We studied the accuracy of these codes using genetic validation.Stroke in UK Biobank (UKB) is ascertained We compiled stroke-specific and broad cerebrovascular disease (CVD) code lists  for medical settings  and self-report. Among 408,210 UKB participants, we identified all with a relevant code, creating 12 stroke definitions based on the code type and source. We performed genome-wide association studies (GWASs) for each definition, comparing summary results against the largest published stroke GWAS (MEGASTROKE), assessing genetic correlations, and replicating 32 stroke-associated loci.The stroke case numbers identified varied widely from 3,976 (primary care stroke-specific codes) to 19,449 . All 12 UKB stroke definitions were significantly correlated with the MEGASTROKE summary GWAS results (rg.81-1) and each other (rg.4-1). However, Bonferroni-corrected confidence intervals were wide, suggesting limited precision of some results. Six previously reported stroke-associated loci were replicated using ≥1 UKB stroke definition.Stroke case numbers in UKB depend on the code source and type used, with a 5-fold difference in the maximum case-sample size. All stroke definitions are significantly genetically correlated with the largest stroke GWAS to date. It is an open-access resource, established to facilitate research into the determinants of a wide range of health outcomes, particularly those relevant in middle and older age (UK Biobank (UKB) is a prospective population-based cohort study with extensive phenotype and genotype information on >500,000 participants from England, Scotland, and Wales  category. Stroke-specific codes are used to code acute stroke events where the clinician is confident about the diagnosis and can usually assign a subtype. In contrast, broad CVD codes also capture cases with: (i) phenotypes that pose a high risk for a subsequent stroke ; (ii) a past history of stroke with residual symptoms ; (iii) events where there may be some diagnostic uncertainty ; and (iv) intracranial hemorrhages other than intracerebral or subarachnoid hemorrhage . Including codes from the broad CVD category will therefore significantly increase the overall number of cases identified, but while this is likely to include at least some misclassified true acute stroke cases, non-stroke cases will also be included.Disease outcomes in UKB are ascertained chiefly In a systematic review of studies validating stroke code accuracy from case-note review, the overall positive predictive value  for identifying acute stroke cases was consistently >70% for stroke-specific codes, dropping to <50% in many studies when broad CVD codes were included , 19. ForWe set out to supplement current knowledge about the accuracy of stroke codes with a method making use of large-scale genetic data, which we refer to as ‘genetic validation'. The fundamental idea is to use existing knowledge of genetic associations with a disease (in this case acute stroke), to assess how well various potential code lists capture people who truly have this disease, which in turn could be used to harmonize disease definitions across cohorts and health systems . If the We included all 408,210 UKB white British ancestry participants in this study. We restricted our analyses to this ancestry subgroup because it covers 94% of the UKB participants and allowed us to achieve a good balance between attaining sufficient case numbers while reducing population stratification and analytic complexity. As part of the UK Biobank recruitment process, informed consent was obtained from all individual participants included in the study. At the time of the study, UKB had linked hospital admissions and death registry administrative coded data available for all participants, and primary care administrative coded data for 47% of the cohort , covering the time period up to March and September 2019, respectively . In addiWe compiled stroke-specific and broad CVD code lists for each medical setting  and self-report. This process was informed by previously published codes where available , 19, supNext, we identified all participants with a relevant code from any of the code lists and created 12 different ways of defining stroke cases in UKB based on the code type  and source . This resulted in 12 partially overlapping case-control groups, where cases were all the individuals with a stroke code for the particular stroke definition, and all the remaining participants acted as controls. A specific UKB participant could therefore be a stroke case for one definition and control for another definition.https://shiny.cnsgenomics.com/LMOR/ (We performed 12 genome-wide association studies (GWASs), one for each case-control set . We applied a linear mixed model method using the BoltLMM software package (v2.3.4) software . We inclom/LMOR/ . We alsoom/LMOR/ . We thenom/LMOR/  for compWe compared summary results from our 12 GWASs against the largest published stroke GWAS meta-analysis project-the MEGASTROKE study. The MEGASTROKE study is a meta-analysis of 29 stroke GWASs  and does not include UKB data. Almost all studies included in MEGASTROKE (covering >95% included cases) required the stroke diagnosis to be confirmed by a medical professional or required evidence of stroke from >1 source, even if the initial case ascertainment included using administrative codes . All anap-value significance threshold to 0.0042 after a Bonferroni correction for the 12 tests. We used the LD matrix calculated from the UKB for the reference panel provided as part of the HDL package, therefore restricting analyses to 1,029,876 QCed imputed HapMap3 SNPs. We displayed the results (correlation measured as rg) on a heatmap. We also display Bonferroni corrected confidence intervals to aid interpretation.We applied a high-definition likelihood method using the HDL software  to assesWe then used the HDL software to assess genetic correlations within our study across the 12 definitions. We set the significance threshold to 0.0024 after a Bonferroni correction for seven independent non-overlapping case-control definitions . We compared the number of replicated loci across our summary definitions. We compared the effect sizes of the associations between MEGASTROKE trans-ethnic and European ancestry GWASs and our GWAS summary results. Where the lead SNP was not available in our data, we identified SNPs in moderate LD  with the lead SNP, and if any SNPs in LD available in our data were identified, we examined their associations instead. We displayed results for five of our summary definitions of stroke cases and their controls: stroke-specific code from any medical setting; broad CVD code from any medical setting; stroke-specific or broad CVD code from any medical setting; specific or non-specific self-reported stroke event; any code or self-reported event. We highlighted significantly associated  loci.The MEGASTROKE study identified 32 genetic loci significantly associated with stroke. We identified these loci (the lead SNP for each locus) in our GWAS summary results and considered a locus to be replicated  if the http://csg.sph.umich.edu/abecasis/gas_power_calculator/index.html), assuming a stroke prevalence of 2.26% and inputting the disease allele frequency and genotype relative risk estimates from the MEGASTROKE publication We also calculated our expected power to replicate the 32 loci using the Genetic Association Study (GAS) Power Calculator (The number of relevant codes for identifying stroke cases varied widely depending on the coding system (ICD vs. Read vs. self-report) and code type (stroke-specific vs. broad CVD code)–from less than five codes for a specific self-reported stroke event, to >500 codes when including all possible codes across all coding systems. The stroke-specific and broad cerebrovascular disease (CVD) code lists for each medical setting and self-report are shown in the The number of stroke cases identified among the 408,210 participants also varied widely depending on the code type and source used–from 3,976 cases in primary care when using stroke-specific codes, to 19,449 cases when including all possible code combinations (stroke-specific and broad CVD) across all sources  .The code source for cases with a stroke-specific code was: self-report only in 27%, primary care only for 9%, hospital/death record code only for 29%, and >1 source for 35% . The codThe overall proportion of prevalent codes  vs. incident codes  was the same for stroke-specific and broad CVD categories: 38% prevalent vs. 62% incident codes. These proportions are dependent on the updates to different linked health datasets and the proportion of incident codes will continue to increase with increasing duration of follow-up .Mean and median age at recruitment was higher among stroke cases  than for the whole cohort of UKB participants . Mean and median age at the time of stroke  was higher for coded diagnoses from the medical setting compared to self-reported events . This is to be expected, considering that all self-reported events were recorded at the time of recruitment, whereas medical codes also capture diagnoses after recruitment during follow-up. The proportion of women was lower among stroke cases than across all UKB participants . This is to be expected as age-specific incidence rates are substantially lower in women than men in younger and middle-age groups, but these differences narrow down so that in the oldest age groups, incidence rates in women are approximately equal to or even higher than in men  Table 1Table 1.Manhattan plots, QQ plots, and genomic inflation factors  are displayed in p-values for the difference from one were not significant, compatible with perfect correlation. However, the Bonferroni corrected CIs were wide, especially for five of the 12 tests, where the lower confidence limit was <0.7, limiting the precision of some of these results  ranging from 0.81 to 1, and confidence intervals overlapping. The  results .p-values significantly different from 0), with rg ranging from 0.4 to 1. Again, the Bonferroni corrected CIs were wide, with the lower confidence limit even suggesting the possibility of a negative correlation for two comparisons  . This rap-values were smaller in the larger dataset  suggesting a more robust replication when using the broadest definition of stroke in UKB. Within our data, effect sizes (expressed as odds ratios) were similar across the stroke definitions, with overlapping Bonferroni corrected confidence intervals . The power from additional cases for the latter category did not result in replicating more loci than stroke-specific codes alone. However, for three of the five replicated loci, the ntervals .For two of the six replicated loci (PITX2 and HDAC9–TWIST1), the effect size of the association (odds ratio) was bigger in the MEGASTROKE dataset than in our data . These two loci are known to be associated with particular stroke subtypes–PITX2 with cardioembolic and HDAC9–TWIST1 with