, trained interviewers also attempted to contact non-responders by telephone if they did not complete the online survey. One contact attempt was made for cases and up to two contact attempts for controls. The online survey was available in English and Spanish, and interviewers had access to a language line interpreter. In order to focus on community exposures, cases and controls were excluded if they reported living in an institution or close contact with a household member with confirmed or suspected COVID-19. Other exclusion criteria included duplicate or missing telephone number or missing zip code; symptom onset date >7 days from the specimen collection date (cases); a prior positive COVID-19 test result (controls); or failed data quality checks . Self-reported COVID-19 vaccination  was recorded after May 13, 2021; before then, participants reporting any vaccination were excluded.Occupation and industry types were assigned using the National Institute for Occupational Safety and Health (NIOSH) Industry and Occupation Computerized Coding System (NIOCCS), a web-based software tool designed to translate industry and occupation text to standardized industry and occupation codes. Counties were classified as urban or rural/frontier based on Colorado census designations.An unmatched analysis using logistic regression assessed differences between cases and controls adjusting for matched criteria  and potential confounders selected a priori. A matched analysis using conditional logistic regression was also performed. A separate model restricted the analysis to symptomatic controls to make the control group more comparable to cases and to adjust for potential differences in behaviors and recall between asymptomatic and symptomatic persons. Statistical analyses were conducted using SAS software . The Colorado Multiple Institutional Review Board determined this study to be public health surveillance and not human subjects research and therefore exempt from full approval and requirements for informed consent.This study included 4,803 cases and 8,333 controls. Of the eligible cases randomly selected for the supplemental interview , 4,803 were enrolled (response rate 19%). Of the potentially eligible controls randomly selected and matched to cases , 8,333 were enrolled (response rate 7%) . CompareMost cases  were matched to at least one control. Matched and unmatched analyses produced similar findings; to include all cases, unmatched results are presented here. Cases were more likely than controls to work or volunteer outside the home  , Fig 2. Cases were more likely than controls to report contact with a non-household member with confirmed or suspected COVID-19 . The most common exposure locations reported by both cases and controls were place of employment and social events or gathering. The most frequently reported exposure relationship was co-worker or friend. Controls were more likely than cases to report travel, grocery or retail shopping, restaurant, café, or coffee shop, healthcare setting, salon, spa, or barber, and public transportation. Cases were more likely than controls to report dining indoors at a restaurant, café, or coffee shop , going to a bar or club , and attending a church, religious, or spiritual gathering . Findings were consistent among cases and controls with no known close contact exposure . In the In this case-control study in Colorado, working or volunteering outside the home and close contact with a non-household member with COVID-19 were risk factors for COVID-19. Moreover, among cases who reported close contact with a non-household member with COVID-19, the most frequently reported location was the workplace and the most frequently reported relationship was a co-worker. These findings emphasize the importance of workplace precautions, including sick leave, improved ventilation, reduced crowding, and vaccination, in preventing the ongoing transmission of SARS-CoV-2 and other respiratory viruses .Cases were more likely to leave their home to work as opposed to working from home or teleworking. This finding is congruent with a study that found cases with COVID-19 were more likely to report exclusively working in an office or school setting when compared to controls and another that found that working on-site was positively associated with SARS-CoV-2 infection , 5. As mClose contact with a non-household member with confirmed or suspected COVID-19 was more frequently reported among cases. This finding is similar to previous reports  and undeWhile cases were more likely to work in food service industries such as restaurants, cafés, or coffee shops, reported non-work related exposures in those settings were not associated with infection. This may be related to the timing of this study, which was conducted when some communities encouraged people to return to restaurant dining, and facilities may have been implementing measures to limit dining capacity, use open-air spaces (consistent with our finding cases were more likely to dine indoors), or improve ventilation, although implementation of these guidelines varied by facility and jurisdiction. Evidence from other case-control studies is mixed. While a case-control study in United States conducted in 2020 found that adults with positive SARS-CoV-2 tests were twice as likely to report dining at a restaurant than those with negative SARS-CoV-2 tests, a case-control study in Portugal in 2020 did not find a positive association between going to a restaurant and SARS-CoV-2 infection . SimilarThe findings in this report are subject to several limitations. First, the COVID-19 positive and negative persons included in this study could differ from those in the general population. Enrolled cases and controls differed from the eligible population from which they were sampled; in particular, cases were more likely to be vaccinated. This study only included cases reported to Colorado’s COVID-19 surveillance system who completed both the case investigation interview and responded to the survey. There was a higher proportion of healthcare workers among controls, who are subject to more frequent testing and may be more willing to answer a survey. Moreover, controls were more likely to travel and have certain community exposures; this may be because controls were more likely to seek testing prior to travel or other planned exposures and were not necessarily representative of the general population who did not have COVID-19 infection. Second, recruitment was limited to symptomatic cases, whose behavior may differ from asymptomatic cases. To control for any resulting bias, the adjusted analysis was restricted to symptomatic controls, which may have led to overmatching because the symptomatic controls likely had other respiratory viruses linked to similar risk behaviors associated with risk for SARS-CoV-2 infection. Third, both cases and controls were aware of their test results, which may have led to social desirability bias; in particular, cases may have underreported exposures.Knowledge on the settings and activities associated with a higher risk of SARS-CoV-2 transmission is essential for informing prevention measures aimed at reducing the transmission of COVID-19. These findings emphasize the risk of community exposure to persons with COVID-19 and the need for workplace precautions in preventing the ongoing transmission of SARS-CoV-2.


Population-based studies showing the advantage of computer-assisted total knee arthroplasty (CATKA) over conventional total knee arthroplasty (TKA) are outdated. More recent institution-based studies with relatively small sample sizes may hinder wider adoption. This cohort-based study aimed to compare postoperative CATKA and TKA in-hospital complications and 90-day all-cause readmissions using 2017-2018 data.Patients who underwent a primary unilateral CATKA or TKA were identified in the New York Statewide Planning and Research Cooperative System database. In-hospital complications were defined based on the 2020 Centers for Medicare & Medicaid Services total hip arthroplasty and TKA complications measure. Ninety-day readmissions were identified using unique patient identifiers. Logistic regression with a generalized estimating equation was used to assess associations of computer assistance with in-hospital complications and 90-day all-cause readmissions.P < 0.001); patients who had CATKAs had fewer 90-day all-cause readmissions compared with those who underwent TKAs . Computer assistance was associated with significantly lower odds of in-hospital complications  but not 90-day all-cause readmissions.A total of 80,468 TKAs were identified during the study period, of which 7,395 (9.2%) were CATKAs. Significantly fewer complications occurred among patients who had CATKAs compared with conventional TKAs (0.4% of total CATKAs vs 2.6% of total conventional TKAs, Patients undergoing CATKAs had markedly lower odds of in-hospital complications, compared with patients having TKAs, which has implications for both patient outcomes and hospital reimbursement. These more recent cohort-based findings encourage wider CATKA adoption. Moreover, the few published cohort-based studies were conducted using International Classification of Diseases, Ninth Revision (ICD-9) data, which lack the clinical specificity of ICD-10 and are not reflective of utilization in recent years.8910111213Compared with conventional TKAs, computer-assisted navigation technology (image based and imageless) increases the accuracy of implant positioning and optimizes the function and longevity of the prosthesis. In a recently published overview of systematic reviews, computer-assisted total knee arthroplasties (CATKAs) were associated with more favorable functional, radiological, and patient safety outcomes.14 To our knowledge, however, the relationship between CATKAs and patient outcomes in the ICD-10 era remains understudied. The primary objective of this study was to compare the rates of postoperative in-hospital complications and 90-day all-cause readmissions for patients having CATKAs and conventional TKAs. We hypothesized that CATKAs would be associated with lower postoperative complications and 90-day all-cause readmissions, compared with conventional TKAs.In a recently published study spanning 2010 to 2017, a modest increase in the adoption of CATKAs was observed, potentially driven by better patient outcomes.15 In-hospital data in SPARCS include discharges from all short-term acute care hospitals in New York State. We identified patients who underwent a conventional TKA or CATKA between January 1, 2017, and September 30, 2018. After obtaining institutional review board approval, we queried the SPARCS.This was a retrospective study using an all-payer discharge database, the New York Statewide Planning and Research Cooperative System (SPARCS), that collects data on patient characteristics, diagnoses, treatments, outpatient services, ambulatory surgery, and emergency department visits from all nonfederal hospitals in New York that are required to send data to the SPARCS database.16 across practically every type of hospital.16 The SPARCS data set has also been used previously for examining complication rates in total joint arthroplasty.18 Thus, there was a precedent for analyzing in-hospital postoperative complications in New York, such that it may provide important insights into postoperative CATKA complications nationally.Although the state of New York is not nationally representative, it makes up about 6% of the total US cohort with a notable proportion of racial and