Retrospective analysis of hospital electronic health records reveals unseen cases of acute hepatitis with unknown aetiology in adults in Oxfordshire

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It is possible that evidence of such an outbreak could be identified at a population level based on routine data captured by electronic health records (EHR). Methods: We used anonymised EHR to collate retrospective data for all emergency presentations to Oxford University Hospitals NHS Foundation Trust in the UK, between 2016-2022, for all ages from 18 months and older. We investigated clinical characteristics and temporal distribution of presentations of acute hepatitis and of adenovirus infections based on laboratory data and clinical coding. We relaxed the stringent case definition adopted during the AS-Hep-UA to identify all cases of acute hepatitis with unknown aetiology (termed AHUA). We compared events within the outbreak period (defined as 1st Oct 2021 - 31 Aug 2022) to the rest of our study period. Results: Over the study period, there were 903,433 acute presentations overall, of which 391 (0.04%) were classified as AHUA. AHUA episodes had significantly higher critical care admission rates (p<0.0001, OR=41.7, 95% CI:26.3-65.0) and longer inpatient admissions (p<0.0001) compared with the rest of the patient population. During the outbreak period, significantly more adults (≥16 years) were diagnosed with AHUA (p<0.0001, OR=3.01, 95% CI: 2.20-4.12), and there were significantly more human adenovirus (HadV) infections in children (p<0.001, OR=1.78, 95% CI:1.27-2.47). There were also more HAdV tests performed during the outbreak (p<0.0001, OR=1.27, 95% CI:1.17-1.37). Among 3,707 individuals who were tested for HAdV, 179 (4.8%) were positive. However, there was no evidence of more acute hepatitis or increased severity of illness in HadV-positive compared to negative cases. Conclusions: Our results highlight an increase in AHUA in adults coinciding with the period of the outbreak in children, but not linked to documented HAdV infection. Tracking changes in routinely collected clinical data through EHR could be used to support outbreak surveillance. acute hepatitis adenovirus outbreak AAV epidemiology electronic health records surveillance Figures Figure 1 Figure 2 Figure 3 INTRODUCTION In April 2022, the United Kingdom Health Security Agency (UKHSA) alerted the World Health Organization to a significant increase in acute severe hepatitis in children aged less than 10 years, who were otherwise clinically fit and well ( 1 ). Concerningly, a proportion of these children had sufficiently severe disease to warrant liver transplantation ( 2 ). Initial investigations and evaluation demonstrated no link to Hepatitis viruses A-E, other known causes of acute hepatitis, toxins, common exposures, or foreign travel; these cases were therefore designated ‘acute severe hepatitis of unknown aetiology’ (AS-Hep-UA). Subsequent detailed investigation of samples from affected children suggested a likely infectious aetiology, with metagenomic sequencing identifying adeno-associated virus 2 (AAV2) in 81–96% AS-Hep-UA patients (versus 4–7% in controls), alongside a higher than expected prevalence of human adenovirus (HAdV) ( 3 – 5 ). In addition to HAdV, a likely contribution was made by AAV coinfection with other ‘helper’ viruses including acute infections or reactivation of latent infections, particularly with Epstein-Barr Virus (EBV), human herpes-virus 6 (HHV6) and enteroviruses ( 3 – 5 ), and/or a contribution from superantigen-mediated immune activation ( 6 ). A significant enrichment of the Human Leucocyte Antigen (HLA) class II allele DRB1*04:01 has been identified among AS-Hep-UA cases compared to the background population, suggesting a specific immune susceptibility ( 3 ). Following the initial reporting of AS-Hep-UA in Scotland, several cases were retrospectively identified in the United States dating back to October 2021 ( 6 ). By the start of July 2022, > 1000 probable cases had been identified worldwide ( 7 ). The outbreak in Europe peaked between the end of March and early May 2022 (week 12 to 18), and subsequently declined between May and August ( 6 ). The case definition of AS-Hep-UA was refined to include age 500 IU/L) which could not be accounted for by other causes ( 7 ). Despite AS-Hep-UA being identified worldwide, there were geographical disparities in the incidence of cases and liver transplantation; rates in the UK and across parts of Europe clearly exceeded expected averages, in contrast to no significant deviation from baseline across the US, Brazil, India, and Japan ( 8 , 9 ). More than a quarter of global cases were identified in the UK, which had a 100-fold relative incidence rate compared to France, despite the countries being geographical neighbours of almost identical population sizes ( 10 ). However, the relative contribution of enhanced surveillance, population susceptibility, and circulation of any causative agent to these differing rates has remained unclear. Furthermore, patients with acute hepatitis with unknown aetiology, but not meeting the stringent case definitions would not have been reported as AS-Hep-UA cases (i.e. those with ALT and/or AST elevated but both < 500IU/L; age ≥16 years). Therefore, it is not known whether the AS-Hep-UA outbreak was the ‘tip of an iceberg’ of milder cases of disease in the population, and/or cases among older adolescents and adults. Routinely collected clinical data (e.g. patient diagnoses, liver enzyme and microbiology test results) in the form of electronic health records (EHR) present an opportunity to investigate population trends that could be associated with this outbreak. There is potential to use routine clinical laboratory parameters as a surveillance tool at a population level, for example as a sentinel marker for circulation of an infectious trigger. In this study, we used hospital EHR data from Oxfordshire, UK, to explore trends before, during and after the period of the AS-Hep-UA outbreak. We addressed the following specific aims: (i) to explore any changes in liver enzyme levels in adults and children/adolescents presenting to hospital, and (ii) to determine any changes in incidence and severity of acute hepatitis and HAdV infection. METHODS Data source We analysed EHR representing children, adolescents and adults presenting as an emergency to Oxford University Hospitals (OUH) NHS Foundation Trust, a large tertiary referral hospital in the South East of England, serving a population of ~725,000. Data were accessed through the Infections in Oxfordshire Research Database (IORD) (11), and were held, accessed and analysed in accordance with NHS standards for data management and protection (more details in Supplementary methods ). This study, as part of the Infections in Oxfordshire Research Database (IORD), was approved by the National Research Ethics Service South Central – Oxford C Research Ethics Committee (19/SC/0403), the Health Research Authority and the national Confidentiality Advisory Group (19/CAG/0144), including provision for use of pseudonymised routinely collected data without individual patient consent. In this retrospective cohort study, we reviewed data from 1 st March 2016 to 31 st December 2022 for all individuals aged 18 months and older presenting to the Emergency Department or acute medical/surgical assessment units at OUH. We recorded subsequent admission to hospital, admission to the Intensive Care Unit (ICU), duration of hospital admission, and mortality during the admission. Laboratory data Laboratory data were generated by externally ISO accredited clinical biochemistry and microbiology laboratories at OUH. A full list of laboratory assays and platforms is provided in Suppl methods , and reference intervals for liver enzymes and inflammatory markers are provided in Table 1 . Laboratory data were based on those routinely collected, where a request for ‘liver function tests’ (LFTs) prompts a clinical biochemistry profile comprising alanine transferase (ALT), alkaline phosphatase (ALP), bilirubin and albumin. Additional laboratory investigations were requested at the discretion of the clinical team. Abnormalities in these biomarkers were classified based on the upper limit of normal (ULN) for all ages and both sexes – mild, moderate and severe derangement was defined as up to 2x, 2-5x and >5x ULN, respectively, with the exception of albumin, which was classified as deranged if levels were less than the lower limit of normal (LLN) of 32g/L. HAdV testing was undertaken using a PCR-based multiplex test on respiratory samples or using an HadV-specific PCR on whole blood based on specific clinician request, which usually focuses on patients requiring critical care or in immunocompromised patients under the care of haematology/oncology teams. Classification and definitions Patients were stratified into three categories based on their ages at presentation: younger children (<7 years), older children (7-15 years) and adults (≥16 years). Epochs were considered as pre-COVID-19 (1st March 2016 - 10 March 2020), COVID-19 pandemic period (11th March 2020 - 31st December 2022), and nested within the COVID-19 pandemic period, the AS-Hep-UA outbreak (1st Oct 2021 - 31 Aug 2022). We applied the established strict case definition for AS-Hep-UA, as someone 500 IU/L), which cannot be accounted for by other causes (7). We additionally applied a more relaxed definition of acute hepatitis of unknown aetiology (AHUA), to identify cases in adults, and also milder cases that would not meet criteria for AS-Hep-UA. We defined AHUA as patients assigned either a primary or secondary diagnostic code from the International Classification of Diseases 10th Revision consistent with hepatitis of an uncertain cause ( Table 2; Supplementary Methods ) or patients with ALT>2x ULN. Diagnostic codes were assigned by hospital admission coders following patient discharge, based on national clinical coding standards. We also considered presentations of diagnosed acute or chronic viral hepatitis A-E virus infection as a baseline control, and to ensure these cases were excluded from the AHUA category. Data analysis and statistical testing Each presentation episode was considered independently; thus