large artery stroke [10] .Power calculations suggested we had ≥80% power to replicate all 32 loci for any code or self-reported event definition while having ≥80% power for only 11/32 loci for the definition including a stroke-specific code from any medical setting .Our analyses show, that depending on the code source and type used for identifying stroke cases in the UKB, the currently achieved maximum case-sample size can range from ~4,000 to ~20,000–a remarkable 5-fold difference. We go on to demonstrate, that regardless of the code source and type used, the resulting GWAS summary results are significantly genetically correlated with the largest stroke GWAS to date, with similar  heritability estimates. Finally, when we try to replicate known stroke-significant loci in our data, both stroke-specific codes from any medical setting as well as a broad definition including any code or self-reported event, replicate five of the 32 loci. Replication generated broadly similar effect sizes for all but two stroke subtype-specific loci, which is likely explained by our dataset including a mix of stroke subtypes. Another possible explanation is the “winner's curse” phenomenon .The correlation of all definitions with the MEGASTROKE study results suggests one or more of the following: (i) all definitions retrieve true-positive acute stroke cases, meaning that broad CVD codes include additional true-positive cases not identified by stroke-specific codes; (ii) cases coded with a broad CVD code have not necessarily suffered an acute stroke, but represent a range of phenotypes with a similar genetic architecture to acute stroke ; (iii) Previous case-note validation studies suggested that broad CVD codes are better at identifying the broad conditions they signify as opposed to ascertaining acute stroke cases , supportDespite the definition using any code or self-reported event increasing the sample size by more than 2-fold compared to the definition using only stroke-specific codes from a medical setting, it did not replicate a higher number of known stroke-associated loci. This could suggest that there is still insufficient power to replicate additional loci using any of our definitions despite power calculations suggesting ≥80% power for all loci. Also, associations for nine of the 32 loci in MEGASTROKE were only found for specific stroke subtypes and 11 of the 32 loci were significant in analyses including only ischemic stroke cases, the proportions of which are unlikely to be identical between the two datasets. For example, the MEGASTROKE study sample included 90% confirmed ischemic stroke cases. The stroke subtype breakdown among the UKB participants is available only for stroke-specific codes from the hospital, death record, and self-reported data  and shows a proportion of confirmed ischemic stroke cases of 47%, with 10% cases being intracerebral hemorrhage and 11% subarachnoid hemorrhage and the remainder of unspecified stroke subtype. Furthermore, case-note validation suggests that while ischemic stroke cases can be identified with good accuracy using stroke-specific codes, further work is needed to understand the accuracy of hemorrhagic stroke codes (Self-reported cases (both stroke-specific and broad CVD) also showed a close genetic correlation with the MEGASTROKE study, supporting the use of self-report as a means of identifying additional stroke cases in the UKB. This was so despite the highly variable results from the previous case-note-based validation studies of self-report for ascertaining stroke cases. Studying this by case-note validation in UKB itself would be challenging, given the difficulties accessing NHS records which predate recruitment by many years and the fact that participants may have moved between UK regions during their life-course. Other studies have also reported a close genetic correlation between a wide range of self-reported diseases and medical setting diagnoses. Examples include both acute and chronic conditions  –17.We used the HDL method for assessing genetic correlations to fully account for LD across the genome and improve precision in genetic correlation estimation. Compared to the LD Score Regression method, HDL reduces the variance of genetic correlation estimates by about 60%, equivalent to a 2.5-fold increase in sample size . For somIn our analyses, the case-control groups were partially overlapping and a specific UKB participant could therefore be a stroke case for one definition and control for another definition. We used this study design to mimic the “real world” situation, creating binary case-control definitions based on each code list. In theory, this could reduce the power of some of the analyses, since it means controls can end up including some true-positive stroke cases. However, in reality, it is unlikely to have a significant effect given the overall large number of controls. For example, for analyses using stroke-specific codes from any medical setting, just over 2% of controls have a broad CVD code and/or have self-reported a stroke event.We used BoltLMM for running the GWAS . Our casThe strengths of our study are: (i) we included-and have made available to re-use-a clinically informed, comprehensive set of codes across all relevant coding systems; (ii) we compared our results against the largest stroke GWAS to date; (iii) we used multiple methods for comparison accounting for both GWAS significant loci but also SNPs across the whole genome–i.e., correlation and replication; (iv) we have added novel data to what is already known from case-note validation.Our study also has some limitations: (i) some of our definitions included relatively small case numbers compared to the MEGASTROKE study, reducing our power to replicate known loci; (ii) uneven numbers across definitions not allowing direct comparisons, but rather reflecting the real-world situation; (iii) our definitions included the subarachnoid hemorrhage stroke subtype codes, whereas the MEGASTROKE study did not, resulting in a slightly different mix of stroke cases; (iv) the UKB participants' demographic characteristics differ from those of the UK general population with evidence of a healthy-volunteer selection bias, which needs to be considered when extrapolating these results to other settings ; and, .Further research is needed: to better understand the underlying reasons for the close genetic correlation between stroke-specific and broad CVD codes; to dissect the underlying explanation for our results with targeted case-note validation; to replicate our results in other datasets. In addition, more data is needed on the accuracy of different coding systems for identifying specific pathological stroke subtypes  and etiological stroke subtypes .This work has been published as a preprint on MEDRXIV (MEDRXIV/2021/264348-Version 1).The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The studies involving human participants were reviewed and approved by UK Biobank project 2532 approval. The patients/participants provided their written informed consent to participate in this study.All authors made substantial contributions to the conception or design of the work, or the acquisition, analysis, or interpretation of data for the work, drafting the work or revising it critically for important intellectual content, final approval of the version to be published, and agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.https://www.megastroke.org/acknowledgements.html and the author list for the MEGASTROKE consortium is added in KR was funded by Health Data Research UK Rutherford fellowship MR/S004130/1. AF was funded by BHF award RE/18/5/34216 and MR/S004130/1. This work was supported by the Wellcome Trust-University of Edinburgh Institutional Strategic Support Fund. AT was funded by HDR-UK awards HDR-9004 and HDR-9003. The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript. The MEGASTROKE project received funding from sources specified at 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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Typhoid fever remains a major cause of morbidity and mortality in low-income and middle-income countries. Vi-tetanus toxoid conjugate vaccine (Vi-TT) is recommended by WHO for implementation in high-burden countries, but there is little evidence about its ability to protect against clinical typhoid in such settings.www.isrctn.com, ISRCTN11643110.We did a participant-masked and observer-masked cluster-randomised trial preceded by a safety pilot phase in an urban endemic setting in Dhaka, Bangladesh. 150 clusters, each with approximately 1350 residents, were randomly assigned (1:1) to either Vi-TT or SA 14-14-2 Japanese encephalitis (JE) vaccine. Children aged 9 months to less than 16 years were invited via parent or guardian to receive a single, parenteral dose of vaccine according to their cluster of residence. The study population was followed for an average of 17·1 months. Total and overall protection by Vi-TT against blood culture-confirmed typhoid were the primary endpoints assessed in the intention-to-treat population of vaccinees or all residents in the clusters. A subset of approximately 4800 participants was assessed with active surveillance for adverse events. The trial is registered at 41 344 children were vaccinated in April–May, 2018, with another 20 412 children vaccinated at catch-up vaccination campaigns between September and December, 2018, and April and May, 2019. The incidence of typhoid fever (cases per 100 000 person-years) was 635 in JE vaccinees and 96 in Vi-TT vaccinees . Total vaccine protection was consistent in different age groups, including children vaccinated at ages under 2 years . The incidence was 213 among all residents in the JE clusters and 93 in the Vi-TT clusters . We did not observe significant indirect vaccine protection by Vi-TT . The vaccines were well tolerated, and no serious adverse events judged to be vaccine-related were observed.Vi-TT provided protection against typhoid fever to children vaccinated between 9 months and less than 16 years. Longer-term follow-up will be needed to assess the duration of protection and the need for booster doses.The study was funded by the Bill & Melinda Gates Foundation. Salmonella enterica serovar Typhi (S Typhi), remains high. Analyses have estimated that more than 9 million cases and 110 000 deaths occur per year, with the burden concentrated among the poor residing in low-income and middle-income countries.S Typhi.Despite decades of worldwide effort to improve water quality, hygiene, and sanitation, the global burden of typhoid fever, caused by S Typhi organisms (strain Ty21a), or of parenterally administered Vi polysaccharide , were found to be safe and moderately protective.9Older-generation, parenterally administered typhoid vaccines, consisting of killed typhoid organisms, were shown in some trials to provide substantial protection, but their use was limited by their reactogenicity.Evidence