ethnic minorities. Its cohort resides in small towns, small cities, and a metropolis. A large number of TKAs are performed in the state,ICD-10 Procedure Coding System codes) identifiable in the SPARCS data were included in the study. Of those, patients whose data also included codes for computer assistance  were identified as CATKA cases. Those who underwent total hip arthroplasty (THA) within the same hospitalization, who had a bilateral TKA or CATKA, or who had missing discharge data and other covariate information were excluded from our analyses.Patients who were older than 45 years and received a primary unilateral TKA or CATKA  risk-standardized THA/TKA complications measure. This measure is currently implemented in the Medicare Hospital Value-Based Purchasing program and the Comprehensive Care for Joint Replacement Bundled Payment Model.The SPARCS database has unique patient-specific identifiers, which permits longitudinal tracking of patient readmissions in New York State within the period of interest. Any readmission within a 90-day period of discharge was recorded and used for analysis.t-tests, chi-square tests, or Fisher exact tests, where appropriate. Means with SDs or numbers of patients with their percentages were used to describe normally distributed continuous variables and categorical variables.Demographic and clinical characteristics were compared between patients who received conventional TKA and CATKA using Student 15 We adjusted for variables known to be associated with patient outcomes. The primary variable of interest was the use of computer-assisted navigation technology.891011121314 Covariates included age21 and insurance type,22 as well as the Charlson Comorbidity Index, which was applied to clinical comorbidity data to calculate a measure of disease burden.23-25 Race/ethnicity was also included as a covariate because of its known effect on patient access and other outcomes.26-28 Finally, we adjusted for hospital volume, which was defined using quartiles of combined 2-year procedure volume.30 Odds ratios (ORs) and their 95% confidence intervals (95% CIs) were estimated and reported, and all P values were two-sided with statistical significance evaluated at an alpha level of <0.05. Statistical tests and analyses were performed using SAS 9.4 (SAS Institute).To evaluate the association of practice patterns  with these outcomes, we used logistic regression models with generalized equation estimation to account for the correlation of patients who underwent conventional TKA and CATKA in the same hospital, while adjusting for demographic and clinical factors. The SPARCS database collects demographic and clinical patient data, including age, sex, race/ethnicity, insurance type, and clinical comorbidities.P < 0.0001), whereas Blacks , Hispanics , and Asian/Pacific Islanders (1.7% vs 3.1%) made up a much smaller proportion of the overall TKA cohort, particularly the CATKA subgroup. Medicare and Commercial coverage were the most common insurance types among both patients who had CATKAs  and conventional TKAs , whereas Medicaid and Workers' Compensation coverage made up a very small proportion of the CATKAs  and conventional TKAs .Over the study period, 80,468 TKAs were performed in New York, of which 7,395 (9.2%) were CATKAs. Bivariate analysis revealed that demographic variables were generally similar across the conventional TKA and CATKA cohorts, although some differences were observed among the race and insurance-type covariates Table . Non-HisP < 0.0001) (Table P < 0.01). Male sex , Medicaid insurance , and having three or more comorbidities  were all found to be markedly associated with higher odds of 90-day all-cause readmissions after CATKA. High hospital volume, defined as more than 661 TKA procedures , was markedly associated with lower odds of 90-day all-cause readmissions after CATKA , followed by pneumonia and pulmonary embolisms. In the unadjusted analyses, there were significantly fewer complications after CATKAs compared with conventional TKAs (0.4% vs 2.6%, Using a New York all-payer cohort-based data set, this study compared in-hospital complications and 90-day all-cause readmissions among patients aged 45 years and older who underwent either a CATKA or a conventional TKA. We found that patients who underwent CATKAs had notably fewer in-hospital complications. Mechanical complications, periprosthetic joint infections/wound infections, and pneumonia were the most common. After adjusting for covariates, CATKA patients had 85% lower odds of in-hospital complications, compared with those who had conventional TKAs. However, there was no notable association between CATKA and 90-day all-cause readmissions. To our knowledge, this is the first study to analyze complication rates of both conventional TKAs and CATKAs in recent years.3 Our findings have shown that patients who had CATKAs suffered fewer complications across several categories, which is consistent with older ICD-9 studies.8910111213 We have shown that CATKA patients suffered fewer mechanical complications, compared with conventional TKAs. Our results also showed that patients who had CATKAs had fewer pulmonary embolisms, potentially because of the use of computer-assisted alignment guides that lead to fewer emboli.32 Previous studies have also reported that embolization that occurs during surgical procedures is associated with both intraoperative and postoperative cardiac complications.33-35 As such, utilization of CATKAs may prevent emboli from occurring, therefore reducing emboli-related cardiac complications. In our CATKA cohort, none had a pulmonary embolism or acute myocardial infarction. Our finding that CATKAs were associated with lower odds of postoperative complications compared with conventional TKAs is further supported by a review by Hasan et al,7 which found that CATKAs have more favorable functional, radiological, and patient safety outcomes. Greater integration of computer navigation technology in practice may have a notable impact on the overall success of TKAs, by mitigating the odds of serious complications and improving patient outcomes.The success of TKAs is rooted in accurate implant positioning to prevent axis malalignment and optimize prosthesis durability while also preventing wear damage and aseptic loosening. Although optimal position for implants is still debatable, CATKA aims to deliver accurate implant positioning and may provide other benefits.37 Previous literature has shown that minority race/ethnicity, lower socioeconomic status, lower volume hospitals, and having public insurance are all associated with higher odds of 90-day all-cause readmissions.36-38 In the current reimbursement environment, these findings do bring into question whether hospitals—particularly those participating in bundled payment programs—may be motivated to invest in computer-assisted technology. Higher initial costs associated with CATKAs can be attributed to preoperative imaging, materials, and software. Lower in-hospital complication rates are likely to decrease hospital costs under a bundled payment model, allowing them to increase their potential profit sharing. However, to better understand whether cost savings associated with reduced in-hospital complications are sufficient to financially incentivize the adoption of computer-assisted technology despite no apparent cost savings related to 90-day readmissions, a follow-up cost analysis is warranted.Although our results showed that the use of computer navigation technology itself was associated with lower odds of postoperative in-hospital complications when compared with conventional TKA, CATKA was not associated with lower odds of 90-day all-cause readmissions. Other risk factors may be more influential than the technology and surgical approach when examining a 90-day episode of care that is primarily outpatient managed.7891011123132333440 In the current study, CATKA represented slightly less than 10% of all TKAs. In another recent study of CATKA trends, modest increases were observed from 2010 to 2017.14 The use of computer-assisted navigation technology for TKAs has yet to become the primary recommendation/standard of care for unilateral uncomplicated TKA.41 Comparatively, increased adoption of the da Vinci Surgical System for robot-assisted prostatectomy and increased utilization of minimally invasive robot-assisted urologic surgeries were driven by greater surgical efficiency and improved patient outcomes.43 Given the better surgical and patient outcomes associated with CATKAs, the endorsement of CATKAs as a benchmark for certain patient cohorts may improve adoption.We hope that the more recent cohort-based results of this study will help increase wider adoption of CATKA. Our study corroborates a growing body of evidence that shows CATKAs are associated with lower odds of postoperative in-hospital complications, but the adoption of this technology does not reflect this.ICD-10–based study that provides insight on in-hospital complication rates after conventional TKAs and CATKAs, it has some limitations. The study relies on a single administrative database, which likely underreports the number of CATKAs performed in the State of New York. Although coding errors are possible, studies have shown that administrative claims data are reliable for studying outcomes after joint arthroplasty.18 In addition, the data were derived from only one state. Although New York is a large, diverse state and may serve as a surrogate for national CATKA complication rates, the findings of the study are not necessarily representative of all the CATKA complication rates across the United States. Moreover, some hospital characteristics not included in our model, such as teaching status, size, and geographical location, may be associated with CATKA uptake and subsequent postoperative CATKA complication rates. For instance, large size, urban location, and nonteaching status have all been found to be associated with higher rates of CATKAs and technology-assisted KAs.44 Our analysis did not account for the possibility of selection bias, whereby the unmeasured characteristics of patients who underwent CATKA may be different from those who underwent conventional TKA. In addition, although we were able to comprehensively capture postoperative in-hospital complications, the patient-specific identifiers used in this study did not capture postdischarge complications requiring readmission if patients were readmitted into facilities outside of New York, which represents another important outcome currently captured in the Hospital Value-Based Purchasing and Comprehensive Care for Joint Replacement programs. Therefore, readmission outside of New York could be a confounding variable because patients who undergo surgery in New York may return home with complications. However, we believe that the likelihood of this happening applies similarly to patients who underwent CATKA and conventional TKA. Furthermore, the study includes data on in-hospital surgeries and does not include any surgeries performed in ambulatory surgery centers. Despite these limitations, we believe that the large sample size of the study provides valuable insight about the relationship between CATKAs and in-hospital complications across a diverse cohort.Although this is the first In our study cohort, patients who had CATKAs had markedly lower odds of postoperative in-hospital complications compared with those who had conventional TKAs. Our findings have important implications for increasing adoption and access to this technology.