individuals may have featured more than once across the study duration. We used the first set of blood tests taken on presentation for analysis. An infecting pathogen was reported if at least one microbiology test was positive. Data were analysed using R v4.3.2 and visualised using ggplot v3.4.4. We tested for the presence of a non-monotonic trend using the non-parametric WAVK test (12), using its implementation in the R package funtimes (13). Fisher’s exact tests and Mann-Whitney U tests were performed using the fisher.test and wilcox.test functions in R . Odds ratios (OR) were calculated using conditional maximum likelihood estimation as part of the fisher.test function. An interupted time series analysis was performed to assess changes in the incidence of AHUA or viral hepatitis A-E (i.e., the number of new episodes identified per month) during the study duration, using a segmented regression framework (14), as follows: Where y t , β i , α outbreak , t start and ε t represent the number of new episodes identified in month t, the parameter estimates, a binary variable encoding the AS-Hep-UA epoch, the start of the AS-Hep-UA epoch, and model residuals respectively. Autocorrelation and normality of ε t was assessed using the Durbin Watson test in the lmtest R package (15), and Shapiro-Wilk test, respectively. The statistical significance of parameter estimates was assessed using a Student’s t-test. Associations between HAdV infection and routinely collected blood biomarker data were assessed using Fisher’s exact test. In particular, for each we tested if the proportion of patients falling into each derangement category (described above) differed significantly between patients with HAdV infections or otherwise. Benjamini-Hochberg procedure was used to correct for multiple testing and adjusted p-values, where available, were annotated. RESULTS No evidence for an increase in hospital presentations or elevated liver enzymes during AS-Hep-UA outbreak We analysed data for 903,433 acute hospital presentations, from 441,780 males and 461,632 females (and 21 individuals for whom sex was not recorded). The median age was 44 years (IQR 22-69 years), with 7.9%, 9.5%, and 82.6% classified as younger children, older children/adolescents, and adults, respectively ( Table 1 ). A median of 11,023 patients presented to the hospital per month, with a marked decline in the number of presentations in April 2020 coinciding with the implementation of SARS-CoV-2 (COVID-19) pandemic lockdown measures in the UK introduced on 26th March 2020 ( Supplementary Figure 1a ). During the AS-Hep-UA outbreak, minimal changes in the number of acute presentations per month was observed across any of the three age groups (mininum WAVK test p=0.998; Supplementary Figure 1a ). There was an overall increasing trend in the number of ALT tests requested for acutely presenting patients over time since March 2016 regardless of sex ( WAVK statistic =13.4, p <0.0001; Supplementary Figure 1b ). However, there was also an increasing trend in the number of ALT observations compared to WBC observations, which indicates increased ‘liver function scrutiny’ over time ( Supplementary Figure 1c ; WAVK statistic =91.1, p <0.0001). Across the study duration, 59% of patient episodes had recorded blood tests. Among these, 90% had an ALT test and 1.2% an AST test. There was no evidence of change in the median or IQR of ALT levels over time for any age group ( Figure 1a ; minimum WAVK test p =0.344), with no observable peak during the AS-Hep-UA outbreak. Similarly, the proportion of individuals with mild, moderate or severe derangement of ALT levels remained relatively stable over time ( Figure 1b ; minimum WAVK test p=0.468). Therefore, there was no temporal association between the period of the AS-Hep-UA outbreak and elevated liver enzymes in patients presenting acutely to hospital. Increased incidence of AHUA in adults coinciding with AS-Hep-UA outbreak We further investigated the use of primary or secondary diagnostic codes for identifying increased incidence of AHUA in the patient population. We compared the temporal trend for AHUA diagnoses ( Table 2; Supplementary Methods ) against those for viral hepatitis A-E, the latter which are likely to be relatively stable and therefore serve as an appropriate control. Across the study duration, 3729 diagnostic codes representing AHUA (total 1005) or viral hepatitis A-E (total 2724) were assigned to 1531 distinct patient episodes ( Table 2 ), of which 98% were adults (eight younger children, 11 older children/adolescents, 1512 adults). Overall, there were 391 patient episodes where only diagnostic codes classified as AHUA were assigned, representing 0.04% of all 903,433 patient episodes. The number of acute hepatitis diagnoses classified as AHUA or viral hepatitis A-E per month remained relatively constant over time ( Figure 2a ). However, an increase in the number of AHUA cases coinciding with the AS-Hep-UA outbreak period was observed ( Figure 2b ). The proportion of patients diagnosed with AHUA was higher during the AS-Hep-UA outbreak than outside this period (Fisher’s exact test p<0.0001; OR 3.01, 95% CI:2.20-4.12). Interrupted time series analysis indicated a significantly increased number of AHUA cases per month during the AS-Hep-UA outbreak period (estimate=2.92, 95% CI: 1.57-5.69; t=3.50, d.f.=78, p<0.001), but there was no evidence of change in the number of viral hepatitis A-E cases (estimate=-0.785, 95% CI: -4.34-2.77; t=-0.440, d.f.=78, p=0.661). Overall, these observations support an increased incidence of AHUA amongst adults during the AS-Hep-UA outbreak. We could not determine if this was the case for children, since only 2% of relevant diagnostic codes were assigned to children. AHUA associated with critical care admission, duration of hospitalisation and mortality Compared to patients without AHUA, patients with AHUA had significantly higher ICU admission rates ( Table 3 ; Fisher’s exact test p<0.0001; OR 41.7 within and 23.7 outside the AS-Hep-UA epoch) and mortality rates (Fisher’s exact tests p=0.035 within and p<0.0001 outside; OR 6.99 within and 11.3 outside), and longer hospitalisations (both Mann-Whitney U tests p<0.0001; Table 3 ). We also compared patients with AHUA to those diagnosed with viral hepatitis A-E; those with AHUA had significantly higher ICU admission rates (both Fisher’s exact test p<0.0001; OR 5.01 within AS-Hep-UA outbreak and 3.90 outside outbreak) and longer hospitalisation periods (Mann-Whitney U tests p<0.05), both within and outside the AS-Hep-UA epoch. Inpatient mortality was significantly higher for the AHUA group than those with viral hepatitis A-E outside the AS-Hep-UA epoch (Fisher’s exact test p<0.0001; OR 19.8, 95% CI: 4.29-185), but not within the AS-Hep-UA epoch (p=1, OR=1.16, 95% CI: 0.0825-16.3). Increased incidence of HAdV infections during AS-Hep-UA outbreak but not associated with deranged liver enzymes or poorer patient outcomes Across the study duration, we retrieved 3707 distinct patient records that included microbiology tests for HAdV infection, of which 179 were positive (4.8%). The positivity rate was highest in younger children, among whom 124/781 (15.9%) of HAdV tests were positive, compared to older children/adolescents (9/440, 2.0% positive) and adults (46/2486, 1.9% positive), in keeping with the known epidemiology of HAdV infection (16–18). None of the HAdV-infected patients were given ICD10 codes indicative of AHUA across the study duration. A minority (16/179; 9%) of HadV positive results were derived from eye swabs, which is unlikely to have influenced any overall trends. There was an increase in the number of HAdV-tests undertaken between April 2021 and April 2022 ( Figure 3a ), and a significantly higher number of HAdV tests performed relative to all microbiology tests performed during the AS-Hep-UA epoch (Fisher’s exact test p<0.0001; OR 1.27, 95% CI:1.17-1.37). These findings indicate increased clinician scrutiny for HAdV during the outbreak. The proportion of HAdV-positive tests during the AS-Hep-UA epoch was significantly higher than outside of the AS-Hep-UA epoch at 60/839 (7.2%) vs 119/2868 (4.1%) respectively (Fisher’s exact test p <0.001; OR 1.78, 95% CI:1.27-2.47). Additionally, there was an increase in the incidence and proportion of HAdV-positive tests in younger children during the AS-Hep-UA outbreak relative to the period preceding the outbreak ( Figure 3b ). However, there were also other peaks in the proportion of HAdV-positive tests across the entire study duration ( Figure 3b ), indicating previous periods of high HAdV-positivity before the AS-Hep-UA epoch. There was no evidence that the proportion of patients with mild, moderate or severe derangement of ALT, AST, bilirubin, GGT, CRP or WBC differed significantly between those testing positive vs. negative for HAdV (minimum Fisher’s exact test p=0.62; Supplementary Figure 2 ). The proportion of patients with low albumin and mild derangement of ALP was significantly smaller for those testing HAdV-positive vs. negative (Fisher’s exact test p=0.024 and p=0.004 respectively; Supplementary Figure 2 ). A similar association with raised ALP was also present in confirmed rhinovirus/enterovirus infections (Fisher’s exact test p=0.002), indicating that mild derangement of ALP is not unique to HAdV infections (data not shown). HAdV testing focuses primarily on a clinically vulnerable group, shown by higher rates of hospital admission, ICU admission and inpatient mortality among those receiving a HAdV test (irrespective of the test result) compared to the untested population (78.5% vs 30.7%, 18.2% vs 1.2%, and 0.59% vs 0.34%, respectively; Table 3 ). However, the HAdV-positive group fared somewhat better than those who tested negative, with lower hospital admission (both Fisher’s exact tests p<0.001) and significantly shorter hospital stays (both Mann-Whitney U tests p<0.05), whether within or outside the AS-Hep-UA epoch. Additionally, the HAdV-positive group had significantly lower ICU admission rates within the AS-Hep-UA epoch, but not outside the epoch (Fisher’s exact tests p=0.015 and p=0.35, respectively) (data not shown). No HAdV-positive patients died across the entire study duration. Characteristics of the