before this studySalmonella enterica serovar Typhi is chemically conjugated to a carrier protein have generated considerable interest due to their ability to elicit T-cell-dependent antibody responses, and thus possible protection of infants and young children as well as immunological memory. A Vi-tetanus toxoid conjugate vaccine (Vi-TT) produced in India, which is the subject of the study report here, has been licensed and WHO-prequalified on the basis of trials in typhoid-endemic populations, which evaluated immunological endpoints, but did not evaluate clinical typhoid disease, and a volunteer typhoid challenge experiment in adults in the UK, where typhoid is not endemic. We searched PubMed for reports on randomised trials of the clinical efficacy of Vi-conjugate vaccines using the search terms “Vi polysaccharide vaccine”, “Vi-conjugate vaccine”, and “typhoid vaccine”. We found publications on results of an individually randomised efficacy trial of clinical protection by Vi-rEPA (recombinant Pseudomonas aeruginosa exoprotein A) vaccine in Vietnamese children aged 2–5 years given two injections 6 weeks apart, in which 91·5%  protection was detected over 27 months of follow-up. Furthermore, a school-based cluster-randomised trial of a two-dose regimen of Vi-TT vaccine  found 100% (95% CI 97·6–100%) protection, but this trial was limited by a small sample size of 1765 children. An individually randomised trial of the same Vi-TT vaccine evaluated in the current paper, given as a single dose to children aged 9 months to less than 16 years in Nepal, found 81·6%  protection in an interim analysis covering 12 months follow-up after dosing.Typhoid vaccines in which the Vi polysaccharide outer capsule of Added value of this studyOur trial confirms the findings of the earlier reported individually randomised trial in Nepal, in which a single dose of Vi-TT given to children aged 9 months to less than 16 years was safe and conferred approximately 80% protection against typhoid fever, and adds to the Nepal trial in three significant ways.Implications of all the available evidenceTaken together with the findings of the Nepal trial, our data confirm that Vi-TT, administered in a single dose, is safe and effective when given to children in settings with a high incidence of endemic typhoid and support efforts by WHO and the Global Alliance for Vaccines and Immunizations to roll out this vaccine in such settings. The finding of significant protection of infants and children under 2 years of age indicate that the Vi-TT can be effectively incorporated into the EPI schedule of immunisation of infants and young children. Finally, to date no study has done sufficiently long post-vaccination follow-up to measure long-term protection by Vi-conjugate vaccines against typhoid. Such data will be needed to fully measure the cost-effectiveness of these vaccines and to establish the need for and timing of booster doses.Among new-generation vaccines against typhoid, ViPS-protein conjugate vaccines have been most promising, owing to their ability to elicit robust immune responses in infants and young children and their capacity to elicit T-cell-mediated immune memory responses, offering the potential for long-term protection.One such vaccine, consisting of ViPS conjugated to tetanus toxoid , has been licensed as a single-dose vaccine for people younger than 12 months in multiple countries ,We did a participant-masked and observer-masked, cluster-randomised trial in Mirpur, a densely populated urban area of Dhaka, Bangladesh using eight clinical facilities known to provide care for enteric fever to the catchment population , which had previously been documented to have a high incidence of typhoid fever (manuscript submitted). Before the trial, we did a small, individually randomised pilot study that showed the safety of a single dose of the Vi-TT vaccine and the SA 14-14-2 Japanese encephalitis (JE) control vaccines in 200 children aged 9 months to less than 16 years in Dhaka. For the main trial, eligible children aged 9 months to less than 16 years residing in an area with a total population of 205 760 were assigned by geographical cluster to receive either a single dose of Vi-TT or the SA 14-14-2 vaccine. The clusters were then followed-up after dosing to detect episodes of typhoid and paratyphoid fever.We did a baseline census of the entire population of the study area, excluding people who were not permanent residents and those who planned to move away within the next month, between Feb 14 and March 25, 2018, to collect individual-level and household-level demographic, socioeconomic, water–sanitation–hygiene, and geo-positioning information, after obtaining informed consent from participants. For this purpose, a household was defined as people who shared the same cooking pot. Each household and individual were given a unique study number, and each household was given a household card providing basic information about each household member. This population was divided into 150 geographical clusters of contiguous households with approximately equal total populations, by means of natural borders wherever possible . The cenThe trial received ethical approval from the Research and Ethical Review Committees of the International Centre for Diarrhoeal Disease Research, Bangladesh , as well as the institutional review boards of Oxford University, Oxford, UK and University of Maryland, Baltimore, MD, USA. Written informed consent was obtained from all participants, including parent or guardian consent for all those younger than 16 years and participant assent for those aged 11 to less than 16 years. Full details of the study protocol and analysis plan are available in the vs at or below the median), and the number of children aged 9 months to less than 16 years in the cluster (above the median vs at or below the median). We generated approximately 3000 randomisation lists using stratified block randomisation to identify 1000 randomisation lists meeting the condition of 75 clusters in each group and then randomly selected one as the final randomisation list.Each cluster was allocated at random in a 1:1 ratio to receive either Vi-TT or SA 14-14-2 vaccine, with 75 clusters in each group, blocked within 12 prespecified strata. We stratified allocation by the three geographical wards of residence, the distance from the midpoint of the cluster to the nearest surveillance health-care facilities . We took several steps to keep participants and observers masked to the assigned and received study agent. First, assessment of eligibility and acquisition of informed consent were done without knowledge of the assigned agent. Secondly, randomisation and vaccine preparation were completed by staff members who were physically shielded by a curtain from the participant. Thirdly, although vaccinators were aware of the agent administered, they were instructed not to inform participants; and fourthly, all people involved in post-vaccination follow-up for assessment of vaccine safety, immunogenicity, and protection were kept unaware of vaccine assignment.We evaluated the safety, immunogenicity, and protection conferred by a single dose of Vi-TT vaccine, in which ViPS (25 μg) was chemically conjugated to tetanus toxoid. The control agent, also given as a single dose to the same age group, was SA 14-14-2, live attenuated JE vaccine . Both vaccines were supplied in their commercial presentations as five-dose vials and stored at 2–8°C. Vi-TT vaccine was given as an intramuscular injection, whereas SA 14-14-2 vaccine was given subcutaneously. Children aged 9 months to less than 16 years were targeted for vaccination. We inoculated both vaccines at the same anatomical location depending on the ages of the children  to help maintain the masking.vs older), were given diary cards to record adverse events daily post-vaccination, to help recall symptoms and duration for the active surveillance of adverse events. As part of active surveillance follow-up, parents or guardians of participants were contacted by telephone at 7 days post-vaccination to ascertain both solicited and unsolicited adverse events. In addition, parents or guardians of all participants not in the subset were encouraged to go to the Adverse Event Monitoring Cell at the Mirpur Field Clinic where a medical doctor was available from 0830 h to 2100 h and on call 24 h a day for adverse event monitoring throughout the whole vaccination period and 7 days thereafter. In addition, surveillance for severe adverse events was also done throughout the post-vaccination period of the trial for all participants by study physicians at all clinical sites used for surveillance for enteric fever (see below), via queries of participants during the 6-monthly census follow-ups, and every 2 weeks visits to all households by community health workers. Verbal autopsies were done for deaths detected in the censuses and in the community health worker visits. For these verbal autopsies, information obtained by the trained study staff was further investigated and corroborated by study physicians, who assigned a cause of death. Structured data forms were used for all of these activities. All adverse events were graded as severe, moderate, or mild, as were events classified serious adverse events, and severity was judged by a clinician, as observed by the investigator, members of the study team, or reported by the parent or guardian.All participants were requested to remain in the vaccination site for 15 min after vaccination to detect and manage acute events. A subset of approximately 4800 participants at the baseline vaccination campaign, selected in approximately equal numbers from each of the 150 clusters and stratified by age at vaccination  by an independent, unmasked statistician. These participants were invited to contribute 3–5 mL of blood just before dosing and at 28 days, 18 months, and 24 months after dosing. Anti-Vi IgG titres in plasma were assessed by means of a commercial enzyme-linked immunosorbent assay kit .Approximately 1500 vaccinees were enrolled in an immunogenicity subcohort. Participants from 18 randomly selected clusters, yielding an approximate 2:1 ratio of Vi-TT to SA 14-14-2 recipients, were randomly selected in an age-stratified manner . Salmonella polyvalent (group A-S), Salmonella group D, Salmonella H-d, and Salmonella Vi antisera were used for identification of S Typhi. To identify S Paratyphi A, Salmonella polyvalent (group A-S), Salmonella group A, and Salmonella H-a antisera were used. Antimicrobial susceptibility profiles were assessed by the disk diffusion method and the resistance pattern was established following the Clinical and Laboratory Standards Institute guidelines.S Typhi or S Paratyphi A was isolated, a team was dispatched within 14 days of isolation to the stated home of the participant to confirm that the person whose name was given at the surveillance site had sought care at the site on the date of