Treatments for COVID-19 infections have improved dramatically since the beginning of the pandemic, and glucocorticoids have been a key tool in improving mortality rates. The UK’s National Institute for Health and Care Excellence guidance is for treatment to be targeted only at those requiring oxygen supplementation, however, and the interactions between glucocorticoids and COVID-19 are not completely understood. In this work, a multi-omic analysis of 98 inpatient-recruited participants was performed by quantitative metabolomics (using targeted liquid chromatography-mass spectrometry) and data-independent acquisition proteomics. Both ‘omics datasets were analysed for statistically significant features and pathways differentiating participants whose treatment regimens did or did not include glucocorticoids. Metabolomic differences in glucocorticoid-treated patients included the modulation of cortisol and bile acid concentrations in serum, but no alleviation of serum dyslipidemia or increased amino acid concentrations (including tyrosine and arginine) in the glucocorticoid-treated cohort relative to the untreated cohort. Proteomic pathway analysis indicated neutrophil and platelet degranulation as influenced by glucocorticoid treatment. These results are in keeping with the key role of platelet-associated pathways and neutrophils in COVID-19 pathogenesis and provide opportunity for further understanding of glucocorticoid action. The findings also, however, highlight that glucocorticoids are not fully effective across the wide range of ‘omics dysregulation caused by COVID-19 infections. Whilst great strides have been made in testing for and vaccinating against COVID-19, due to the rapid spread of new variants, the disease is progressing towards a globally endemic status. More positively, treatment of the illness has also improved, with mortality risk declining since the initial wave of COVID-19 infections in early 2020 .Glucocorticoids act through trans-repression (the decreased expression of pro-inflammatory genes), trans-activation (the increased expression of anti-inflammatory genes) and also in part through non-genomic mechanisms, and have a long history in the treatment of chronic upper airways disease . As withMetabolomic and proteomic analyses of COVID-19-positive individuals have so far mainly concentrated on identifying biomarkers for prognosis and diagnosis ,17,18, rThe objective of this study was to quantitatively investigate the serum metabolome, lipidome and proteome of hospital inpatients with COVID-19 to identify the combined influence of glucocorticoid treatment and COVID-19 infection, benefiting from the additional insight offered by combining multiple ‘omics approaches ,24. BecaThe results presented here are consistent with previous work that the glucocorticoid dexamethasone modulates the neutrophil response, and also modulates cortisol levels and bile acid dysregulation. The data also, however, illustrate that in many cases, glucocorticoid treatment is not sufficient to reverse the observed phenomena of the impact of COVID-19, for example in compensating for dyslipidemia or elevated amino acid levels in serum.n = 25) with a negative COVID-19 RT-PCR test but with symptoms consistent with a suspected COVID-19 infection was also recruited, in a shorter timeframe between May 2020 and July 2020. Of the 37 participants treated with synthetic glucocorticoids, 30 were treated with dexamethasone, 6 with prednisolone, 1 with methylprednisolone and none with betamethasone or hydrocortisone. A summary of the population characteristics is shown in The study population analysed in this work totalled 98 hospital inpatients recruited between May 2020 and March 2021. These included 37 participants with a positive COVID-19 RT-PCR test and treated with glucocorticoids up to 48 h prior to sampling, and 36 participants with a positive RT-PCR test but not treated with glucocorticoids. A control group , but levels of lymphocytes were not. Within the treated cohort, participants were more likely to present with bilateral chest X-ray changes , more likely to require continuous positive airways pressure  and were also escalated to the hospital Medical Acute Dependency Unit more frequently , indicating that those treated with glucocorticoids are more severely affected by COVID-19 than those not treated.Age distributions for the glucocorticoid-treated COVID-19-positive cohort and the untreated cohort were similar, but the treated cohort included proportionately more males. Comorbidities are associated with severity, representing both a causative and confounding factor. Due to hospital recruitment, however, comorbidities including type 2 diabetes mellitus, hypertension, high cholesterol and ischaemic heart disease were present in both the treated and untreated groups; former smokers were somewhat more represented in the glucocorticoid-treated group. Levels of C-Reactive Protein (CRP) and eosinophils were reduced by glucocorticoid treatment . We then identified those metabolites that were differentiated between those treated with glucocorticoids and those not treated at a For proteomics, SWATH-MS identified 754 proteins. This initial dataset was then filtered by the same method as for metabolites in order to identify proteins differentially expressed between glucocorticoids-treated and -untreated participants. A total of 68 proteins were found to be modulated.The top 15 altered metabolites  and protThe initial data frame of metabolites measured in participants was subjected to pathway analysis. The most statistically significant pathway when controlled for false discovery was steroid hormone biosynthesis . SeveralUnivariate analyses by boxplot for cortisol, bile acids and amino acids  were over-represented in the relevance network, comprising 8 of the 20 metabolites identified. In the overall feature set, lipids incorporating arachidonic acid comprised 13 of the 483 metabolites measured by the targeted LC-MS/MS method used in this work.We have employed a quantitative multiomics approach combined with use of clinical data to investigate glucocorticoid action in patients with COVID-19 across metabolomic and proteomic features. Metabolomic pathway analysis shows that the largest difference between the glucocorticoid-treated and -untreated groups was in steroid hormone biosynthesis, as would be expected. Cortisol levels in the treated group were below those seen in either the untreated group, or the control group. The remaining pathways influenced were predominantly related to amino acids. Amino acid dysregulation was greater in glucocorticoid-treated participants than in -untreated participants. Consequently, these data show no evidence of glucocorticoids modulating COVID-19-driven amino acid dysregulation. Furthermore, glucocorticoid treatment did not alleviate increased serum concentrations of triglycerides. This result is consistent with dexamethasone previously being identified as potentially causing hypertriglyceridemia in non-COVID-19 settings , and sugFor the proteomic dataset, the most statistically significant pathway change caused by glucocorticoid treatment was that for neutrophil degranulation. This pathway is related to platelet activation and degranulation in an inflammatory response. The importance of neutrophil- and platelet-associated pathways described here is consistent with the COVID-19 proteomics literature, especially Sinha et al. and Panda et al. , and revThe relevance network presented in this work  also shoIt should further be noted that the World Health Organization recommends the use of dexamethasone only in severe/critical patients, and not routinely, as do the UK’s NICE guidelines. More recently, clinical data have shown that treatment with dexamethasone in patients that do not require intensive respiratory support is not associated with any improvement in outcomes, and that there is evidence of potential harm in such cases . We now This study does include clear limitations. As a retrospective observational study, results will naturally be less robust than those that would be obtained from a prospective randomized and controlled trial. Furthermore, dexamethasone became standard of care in the UK in June 2020, albeit the implementation was not uniform , which mIn conclusion, these results provide the first multi-omics investigation into the action of synthetic glucocorticoids in COVID-19 treatment, and are concordant with previous findings that glucocorticoids modulate the neutrophil response , potentiAll participants for this study were recruited as part of an observational cohort study. Ethical approval (IRAS project ID 155921) was obtained via the NHS Health Research Authority (REC reference: 14/LO/1221). The participants were recruited consecutively at Frimley Park NHS Trust, UK, between May 2020 and March 2021. Participants were identified by clinical staff to ensure that they had the capacity to consent to the study and were asked to sign an Informed Consent Form based on the International Severe Acute Respiratory and emerging Infection Consortium/World Health Organisation (ISARIC/WHO) Clinical Characterisation Protocol for Severe Emerging Infections. Those patients that did not have this capacity were not sampled. Signatures were witnessed by University of Surrey researchers. At the time of recruitment, participants were categorised by