population testing positive for HAdV are presented in Table 4 . DISCUSSION Anonymised clinical data from EHRs offers access to large datasets, providing power in numbers to determine overall trends reflecting clinical epidemiology and its influence on morbidity, mortality and health service workload. Monitoring of EHRs may be an effective and low-cost surveillance tool that allows identification of trends that could be of concern - e.g. deranged laboratory parameters and/or changes in recorded diagnoses based on microbiology tests or coding. Such strategies could potentially be developed to provide an ‘early warning’ system to allow clinical and public health authorities to review data in real time, cross-compare between regions, identify possible outbreaks, and implement enhanced surveillance and public-health messaging if necessary. Among patients presenting acutely for hospital-based care, HAdV testing is largely reserved for vulnerable groups. Even in this high-risk population, those testing positive for HAdV had lower admission rates than those testing negative, reinforcing the view that this virus is generally benign and self-limiting, with a low risk of serious complications. The lack of associations between HAdV infections and deranged liver enzymes is concordant with the fact that HAdV infections typically lead to mild respiratory or gastrointestinal disease, and that hepatitis is an unusual complication ( 18 ). Co-infections with HAdV and other viruses such as respiratory syncytial virus (RSV) have been linked to poorer outcomes ( 18 ), but the small number of HAdV infections identified in this study precluded robust analysis of mixed infections. Despite an increased incidence of HAdV infections during the AS-Hep-UA outbreak, this was not an unusual aspect of local HAdV epidemiology, and could have been partly accounted for by increased clinician scrutiny. It has emerged that severe clinical outcomes during the AS-Hep-UA outbreak in children was likely driven by AAV2 as a leading aetiological agent. However, AAV2 requires co-infection with a ‘helper’ virus (including, but not limited to, HAdV) to replicate, and this appears to be a requirement for the development of liver pathology ( 3 – 5 ). Retrospective analysis of EHRs cannot be used to investigate the epidemiology of AAV infection, as these viruses are not part of clinical diagnostic testing pathways and have only been identified as an agent of AS-Hep-UA through retrospective metagenomic sequencing ( 3 – 5 ). The specific subtype of HAdV implicated in the outbreak, 41F, is not routinely discriminated from other types by clinical testing. Other possible ‘helper viruses’ include human herpesviruses ( 5 ), many of which are ubiquitous in the population and characterised by long-term carriage and latency, making it difficult to distinguish between clinically relevant episodes and subclinical reactivation. Thus routinely-collected clinical datasets do not include screening for all relevant pathogens, and detection of implicated pathogens can be difficult to interpret due to the detection of commensal or bystander organisms. Overall, wider adoption of metagenomics-based diagnostics has the potential to further enhance the utility of EHRs to investigate future outbreaks but interpretation is complex ( 4 ). There are various caveats to our analysis approach. Although we have captured data for a large population over a period of almost seven years, this remains a small data set within which to identify rare events, and our region was not known to be directly affected by the AS-Hep-UA outbreak. Additionally, if there were milder cases of disease in the population during or preceding the documented AS-Hep-UA outbreak, these may not have presented to hospital at all. Even among those presenting to healthcare, many patients did not have liver enzymes measured. Thus, the IORD dataset provides only a limited view of the whole population, which is not true community surveillance. Hospital admission data are also biased by repeated representation of the same individuals, and over-represent populations who preferentially present to emergency care rather than accessing primary care, or those admitted through different routes. Further, as we did not include children < age 18 months, we may have missed relevant signals in the younger population. In the intensive care population, some cases meeting our criteria for AHUA may be related to liver ischaemia or injury from diverse causes associated with critical illness, rather than liver-specific pathology. Finally, our analyses relies on consistent clinical coding of patient episodes, which has been shown to be subject to some inaccuracy and/or variability ( 19 ). Conclusion We identified an increased incidence of episodes coded as AHUA in adults and an increased incidence of HAdV infections in younger children coinciding with the AS-Hep-UA outbreak in children during 2022. While the latter may be partially accounted for by increased clinician scrutiny during this period, the pattern was not observed to the same level in older children/adolescents, and not at all in adults despite similar increases in testing for all age groups. There was no evidence for increased incidence of abnormal liver enzymes in children or adults, nor associations between HAdV infections and elevated liver transaminases. Our findings suggest that the use of routinely collected liver enzyme EHR data lacks sensitivity for tracking this outbreak, which is likely due a large number of confounding aetiologies that may lead to elevated transaminases. However, the identification of increased incidence of AHUA in adults, which was largely ignored during the AS-Hep-UA outbreak in children, highlights the potential of using hospital diagnostic codes for cost-effective disease surveillance. Longer-term collation of EHR data from multiple regions could potentially offer a powerful surveillance approach that could be extended to other diseases. However, this would require the establishment of suitable and systematic data-processing infrastructure and governance frameworks, in addition to investment of personnel and resources, if it is to become a real-world surveillance tool. Abbreviations United Kingdom Health Security Agency (UKHSA) Acute severe hepatitis with unknown aetiology in children (AS-Hep-UA) Acute hepatitis with unknown aetiology (AHUA) Infections in Oxfordshire Research Database (IORD) Adeno-associated virus 2 (AAV2) Human adenovirus (HAdV) Epstein-Barr Virus (EBV) Human herpes-virus 6 (HHV6) Human leukocyte antigen (HLA) Upper limit of normal (ULN) Lower limit of normal (LLN) Alanine transaminase (ALT) Aspartate aminotransferase (AST) C-reactive protein (CRP) Alkaline phosphatase (ALP) White blood cell count (WBC) Gamma-glutamyl transferase (GGT) Intensive care unit (ICU) Interquartile range (IQR) Confidence interval (CI) Oxford University Hospitals NHS Trust (OUH) Declarations ETHICS APPROVAL AND CONSENT TO PARTICIPATE All data used for this study was pseudonymised prior to its use. The study, as part of the Infections in Oxfordshire Research Database (IORD), was approved by the National Research Ethics Service South Central – Oxford C Research Ethics Committee (19/SC/0403), the Health Research Authority and the national Confidentiality Advisory Group (19/CAG/0144), including provision for use of pseudonymised routinely collected data without individual patient consent. As such, individual patient consent was not obtained for the purposes of this study. Individuals who choose to opt out of their data being used in research are not included in the study. The study sponsor was OUH. All patients were assigned an anonymised ‘cluster ID’, with no identifying details handled by the research team. Only month/year of birth was available rather than specific date of birth. Data were held within a password protected, encrypted database and accessed only by named investigators in accordance with NHS standards for data management and protection. More details can be found at the following URL: https://oxfordbrc.nihr.ac.uk/research-themes-overview/antimicrobial-resistance-and-modernising-microbiology/infections-in-oxfordshire-research-database-iord/. CONSENT FOR PUBLICATION Not applicable. AVAILABILITY OF DATA AND MATERIALS The data that support the findings of this study are available from the IORD database but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. The IORD database can only be accessed by named investigators in accordance with NHS standards for data management and protection. For further details on how to apply for access to the data and for a research proposal template please email [email protected] . All custom scripts used for the analyses presented in this manuscript are hosted on GitHub (https://github.com/cednotsed/iORD_hepatitis.git). COMPETING INTERESTS The authors declare no competing interests. FUNDING CCST is supported by doctoral funding from the A*STAR National Science Scholarship. PCM is supported by core funding from the Francis Crick Institute, a Wellcome fellowship (ref 110110/Z/15/Z), and University College London NIHR Biomedical Research Centre (BRC). PCM receives funding from GSK to support a doctoral student in her team, outside the scope of this paper. ASW is supported by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford in partnership with the UK Health Security Agency (UK HSA) (NIHR200915), by the NIHR Oxford Biomedical Research Centre, has core support from the Medical Research Council UK to the MRC Clinical Trials Unit [MC_UU_12023/22] and is an NIHR Senior Investigator. DWE is supported by a Robertson Fellowship and an NIHR Oxford BRC Senior Fellowship. NS is an Oxford Martin Fellow and holds an NIHR Oxford BRC Senior Fellowship. 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Package ‘lmtest.’ Test Linear Regres Models Httpscran R-Proj Orgwebpackageslmtestlmtest Pdf Accessed. 2015;6. Duan Y, Zhu Y, Xu B, Li C, Chen A, Deng L, et al. Multicenter study of human adenovirus infection in pediatric community-acquired pneumonia in China. Zhonghua Er Ke Za Zhi Chin J Pediatr. 2019;57(1):27–32. Tran A, Talmud D, Lejeune B, Jovenin N, Renois F, Payan C, et al. Prevalence of rotavirus, adenovirus, norovirus, and astrovirus infections and coinfections among hospitalized children in northern France. J Clin Microbiol. 