presentation, and to ensure that clinical progress was satisfactory, including that antibiotic treatment was appropriate to the antimicrobial sensitivity patterns of blood culture isolates. Febrile visits in which the onset of fever was less than 14 days after discharge from the previous febrile visit were concatenated into a single febrile episode. Febrile episodes in which one or more blood cultures were positive for S Typhi were defined as typhoid or for S Paratyphi A were defined as paratyphoid fever sodes, and the onset of fever before the first visit of the episode was taken as the date of disease onset. Only episodes in which domiciliary visits confirmed patients' identities were eligible for inclusion in analyses of vaccine protection.We initiated surveillance for enteric fever from Feb 26, 2018. We used a common protocol at all eight clinical facilities see . CommuniPrimary and secondary analyses were prespecified and were undertaken only after formal locking of the dataset. The primary analyses assessed total vaccine protection by Vi-TT against typhoid (protection of vaccinees owing to the direct immunising effect of the vaccine as well as reduction of transmission due to proximity to other vaccinees) and overall vaccine protection by Vi-TT against typhoid . Secondary analyses were assessment of vaccine safety; vaccine immune responses; indirect vaccine protection by Vi-TT against typhoid (protection of non-vaccinees owing to reduction of transmission due to proximity to vaccinees); and total protection by Vi-TT against paratyphoid fever.The study protocol originally specified that primary analyses would target the inner 80% of the population of each cluster and would consider 24 months of surveillance after baseline. A sample size of 187 500 residents with two groups of 75 equal-sized clusters was calculated accordingly (available in the study protocol). Before locking and unmasking of the data, and with consent of the Data Safety and Monitoring Boards (DSMBs), we decided to analyse the entire populations of the clusters, as these were the units originally allocated to the two study groups, and to restrict our analyses to episodes and person-time up to the 18-month census update, because the COVID-19 pandemic forced cessation of all study activities in Dhaka, including doing a final study census at 24 months of follow-up.Because the study population had high migration rates and because we had adopted a strategy of catch-up vaccinations every 6 months after baseline, it was necessary to evaluate vaccine protection in a dynamic population. We defined the start date of residence as the midpoint of the baseline vaccination campaign  for non-vaccinees living in the study area at that time, the date of vaccination for individuals vaccinated at baseline, or the date of first migration into or birth in the study area for in-migrants and births post-baseline. Total vaccine protection was assessed by comparing the incidence of disease in recipients of Vi-TT vaccine versus recipients of SA 14-14-2 vaccine, taking the start date for counting events as the day after vaccination. Overall vaccine protection was evaluated by comparing the incidence in all residents of the Vi-TT clusters versus all residents of the SA 14-14-2 clusters, taking the start date for counting events as the day after the start date of residence. Indirect vaccine protection was assessed by comparing the incidence of typhoid in non-recipients of Vi-TT in the Vi-TT clusters versus non-recipients of SA 14-14-2 in the SA 14-14-2 clusters, taking the start date for counting events as the day after the start date of residence. We analysed individuals according to a first in, first out rule, including only individuals from the time of their first residence in a study cluster and censoring their follow-up based on the earliest of the date of death, the end of surveillance, or movement out of the study cluster in which they were first captured in this study (regardless of destination of that movement). For estimation of indirect vaccine protection of initially unvaccinated children who later became vaccinated, an additional censoring criterion was the date of vaccination.2 tests (or Fisher's exact test for sparse data) for categorical data and Student's t test (or the Mann-Whitney U test when parametric assumptions were not satisfied) for dimensional data. To evaluate vaccine protection against typhoid fever in simple analyses, we compared time to event using Kaplan-Meier survival curves, evaluated statistically with the log-rank test.www.isrctn.com, ISRCTN11643110 and is ongoing.Inter-group comparisons of the occurrence of adverse events and plasma immune responses were statistically evaluated with χThe funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.Baseline immunisation was done between April 15 and May 15, 2018. Because of the high migration rate of the study population and the need to maintain stable coverage after baseline, we did catch-up vaccination of children who had not been dosed in earlier rounds at approximately 6-month intervals between September and December, 2018, and April and May, 2019. At baseline, there were 205 760 residents in the study area, including 61 654 children aged 9 months to less than 16 years old who were age-eligible for vaccination. 41 344 age-eligible children were vaccinated in April–May, 2018, with another 20 412 children vaccinated at catch-up vaccination campaigns between September and December, 2018, and April and May, 2019. During an average of 17·1 months of follow-up of the study population, there were 105 529 births and in-migrations, whereas the number of deaths were 1146 and out-migrations were 71 846 . 12 348 The incidence rate difference between SA 14-14-2 and Vi-TT groups was 539  per 100 000 person-years among vaccinees, and 120  per 100 000 person-years in all residents. Vi-TT conferred 85%  total protection against typhoid fever . OverallAmong the 7-day, active surveillance subset of 2507 children who received SA 14-14-2 and 2500 who received Vi-TT, including participants in the pilot study, the most common complaints were fever (5·3%), a general feeling of unwellness (4·3%), diarrhoea (2·1%), and pain at the injection site (1·6%). Risks of these events were similar in the two vaccine groups. In the 6 months post-vaccination , there were 370 serious adverse events reported in the SA 14-14-2 vaccine group and 314 in the Vi-TT group (p=0·029) among all vaccinees. None was judged related to receipt of vaccine, and no component cause appeared to account for the overall difference . During Blood samples were obtained from 1515 vaccinees at baseline and 1433 vaccinees 28 days after dosing. Plasma IgG anti-Vi titres were similar for the Vi-TT and SA 14-14-2 vaccine groups at baseline. On day 28 after dosing, the median titres (in ELISA units) were 3·7 (IQR 3·7–3·7) for the SA 14-14-2 group and 3222·4 (IQR 1757·0–5471·6) for the Vi-TT group , a disease clinically resembling typhoid fever and with a similar transmission route but not expected to be prevented by Vi-TT due to an absence of ViPS capsule in S Paratyphi A organisms. Another limitation was that, in order to maintain a reasonably high degree of Vi-TT coverage of the target age group over time, catch-up vaccination campaigns were done at 6-month intervals, a strategy unlikely to be feasible in routine public health practice.Before considering the implications of these findings, it is important to discuss the potential limitations of the study. The trial was not formally double-blinded, although measures were taken to help ensure that it was done in a participant-masked and observer-masked fashion. Also weighing against bias was the observation that Vi-TT conferred no protection against enteric fever due to Our trial failed to find evidence of significant indirect (herd) protection from Vi-TT. This could have occurred for methodological reasons. On the basis of mathematical model simulations, we expected indirect protection to be approximately 20% and established that the trial would not be adequately powered to evaluate this amount of vaccine protection; we therefore evaluated indirect protection in a prespecified secondary analysis.Pseudomonas aeruginosa exoprotein A) vaccine has reported 91·5%  protection in Vietnamese children aged 2–5 years.A few studies done with typhoid conjugated vaccines have shown different protection level. An experiment on a human challenge model with Vi-TT vaccine done in the UK, a typhoid non-endemic area, evaluated only the immunological endpoints.The implications of our findings are both biological and programmatic. From a biological perspective, the protection of children vaccinated at younger than 2 years of age is consistent with the concept that Vi-TT induces T-cell-dependent antibody responses; it is believed that the reason earlier ViPS-only vaccines were not efficacious in very young children was that they fail to elicit T-cell-independent antibody responses. Although these T-cell-dependent antibody responses might also entail induction of immune memory and sustained protection by Vi-TT, this needs to be shown. Programmatically, although our trial was not adequately powered to evaluate the clinical protection by Vi-TT when given at specific contact visits of the schedule, it is nonetheless reassuring that substantial clinical protection was observed when the vaccine was given to participants as young as 9 months of age.In conclusion, our findings strongly support global efforts by WHO and the Global Alliance for Vaccines and Immunization to introduce Vi-TT vaccine into public health programmes for populations affected by high rates of endemic typhoid fever, including those with a high burden of typhoid in very young children. Fine tuning such efforts, including the potential use of booster doses of Vi-TT, will require further work to define the duration of vaccine protection.fqadri@icddrb.org). Only after approval of a proposal can data be shared through a secure online platform. Approval of the proposal will be subject to scientific review by the icddr,b Research Review Committee and by the institutional review board at icddr,b. Sharing of data will also be subject to the published data access rules of the icddr,b. The requestor will need to sign a standard data access agreement required by the icddr,b.Deidentified individual participant data including a data dictionary for each variable analysed in the published paper, as well as the study protocol, the statistical analysis plan, and the informed consent form will be made available when the trial is complete, on requests directed to the corresponding author (VEP has received reimbursement from Merck and Pfizer for travel expenses to scientific input engagements unrelated to the topic of this manuscript and is a member of the WHO Immunization and Vaccine-related Implementation Research Advisory Committee. All other authors declare no competing interests.