the hospital as either “query COVID”  or “COVID positive” (meaning that a positive test result had been recorded). All participants were provided with a Patient Information Sheet explaining the goals of the study. All methods part of this study were performed in accordance with the relevant guidelines and regulations.Collection of the samples was performed by researchers from the University of Surrey at Frimley Park NHS Foundation Trust hospitals; collection took place on admission or in some cases shortly afterwards. Alongside the collection of blood samples, metadata for all participants were also collected, covering the inter alia medication regime , sex, age, comorbidities (based on whether the participant was receiving treatment), the results and dates of COVID-19 PCR (polymerase chain reaction) tests, bilateral chest X-Ray changes, smoking status and whether the participant presented with clinical symptoms of COVID-19. Values for lymphocytes, CRP and eosinophils were also taken—here, values within five days of biofluid sampling were recorded.g for 10 min at 4 °C. All samples with a sampling time interval greater than four hours were rejected. Serum was then decanted into 100 µL aliquots and stored at −80 °C until processing. Prior to analysis, the serum was sterilised using 200 µL of ethanol into 100 µL of serum (2:1 v/v solvent/sample ratio).Serum collection and extraction followed the protocols set out by the COVID-19 Coalition for metabolomics  and protn = 25), a group of COVID-19-positive patients treated with a glucocorticoid in the 48 h prior to sampling (n = 37), and a group of COVID-19-positive patients not treated with a glucocorticoid in in the 48 h prior to sampling (n = 36).From the initial population of 115 recruited participants, a number of exclusions were made to remove participants where incomplete metadata were provided, where the gap between the initial positive COVID-19 test and sampling exceeded 14 days or where the participants were already being medicated with glucocorticoids prior to COVID-19 treatment. The remaining population of 98 was then segmented into a COVID-19-negative control group for comparison purposes (n = 5) and between plates (n = 3) using Biocrates METIDQ software (QC2 correction). Metabolites where >25% concentrations were at or below the limit of detection (≪LOD), above the limit of quantification (>LOQ), or where the blank was out of range were excluded . The remaining 474 quantified metabolites comprised of 8 acylcarnitines, 20 amino acids, 26 biogenic amines, 11 bile acids, 53 ceramides, 15 cholesteryl esters, 1 cresol, 9 diglycerides, 4 carboxylic and fatty acids, 85 phosphatidyl cholines, 14 sphingolipids, 222 triglycerides, 2 hormones, 2 indoles, 1 nucleobase and 1 vitamin.Serum samples were analysed using the Biocrates MxP Quant 500 system using a Xevo TQ-S Triple Quadrupole Mass Spectrometer coupled to an Acquity UPLC system . The MxP Quant 500 system provides targeted quantification of metabolites including amino acids and derivatives, bile acids, biogenic amines, acylcarnitines, carbohydrates and other small molecule metabolites, plus a wide array of lipids. Analysis took place via a single assay, and two analytical procedures. The first of these procedures operated by liquid chromatography (operated in both positive and negative ion mode) and the second by flow injection analysis (positive ion mode), both coupled to tandem mass spectrometry with isotopically labelled internal standards for quantification. The sample order was randomised across 96-well plates, and 3 levels of quality controls (QC) were run on each plate. Blank PBS  samples  were used for the calculation of the limits of detection (LOD). Biogenic amines and amino acids were quantified for each plate using a seven-point calibration curve, with other analytes semi-quantitated with a single point standard . The levels of metabolites present in each QC were compared to the expected values and the CV% calculated. Data were normalised between the three batches using the results of quality control level 2 (QC2) repeats across the plate  approach for precision identification and accurate quantification of proteins. Samples were quality-checked, assigned a unique in-ternal study ID and groups were randomized. Immunodepletion was performed by using Top14 Abundant Protein Depletion Resin  in a 96-well format. Samples were reduced, alkylated and digested with trypsin  using S-trap columns  prior to lyophylisation. Digitized proteomic maps were generated by using 100 variable window SWATH-MS (68 min) with a micro-flow LC-MS system. SWATH-MS analysis was performed on a 6600 TripleTOF mass spectrometer with DuoSpray Ion Source  coupled to an Eksigent 425 LC (AB Sciex Limited) with specific mass spectrometric conditions , as previously described. All study samples were run in duplicate and CV percentages were calculated across the two injections. Only samples with median CV’s across all proteins quantified with less than 20% were taken forward for analysis, with the best injection for each sample included in the final protein quantification. To ensure instrument performance remained consistent throughout the study and to control for batch-related effects, both commercially available plasma  and a total pool of all study samples (TOTs) were processed, digested and run alongside each batch of samples.m/z values referenced to library values as well as orthogonal information in the form of MS/MS fragmentation spectra and retention time matching against isotopically labelled internal standards. The mass spectrometry conditions were used as optimised and provided by Biocrates.Serum metabolites were identified and quantified using isotopically labelled internal standards, retention times and multiple reaction monitoring. In this study, identifications were made in accordance with the Metabolomics Standards Initiative for metabolite identification , using bFor the proteomic workflow, extraction and processing followed the method described in Salie et al. . BrieflyThe two approaches described generated two data frames, each comprising a matrix of n participants by p features, with matched n of 98 participants. These data frames are provided in p-value. Specifically, a p-value threshold of 0.05 was used for significance by Wilcoxon rank-sum test.Both the metabolomic and proteomic data frames were log2 transformed to stabilize variance and reduce heteroscedasticity. In order to identify proteins and metabolites differentially expressed between the two conditions , the difference between the two populations for each feature was measured by Pathway analysis for the proteomic differences between the two classes of participants was conducted using the ClueGo application (version 2.5.9) within Cytoscape (version 3.9.1) ,48, matcThe relevance network was constructed using the R package MixOmics . This ap


Several variants of the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are emerging all over the world. Variant surveillance from genome sequencing has become crucial to determine if mutations in these variants are rendering the virus more infectious, potent, or resistant to existing vaccines and therapeutics. Meanwhile, analyzing many raw sequencing data repeatedly with currently available code-based bioinformatics tools is tremendously challenging to be implemented in this unprecedented pandemic time due to the fact of limited experts and computational resources. Therefore, in order to hasten variant surveillance efforts, we developed an installation-free cloud workflow for robust mutation profiling of SARS-CoV-2 variants from multiple Illumina sequencing data. Herein, 55 raw sequencing data representing four early SARS-CoV-2 variants of concern  from an open-access database were used to test our workflow performance. As a result, our workflow could automatically identify mutated sites of the variants along with reliable annotation of the protein-coding genes at cost-effective and timely manner for all by harnessing parallel cloud computing in one execution under resource-limitation settings. In addition, our workflow can also generate a consensus genome sequence which can be shared with others in public data repositories to support global variant surveillance efforts. A novel coronavirus species caused a previously unidentified human pneumonia-like disease for the first time in Wuhan, China, on 12 December 2019  followinMeanwhile, in order to describe variants, it is critical to identify and track mutations from genome sequencing. The global health effort is shown by deposition of the sequences of SARS-CoV-2 samples from many countries in FASTA format in public data repositories, such as the Global Initiative on Sharing All Influenza Data (GISAID) EpiCoV™  and the The challenge of processing SARS-CoV-2 raw sequencing data in the FASTQ format is that it is of a greater size compared to the genome sequence in FASTA format which also persists. Recently, a web-based COVID-19 analysis platform  was publIn this study, we built a cloud-powered bioinformatics workflow that was optimized for mutation profiling of SARS-CoV-2 variants to accommodate limitations of both computational and expert resources. Our installation-free workflow on a specified public cloud platform can diminish the implementation barrier in resource-poor settings to allow reliable variant