2010;48(5):1943–6. Probst V, Spieker AJ, Stopczynski T, Stewart LS, Haddadin Z, Selvarangan R, et al. Clinical Presentation and Severity of Adenovirus Detection Alone vs Adenovirus Co-detection With Other Respiratory Viruses in US Children With Acute Respiratory Illness from 2016 to 2018. J Pediatr Infect Dis Soc. 2022 Oct 1;11(10):430–9. CHKS. The quality of clinical coding in the NHS. 2014 Sep; Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/364476/The_quality_of_clinical_coding_in_the_NHS.pdf International Severe Acute Respiratory and emerging Infection Consortium (ISARIC) 4c. Summary Analysis of Laboratory Tests (SALT). [cited 2023 Feb 13]; Available from: https://isaric4c.net/salt/ Tables Table 1: Characteristics of population of adults and children aged ≥18 months presenting as an emergency to Oxford University Hospitals NHS Foundation Trust (UK) between 2016 and 2022. Characteristic Younger children (18 months-6 years) n=70,962 Older children / adolescents (7-15 years) n=86,217 Adults ( ³ 16 years) n=746,254 Age in years at presentation (median, IQR) 3 (2-5) 11 (9-13) 53 (32-74) Male sex (%) 57.1 54.2 47.5 Proportion admitted to hospital following emergency presentation (%) 17.0 13.2 34.3 Proportion admitted to ICU (%) 0.4 0.3 1.5 Mortality during admission episode (%) 0.01 0.01 0.41 Admission duration in hours if admitted, median (IQR) 20 (12-42) 24 (14-49) 56 (22-160) Biomarker Ref. Range Liver biomarkers (median, IQR) ALT 10-45 IU/L 15 (12-21) 14 (11-20) 19 (13-30) AST 15-42 IU/L 41 (31-144) 33 (21-119) 41 (22-110) Bilirubin 0-21 µmol/L 5 (3-7) 7 (5-11) 9 (6-14) Albumin 32-50 g/L 38 (35-40) 40 (38-43) 37 (33-40) ALP 30-130 IU/L 195 (160-237) 181 (115-247) 79 (64-101) GGT 15-40 IU/L 17 (10-104) 20 (13-62) 85 (31-280) Infection biomarkers (median, IQR) CRP 0-5 mg/L 10 (1.3-42.3) 2.5 (0.4-20.5) 8.6 (2.1-43.4) WBC count 4-11 x10^9/L 10 (7.51-13.8) 8.53 (6.58-11.5) 8.87 (6.9-11.6) Table 2: Diagnostic codes representing Acute Hepatitis of Unknown Aetiology (AHUA) and confirmed viral hepatitis A-E infection assigned to patients presenting as an emergency to Oxford University Hospitals NHS Foundation Trust (UK) between 2016 and 2022. In order to protect against the risk of identification of individual cases, any subgroups numbering fewer than 5 individuals are not numerated. ICD10 code Condition(s) coded Younger children (18 months-6 years) n=15 Older children (7-15 years) n=12 Adults (≥16 years) n=3702 Codes representing acute hepatitis of unknown aetiology (AHUA; n =1005) K716 Toxic liver disease with hepatitis, not elsewhere classified <5 <5 32 K720 Acute and subacute hepatic failure <5 <5 534 K752 Nonspecific reactive hepatitis <5 <5 <5 K759 Inflammatory liver disease, unspecified <5 <5 170 B178 Other specified acute viral hepatitis <5 <5 13 B179 Acute viral hepatitis, unspecified 9 <5 186 B199 Unspecified viral hepatitis without hepatic coma <5 <5 46 Codes representing viral hepatitis A-E ( n =2724) B159 Hepatitis A <5 <5 67 B162, B169, B180, B181 Acute or chronic viral hepatitis B ± delta virus <5 <5 491 B171, B182 Acute or chronic viral hepatitis C <5 <5 2057 B172 Acute viral hepatitis E <5 <5 54 Table 3: Outcomes of presentation to hospital among individuals presenting as an emergency to Oxford University Hospitals NHS Foundation Trust (UK) between 2016 and 2022 Population Hospital admission rate (%) ICU admission rate (%) Duration of hospital admission in hours (median, IQR) Mortality rate (%) All patients (n=903,433) 30.9 1.3 51 (21-146) 0.34 HAdV tested (n=3707) 78.5 18.2 101 (45-221) 0.59 HAdV untested (n=899,726) 30.7 1.2 50 (21-145) 0.34 HAdV tested and positive (n=179) 60.9 11.7 65 (31-139) 0 HAdV tested and negative (n=3528) 79.4 18.5 102 (46-224) 0.62 Patients with diagnostic codes relevant to the study (n=1531) 100 12.3 93 (35-234) 1.1 Patients with only diagnostic codes indicative of AHUA (n=391) 100 26.1 146 (57-333) 3.3 Patients with diagnostic codes indicating viral hepatitis A-E (n=1140) 100 7.5 79 (29-199) 0.35 Patients without AHUA (903,042) 30.9 1.3 50 (21-145) 0.34 Table 4: Characteristics of 179 patient episodes with HAdV-positive microbiology tests among children and adults presenting as an emergency to Oxford University Hospitals NHS Foundation Trust (UK) between 2016 and 2022. Characteristic Younger children (18 months-6 years) Older children (7-15 years) Adults (≥16 years) Total number tested for HAdV infection 781 440 2486 Total number positive for HAdV infection (% of those tested) 124/781 (15.9%) 9/440 (2.0%) 46/2486 (1.9%) Epoch of presentation a Pre-COVID-19 (n=79 HAdV-positive) 45/79 (57.0%) 4/79 (5.1%) 30/79 (38.0%) COVID-19 pandemic (n=100 HAdV-positive) 79/100 (79.0%) 5/100 (5.0%) 16/100 (16.0%) AS-Hep-UA (n=60 HAdV-positive) 51/60 (85.0%) 2/60 (3.3%) 7/60 (11.7%) Levels of derangement for ALT Not tested 66 3 10 Normal 53 5 25 Mild (Up to 2x ULN) <5 0 7 Moderate (2-5x ULN) <5 <5 5x ULN) 0 0 <5 Inflammatory markers (median, IQR) CRP (mg/L) 47.2 (10.6-73.3) 24.0 (11.8-88.6) 81.9 (23.3-143.7) White cells (x10^9/L) 11.6 (8.53-17.7) 9.1 (6.60-9.61) 10.4 (6.73-14.6) Admission rate 74/124 (59.7%) 5/9 (55.6%) 30/46 (65.2%) ICU admission rate 9/124 (7.3%) 0/9 (0%) 12/46 (26.1%) Duration of hospital admission in hours (median, IQR) 56 (26-95) 118 (33-309) 125 (49-450) Death rate during admission 0/124 (0%) 0/9 (0%) 0/46 (0%) Vital signs at presentation (median, IQR) Heart rate 134 (112-145) 111 (109-123) 92 (84-106) Diastolic blood pressure 61 (54.0-71.5) 81.5 (74.0, 86.3) 73.0 (61.3-82.0) Systolic blood pressure 104.0 (94.5-110.0) 109.0 (105.0-116.8) 123.5 (113.5-135.8) Tympanic temperature 37.5 (36.7-38.1) 37.3 (36.9-37.9) 36.9 (36.4-37.6) Oxygen saturation 98.0 (96.0-99.3) 98.0 (98.0-100.0) 96.0 (95.0-98.3) Respiratory rate 28.0 (24.0-35.0) 27.5 (22.5-30.5) 19.0 (17.0-21.0) a AS-Hep-UA epoch is nested within the Covid-19 pandemic period. Epochs were considered as pre-COVID-19 (1st March 2016 - 10 March 2020), COVID-19 pandemic period (11th March 2020 - 31st December 2022), and the AS-Hep-UA outbreak (1st Oct 2021 - 31 Aug 2022). Additional Declarations No competing interests reported. Supplementary Files SUPPLEMENTARYMATERIALS.docx Cite Share Download PDF Status: Published Journal Publication published 15 Jul, 2024 Read the published version in BMC Public Health → Version 1 posted Editorial decision: Revision requested 15 Apr, 2024 Reviews received at journal 27 Mar, 2024 Reviewers agreed at journal 20 Feb, 2024 Reviews received at journal 19 Feb, 2024 Reviewers agreed at journal 11 Feb, 2024 Reviewers invited by journal 28 Dec, 2023 Editor assigned by journal 19 Dec, 2023 Editor invited by journal 11 Dec, 2023 Submission checks completed at journal 11 Dec, 2023 First submitted to journal 10 Nov, 2023 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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14:29:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3591392/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3591392/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12889-024-19292-1","type":"published","date":"2024-07-15T16:13:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":48148162,"identity":"59efcc7c-2a88-4272-a217-3d4a87b1602a","added_by":"auto","created_at":"2023-12-13 19:14:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":401622,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal trends in ALT levels in patients presenting acutely to OUH\u003c/strong\u003e (a) median, 25\u003csup\u003eth\u003c/sup\u003e and 75\u003csup\u003eth\u003c/sup\u003e percentiles of ALT levels in patients presenting to OUH and (b) proportion of patients with mild, moderate and severe derangement in ALT levels aggregated at two month intervals. Relevant epochs are highlighted in grey (pre-COVID-19-pandemic), yellow (COVID-19 pandemic), and with dashed lines (start of AS-Hep-UA outbreak to end of first quarter of 2022).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3591392/v1/90acc5773e774c5854037623.png"},{"id":48148160,"identity":"fa478059-839a-42b2-9de8-5cd158df691d","added_by":"auto","created_at":"2023-12-13 19:14:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":255025,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal trends of acute hepatitis with unknown aetiology (AHUA) based on clinical coding at Oxford University Hospitals from 2016 to 2022.\u003c/strong\u003e Twelve-month moving averages (means) of (a) overall number of hepatitis-related diagnoses (viral hepatitis A-E or AHUA) per month regardless of age group or sex, and (b) liver-related diagnoses with or without a specified causal agent (AHUA). ICD10 codes (primary or secondary) and their described causal agents are annotated. B159, Hepatitis A virus infection without hepatic coma; B169, Acute hepatitis B virus infection without delta-agent and without hepatic coma; B171, \u003ca href=\"https://1up.health/health-data/icd10/id/B171#:~:text=icd10%20%2D%20B171%3A%20Acute%20hepatitis%20C\"\u003eAcute hepatitis C\u003c/a\u003e virus infection; B172, Acute hepatitis E virus infection; B181, chronic viral hepatitis B virus infection without delta agent; B182, chronic hepatitis C virus infection. Relevant epochs are highlighted in grey (pre-COVID-19-pandemic), yellow (COVID-19 pandemic), and with dashed lines (start of AS-Hep-UA outbreak to end of first quarter of 2022).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3591392/v1/4276762f22d172c1007223ed.png"},{"id":48148163,"identity":"ec218d76-5b51-48b0-812c-92794fc76814","added_by":"auto","created_at":"2023-12-13 19:14:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":250528,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal trends of HAdV-related microbiological tests requested at OUH from 2016 to 2022. \u003c/strong\u003e(a) Number of HAdV tests requested and (b) the proportion of all HadV tests that were positive per month. Relevant epochs are highlighted in grey (pre-COVID-19-pandemic), yellow (COVID-19 pandemic), and with dashed lines (start of AS-Hep-UA outbreak to end of first quarter of 2022). Red, green, blue and black lines show data for for younger children (\u0026lt;7 years), older children/adolescents (7-15 years), adults (³16), and 12-month simple moving average, respectively.