Introduction: COVID-19 can be worsened by hyper-production of cytokines accompanied by increased level of oxidative stress. The aim of this study was to investigate the correlation between a set of cytokines and the markers of the oxidative stress.Methods: The levels of cytokines IL-2, IL-4, IL-6, IL8, IL-10, VEGF, IFN-γ, TNF-α, IL-1α, MCP-1 and EGF were determined by using High Sensitivity Evidence Investigator™ Biochip Array technology. The oxidative stress parameters  were measured in serum on FRAS5 analytical photometric system.Results: IL-6, IL-8, IL-10, VEGF, MCP-1 and EGF were significantly higher (p<0.05) in the patients with severe COVID-19 with increased levels of IL-2, IFN-y, TNF-α and IL-1α. The d-ROM, OS index, and PAT were significantly higher (p<0.05) in severe COVID-19 patients. IL-6 demonstrated the strongest correlation with all of the markers of the oxidative stress, d-ROM , PAT  and OS index . Similar behavior was evidenced between IFN-y and d-ROM , PAT  and OS index .Conclusion: The oxidative stress markers show good correlation with the tested cytokines which can be measured at the beginning of the disease in a primary care setting to predict the course of COVID-19. Oxidative stress is typical for infection of human respiratory syncytial virus, rhinoviruses, and many other viruses. This has been discussed in previously published reviews and as well, several experimental studies suggest that cytokine storm correlated with direct tissue injury and lead to unfavourable prognosis of severe form of the COVID-19 disease. Briefly, particularly high levels of IL-6, IL-10, IL-2R and TNF-α have been reported in patients with severe form of the disease although other authors suggest that more cytokines, such IL-1β, IL-1RA, IL-8, IL-18 are included in the COVID-19 pathogenesis.Cytokine storm syndrome has been widely discussed and proposed as one of the underlying aetiologies of respiratory failure in patients infected with SARS-CoV-2. Pro-inflammatory cytokines play a key role in large number of respiratory viral infections by activation of the adaptive immune response and, when this response is not controlled, it can lead to involvement of the lung tissue in the course of ARDS or can result in severe damages of multiple organs. For example, following influenza viral infection, an excessive amount of reactive oxygen species (ROS) is produced in several tissues including alveolar epithelium and endothelium Namely, ROS is a strong ligand and a direct mediator in the NLPR3 (inflammasome) trigger. Moreover, NF-κB, which is activated by ROS, triggers transcriptional levels of NLPR3 are enhanced by TLR and NLR ligands. This means that the inflammasome is increased by ROS either directly or indirectly. To the addition of ROS, H2O2 activates NF-κB to produce inflammatory cytokines. Hyperproduction of IL-6, TNF-α, IL-1β, IP-10, GCSF, MCP-1, MIP1-α/CCL3 and elevated blood ferritin are also observed in patients infected with SARS-CoV-2.Authors have suggested that the innate immune response follows same pathway for SARS-CoV-2 infection.For this purpose, and in the light to share more experimental data as evidence to the suggested pathogenesis of COVID-19 with the scientific community, we have utilized a highly standardized cytokine assay to measure plasma levels of 11 inflammatory cytokines potentially associated as key factors with the cytokine storm syndrome. Afterwards, we have investigated which of these cytokines involved in the cytokine storm of COVID-19 show good association/correlation with the oxidative stress markers determined with fast and inexpensive photometric analytical method. Moreover, the relation between the cytokines, oxidative stress markers and the most commonly used inflammation-related biomarkers  in severe form of the disease was investigated.2 <90% on room air, respiratory rate >30 breaths/minute or presence of severe respiratory distress. All patients were confirmed to have SARS-CoV-2 infection by real-time reverse transcriptase-polymerase chain reaction assay (RT-PCR). Severe form of COVID-19 as primary exposure variable, demographic characteristics, medical history, clinical symptoms and signs, concomitant medication, outcome data, as well as laboratory analyzes were obtained from the patients’ medical records were other predictor variables. For purpose of control and comparison between groups, we have analyzed samples of 20 healthy individuals with negative RT-PCR test for SARS-CoV-2 .  This group had no defined risk factors, were in good health as determined by the medical history, complete physical examination, electrocardiogram (ECG), and clinical laboratory tests. The female individuals had negative serum pregnancy test and absence of lactation. The study flow chart is shown in52 patients with COVID-19 were hospitalized at the University Clinic for Infectious Diseases and Febrile Conditions, Skopje, Republic of North Macedonia at the beginning of the pandemic within a period of 1 month. 14 patients classified with severe COVID-19  with a mean age of 58.36 years (range from 36 to 71 years) were included in this study (study group). The diagnosis and classification of COVID-19 were based on the Interim Guidance for Clinical Management of COVID-19 issued by WHO. Severe cases in addition to severe pneumonia met at least one of the following conditions: SpO2O2 and ascorbic acid, respectively. Oxidative stress index (OSI) presents information obtained from d-ROMs Fast test and the PAT test that is automatically calculated by the manufacturer’s software  with normal reference values less than 40.PAT  and d-ROMs (plasma peroxides) were measured on a FRAS5 analytical photometric system . Samples were collected and analyzed immediately after hospital admission. The instructions of the manufacturer were followed for the both tests. The d-ROM and PAT are reported in equivalents of HThe High Sensitivity Evidence Investigator™ Biochip Array technology  was used to perform simultaneous quantitative detection of multiple analytes from a single patient sample .100 μL of plasma was used in biochip carriers, following by incubation on thermo-shaker for 1 hour at 37°C and 370 rpm and 16–20 hours incubation at 4°C. Afterwards, carry out of two wash cycles and 300 μL conjugate was added into each well followed by another incubation of 1 hour at 37°C and 370 rpm. At the final step after twice washing the carriers, fluorescent dye was added to carriers according to protocol and carriers were captured by Evidence Investigator Array. Results were processed automatically using EvInvest software and levels of cytokines IL-2, IL-4, IL-6, IL-8, IL-10, VEGF, IFN-γ, TNF-α, IL-1α, MCP-1 and EGF were calculated as pg/mL.Exposure variables were summarized using descriptive statistics. Data were described as number and/or percentage, or median and range or mean and standard deviation (SD) or standard error of mean (SEM), where appropriate. Differences between groups were explored using the t-test followed by Mann–Whitney where appropriate. A p-value less than 0.05 was considered significant. Spearman r coefficient of correlation was performed. All analyses were made using the statistical program GraphPad Prism 9 (USA) (RRID:SCR_000306); an open-access alternative is JASP (RRID:SCR_015823).All 14 patients with a mean age of 58.36 years had severe form of the disease. The average time from onset of symptoms to hospital admission was 10.52±2.33 days (range 7–16 days). All of them had underlying medical conditions at admission. The most frequently reported comorbidities were hypertension, diabetes and chronic cardiac disease. The most prominent and disturbing symptoms reported by the patients on admission were high body temperature (80%), dyspnea (64%), malaise (62%) and cough (56%). The mean value of all clinical laboratory parameters upon hospitalization are presented inAs presented in Herein, we share our results to give an add-on to the clinical evidences that oxidative stress is increased in patients with severe form of COVID-19 and that the measured oxidative stress parameters had shown a good correlation with the cytokines and the commonly used laboratory biomarkers. This pilot study focused on the possibility to utilize the oxidative stress parameters  as a fast and inexpensive prognostic tool for disease progression and potentially predict the outcome of COVID-19 in patients. Several retrospective studies and reviews have been published where abnormal levels of cytokines involved in the adaptive immunity  or pro-inflammatory cytokines and interleukins  were reported.Cytokines including chemokines and growth factors together with lipid metabolites are among the main factors of immune cell function and their differentiation, hence upon their dysregulation various diseases can arise.et al. (2020) reported that MCP-1 levels were much higher in critical ICU patients and additionally that the platelet count was lower in those patients that do not survive. Patients from our study were all with severe form of COVID-19 and all of them had died during hospitalization. Moreover, in our patients several of the cytokines had been increased more than 10-fold above the levels of the non-infected that we considered as a baseline. It is worth noting, the statistically significant increase of the VEGF levels more than 10-fold that can be related to the essential role of VEGF in endothelial cell activation by binding to cell surface VEGF receptors. VEGF up-regulation was observed in several viral infections and it has been investigated as a target for potential therapy development. In addition, Huanget al., report higher levels of VEGF in hospitalized COVID-19 patients. Tanacanet al., had investigated the cytokine profile in pregnant women with COVID-19 where their results indicated significantly higher values for IFN-g andIL-6, and lower values of IL-2, IL-10 and IL-17, especially in those where complications like miscarriage and preterm delivery was evidenced.Our study revealed that several cytokines and biomarkers were significantly increased in infected SARS-CoV-2 patients with severe form of the disease in comparison to those who were not, which was accompanied with coagulopathy as determined by deterioration of the platelet related parameters  and MCP-1 as thrombosis related indicator. HuangThe strong correlation between the investigated cytokines (including chemokines and growth factors), the oxidative stress parameters and some of the commonly used biomarkers  are in line with the proposed cytokine storm as underlying mechanism of the infection. The cytokine storm syndrome occurs when large numbers of leukocytes are activated and release a high concentration of proinflammatory cytokines, with IL-6, IL-10, IFN, MPC-1, IL-1, IL-2 and IL-8 being the foremost. Generally, SARS-CoV-2 infection is associated with oxidative stress, the proinflammatory state, cytokine production, and cell death demonstrated by increase in ROS levels and an alteration of antioxidant defense during the infection. We have demonstrated the significantly higher level of the d-ROM and OS index values in the infected patients with SARS-CoV-2 when compared with those who were not infected, supporting the hypothesis that viral infection will increase the oxidative stress and complicate the course of the disease. Whilst we consider that the OS index value presents an important parameter that we can have an impact on against COVID-19, by supplementation with antioxidants especially when there is applicable knowledge for several nutraceuticals/vitamins  with proven anti-inflammatory, antioxidant and antiviral capacity. Clinical trials of several monoclonal antibodies  against selected cytokines are underway, some of which have already been published. Although, data on their effectiveness are emerging additional studies are needed for those treatments to be given routinely in COVID-19.Even though limited published data are available, we believe that SARS-CoV-2 in line with other RNA viruses triggers oxidative stress by disturbing the pro-antioxidant–antioxidant balance.There are several limitations of the study besides being a single-center experience and a pilot study with only severe and critically ill patients. The herein presented patients were hospitalized at the beginning of the global pandemic when no specific and official guidelines were issued and available to assist the need for hospitalization. They had symptoms developed several days prior being hospitalized, however we believe that these symptoms were not life threatening and the hyper-inflammatory phase was at its beginning stage which is deemed by the obtained levels of the cytokines and the oxidative stress index. Nevertheless, further studies concerning COVID-19 patients with high levels of d-ROMs and OS index are warranted to determine whether supporting antioxidant therapy can reduce the possibility for the fatal outcome of the critically ill COVID-19 patients.Our study demonstrates that the levels of IL-6, IL-8, IL-10, VEGF, MCP-1 and EGF were significantly increased in the severe COVID-19 patients. Among the tested correlations between the oxidative stress parameters and the cytokines, the strongest one was between oxidative stress index and IL-6 . The presented results will contribute to support the evidences that the cytokine storm syndrome lies as an immune-pathogenesis during SARS-CoV-2 infection. By using the oxidative stress parameters  physicians can provide timely and early interventions in COVID-19 patients. Namely, we consider that the investigated parameters can be used as a tool at the beginning of COVID-19 disease for the general assessment of oxidative stress and hence enabling a better triage of the patients in terms of disease severity.MP, DZ, EA contributed to the conception and design of the study. MP and DZ contributed to the oxidative stress parameters analyses, collated the data for the study, and completed all statistical analysis of data. MP wrote the first draft of the manuscript. AE performed the cytokine assay. KS and EA contributed to the clinical evaluation and medical data collection from the COVID-19 patients. All authors read and approved the final version of the manuscript.https://doi.org/10.5061/dryad.gf1vhhmqg.DataDryad: Underlying data for ‘Presentation of cytokine profile in relation to oxidative stress parameters in patients with severe COVID-19: an observational pilot study’.Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).Data are available under the terms of the The authors have performed the recommended revisions the manuscript is now appropriate for indexing.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Perinatology, maternal-fetal medicine, prenatal diagnosisI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The authors have addressed all my comments and concerns.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?PartlyIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Infectious and non-communicable diseases, biomarkers discovery, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. The current study examined the levels of cytokines and oxidative stress markers among COVID-19 patients and correlated them with disease severity. The manuscript is well written except that in the Methods section the authors failed to state the study design. I inferred from the statistical section that the study design was a case-control study. The authors should describe the control very well - how they were recruited to ensure they were devoid of confounding factors, more especially since cytokines are involved. Cytokines are affected by many factors. Additionally, conclusion statements should explain the statistical terms correlation.Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?PartlyIs the study design appropriate and is the work technically sound?PartlyAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Infectious and non-communicable diseases, biomarkers discovery, I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Thank you for reviewing our manuscript and for your contribution to increasing the quality of our manuscript. We have accepted all your remarks, and we made changes in the material section in order to describe the control . All changes are marked in red and with track changes.  Additionally, in the Section Conclusion - we have elaborated in detail on the statistical correlation. The inflammatory background of COVID-19 should be discussed.Treatment options and targeted therapies should be discussed in more detail.Severe COVID-19 complications should be discussed in more detail.The clinical implication of findings obtained from the present study should be underlined.et al. 2021may be useful and in my opinion, the mechanisms behind the possible immunologic processes behind COVID-19 may help the authors to improve their manuscript.The following literature by TanacanI have read the manuscript with great interest. The authors have made a comprehensive study on an important topic. I have some minor recommendations for the authors:Is the work clearly and accurately presented and does it cite the current literature?YesIf applicable, is the statistical analysis and its interpretation appropriate?YesAre all the source data underlying the results available to ensure full reproducibility?YesIs the study design appropriate and is the work technically sound?YesAre the conclusions drawn adequately supported by the results?YesAre sufficient details of methods and analysis provided to allow replication by others?YesReviewer Expertise:Perinatology, maternal-fetal medicine, prenatal diagnosisI confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. nd version of the manuscript after we receive an additional report from another reviewer. Thank you for reviewing our manuscript and for your contribution to the increase in the quality of the manuscript. Below I attend to all of your comments. The changes will be included in the 2Comment 1, Comment 2, and Comment 5:Section Discussion on page 6 of 10 (first paragraph). A revision will be inserted where the reference Tanacan et al. 2021 will be included, additionally, in this section, we will discuss treatment options and targeted therapies. The authors have discussed the inflammatory background of the disease in theComment 3: COVID-19 complications are also presented and well referenced in the manuscript. We believe that this has been a subject of numerous publications in the past year. Also, we discussed the most prominent and disturbing symptoms and their onset followed by the clinical biochemical parameters.Comment 4:ection Conclusion. However, we will elaborate in more detail as stated below: The clinical implication of findings has been pointed out in the S "The presented results will contribute to supporting the evidence that the cytokine storm syndrome lies as an immunopathogenesis during SARS-CoV-2 infection. By using the oxidative stress parameters  physicians can provide timely and early interventions in COVID-19 patients. Namely, we consider that the investigated parameters can be used as a tool at the beginning of COVID-19 disease for the general assessment of oxidative stress and hence enabling a better triage of the patients in terms of disease severity."


During the COVID-19 pandemic, utilization of remote monitoring platforms was recommended. The HeartLogic algorithm identifies patients at risk of heart failure events, combining multiple sensors available on implantable cardioverter defibrillators. This analysis examined how multiple CIED sensors behave in periods of anticipated restrictions pertaining to physical activity. We demonstrated a significant drop in median activity level immediately after the implementation of stay-at-home orders, whereas there was no difference in the other contributing sensors. The weekly rate of heart failure alerts was significantly higher during the lockdown and post-lockdown than that reported in the pre-lockdown. p = 0.014) and post-lockdown  than that reported in pre-lockdown . However, the median duration of alert state and the maximum index value did not change among phases, as well as the proportion of alerts followed by clinical actions at the centers and the proportion of alerts fully managed remotely. Conclusions: During the lockdown, the system detected a significant drop in the median activity level and generated a higher rate of alerts suggestive of worsening of the HF status. Aims: The utilization of remote monitoring platforms was recommended amidst the COVID-19 pandemic. The HeartLogic index combines multiple implantable cardioverter defibrillator (ICD) sensors and has proved to be a predictor of impending heart failure (HF) decompensation. We examined how multiple ICD sensors behave in the periods of anticipated restrictions pertaining to physical activity. Methods: The HeartLogic feature was active in 349 ICD and cardiac resynchronization therapy ICD patients at 20 Italian centers. The period from 1 January to 19 July 2020, was divided into three phases: pre-lockdown (weeks 1–11), lockdown (weeks 12–20), post-lockdown (weeks 21–29). Results: Immediately after the implementation of stay-at-home orders (week 12), we observed a significant drop in median activity level whereas there was no difference in the other contributing parameters. The median composite HeartLogic index increased at the end of the Lockdown. The weekly rate of alerts was significantly higher during the lockdown (1.56 alerts/week/100 pts, 95%CI: 1.15–2.06; IRR = 1.71,  The spread of the coronavirus disease 2019 (COVID-19) epidemic required a rapid response. The primary modes of disease prevention have involved limiting exposure and social distancing. At various times, stay-at-home orders were issued in many geographies and were shown to result in a rapid global reduction in physical activity . During To date, scant data exist on the consequences of the home confinement on the clinical status of heart failure (HF) patients with CIEDs . MoreoveThe home confinement for the COVID-19 pandemic in Italy was imposed from March 8th to May 18th. For this analysis we identified all of the HF patients with reduced left ventricular ejection fraction (≤35% at the time of implantation) who had received a HeartLogic-enabled implantable cardioverter defibrillator (ICD) or cardiac resynchronization therapy ICD (CRT-D)  in accordance with standard indications  at 20 stThe details of the HeartLogic algorithm have been reported previously . BrieflyWe assessed the trend of all HeartLogic sensors from 1 January to 19 July. The period was divided in 3 phases: pre-lockdown (weeks 1–11), lockdown (weeks 12–20), post-lockdown (weeks 21–29). We calculated the change in all variables among phases, as well as the rate of alerts occurred, and the clinical actions or extra in-office visits performed to manage them. We also compared the sensed parameters that contributed to the calculation of the HeartLogic index in case of alerts among study phases.p value < 0.05 was considered significant in all tests. All statistical analyses were performed by means of R: a language and environment for statistical computing .Descriptive statistics are reported as means ± SD for normally distributed continuous variables, or medians with 25th to 75th percentiles in the case of skewed distribution. Normality of distribution was tested by means of the nonparametric Kolmogorov–Smirnov test. Categorical data were expressed as percentages. Differences between continuous variables were performed using a Student’s T test for Gaussian variables, and a Mann–Whitney U test non-parametric test for non-Gaussian variables. Differences in proportions were compared by applying chi-squared analysis or Fisher’s exact test, as appropriate. Incidence rates with confidence intervals were calculated and compared, together with the incidence rate ratio. One-way analysis of variance for repeated measures was used to test for differences among phases. A In the analysis, we included 349 ICD and cardiac resynchronization therapy ICD patients who had received the device at the study centers before November 2019. p = 0.014), and 43 times during the post-lockdown phase   .Of the 127 reported HeartLogic alerts, 113 (89%) did not require extra in-office visits and were managed remotely. Alert-triggered actions  were reported in 34 (27%) cases. In the remaining cases, physicians adopted an active monitoring approach, intensifying the frequency of contacts but not intervening proactively. The proportion of alerts managed remotely, as well as the proportion of alerts triggering clinical actions, remained constant among study phases . The aveIn this analysis, we showed a marked decrease in the ICD-measured physical activity, whereas no changes were detected in the other sensors, i.e., heart sounds, intrathoracic impedance, respiration parameters, heart rate. Indeed, the home confinement had no significant impact on single physiologic parameters beyond activity, but we observed an increased number of device-defined HF events, as detected by the combined HeartLogic index. This occurred at the end of the lockdown period and seemed to persist for some weeks after the end of home confinement.The decline in physical activity during the lockdown was significant, as demonstrated through the use of smartphone accelerometers and algorithms for step counting in the general population  and in aThe rate of HeartLogic alerts was higher during the lockdown, as well as the average index after 8 weeks of home confinement. The analysis of the individual sensors during the weeks before the alert onset showed comparable contribution from all sensors to the combined HeartLogic index among study phases, except for higher activity levels in the post-lockdown phase. This suggests that the trigger mechanism of HF decompensation is the same under different activity conditions. Moreover, the decline in activity alone cannot lead to an increase in the index , but lowIn our centers with experts in the remote management of alerts, the COVID-19 restrictions had no impact on their standard practice. Indeed, the proportion of alerts managed remotely and alerts followed by clinical actions did not change between different phases of the pandemic. Consequently, the severity of the individual events was equivalent, since the in-alert states had similar durations and extents.In previous works, the ability of HeartLogic to detect actionable HF events has been demonstrated, facilitating effective remote management ,27,28,29The main limitation of this study is its observational non-randomized design. Moreover, as mentioned above, no predetermined actions were prescribed in response to HeartLogic alerts or to the individual subject’s reported signs or symptoms.The HeartLogic multisensor platform detected the decrease in activity, although the home confinement had no impact on the other sensors. The increased number of alerts during the lockdown suggests that the home confinement had a negative effect on patients’ outcome. This must be taken into account in periods of prolonged social distancing and confirms the importance of ensuring safe and secure access to healthcare facilities for everybody who needs continuity of care.