identification from SARS-CoV-2 Illumina raw sequencing data in a high-throughput manner by utilizing parallel computation. Mutation profiling that consists of mutation identification and annotation is the central focus of our cloud workflow, and it is complemented by the generation of consensus sequences of the SARS-CoV-2 genome which can subsequently be deposited in public data repositories to support global health efforts.We selected sequencing data from four early VOCs , which are considered highly transmissible variants in human populations . These fIn this study, several data sets that collected raw sequencing data of SARS-CoV-2 from other wet-lab experiments were used to represent VOCs. The registered data sets from 2020~2021 were selected in the NCBI BioProject public repository [https://cgc.sbgenomics.com, accessed on 13 November 2021) platform [We used the Cancer Genomics Cloud  from the SRA Toolkit  to facilOur own tailored workflow is a series of several well-established bioinformatics tools, starting from alignment, variant calling, and variant annotation, to genome reconstruction built in the CGC platform. We used Burrows-Wheeler Alignment Maximal Exact Matches (BWA-MEM) for alignment , Haplotyhttp://ftp.ebi.ac.uk/ensemblgenomes/pub/viruses/variation/vep/sars_cov_2_vep_101_ASM985889v3.tar.gz, accessed on 13 November 2021). Then, the BWA INDEX tool was used to transform the reference genome in the full-text index in Minute space (FM-index) prior to the alignment process. At the same time, the reference genome was also processed by SBG FASTA Indices to create the FASTA dictionary and index which are later required for running GATK tools. The raw reads were aligned to the reference genome in the FM-index by BWA-MEM tool (v.0.7.17) and embedded with Biobambam2 sortmadup (v.2.0.87) [In general, our workflow can be explained as follows. In the beginning, our workflow needs three different input files provided by the user: the raw sequencing read files as FASTQ, a reference genome as FASTA, and species cache information as zipped files. For this study of SARS-CoV-2 mutation profiling purposes, we used previously preprocessed files of selected VOC data sets as input reads, the original genome sequence of viral SARS-CoV-2 ribonucleic acid (RNA) with a length of 29,903 nucleotides  availabl.2.0.87)  internal.2.0.87) , a standhttps://pangolin.cog-uk.io; v.3.1.19, with Pango lineages v. accessed on 20 January 2022) [https://covidcg.org/?tab=report accessed on 31 December 2021) web-based tool (v. 2.4.5) [https://genome.ucsc.edu/cgi-bin/hgTracks?db=wuhCor1 accessed on 31 December 2021) based on its 2021 updated database [All genome sequences reconstructed by our workflow were then passed to the web-based Pango lineage assigner tool   in a mul. 2.4.5)  to valid. 2.4.5)  was useddatabase .In our experiment, 98% of reads  across 110 paired-ended read files of 55 samples passed our data preprocessing step that were used as input for our defined cloud workflow in mutation profiling steps. The 98% of reads came from data sets that had no additional file when downloaded as paired ended files containing forward and reverse reads.All data preprocessing and mutation profiling steps were performed in the CGC platform under the availability of diverse computed-optimized AWS instances. For twenty minutes, US$ 2.22, with default instance configuration settings, was spent during this study as shown in more detail in Our workflow successfully identified 96% of samples (53 of 55) after the lineage of constructed genome sequence output for each sample was confirmed through the web-based Pangolin tool which belonged to lineages or sub-lineages of the respective lineage mentioned in corresponding data set metadata information with two samples withdrawn: SRR13907331 and SRR13907335 from PRJNA708134 Alpha group which belonged to B.1.1.528 and B.1.1.263 lineage respectively  across the four VOCs; 32 for Alpha, 27 for Beta, 34 for Gamma, and 42 for Delta . Our observations of these most frequent mutations also matched very well with the COVID-19 CG. Specifically, all of the most frequent mutations listed in the COVID-19 CG for each VOC  belonged to the list of our most frequent mutations. Meanwhile, all key mutations mentioned in the UCSC SARS-CoV-2 Genome Browser for each VOC  were also found in these validated most frequent mutations , and mutation identification and annotation when cross-validated with other dedicated SARS-CoV-2 resources, ranging from the web-based Pangolin tool to COVID-19 CG and UCSC SARS-CoV-2 Genome Browser. In addition, this study also suggested that when different data sets that come from different experiments are taken into consideration, we can still conclude the similar most frequent mutations even with limited sample sizes. Taking our experiment as an example, samples in the Alpha and Gamma groups that came from more than one data set within the sub-lineage found for each group, Q.4 for Alpha and P.1.14 for Gamma, still reflected the most frequent mutations found in larger sample sizes available from other resources. This phenomenon can be explained by the concordance of genomes deposited in the larger sample size in public repository data which were also generated from many experiments around the world resulting in high diversity levels similar to our distinct data sets used in the Alpha and Gamma groups. Meanwhile, our workflow also ensures that there is an unbiased bioinformatics analysis across data, since we used raw sequencing data as our input and applied uniform analytical tools to process these data from the beginning. This approach is critical when dealing with data that come from multiple data sets from different experiments as seen in this study due to different experiments that may have been conducted by different sequencing data analyses based on each lab’s protocol settings. Finally, our workflow that integrated many publicly available resources ensures its reproducibility for use by a wide variety of users from diverse backgrounds in their workspace by extending its capability to process their own sequencing data sets in real-world settings beyond the open-access data sets shown in this study by dragging and dropping their own sequencing files along with all necessary data using the CGC web uploader to make it run.Understanding SARS-CoV-2 mutations is also very helpful to elucidate genomic variability that allows the virus to evade host immunity and acquire drug resistance. For example, some vaccines to prevent the COVID-19 infection were developed to target proteins of SARS-CoV-2 with the S protein as the main protein target due to its role in initiating viral entry into host cells . Some muOur workflow can be a solution to a lack of resources for analyzing the SARS-CoV-2 genome. Our workflow has an affordable cost which will be beneficial for implementation especially in those countries with limited computational and expert resources. Given the fact that many VOCs and VOIs that appeared in developing countries as determined by the United Nations , such asOur present workflow has several limitations. Although our workflow uses standardized workflow definitions in CWL, since our workflow utilizes the CGC platform-agnostic code, it might not work optimally outside the CGC platform. Therefore, prior registration in the CGC platform is needed in order to use our cloud workflow. Meanwhile, parallel computations on the CGC platform can only perform up to 80 samples in one execution turn by default. If a user wishes to use a larger number, a request should be made to the CGC team in advance. Furthermore, our workflow only works with Illumina sequencing data as the input due to genomics technological differences that exist behind each sequencing platform, while Illumina works in short read sequencing by a synthesis approach .There are a number of SARS-CoV-2 variants circulating around the world which have made the end of the novel COVID-19 pandemic unpredictable. It is important to know particular variants based on the identified mutations of the genes, since such variants may have different virulence activities. Cloud-powered diagnostic tools have accelerated the identification of viral mutations through sequencing which has become critical to fighting COVID-19. Our workflow that collects a set of well-established bioinformatics tools for mutation profiling purposes, including viral mutation identification and annotation, can be an option to meet this demand. Our workflow designed for Illumina sequencing works in parallel, in a rapid and cost-effective manner suitable for resource-poor settings and may be best suited for application worldwide for detecting current and emerging variants so that the spread of specific variants can be limited as early as possible. In addition, our workflow is able to generate a genome sequence of SARS-CoV-2 to support global genomics surveillance by accession to public data repositories. In the future, our cloud workflow capability can be extended to predict upcoming variants with a prominent machine learning approach due to the abundant SARS-CoV-2 sequences available in public data repositories. Furthermore, our workflow can also be easily operated to detect variants of other pathogens species beyond SARS-CoV-2 as long as sequencing data, reference genome, and the related species annotation data are available.