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-3591392/v1/49cc1bce815be188736c9475.png"},{"id":61595260,"identity":"3106bf7d-b5a8-420f-b573-38f78cb11276","added_by":"auto","created_at":"2024-08-01 17:21:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1923241,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3591392/v1/e30b0575-dce2-49a7-9143-6d8fe253c509.pdf"},{"id":48148856,"identity":"9cda5ea9-ace0-46a5-8ed5-afd71ee4b00b","added_by":"auto","created_at":"2023-12-13 19:22:04","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":557546,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYMATERIALS.docx","url":"https://assets-eu.researchsquare.com/files/rs-3591392/v1/6cc1f52c50f8e8daae4d29d7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Retrospective analysis of hospital electronic health records reveals unseen cases of acute hepatitis with unknown aetiology in adults in Oxfordshire","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eIn April 2022, the United Kingdom Health Security Agency (UKHSA) alerted the World Health Organization to a significant increase in acute severe hepatitis in children aged less than 10 years, who were otherwise clinically fit and well (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Concerningly, a proportion of these children had sufficiently severe disease to warrant liver transplantation (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Initial investigations and evaluation demonstrated no link to Hepatitis viruses A-E, other known causes of acute hepatitis, toxins, common exposures, or foreign travel; these cases were therefore designated \u0026lsquo;acute severe hepatitis of unknown aetiology\u0026rsquo; (AS-Hep-UA).\u003c/p\u003e \u003cp\u003eSubsequent detailed investigation of samples from affected children suggested a likely infectious aetiology, with metagenomic sequencing identifying adeno-associated virus 2 (AAV2) in 81\u0026ndash;96% AS-Hep-UA patients (versus 4\u0026ndash;7% in controls), alongside a higher than expected prevalence of human adenovirus (HAdV) (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In addition to HAdV, a likely contribution was made by AAV coinfection with other \u0026lsquo;helper\u0026rsquo; viruses including acute infections or reactivation of latent infections, particularly with Epstein-Barr Virus (EBV), human herpes-virus 6 (HHV6) and enteroviruses (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), and/or a contribution from superantigen-mediated immune activation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). A significant enrichment of the Human Leucocyte Antigen (HLA) class II allele DRB1*04:01 has been identified among AS-Hep-UA cases compared to the background population, suggesting a specific immune susceptibility (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFollowing the initial reporting of AS-Hep-UA in Scotland, several cases were retrospectively identified in the United States dating back to October 2021 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). By the start of July 2022, \u0026gt;\u0026thinsp;1000 probable cases had been identified worldwide (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The outbreak in Europe peaked between the end of March and early May 2022 (week 12 to 18), and subsequently declined between May and August (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). The case definition of AS-Hep-UA was refined to include age\u0026thinsp;\u0026lt;\u0026thinsp;16 presenting no earlier than October 1st 2021 with an acute hepatitis and deranged serum liver enzymes (alanine transaminase (ALT) or aspartate transaminase (AST)\u0026thinsp;\u0026gt;\u0026thinsp;500 IU/L) which could not be accounted for by other causes (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite AS-Hep-UA being identified worldwide, there were geographical disparities in the incidence of cases and liver transplantation; rates in the UK and across parts of Europe clearly exceeded expected averages, in contrast to no significant deviation from baseline across the US, Brazil, India, and Japan (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). More than a quarter of global cases were identified in the UK, which had a 100-fold relative incidence rate compared to France, despite the countries being geographical neighbours of almost identical population sizes (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, the relative contribution of enhanced surveillance, population susceptibility, and circulation of any causative agent to these differing rates has remained unclear. Furthermore, patients with acute hepatitis with unknown aetiology, but not meeting the stringent case definitions would not have been reported as AS-Hep-UA cases (i.e. those with ALT and/or AST elevated but both \u0026lt;\u0026thinsp;500IU/L; age \u0026ge;16 years). Therefore, it is not known whether the AS-Hep-UA outbreak was the \u0026lsquo;tip of an iceberg\u0026rsquo; of milder cases of disease in the population, and/or cases among older adolescents and adults.\u003c/p\u003e \u003cp\u003eRoutinely collected clinical data (e.g. patient diagnoses, liver enzyme and microbiology test results) in the form of electronic health records (EHR) present an opportunity to investigate population trends that could be associated with this outbreak. There is potential to use routine clinical laboratory parameters as a surveillance tool at a population level, for example as a sentinel marker for circulation of an infectious trigger. In this study, we used hospital EHR data from Oxfordshire, UK, to explore trends before, during and after the period of the AS-Hep-UA outbreak. We addressed the following specific aims: (i) to explore any changes in liver enzyme levels in adults and children/adolescents presenting to hospital, and (ii) to determine any changes in incidence and severity of acute hepatitis and HAdV infection.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003e\u003cstrong\u003eData source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analysed EHR representing children, adolescents and adults presenting as an emergency to Oxford University Hospitals (OUH) NHS Foundation Trust, a large tertiary referral hospital in the South East of England, serving a population of ~725,000. Data were accessed through the Infections in Oxfordshire Research Database (IORD)\u0026nbsp;(11), and were held, accessed and analysed in accordance with NHS standards for data management and protection (more details in\u003cstrong\u003e\u0026nbsp;Supplementary methods\u003c/strong\u003e). This study, as part of the Infections in Oxfordshire Research Database (IORD), was approved by the National Research Ethics Service South Central \u0026ndash; Oxford C Research Ethics Committee (19/SC/0403), the Health Research Authority and the national Confidentiality Advisory Group (19/CAG/0144), including provision for use of pseudonymised routinely collected data without individual patient consent.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this retrospective cohort study, we reviewed data from 1\u003csup\u003est\u0026nbsp;\u003c/sup\u003eMarch 2016 to 31\u003csup\u003est\u003c/sup\u003e December 2022 for all individuals aged 18 months and older presenting to the Emergency Department or acute medical/surgical assessment units at OUH. We recorded subsequent admission to hospital, admission to the Intensive Care Unit (ICU), duration of hospital admission, and mortality during the admission. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLaboratory data were generated by externally ISO accredited clinical biochemistry and microbiology laboratories at OUH. A full list of laboratory assays and platforms is provided in \u003cstrong\u003eSuppl methods\u003c/strong\u003e, and reference intervals for liver enzymes and inflammatory markers are provided in \u003cstrong\u003eTable 1\u003c/strong\u003e. Laboratory data were based on those routinely collected, where a request for \u0026lsquo;liver function tests\u0026rsquo; (LFTs) prompts a clinical biochemistry profile comprising alanine transferase (ALT), alkaline phosphatase (ALP), bilirubin and albumin. Additional laboratory investigations were requested at the discretion of the clinical team. Abnormalities in these biomarkers were classified based on the upper limit of normal (ULN) for all ages and both sexes \u0026ndash; mild, moderate and severe derangement was defined as up to 2x, 2-5x and \u0026gt;5x ULN, respectively, with the exception of albumin, which was classified as deranged if levels were less than the lower limit of normal (LLN) of 32g/L.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHAdV testing was undertaken using a PCR-based multiplex test on respiratory samples or using an HadV-specific PCR on whole blood based on specific clinician request, which usually focuses on patients requiring critical care or in immunocompromised patients under the care of haematology/oncology teams. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassification and definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients were stratified into three categories based on their ages at presentation: younger children (\u0026lt;7 years), older children (7-15 years) and adults (\u0026ge;16 years). Epochs were considered as pre-COVID-19 (1st March 2016 - 10 March 2020), COVID-19 pandemic period (11th March 2020 - 31st December 2022), and nested within the COVID-19 pandemic period, the AS-Hep-UA outbreak (1st Oct 2021 - 31 Aug 2022). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe applied the established strict case definition for AS-Hep-UA, as someone \u0026lt;16 years of age presenting no earlier than 1st October 2021 with an acute hepatitis (ALT and/or AST \u0026gt;500 IU/L), which cannot be accounted for by other causes\u0026nbsp;(7). We additionally applied a more relaxed definition of acute hepatitis of unknown aetiology (AHUA), to identify cases in adults, and also milder cases that would not meet criteria for AS-Hep-UA. We defined AHUA as patients assigned either a primary or secondary diagnostic code from the International Classification of Diseases 10th Revision consistent with hepatitis of an uncertain cause (\u003cstrong\u003eTable 2; Supplementary Methods\u003c/strong\u003e) or patients with ALT\u0026gt;2x ULN. Diagnostic codes were assigned by hospital admission coders following patient discharge, based on national clinical coding standards. We also considered presentations of diagnosed acute or chronic viral hepatitis A-E virus infection as a baseline control, and to ensure these cases were excluded from the AHUA category. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis and statistical testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEach presentation episode was considered independently; thus individuals may have featured more than once across the study duration. We used the first set of blood tests taken on presentation for analysis. An infecting pathogen was reported if at least one microbiology test was positive. Data were analysed using \u003cem\u003eR\u0026nbsp;\u003c/em\u003ev4.3.2 and visualised using \u003cem\u003eggplot\u0026nbsp;\u003c/em\u003ev3.4.4. We tested for the presence of a non-monotonic trend using the non-parametric WAVK test (12), using its implementation in the \u003cem\u003eR\u003c/em\u003e package \u003cem\u003efuntimes\u003c/em\u003e (13). Fisher\u0026rsquo;s exact tests and Mann-Whitney U tests were performed using the \u003cem\u003efisher.test\u003c/em\u003e and \u003cem\u003ewilcox.test\u003c/em\u003e functions in \u003cem\u003eR\u003c/em\u003e. Odds ratios (OR) were calculated using conditional maximum likelihood estimation as part of the \u003cem\u003efisher.test\u003c/em\u003e function. An interupted time series analysis was performed to assess changes in the incidence of AHUA or viral hepatitis A-E (i.e., the number of new episodes identified per month) during the study duration, using a segmented regression framework (14), as follows:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" height=\"54\" width=\"672\"\u003e\u003c/p\u003e\n\u003cp\u003eWhere \u003cem\u003ey\u003csub\u003et\u003c/sub\u003e\u003c/em\u003e, \u003cem\u003e\u0026beta;\u003csub\u003ei\u003c/sub\u003e\u003c/em\u003e, \u0026alpha;\u003csub\u003eoutbreak\u003c/sub\u003e, \u003cem\u003et\u003csub\u003estart\u003c/sub\u003e\u003c/em\u003e and \u003cem\u003e\u0026epsilon;\u003csub\u003et\u003c/sub\u003e\u0026nbsp;\u003c/em\u003erepresent the number of new episodes identified in month t, the parameter estimates, a binary variable encoding the AS-Hep-UA epoch, the start of the AS-Hep-UA epoch, and model residuals respectively. Autocorrelation and normality of \u003cem\u003e\u0026epsilon;\u003csub\u003et\u003c/sub\u003e was\u0026nbsp;\u003c/em\u003eassessed using the Durbin Watson test in the \u003cem\u003elmtest\u003c/em\u003e \u003cem\u003eR\u003c/em\u003e package (15), and Shapiro-Wilk test, respectively. The statistical significance of parameter estimates was assessed using a Student\u0026rsquo;s t-test.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAssociations between HAdV infection and routinely collected blood biomarker data were assessed using Fisher\u0026rsquo;s exact test. \u0026nbsp;In particular, for each we tested if the proportion of patients falling into each derangement category (described above) differed significantly between patients with HAdV infections or otherwise. Benjamini-Hochberg procedure was used to correct for multiple testing and adjusted p-values, where available, were annotated.\u0026nbsp;\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNo evidence for an increase in hospital presentations or elevated liver enzymes during AS-Hep-UA outbreak\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe analysed data for 903,433 acute hospital presentations, from 441,780 males and 461,632 females (and 21 individuals for whom sex was not recorded). The median age was 44 years (IQR 22-69 years), with 7.9%, 9.5%, and 82.6% classified as younger children, older children/adolescents, and adults, respectively (\u003cstrong\u003eTable 1\u003c/strong\u003e). A median of 11,023 patients presented to the hospital per month, with a marked decline in the number of presentations in April 2020 coinciding with the implementation of SARS-CoV-2 (COVID-19) pandemic lockdown measures in the UK introduced on 26th March 2020 (\u003cstrong\u003eSupplementary Figure 1a\u003c/strong\u003e). \u003c/p\u003e\n\n\u003cp\u003eDuring the AS-Hep-UA outbreak, minimal changes in the number of acute presentations per month was observed across any of the three age groups (mininum WAVK test p=0.998; \u003cstrong\u003eSupplementary Figure 1a\u003c/strong\u003e). There was an overall increasing trend in the number of ALT tests requested for acutely presenting patients over time since March 2016 regardless of sex (\u003cem\u003eWAVK statistic\u003c/em\u003e=13.4, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001; \u003cstrong\u003eSupplementary Figure 1b\u003c/strong\u003e). However, there was also an increasing trend in the number of ALT observations compared to WBC observations, which indicates increased \u0026lsquo;liver function scrutiny\u0026rsquo; over time (\u003cstrong\u003eSupplementary Figure 1c\u003c/strong\u003e; \u003cem\u003eWAVK statistic\u003c/em\u003e=91.1, \u003cem\u003ep\u003c/em\u003e\u0026lt;0.0001). \u003c/p\u003e\n\n\u003cp\u003eAcross the study duration, 59% of patient episodes had recorded blood tests. Among these, 90% had an ALT test and 1.2% an AST test. There was no evidence of change in the median or IQR of ALT levels over time for any age group (\u003cstrong\u003eFigure 1a\u003c/strong\u003e; minimum WAVK test \u003cem\u003ep\u003c/em\u003e=0.344), with no observable peak during the AS-Hep-UA outbreak. Similarly, the proportion of individuals with mild, moderate or severe derangement of ALT levels remained relatively stable over time (\u003cstrong\u003eFigure 1b\u003c/strong\u003e; minimum WAVK test p=0.468). Therefore, there was no temporal association between the period of the AS-Hep-UA outbreak and elevated liver enzymes in patients presenting acutely to hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIncreased incidence of AHUA in adults coinciding with AS-Hep-UA outbreak\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe further investigated the use of primary or secondary diagnostic codes for identifying increased incidence of AHUA in the patient population. We compared the temporal trend for AHUA diagnoses (\u003cstrong\u003eTable 2; Supplementary Methods\u003c/strong\u003e) against those for viral hepatitis A-E, the latter which are likely to be relatively stable and therefore serve as an appropriate control. \u003c/p\u003e\n\n\u003cp\u003eAcross the study duration, 3729 diagnostic codes representing AHUA (total 1005) or viral hepatitis A-E (total 2724) were assigned to 1531 distinct patient episodes (\u003cstrong\u003eTable 2\u003c/strong\u003e), of which 98% were adults (eight younger children, 11 older children/adolescents, 1512 adults). Overall, there were 391 patient episodes where only diagnostic codes classified as AHUA were assigned, representing 0.04% of all 903,433 patient episodes. The number of acute hepatitis diagnoses classified as AHUA or viral hepatitis A-E per month remained relatively constant over time (\u003cstrong\u003eFigure 2a\u003c/strong\u003e). However, an increase in the number of AHUA cases coinciding with the AS-Hep-UA outbreak period was observed (\u003cstrong\u003eFigure 2b\u003c/strong\u003e). The proportion of patients diagnosed with AHUA was higher during the AS-Hep-UA outbreak than outside this period (Fisher\u0026rsquo;s exact test p\u0026lt;0.0001; OR 3.01, 95% CI:2.20-4.12). \u003c/p\u003e\n\n\u003cp\u003eInterrupted time series analysis indicated a significantly increased number of AHUA cases per month during the AS-Hep-UA outbreak period (estimate=2.92, 95% CI: 1.57-5.69; t=3.50, d.f.=78, p\u0026lt;0.001), but there was no evidence of change in the number of viral hepatitis A-E cases (estimate=-0.785, 95% CI: -4.34-2.77; t=-0.440, d.f.=78, p=0.661). Overall, these observations support an increased incidence of AHUA amongst adults during the AS-Hep-UA outbreak. We could not determine if this was the case for children, since only 2% of relevant diagnostic codes were assigned to children. \u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAHUA associated with critical care admission, duration of hospitalisation and mortality\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompared to patients without AHUA, patients with AHUA had significantly higher ICU admission rates (\u003cstrong\u003eTable 3\u003c/strong\u003e; Fisher\u0026rsquo;s exact test p\u0026lt;0.0001; OR 41.7 within and 23.7 outside the AS-Hep-UA epoch) and mortality rates (Fisher\u0026rsquo;s exact tests p=0.035 within and p\u0026lt;0.0001 outside; OR 6.99 within and 11.3 outside), and longer hospitalisations (both Mann-Whitney U tests p\u0026lt;0.0001; \u003cstrong\u003eTable 3\u003c/strong\u003e). We also compared patients with AHUA to those diagnosed with viral hepatitis A-E; those with AHUA had significantly higher ICU admission rates (both Fisher\u0026rsquo;s exact test p\u0026lt;0.0001; OR 5.01 within AS-Hep-UA outbreak and 3.90 outside outbreak) and longer hospitalisation periods (Mann-Whitney U tests p\u0026lt;0.05), both within and outside the AS-Hep-UA epoch. Inpatient mortality was significantly higher for the AHUA group than those with viral hepatitis A-E outside the AS-Hep-UA epoch (Fisher\u0026rsquo;s exact test p\u0026lt;0.0001; OR 19.8, 95% CI: 4.29-185), but not within the AS-Hep-UA epoch (p=1, OR=1.16, 95% CI: 0.0825-16.3). \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e \u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eIncreased incidence of \u003c/em\u003eHAdV\u003c/strong\u003e \u003cstrong\u003e\u003cem\u003einfections during AS-Hep-UA outbreak but not associated with deranged liver enzymes or poorer patient outcomes\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcross the study duration, we retrieved 3707 distinct patient records that included microbiology tests for HAdV infection, of which 179 were positive (4.8%). The positivity rate was highest in younger children, among whom 124/781 (15.9%) of HAdV tests were positive, compared to older children/adolescents (9/440, 2.0% positive) and adults (46/2486, 1.9% positive), in keeping with the known epidemiology of HAdV infection (16\u0026ndash;18). None of the HAdV-infected patients were given ICD10 codes indicative of AHUA across the study duration. A minority (16/179; 9%) of HadV positive results were derived from eye swabs, which is unlikely to have influenced any overall trends.