However, due to the heterogeneity of X-ray imaging and the difficulty of annotating infected regions precisely, learning automated infection segmentation on CXRs remains a challenging task. We propose a novel approach, called DRR4Covid, to learn COVID-19 infection segmentation on CXRs from digitally reconstructed radiographs (DRRs). DRR4Covid consists of an infection-aware DRR generator, a segmentation network, and a domain adaptation module. Given a labeled Computed Tomography scan, the infection-aware DRR generator can produce infection-aware DRRs with pixel-level annotations of infected regions for training the segmentation network. The domain adaptation module is designed to enable the segmentation network trained on DRRs to generalize to CXRs. The statistical analyses made on experiment results have indicated that our infection-aware DRRs are significantly better than standard DRRs in learning COVID-19 infection segmentation (p < 0.05) and the domain adaptation module can improve the infection segmentation performance on CXRs significantly (p < 0.05). Without using any annotations of CXRs, our network has achieved a classification score of  and a segmentation score of  on a test set with 558 normal cases and 558 positive cases. Besides, by adjusting the strength of radiological signs of COVID-19 infection in infection-aware DRRs, we estimate the detection limit of X-ray imaging in detecting COVID-19 infection. The estimated detection limit, measured by the percent volume of the lung that is infected by COVID-19, is 19.43% ± 16.29%, and the estimated lower bound of infected voxel contribution rate for significant radiological signs of COVID-19 infection is 20.0%. Our codes are made publicly available at  I.The highly contagious Coronavirus Disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus Radiological imaging, such as Computed Tomography (CT) and Chest X-ray (CXR), is currently used to provide visual evidence for confirming COVID-19 positive patients in clinical practice. CT scan provides accurate 3D images of the lungs that are able to detect very small lesions effectively such as lung nodule and tumor. However, the workflow of CT imaging, involving several pre-scan events et al.Many approaches have been proposed for automated COVID-19 diagnosis based on CXRs, and have claimed notable detection accuracy of COVID-19 infection. However, due to the lack of sufficient CXRs with pixel-level annotations of infected regions, the majority of these approaches are designed by using classification models rather than segmentation models. The projective nature of X-ray imaging causes large overlapping of anatomies, fuzzy object boundaries and complex texture patterns, thus making it extremely difficult to delineate infected regions precisely on CXRs even for experienced clinicians Digitally reconstructed radiograph (DRR) To this end, we propose a novel approach, called DRR4Covid, which can learn automated COVID-19 infection segmentation on CXRs from labeled DRRs. We design DRR4Covid with a modular framework, which consists of an infection-aware DRR generator, a deep segmentation network, and a domain adaptation module. Given a CT volume with voxel-level infection annotations, our infection-aware DRR generator can produce DRRs with adjustable radiological signs of COVID-19 infection, and generate pixel-level annotations of infected regions that match the DRRs accurately. Although such synthetic DRRs are photo-realistic, there is still a gap between synthetic DRRs and real CXRs, which may lead to a poor segmentation performance on real CXRs. Therefore, we introduce a domain adaptation module to train networks on labeled DRRs and unlabeled CXRs together. In this article, we provide a simple but effective implementation of DRR4Covid by using a domain adaptation module based on Maximum Mean Discrepancy (MMD), and a FCN-based 1)We propose a novel approach, i.e., DRR4Covid, to learn automated COVID-19 infection segmentation on CXRs. To our best knowledge, this is the first attempt to learn automated COVID-19 infection segmentation on CXRs by using the labeled DRRs that are generated from Chest CT scans. Owing to the modular framework, our DRR4Covid can be implemented flexibly with the off-the-shelf segmentation networks and domain adaptation algorithms. Moreover, DRR4Covid is a unified approach that can be applied to other lesion segmentation  on X-ray images;2)We design an infection-aware DRR generator to synthesize infection-aware DRRs with pixel-level annotations of infected regions for training segmentation network. The statistical analyses made on experiment results have confirmed that our infection-aware DRRs are significantly better than standard DRRs in learning COVID-19 infection segmentation (p < 0.05);3)We provide a simple but effective implementation of DRR4Covid by using a domain adaptation module based on Maximum Mean Discrepancy (MMD), and a FCN-based network with a classification header and a segmentation header. The statistical analyses made on experiment results have confirmed that the domain adaptation module can improve the infection segmentation performance on CXRs significantly (p < 0.05);4)We estimate the detection limit of X-ray imaging in detecting COVID-19 infection for the first time, which is of great significance for the severity assessment of COVID-19 infection based on X-ray imaging.The novelties and contributions of our study mainly come from four major aspects:II.In this section, we review the related work from three aspects, including DRR, domain adaptation and CXR based screening of COVID-19 in light of infection segmentation.A.et al.A digitally reconstructed radiograph (DRR) The most closely related work is TD-GAN B.Domain adaptation aims at rectifying the distribution discrepancy between the training samples (source domain) and test samples (target domain) et al.Existing deep domain adaptation methods align the distributions of source domain and target domain mainly from three perspectives. The first stream is image alignment, and image-to-image translation models are typically used to reduce the gap between source domain images and target domain images C.et al. in et al.et al.et al.Segmentation is an essential step in automated infection measurement and COVID-19 diagnosis, which can provide the delineation of the regions of interest (ROIs), e.g., infected regions, in the CXRs for further assessment and quantification. Many approaches have been proposed for automated COVID-19 diagnosis based on CXRs. However, the majority of these approaches are based on classification models rather than segmentation models as reviewed by Shen III.In this section, we describe the modular framework of proposed DRR4Covid, and analyze the critical elements in the design of DRR4Covid, followed by an introduction of our implementation of DRR4Covid.A.Given CT scans with voxel-level infection annotations and unlabeled CXRs, we aim to learn deep models to perform automated COVID-19 infection segmentation on CXRs. We design DRR4Covid with a modular framework as shown in 1)The DRR generator is responsible for synthesizing photo-realistic DRRs that resemble real CXRs as much as possible and producing pixel-level infection annotations that match the DRRs precisely by projecting 3D CT annotations along the same trajectories used in synthesizing DRRs. High-quality labeled DRRs in the context of this article can be defined by two conditions. One is a good consistency between DRRs and infection annotation masks; the other one is a good correlation between the radiological signs of COVID-19 infection in DRRs and in real CXRs. As CXRs are typically considered less sensitive than 3D CT scans 2)Although synthetic DRRs are photo-realistic, there is still a gap between DRRs and real CXRs. Thus, we introduce the domain adaptation module into the framework of DRR4Covid. According to the quality of synthetic labeled DRRs, the problem of training deep models on labeled DRRs and unlabeled CXRs for infection segmentation on CXRs by using the domain adaptation module can be divided into two categories. One is deep domain adaptation with fully supervised learning in the source domain  and unsupervised learning in the target domain ; the other one is deep domain adaptation with weakly supervised learning in the source domain and unsupervised learning in the target domain. The condition of the first category is a good consistency between DRRs and infection annotation masks. If such condition is not well satisfied, the problem will be turned into the second category due to the inaccurate synthetic infection annotations. Compared with the second one, the first category of problem is well defined, and has been extensively studied. In this article, we mainly focus on solving the first category of problem. Thus, we first implement a high-quality DRR generator, i.e., the infection-aware DRR generator.B.We design the infection-aware DRR generator to produce high-quality DRRs as defined in 1)By setting the weight of infected voxels to a very small value, our infection-aware DRR generator is able to produce DRRs with no findings, which are essential for training deep classification models for COVID-19 diagnosis;2)By setting the weight of infected voxels to a relatively large value, our infection-aware DRR generator can generate high-quality DRRs with pixel-level annotations of infected regions, which are essential for training deep segmentation models for precise COVID-19 infection segmentation;3)By adjusting the weight of infected voxels from small values to large values, our infection-aware DRR generator will synthesize a serial of labeled DRRs with different strengths of