Horizontal violence (HV) is defined as “persistent exposure to interpersonal aggression and mistreatment from colleagues.” Our objective in this pilot, single-site study was to identify sources of HV toward emergency medicine (EM) residents, using the Negative Acts Questionnaire-Revised (NAQ-R).In this investigation we used a descriptive cross-sectional survey design to categorize HV. All voluntary participants were residents in an Accreditation Council for Graduate Medical Education-approved, three-year academic EM residency. Data were collected via electronic survey and occurred six months into an academic year. We collected demographic information and responses to the NAQ-R in 2020. Horizontal violence is subdivided into three categories: work-related; person-related; and physical intimidation. Emergency medicine residents answered questions as they related to their interactions with residents and support staff, which included nursing.A total of 23 of 26 residents responded (89%). Participants were 56% women, 78% white, 11% Hispanic, and 89% heterosexual. Participant clinical year was 39% first-, 39% second-, and 22% third-year residents. Women reported a higher frequency of HV compared to men . By category, women indicated higher incidence of work-related violence from other residents (P = .05) and staff (P =.02). There was no difference in reported frequency of violence for interns compared to senior residents.Our pilot study demonstrated horizontal violence toward EM residents exists and is more prevalent in women. The hierarchical structure of education in healthcare is a known risk factor for workplace bullying.8Horizontal violence (HV), “persistent exposure to interpersonal aggression and mistreatment from colleagues,”17Worldwide, workplace bullying of residents has been identified.19Overall, there is a paucity of data regarding HV specifically and its adverse effects on residents, especially residents in EM – a specialty that depends on frequent interactions with staff and residents from different services. In this pilot study we hypothesized that women residents in their first year of residency training would experience more HV, specifically from other residents and support staff, as measured by a tailored healthcare version of the 22-item NAQ-R.This pilot study used a descriptive cross-sectional design to determine HV specifically within an EM residency program. All participants were residents within an academic, Level I trauma center in the United States. A voluntary, electronic version of the NAQ-R, that has been used in other healthcare residency settings,We chose the 22-item NAQ-R as the survey instrument as it is considered the gold standard worldwide . The NAQ13Bullying is evaluated in two different ways within the NAQ-R. The NAQ-R originally used an operational definition in 2009; in 2012, the authors reanalyzed the original data to create a cut-off score definition. This was done to improve analysis related to prevalence of workplace bullying. Current literature primarily focuses on the quantitative definition. The operational definition defines bullying as experiencing a negative act once per week during the prior six months; to determine these criteria, survey item responses of “weekly” or any response of “daily” corresponded to each operationalized definition of bullying. The quantitative definition of bullying takes the total score of the 22-item NAQ-R , and those with total scores greater than 33 are classified as bullying. 21P-values were adjusted using the false discovery rate correction. All tests were two-sided, and P-values less than 0.05 were considered significant.Residents were asked to complete the 22 questions as they related specifically to other residents, including co-residents, off-service residents, and consulting service residents. They then answered the 22 questions as they related to support staff, including nurses, respiratory therapists, lab technicians, personal care assistants, care team assistants, and finance representatives. We summarized the data with medians and interquartile ranges or with frequency counts and percentages, as applicable. Survey items were presented to respondents using a descriptive Likert scale and were subsequently coded from 1 to 5 with 1 (never), 2 (now and then), 3 (monthly), 4 (weekly), and 5 (daily). A total response score was computed by adding the responses across all 22 survey items. Additionally, we further grouped the survey items into categories of work-related, person-related, and physical intimidation. Data analysis was completed using R Core Team (2019) software . Gender and postgraduate year responses to event-frequency questions were performed using Wilcoxon rank sum tests. To avoid issues of multiple comparisons, all A total of 23 of 26 residents completed the questionnaire for a response rate of 88.5%. The P =.471). Overall, women reported a higher frequency of HV compared to men . When subdividing HV into the three categories of work-related violence, person-related violence, and physical intimidation categories, women indicated a higher incidence of work-related violence, both from residents (P= .05) and from support staff (P =.02) as viewed in the From the operational NAQ-R definition of bullying, 13.0% of respondents (3/23) reported being bullied by residents, and 11.1% of respondents (2/18) indicated being bullied by support staff once a week. When the quantitative bullying score (>33 points) was used, 17.4% of respondents were bullied by residents (4/23) and 27.8% were bullied by support staff (5/18); there was no significant difference between support staff and resident bullying (The literature has focused on HV experienced by nurses; this study highlights that residents also experience HV. The HV that was reported during the first six months of the clinical year demonstrates more EM women residents experience overall HV from both cohorts – residents and support staff. Overall, there is statistical significance between gender, specifically in work-related HV. This specific subset of HV consists of ignoring orders/withholding of information. Men, on the other hand, seem to have a consistent experience with notably less HV than women. Clinical year did not affect HV reported.Our findings are in line with prior studies that found residents experience more work-related HV overall and that women residents experience more bullying.Limitations to this pilot study include the small sample size of 26 possible residents, which limited the ability to perform a robust statistical analysis. As this was a self-reported questionnaire, the data may be influenced by recall bias. Age was not included in the demographic portion of the questionnaire, which may be an important variable to consider as well. Unfortunately, not everyone completed all the demographic questions.The ED is a complex work environment with high-acuity patients presenting in a time-sensitive manner with frequent communication between sub-specialties and admitting services. The addition of residents adds to the complexity of patient care for learners and staff. It is noteworthy that even with a small sample size and homogeneous resident population, gender is a potential factor as to who experiences horizontal violence and from which sources. Overall, this study highlights an area of opportunity to improve the educational experience of residents. Recognizing that gender may be an indicator for HV during resident training is an important first step to ultimately creating a safer learning environment.


Background: The albumin levels may potentially be used as a prognostic biomarker in patients with cancertreated with immune checkpoint inhibitors (ICIs) due to its close relationship with nutritional and inflammatory status. However, the available data is limited with heterogeneous patient cohorts, sample sizes and variable cut-offs. Therefore, we conducted a systematic review and meta-analysis to evaluate the association between survival outcomes and albumin levels in patients treated with ICIs.Methods: We conducted a systematic review using the PubMed, Web of Science, and Embase databases to filter the published studies up to 1 June 2022. The meta-analyses were performed with the generic inverse-variance method with a random-effects model due to the high degree of heterogeneity. The primary outcome measure was hazard ratio (HR) with 95% confidence intervals (CI). The study protocol was registered with the PROSPERO registry (Registration Number: CRD42022337746).Results: Thirty-six studies encompassing 8406 cancer patients with advanced disease were included in the meta-analyses. Almost half of the studies were conducted in NSCLC cohorts (n = 15), and 3.5 gr/dL was the most frequently used albumin cut-off in the included studies (n = 20). Patients with lower albumin levels had a significantly increased risk of death  than patients with higher albumin levels. Subgroup analyses for study location, sample size, tumor type and albumin cut-off were demonstrated consistent results. Furthermore, in the subgroup analysis of eight studies using albumin levels as a continuous prognostic factor, every 1 gr/dL decrease in albumin levels was associated with significantly increased risk of death by a factor of 10% . Similar to analyses with overall survival, the patients with lower albumin levels had an increased risk of progression or death compared to patients with higher albumin levels .Conclusion: The available evidence demonstrates that albumin levels may be a prognostic biomarker in advanced cancer patients treated with ICIs. Further research is needed to delineate the role of albumin levels in patients treated with ICIs in the adjuvant setting, as well as the possible benefit of therapeutic approaches to improve hypoalbuminemia. Immune checkpoint inhibitors (ICIs) have bocame a vital part of cancer care in the last decade . First, Despite the stunning rate of developments in therapeutic sites with ICIs, biomarker development has been relatively slow. Other than PD-L1 expression in non-small cell lung cancer , gastricSimilar to peripheral blood-based biomarkers derived from blood count, albumin levels are significantly affected by inflammatory pressure , and lowWe conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidance (PRISMA) . The PRIWe included studies that met the following inclusion criteria: 1) prospective and retrospective studies to investigate the prognostic effects of albumin levels in ICI-treated patients with cancer; 2) articles reporting the hazard ratio (HR) for overall survival (OS) and/or progression-free survival (PFS); and 3) articles for which the full text was available in English. The exclusion criteria were as follows: 1) duplicated articles; 2) chapters