\u003c/p\u003e\n\n\u003cp\u003eThere was an increase in the number of HAdV-tests undertaken between April 2021 and April 2022 (\u003cstrong\u003eFigure 3a\u003c/strong\u003e), and a significantly higher number of HAdV tests performed relative to all microbiology tests performed during the AS-Hep-UA epoch (Fisher\u0026rsquo;s exact test p\u0026lt;0.0001; OR 1.27, 95% CI:1.17-1.37). These findings indicate increased clinician scrutiny for HAdV during the outbreak. The proportion of HAdV-positive tests during the AS-Hep-UA epoch was significantly higher than outside of the AS-Hep-UA epoch at 60/839 (7.2%) vs 119/2868 (4.1%) respectively (Fisher\u0026rsquo;s exact test \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001; OR 1.78, 95% CI:1.27-2.47). Additionally, there was an increase in the incidence and proportion of HAdV-positive tests in younger children during the AS-Hep-UA outbreak relative to the period preceding the outbreak (\u003cstrong\u003eFigure 3b\u003c/strong\u003e). However, there were also other peaks in the proportion of HAdV-positive tests across the entire study duration (\u003cstrong\u003eFigure 3b\u003c/strong\u003e), indicating previous periods of high HAdV-positivity before the AS-Hep-UA epoch. \u003c/p\u003e\n\n\u003cp\u003eThere was no evidence that the proportion of patients with mild, moderate or severe derangement of ALT, AST, bilirubin, GGT, CRP or WBC differed significantly between those testing positive vs. negative for HAdV (minimum Fisher\u0026rsquo;s exact test p=0.62; \u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e). The proportion of patients with low albumin and mild derangement of ALP was significantly smaller for those testing HAdV-positive vs. negative (Fisher\u0026rsquo;s exact test p=0.024 and p=0.004 respectively; \u003cstrong\u003eSupplementary Figure 2\u003c/strong\u003e). A similar association with raised ALP was also present in confirmed rhinovirus/enterovirus infections (Fisher\u0026rsquo;s exact test p=0.002), indicating that mild derangement of ALP is not unique to HAdV infections (data not shown). \u003c/p\u003e\n\n\u003cp\u003eHAdV testing focuses primarily on a clinically vulnerable group, shown by higher rates of hospital admission, ICU admission and inpatient mortality among those receiving a HAdV test (irrespective of the test result) compared to the untested population (78.5% vs 30.7%, 18.2% vs 1.2%, and 0.59% vs 0.34%, respectively; \u003cstrong\u003eTable 3\u003c/strong\u003e). However, the HAdV-positive group fared somewhat better than those who tested negative, with lower hospital admission (both Fisher\u0026rsquo;s exact tests p\u0026lt;0.001) and significantly shorter hospital stays (both Mann-Whitney U tests p\u0026lt;0.05), whether within or outside the AS-Hep-UA epoch. Additionally, the HAdV-positive group had significantly lower ICU admission rates within the AS-Hep-UA epoch, but not outside the epoch (Fisher\u0026rsquo;s exact tests p=0.015 and p=0.35, respectively) (data not shown). No HAdV-positive patients died across the entire study duration. Characteristics of the population testing positive for HAdV are presented in \u003cstrong\u003eTable 4\u003c/strong\u003e. \u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAnonymised clinical data from EHRs offers access to large datasets, providing power in numbers to determine overall trends reflecting clinical epidemiology and its influence on morbidity, mortality and health service workload. Monitoring of EHRs may be an effective and low-cost surveillance tool that allows identification of trends that could be of concern - e.g. deranged laboratory parameters and/or changes in recorded diagnoses based on microbiology tests or coding. Such strategies could potentially be developed to provide an \u0026lsquo;early warning\u0026rsquo; system to allow clinical and public health authorities to review data in real time, cross-compare between regions, identify possible outbreaks, and implement enhanced surveillance and public-health messaging if necessary.\u003c/p\u003e \u003cp\u003eAmong patients presenting acutely for hospital-based care, HAdV testing is largely reserved for vulnerable groups. Even in this high-risk population, those testing positive for HAdV had lower admission rates than those testing negative, reinforcing the view that this virus is generally benign and self-limiting, with a low risk of serious complications. The lack of associations between HAdV infections and deranged liver enzymes is concordant with the fact that HAdV infections typically lead to mild respiratory or gastrointestinal disease, and that hepatitis is an unusual complication (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Co-infections with HAdV and other viruses such as respiratory syncytial virus (RSV) have been linked to poorer outcomes (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), but the small number of HAdV infections identified in this study precluded robust analysis of mixed infections. Despite an increased incidence of HAdV infections during the AS-Hep-UA outbreak, this was not an unusual aspect of local HAdV epidemiology, and could have been partly accounted for by increased clinician scrutiny.\u003c/p\u003e \u003cp\u003eIt has emerged that severe clinical outcomes during the AS-Hep-UA outbreak in children was likely driven by AAV2 as a leading aetiological agent. However, AAV2 requires co-infection with a \u0026lsquo;helper\u0026rsquo; virus (including, but not limited to, HAdV) to replicate, and this appears to be a requirement for the development of liver pathology (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Retrospective analysis of EHRs cannot be used to investigate the epidemiology of AAV infection, as these viruses are not part of clinical diagnostic testing pathways and have only been identified as an agent of AS-Hep-UA through retrospective metagenomic sequencing (\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The specific subtype of HAdV implicated in the outbreak, 41F, is not routinely discriminated from other types by clinical testing. Other possible \u0026lsquo;helper viruses\u0026rsquo; include human herpesviruses (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), many of which are ubiquitous in the population and characterised by long-term carriage and latency, making it difficult to distinguish between clinically relevant episodes and subclinical reactivation. Thus routinely-collected clinical datasets do not include screening for all relevant pathogens, and detection of implicated pathogens can be difficult to interpret due to the detection of commensal or bystander organisms. Overall, wider adoption of metagenomics-based diagnostics has the potential to further enhance the utility of EHRs to investigate future outbreaks but interpretation is complex (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThere are various caveats to our analysis approach. Although we have captured data for a large population over a period of almost seven years, this remains a small data set within which to identify rare events, and our region was not known to be directly affected by the AS-Hep-UA outbreak. Additionally, if there were milder cases of disease in the population during or preceding the documented AS-Hep-UA outbreak, these may not have presented to hospital at all. Even among those presenting to healthcare, many patients did not have liver enzymes measured. Thus, the IORD dataset provides only a limited view of the whole population, which is not true community surveillance. Hospital admission data are also biased by repeated representation of the same individuals, and over-represent populations who preferentially present to emergency care rather than accessing primary care, or those admitted through different routes. Further, as we did not include children\u0026thinsp;\u0026lt;\u0026thinsp;age 18 months, we may have missed relevant signals in the younger population. In the intensive care population, some cases meeting our criteria for AHUA may be related to liver ischaemia or injury from diverse causes associated with critical illness, rather than liver-specific pathology. Finally, our analyses relies on consistent clinical coding of patient episodes, which has been shown to be subject to some inaccuracy and/or variability (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe identified an increased incidence of episodes coded as AHUA in adults and an increased incidence of HAdV infections in younger children coinciding with the AS-Hep-UA outbreak in children during 2022. While the latter may be partially accounted for by increased clinician scrutiny during this period, the pattern was not observed to the same level in older children/adolescents, and not at all in adults despite similar increases in testing for all age groups. There was no evidence for increased incidence of abnormal liver enzymes in children or adults, nor associations between HAdV infections and elevated liver transaminases. Our findings suggest that the use of routinely collected liver enzyme EHR data lacks sensitivity for tracking this outbreak, which is likely due a large number of confounding aetiologies that may lead to elevated transaminases. However, the identification of increased incidence of AHUA in adults, which was largely ignored during the AS-Hep-UA outbreak in children, highlights the potential of using hospital diagnostic codes for cost-effective disease surveillance. Longer-term collation of EHR data from multiple regions could potentially offer a powerful surveillance approach that could be extended to other diseases. However, this would require the establishment of suitable and systematic data-processing infrastructure and governance frameworks, in addition to investment of personnel and resources, if it is to become a real-world surveillance tool.