the radiological signs of COVID-19 infection. Such DRRs might be able to be used to estimate the detection limit of X-ray imaging in detecting COVID-19 infection.The strength of the radiological signs of COVID-19 infection in CXRs and DRRs depends on the contribution rate of infected voxels (CRIV) due to the projective nature of X-ray imaging. A higher value of CRIV represents a larger number of infected voxels appear in the X-ray direction, and the radiological signs of COVID-19 infection, e.g., GGOs, will be more significant. Such property of X-ray imaging can be well modeled in “1” and “4” by our infection-aware DRR generator. Increasing the weight of infected voxels will improve the value of CRIV and vice versa. Accordingly, our infection-aware DRR generator can produce DRRs with different strengths of radiological signs of COVID-19 infection simply by adjusting the weight of infected voxel. The synthetic pixel-level annotations of COVID-19 infection are also computed based on the CRIV. Therefore, our infection-aware DRR generator can maintain the consistency between synthetic DRRs and infection annotation masks easily by increasing the weight of infected voxels when the value of CRIV is too small. To sum up, our infection-aware DRR generator has the following advantages:C.1)We design a FCN-based network as depicted in 2)et al.As a nonparametric distance estimate between two distributions, MMD 3)The training of our model is performed by minimizing the classification loss IV.A.1)We use the public COVID-19-CT-Seg dataset 2)We use two public COVID-19 CXR collections B.1)DRRs with no findings are important for training deep classification and segmentation models for COVID-19 diagnosis. Our infection-aware generator is able to generate such DRRs with no findings by setting the weight of infected voxels to a relatively small value to reduce the CRIV in the ray-casting process. In our experiment, we empirically set the weights of background, lung, and COVID-19 infection as 2)It is easy to generate multiple DRRs from a single CT volume by adjusting the pose (position and orientation) of the CT volume within a virtual imaging system. In our experiment, we randomly shift each CT volume between −100 and 100, and rotate it between − 45° and 45° in 3D directions. Several DRRs generated from a single CT volume are illustrated in 3)Our infection-aware DRR generator is able to generate DRRs with different strengths of radiological signs of COVID-19 infection by adjusting the weights of background, lung and COVID-19 infection 4)We empirically set the contribution threshold of infected voxels (CTIV) 5)For each CT volume, we first generate 40 normal DRRs, including 20 front-view DRRs and 20 lateral-view DRRs by randomly adjusting its pose. Next, in the same way we generate 40 DRRs and the corresponding pixel-level annotations of infected regions with given C.1)This article aims at learning automated COVID-19 infection segmentation on CXRs from DRRs. To this end, we propose DRR4Covid, which consists of an infection-aware DRR generator, a FCN-based network and a MMD-based domain adaptation module. To verify the efficacy of our method, we conduct experiments from four aspects: 1) standard DRRs versus infection-aware DRRs; 2) using domain adaptation versus not using domain adaptation; 3) estimating the detection limit of X-ray imaging in detecting COVID-19 infection by searching for the best parameters 2)ResNet-18 is adopted as the backbone of the FCN-based network in our experiments. We train the network with 100 epochs by using Adam optimizer with the parameters of 3)For the classification output of our model, we adopt the commonly used classification metrics, including accuracy, F1-score and area under precision-recall curve (AUC of PR-curve). As the pixel-level annotations of infected regions are not available for the validation and test sets in the target domain (CXRs), we are unable to use the segmentation evaluation metrics directly. To enable evaluate the quality of segmentation output of our model, we convert the segmentation output into classification output by determining whether there exists infected regions in the segmentation output, and then adopt the same three classification metrics. As for the sub-set of 10 CXRs in the BIMCV COVID-19+ dataset, we directly adopt the commonly used segmentation metric, i.e., Dice similarity coefficient (DSC), to evaluate the segmentation results.4)Statistical tests are conducted to determine the significance in performance differences of learning infection segmentation from DRRs between standard DRRs and our infection-aware DRRs and between using our domain adaptation module and not using domain adaptation. According to the experiment design, we perform paired samples t-test to compare the means of scores. Specifically, we remove the outliers of the scores and apply Shaprio-Wilk test for normality. If the variables violate the assumption of normality, we perform Wilcoxon signed-rank test instead of paired samples t-test. The SciPy package is used in these analyses.D.We report the evaluation results of our model trained on the 63 training sets with/without MMD-based domain adaptation module in 1)Firstly, we do qualitative comparison in Secondly, we analyze the classification and segmentation results on the validation and test sets without using domain adaptation in Next, we analyze the classification and segmentation results on the validation and test sets with using domain adaptation in Finally, we perform statistical tests to compare the means of scores in 2)In order to highlight the efficacy of our domain adaptation module, we compare the average scores of domain adaptation and no domain adaptation on the validation and test sets in 3)We specifically use the case of infection-aware DRRs with 4)As mentioned earlier, CXRs are generally considered less sensitive than 3D CT scans 5)We simply use the 4-fold cross validation models trained by using our domain adaptation module to segment the 10 CXRs. The average DSC scores of these segmentation results are reported in V.This study still has a variety of limitations. Firstly, the synthetic annotation masks of infected regions for DRRs are only investigated experimentally, and are not evaluated by the radiologists. The expert annotations of infected regions for DRRs may provide evidence to determine more accurate contribution threshold of infected voxels. Secondly, this study uses publicly available CT and CXR datasets from various sources. The variability in expert annotations of CT scans and CXRs is not assessed, which may introduce implicit biases to the training and evaluation. Thirdly, the evaluation for the segmentation performance of our method in this study is incomprehensive due to the lack of sufficient CXRs with pixel-level annotations of infected regions. The evaluation results on ten CXRs may be biased and unable to give guidance to optimize our method. A comparison between learning infection segmentation from CXRs straightway and learning from DRRs may provide key insights on how to realize high-quality automated COVID-19 infection segmentation on CXRs efficiently. Lastly, this study uses only ten COVID-19 positive CT cases for synthesizing DRRs. The performance of our DRR4Covid and the validity of the estimated detection limit of X-ray imaging in detecting COVID-19 infection can be improved by involving more CT scans from patients in various stages of COVID-19 infection. Nevertheless, the findings of this article provide promising results that encourage the use of DRR4Covid for learning automated COVID-19 infection segmentation on CXRs from DRRs without the need for any expert annotations of CXRs.VI.We propose a novel approach, called DRR4Covid, to learn automated COVID-19 infection segmentation on CXRs from DRRs. DRR4Covid consists of three key components, including an infection-aware DRR generator, a classification and segmentation network, and a domain adaptation module. The infection-aware DRR generator is able to produce DRRs with adjustable strength of radiological signs of COVID-19 infection, and generate pixel-level infection annotations that match the DRRs precisely, thus enabling deep segmentation networks to be trained directly for automated infection segmentation. The domain adaptation module is introduced to reduce the domain discrepancy between DRRs and CXRs by training networks on unlabeled real CXRs and labeled DRRs together. We provide a simple but effective implementation of DRR4Covid by using a domain adaptation module based on Maximum Mean Discrepancy (MMD), and a FCN-based network with a classification header and a segmentation header. Extensive experiment results have confirmed the efficacy of our method; specifically, without using any annotations of real CXRs, our network has achieved a classification score of  and a segmentation score of  on a test set with 558 normal cases and 558 positive cases. Besides, we estimate the detection limit of X-ray imaging in detecting COVID-19 infection by adjusting the strength of radiological signs of COVID-19 infection in synthetic DRRs. The estimated detection limit, measured by the percent volume of the lung that is infected by COVID-19, is 19.43% ± 16.29%, and the estimated lower bound of the contribution rate of infected voxels is 20.0% for significant radiological signs of COVID-19 infection.To our best knowledge, this is the first attempt to realize the automated COVID-19 infection segmentation base on CXRs by using the labeled DRRs that are generated from Chest CT scans. Future work can be carried out by extending the DRR4Covid to DRR4Lesion to enable multiple lung lesion segmentation on CXRs.