of books, case reports, editorial letters, review articles, and opinion papers; 3) animal studies; 4) studies including patients without cancer; 5) studies with clinical endpoints other than PFS or OS; 6) studies without data for HRs and confidence intervals (CIs); and 7) studies without a predefined albumin cut-off to define hypoalbuminemia.Two authors independently extracted the following data from the available studies  following the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) guidelines : lead auThe study’s primary objective was to evaluate the association between OS or PFS and the presence of hypoalbuminemia in patients treated with ICIs. The secondary objective was to evaluate the association between the OS and the presence of hypoalbuminemia in subgroup analyses according to study location , study sample size , albumin cut-off (3.5 or other), and tumor type .p-values below 0.05 were regarded as statistically significant.We performed the meta-analyses with the generic inverse-variance method with a random-effects model due to the high degree of heterogeneity across the studies. The principal summary measure was HRs with 95% two-sided CIs, and the heterogeneity within each subgroup was reported with I-square statistics. Furthermore, we conducted additional analyses witha fixed-effects model after the exclusion of studies that caused a high degree of heterogeneity. The possibility of publication bias was assessed by the visual inspection of funnel plots. The meta-analyses were conducted with Review Manager software, version 5.4 , and Our systematic search retrieved a total of 2965 records. After removing the duplications (n = 1419), we screened the titles of the remaining 1546 records and excluded 1419 more records due to the following reasons: topic irrelevance (n = 944); review, case report, commentary, meta-analysis, or editorial (n = 379); animal studies (n = 70); and studies not in English (n = 26). We screened the titles and abstracts of the remaining 127 articles and excluded 91 records due to the lack of HRs for survival (n = 28) and the lack of separate reporting for albumin levels in studies evaluating albumin-based prognostic scores (n = 63). We evaluated the full texts of the remaining 36 articles and included these 36 studies in the meta-analyses. The PRISMA diagram for article selection is included in the supplement .Thirty-six studies encompassing 8406 patients were included in the meta-analyses. Almost half of the studies were conducted in NSCLC cohorts (n = 15), followed by urothelial (n = 5), gastroesophageal (n = 5), and hepatocellular cancers (n = 3) . In addip < 0.0001) than patients with higher albumin levels (2 = 89%). The important portion of the heterogeneity stemmed from the studies including albumin as a continuous parameter in the analyses. After the exclusion of these studies (n = 8) (p < 0.0001)  , heterog 0.0001) . Sensitip < 0.0001 for the United States, HR: 1.68, 95% CI: 1.37–2.05, p < 0.0001 for Europe, and HR: 2.30, 95% CI: 1.63–3.24, p < 0.0001 for Far East)   (p = 0.0002)   and sampver 200)  demonstrut-offs) . Further 0.0002) . The fun 0.0002) .p < 0.001) (2 = 84%). Sensitivity analyses conducted by the subtraction of individual studies demonstrated consistent results. Heterogeneity was reduced to 31% after the exclusion of studies using albumin levels as a continuous measure in the analyses. An additional fixed-effect meta-analysis conducted after the exclusion of studies causing heterogeneity demonstrated a significantly increased risk of progression or death in patients with lower albumin levels   . The inc< 0.001) . No addiIn this meta-analysis, we observed a significantly increased risk or progression in ICI-treated patients with hypoalbuminemia. The adverse effect of low albumin levels in OS and PFS was consistent across tumor type, albumin cut-off for hypoalbuminemia, study country, and study sample size. To the best of our knowledge, the present study is the first meta-analysis evaluating the association between albumin levels and survival in patients treated with ICIs.Nutritional status is an essential denominator of patients’ general conditions and is closely related to survival outcomes with anti-cancer treatments . LaboratSeveral studies in patients treated with chemotherapy, surgery, targeted therapy, or radiotherapy demonstrated lower OS and PFS in cancer patients with lower albumin levels . Furtherp-values, although the significance of most results was not expected to change, considering most of the analyses had a p-value of <0.0001. In addition, publication bias due to the higher publishing possibility of studies with positive results could not be excluded. Lastly, most of the included studies were conducted on patients treated with ICI monotherapy, while ICI-ICI, ICI-chemotherapy, and ICI-targeted therapy combinations have become standard care for most tumors, especially as first-line treatment (The present meta-analysis is subject to several limitations inherent to the methodology used and the characteristics of the included studies. First, we collected the reported HRs from the studies rather than conducting an individual patient data meta-analysis, so the presence of single-patient variables was not reported. The included studies were heterogeneous regarding tumor type, sample size, and albumin cut-offs, although we conducted subgroup analyses for these factors and observed consistent results. We did not conduct Bonferroni corrections for reatment .In conclusion, the available evidence demonstrates that albumin levels could be a promising prognostic biomarker in patients with metastatic cancer treated with immunotherapy. Further research is needed to delineate the role of albumin levels in patients treated with ICIs in adjuvant settings and patients treated with ICI-based combinations, as well as the possible benefit of therapeutic approaches to improve hypoalbuminemia in ICI-treated patients.


Can a guideline-informed automated clinical decision support system improve use of primary prophylactic colony-stimulating factors for patients with cancer receiving myelosuppressive chemotherapy?In this cluster randomized clinical trial that included 32 community oncology practices and 2946 adult patients with cancer, primary prophylactic colony-stimulating factor use was high and did not differ significantly between groups . Febrile neutropenia rates were not significantly different between the intervention and usual care practices groups (6.1% vs 4.2%).An automated standing order intervention did not improve primary prophylactic colony-stimulating factor use. This cluster randomized clinical trial examines whether a guideline-based standing order for primary prophylactic colony-stimulating factors improves use of this treatment and reduces the incidence of febrile neutropenia among patients undergoing chemotherapy. Colony-stimulating factors are prescribed to patients undergoing chemotherapy to reduce the risk of febrile neutropenia. Research suggests that 55% to 95% of colony-stimulating factor prescribing is inconsistent with national guidelines.To examine whether a guideline-based standing order for primary prophylactic colony-stimulating factors improves use and reduces the incidence of febrile neutropenia.This cluster randomized clinical trial, the Trial Assessing CSF Prescribing Effectiveness and Risk (TrACER), involved 32 community oncology clinics in the US. Participants were adult patients with breast, colorectal, or non–small cell lung cancer initiating cancer therapy and enrolled between January 2016 and April 2020. Data analysis was performed from July to October 2021.Sites were randomized 3:1 to implementation of a guideline-based primary prophylactic colony-stimulating factor standing order system or usual care. Automated orders were added for high-risk regimens, and an alert not to prescribe was included for low-risk regimens. Risk was based on National Comprehensive Cancer Network guidelines.The primary outcome was to find an increase in colony-stimulating factor use among high-risk patients from 40% to 75%, a reduction in use among low-risk patients from 17% to 7%, and a 50% reduction in febrile neutropenia rates in the intervention group. Mixed model logistic regression adjusted for correlation of outcomes within a clinic.A total of 2946 patients  were enrolled; 2287 were randomized to the intervention, and 659 were randomized to usual care. Colony-stimulating factor use for patients receiving high-risk regimens was high and not significantly different between groups . Among high-risk patients, febrile neutropenia rates for the intervention (58 of 947 patients [6.1%]) and usual care (13 of 308 patients [4.2%]) groups were not significantly different. The febrile neutropenia rate for patients receiving high-risk regimens not receiving colony-stimulating factors was 14.9% (17 of 114 patients). Among the 585 patients receiving low-risk regimens, colony-stimulating factor use was low and did not differ between groups . Febrile neutropenia rates did not differ between usual care (1 of 127 patients [0.8%]) and the intervention (7 of 452 patients [1.5%]) groups.In this cluster randomized clinical trial, implementation of a guideline-informed standing order did not affect colony-stimulating factor use or febrile neutropenia rates in high-risk and low-risk patients. Overall, use was generally appropriate for the level of risk. Standing order interventions do not appear to be necessary or effective in the setting of prophylactic colony-stimulating factor prescribing.NCT02728596ClinicalTrials.gov Identifier:  The disconnect between availability of evidence from practice guidelines and what is observed in practice has led researchers and care delivery systems to search for and test new methods aimed at their translation and implementation.Unwanted variation in clinical practice has been a persistent and costly problem around the world. A common approach to address variation is evidence-based practice guidelines. Multiple studiesThe widespread adoption of electronic health records (EHRs) has provided an opportunity to reduce variation. One widely used EHR-enabled approach is guideline-based, computerized clinical decision support systems, consisting of prompts or self-populating orders based on well-defined clinical criteria. Clinical examples of guideline-informed order sets include thromboprophylaxis prescribing and antibiotic prescribing for acute otitis media.9 have shown that between 55% and 95% of PP-CSF prescribing has been inconsistent with recommendations in both directions—that is, underuse of PP-CSF for chemotherapy regimens with high risk of FN and overuse of PP-CSF for low-risk chemotherapy regimens.14The potential benefits of these clinical decision support systems are particularly compelling in the case of primary prophylaxis colony-stimulating factors (PP-CSFs) for patients with cancer receiving myelosuppressive chemotherapy. PP-CSFs reduce the risk of febrile neutropenia (FN), an event associated with some chemotherapy regimens that carries a 10% to 20% mortality risk, as well as its severity and duration. For decades, clinical practice guidelines have recommended PP-CSF for patients receiving chemotherapy regimens with a high risk of FN and discouraged their use for patients receiving chemotherapy with a low risk of FN. Despite strong evidence and consistent guidelines published by multiple expert panels, prior studies15 cluster randomized clinical trial (RCT), the Trial Assessing CSF Prescribing Effectiveness and Risk (TrACER),16 comparing guideline-informed, EHR-enabled, standing order sets for PP-CSF compared with usual care (UC) for patients with breast, lung, and colorectal cancer receiving initial myelosuppressive cancer-directed therapy. Our primary objective was to determine whether automated orders improved guideline adherence and reduced the incidence of FN compared with UC.Accordingly, we designed a stakeholder-informed, prospective, pragmatic ,17We conducted a pragmatic, cluster RCT within the SWOG Cancer Research Network and the National Cancer Institute Community Oncology Research Program (NCORP). NCORP is a network of researchers, public hospitals, physician practices, and other groups that provide health care services in US communities, organized into research bases and community sites. All aspects of the design, analysis, and reporting of the trial were informed by an external stakeholder advisory group.CONSORT) reporting guideline.This study was approved by the Fred Hutchinson Cancer Center institutional review board and by the local institutional review boards at each participating study site. This study follows the Consolidated Standards of Reporting Trials  or intervention. Sites with an existing PP-CSF standing order system were assigned to a nonrandomized cohort group. Intervention sites modified their order system to include guideline-based alerts and automated PP-CSF orders for patients receiving chemotherapy regimens with high FN risk  and excluded PP-CSF orders for patients receiving regimens with low FN risk . The study included a second site randomization for patients receiving regimens with intermediate FN risk, for which PP-CSF guidelines are less clear. Results of this substudy will be described in a separate report. To maximize recruitment efficiency, sites with very low volume  were ineligible. All clinical practices within each site were required to share the same chemotherapy ordering system .Patients aged 18 years or older with a current diagnosis of breast, colorectal, or non–small cell lung (NSCL) cancer of any stage scheduled for first treatment with 1 of the study-allowed chemotherapy regimens were eligible. Further details on patient eligibility are available in 16 The order sets encompassed the spectrum of platforms used by the intervention clinics. In 5 clinics using paper-based orders, preprinted paper templates were incorporated into process workflows. Intervention ordering system modifications were embedded into the chemotherapy regimen order set, pilot tested, and reviewed with clinic practitioners in training sessions before patient enrollment.When clinicians entered chemotherapy orders, the order-entry intervention either autopopulated or did not populate PP-CSF orders according to the regimen’s FN risk. Clinicians could override standing orders at their discretion. Further details of the trial and intervention design are described in a separate publication.18: absolute neutrophil count less than 1000 cells/μL and a single temperature of greater than 38.3 °C (101 °F) or a sustained temperature of 38 °C (100.4 °F) or higher for more than 1 hour. Patients were observed until death or 12 months after registration.The primary outcomes were (1) PP-CSF use relative to recommendations for high-risk and low-risk chemotherapy regimens, with PP-CSF defined as the initiation of granulocyte CSFs during the first cycle of myelosuppressive systemic therapy, given 24 to 72 hours after cessation of systemic therapy; and (2) incidence of FN within 6 months of registration. FN was defined by the National Cancer Institute Common Terminology Criteria for Adverse Events version 4.0Observational cohort study sites already had established PP-CSF automated order entry systems. Decisions on when to use or not use PP-CSF were at the discretion of the practice. The objective of the cohort study was to compare patterns of PP-CSF use and FN rates with those of the intervention and UC groups of the RCT.Clinical and demographic data including race and ethnicity were collected by clinical research staff from the medical record. Race and ethnicity were analyzed in this study because they may affect responses to chemotherapy. Additional patient demographics, comorbidities, and disease status were collected at baseline. Treatment received, dose modifications, and PP-CSF administration were collected at the end of the first cycle of systemic therapy. Antibiotic use was collected 1 month after registration. Incidence of FN was collected at the end of the first cycle and at 6 months.3 There was 90% power to detect a 50% decrease in FN rate from 25% (UC) to 12.5% (intervention) for high-risk patients. To account for possible reduction in power due to correlation of patients among sites, power estimates accommodated an intraclass correlation coefficient (ICC) of 0.02 suggested by prior cluster randomized studies.19 The combined total accrual goal was 3600 eligible patients in the randomized and cohort groups.Randomization occurred at the site level, with sites randomly assigned to the intervention or UC groups using a 3:1 randomization scheme with stratification on clinic size and whether the clinic was classified as serving a predominantly underserved or minority population. Power calculations for the trial indicated 80% power to detect an increase in CSF use from 40% (UC) to 75% (intervention) for high-risk patients and a reduction in CSF use from 17% (UC) to 7% (intervention) for low-risk patients.Data analysis was performed from July to October 2021. Mixed effects multivariable logistic regression was used to compare UC with the intervention groups separately for low-risk and high-risk patients. The models included a random effect for each site assuming a normal distribution for the random effects. Two binary outcomes were tested separately: PP-CSF use and FN. A 2-sided α = .05 was used for each treatment comparison at each risk level and each outcome. Sensitivity analyses were conducted using population-averaged generalized estimating equations (GEEs) with exchangeable working correlation structure within clinics to estimate the ICC. The full statistical analysis plan is included in the protocol . The staWe compared intervention vs UC with adjustment for important covariates . Among high-risk patients, the primary analysis was a mixed effects logistic regression model with the binary outcome of whether PP-CSF was administered at the start of the first cycle of chemotherapy and a random effect for clinic. We adjusted for age group , comorbidity, race, and Hispanic ethnicity. Since all high-risk patients had breast cancer, we did not adjust for sex and cancer type.Among low-risk patients, the primary analysis was a mixed effects logistic regression model with the binary outcome of whether PP-CSF was administered at the start of the first cycle of chemotherapy and a random effect for clinic. The small number of events made it difficult to include many potential prognostic factors in the analysis; we adjusted for age group, sex, and cancer type.We compared the intervention vs UC groups by fitting mixed effects logistic regression models separately among high-risk and low-risk patients. The outcome was incidence of FN within 6 months of registration. We included a random effect for clinic. For high-risk patients, we adjusted for age; for low-risk patients, we adjusted for cancer type. Sensitivity analyses were conducted using population-averaged GEE with exchangeable working correlation structure within clinics to estimate the ICC.A total of 3665 patients were registered at 45 community oncology clinics, with 2946 patients from 32 clinics included in the randomized groups ; 2287 were randomized to the intervention, and 659 were randomized to UC) and 719 in the observational cohort . Forty-eDemographic and baseline characteristics are shown in P = .21) and was in the opposite direction from expected (expected . Unadjusexpected . When coP = .74) (PP-CSF use in low-risk patients was low within and across the randomized groups (36 of 585 patients [6.1%]) and the observational cohort (10 of 121 patients [8.3%]) . PP-CSF P = .74) . PatientP = .74)  showed s21 . Over time, UC site adherence to guidelines increased for both low-risk and high-risk regimens, whereas intervention sites became less adherent in the low-risk group (eTable 2 in P = .26) (eTable 4 in P = .51) . The findings from the study may not apply to practice settings that are distinct from the community practices that participated . Additionally, the study sample was limited to patients with breast, NSCL, and colorectal cancer, and the applicability of the findings to other cancer sites is unknown. Furthermore, it is important to note that most patients in the high-risk group were female patients with breast cancer receiving dose-dense chemotherapy. PP-CSF is typically embedded in these regimens because of the very high risk of FN for these patients. There were far fewer patients with lung cancer in the study, where there are more patients with other FN risk factors such as age and comorbidity.We note the limitations of this study. First, clinics that participated in TrACER were a voluntary subset of NCORP clinics. It is possible that the reasons clinics chose to participate were influenced by their interest in or experience with CSF. The recently published studyIn a large, prospective, cluster RCT of an automated order entry system for PP-CSF prescribing, the intervention did not improve prescribing adherence or FN rates among patients with NSCL, breast, and colorectal cancer receiving first-line myelosuppressive chemotherapy. Overall adherence to clinical practice guidelines was higher than reported historically, suggesting either a selection effect or a temporal trend toward improved prescribing. Use of guideline-based computerized clinical decision support systems in this setting was ineffective and, therefore, not recommended for community oncology practice settings.