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eUnited Kingdom Health Security Agency (UKHSA)\u003c/p\u003e\n\u003cp\u003eAcute severe hepatitis with unknown aetiology in children (AS-Hep-UA)\u003c/p\u003e\n\u003cp\u003eAcute hepatitis with unknown aetiology (AHUA)\u003c/p\u003e\n\u003cp\u003eInfections in Oxfordshire Research Database (IORD)\u003c/p\u003e\n\u003cp\u003eAdeno-associated virus 2 (AAV2)\u003c/p\u003e\n\u003cp\u003eHuman adenovirus (HAdV)\u003c/p\u003e\n\u003cp\u003eEpstein-Barr Virus (EBV)\u003c/p\u003e\n\u003cp\u003eHuman herpes-virus 6 (HHV6)\u003c/p\u003e\n\u003cp\u003eHuman leukocyte antigen (HLA)\u003c/p\u003e\n\u003cp\u003eUpper limit of normal (ULN)\u003c/p\u003e\n\u003cp\u003eLower limit of normal (LLN)\u003c/p\u003e\n\u003cp\u003eAlanine transaminase (ALT)\u003cbr\u003e\u0026nbsp;Aspartate aminotransferase (AST)\u003cbr\u003e\u0026nbsp;C-reactive protein (CRP)\u003c/p\u003e\n\u003cp\u003eAlkaline phosphatase (ALP)\u003c/p\u003e\n\u003cp\u003eWhite blood cell count (WBC)\u003c/p\u003e\n\u003cp\u003eGamma-glutamyl transferase (GGT)\u003c/p\u003e\n\u003cp\u003eIntensive care unit (ICU)\u003c/p\u003e\n\u003cp\u003eInterquartile range (IQR)\u003c/p\u003e\n\u003cp\u003eConfidence interval (CI)\u003c/p\u003e\n\u003cp\u003eOxford University Hospitals NHS Trust (OUH)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eETHICS APPROVAL AND CONSENT TO PARTICIPATE\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll data used for this study was pseudonymised prior to its use. The study, as part of the Infections in Oxfordshire Research Database (IORD), was approved by\u0026nbsp;the National Research Ethics Service South Central \u0026ndash; Oxford C Research Ethics Committee (19/SC/0403), the Health Research Authority and the national Confidentiality Advisory Group (19/CAG/0144), including provision for use of pseudonymised routinely collected data without individual patient consent. As such, individual patient consent was not obtained for the purposes of this study. Individuals who choose to opt out of their data being used in research are not included in the study.\u0026nbsp;The study sponsor was OUH. All patients were assigned an anonymised \u0026lsquo;cluster ID\u0026rsquo;, with no identifying details handled by the research team. Only month/year of birth was available rather than specific date of birth. Data were held within a password protected, encrypted database and accessed only by named investigators in accordance with NHS standards for data management and protection. More details can be found at the following URL:\u003c/p\u003e\n\u003cp\u003ehttps://oxfordbrc.nihr.ac.uk/research-themes-overview/antimicrobial-resistance-and-modernising-microbiology/infections-in-oxfordshire-research-database-iord/.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVAILABILITY OF DATA AND MATERIALS\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available from the IORD database but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. The IORD database can only be accessed by named investigators in accordance with NHS standards for data management and protection. For further details on how to apply for access to the data and for a research proposal template please email [email protected].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll custom scripts used for the analyses presented in this manuscript are hosted on GitHub (https://github.com/cednotsed/iORD_hepatitis.git).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCCST is supported by doctoral funding from the A*STAR National Science Scholarship. PCM is supported by core funding from the Francis Crick Institute, a Wellcome fellowship (ref 110110/Z/15/Z), and University College London NIHR Biomedical Research Centre (BRC). PCM receives funding from GSK to support a doctoral student in her team, outside the scope of this paper. ASW is supported by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford in partnership with the UK Health Security Agency (UK HSA) (NIHR200915), by the NIHR Oxford Biomedical Research Centre, has core support from the Medical Research Council UK to the MRC Clinical Trials Unit [MC_UU_12023/22] and is an NIHR Senior Investigator. DWE is supported by a Robertson Fellowship and an NIHR Oxford BRC Senior Fellowship. NS is an Oxford Martin Fellow and holds an NIHR Oxford BRC Senior Fellowship.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Acute hepatitis of unknown aetiology - the United Kingdom of Great Britain and Northern Ireland. Disease Outbreak News. 2022 Apr 15; Available from: https://www.who.int/emergencies/disease-outbreak-news/item/2022-DON368\u003c/li\u003e\n\u003cli\u003eUK Health Security Agency. Technical briefing 1: investigation into acute hepatitis of unknown aetiology in children in England. 2022 Apr 25; \u003c/li\u003e\n\u003cli\u003eHo A, Orton R, Tayler R, Asamaphan P, Herder V, Davis C, et al. Adeno-associated virus 2 infection in children with non-A-E hepatitis. Nature [Internet]. 2023 Mar 30; Available from: https://doi.org/10.1038/s41586-023-05948-2\u003c/li\u003e\n\u003cli\u003eMorfopoulou S, Buddle S, Montaguth OET, Atkinson L, Guerra-Assun\u0026ccedil;\u0026atilde;o JA, Marjaneh MM, et al. Genomic investigations of unexplained acute hepatitis in children. Nature [Internet]. 2023 Mar 30; Available from: https://doi.org/10.1038/s41586-023-06003-w\u003c/li\u003e\n\u003cli\u003eServellita V, Gonzalez AS, Lamson DM, Foresythe A, Huh HJ, Bazinet AL, et al. Adeno-associated virus type 2 in US children with acute severe hepatitis. Nature [Internet]. 2023 Mar 30; Available from: https://doi.org/10.1038/s41586-023-05949-1\u003c/li\u003e\n\u003cli\u003eMatthews PC, Campbell C, Săndulescu O, Matičič M, Ruta SM, Rivero-Ju\u0026aacute;rez A, et al. Acute severe hepatitis outbreak in children: A perfect storm. What do we know, and what questions remain? 2022; \u003c/li\u003e\n\u003cli\u003eWorld Health Organization. Severe acute hepatitis of unknown aetiology in children - Multi-country. Disease Outbreak News. 2022 Jun 24; \u003c/li\u003e\n\u003cli\u003eKambhampati AK. Trends in acute hepatitis of unspecified etiology and adenovirus stool testing results in children\u0026mdash;United States, 2017\u0026ndash;2022. MMWR Morb Mortal Wkly Rep. 2022;71. \u003c/li\u003e\n\u003cli\u003evan Beek J, Fraaij PL, Giaquinto C, Shingadia D, Horby P, Indolfi G, et al. Case numbers of acute hepatitis of unknown aetiology among children in 24 countries up to 18 April 2022 compared to the previous 5 years. Eurosurveillance. 2022;27(19):2200370. \u003c/li\u003e\n\u003cli\u003eZhang LY, Huang LS, Yue YH, Fawaz R, Lim JK, Fan JG. Acute hepatitis of unknown origin in children: Early observations from the 2022 outbreak. J Clin Transl Hepatol. 2022;10(3):522\u0026ndash;30. \u003c/li\u003e\n\u003cli\u003eNIHR Oxford Biomedical Research Centre. Infections in Oxfordshire Research Database (IORD). Available from: https://oxfordbrc.nihr.ac.uk/research-themes/modernising-medical-microbiology-and-big-infection-diagnostics/infections-in-oxfordshire-research-database-iord/\u003c/li\u003e\n\u003cli\u003eLyubchich V, Gel YR, El-Shaarawi A. On detecting non-monotonic trends in environmental time series: a fusion of local regression and bootstrap. Environmetrics. 2013 Jun 1;24(4):209\u0026ndash;26. \u003c/li\u003e\n\u003cli\u003eLyubchich V, Gel YR, Brenning A, Chu C, Huang X, Islambekov U, et al. Package \u0026lsquo;funtimes.\u0026rsquo; 2022; \u003c/li\u003e\n\u003cli\u003eSchaffer AL, Dobbins TA, Pearson SA. Interrupted time series analysis using autoregressive integrated moving average (ARIMA) models: a guide for evaluating large-scale health interventions. BMC Med Res Methodol. 2021;21(1):1\u0026ndash;12. \u003c/li\u003e\n\u003cli\u003eHothorn T, Zeileis A, Farebrother RW, Cummins C, Millo G, Mitchell D, et al. Package \u0026lsquo;lmtest.\u0026rsquo; Test Linear Regres Models Httpscran R-Proj Orgwebpackageslmtestlmtest Pdf Accessed. 2015;6. \u003c/li\u003e\n\u003cli\u003eDuan Y, Zhu Y, Xu B, Li C, Chen A, Deng L, et al. Multicenter study of human adenovirus infection in pediatric community-acquired pneumonia in China. Zhonghua Er Ke Za Zhi Chin J Pediatr. 2019;57(1):27\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eTran A, Talmud D, Lejeune B, Jovenin N, Renois F, Payan C, et al. Prevalence of rotavirus, adenovirus, norovirus, and astrovirus infections and coinfections among hospitalized children in northern France. J Clin Microbiol. 2010;48(5):1943\u0026ndash;6. \u003c/li\u003e\n\u003cli\u003eProbst V, Spieker AJ, Stopczynski T, Stewart LS, Haddadin Z, Selvarangan R, et al. Clinical Presentation and Severity of Adenovirus Detection Alone vs Adenovirus Co-detection With Other Respiratory Viruses in US Children With Acute Respiratory Illness from 2016 to 2018. J Pediatr Infect Dis Soc. 2022 Oct 1;11(10):430\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eCHKS. The quality of clinical coding in the NHS. 2014 Sep; Available from: https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/364476/The_quality_of_clinical_coding_in_the_NHS.pdf\u003c/li\u003e\n\u003cli\u003eInternational Severe Acute Respiratory and emerging Infection Consortium (ISARIC) 4c. Summary Analysis of Laboratory Tests (SALT). [cited 2023 Feb 13]; Available from: https://isaric4c.net/salt/\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1:\u003c/strong\u003e Characteristics of population of adults and children aged \u0026ge;18 months presenting as an emergency to Oxford University Hospitals NHS Foundation Trust (UK) between 2016 and 2022.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"626\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.9297124600639%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.169329073482427%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYounger children\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(18 months-6 years)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=70,962\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.41214057507987%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOlder children / adolescents\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(7-15 years)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=86,217\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.488817891373802%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdults\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u003c/strong\u003e\u003cstrong\u003e\u0026sup3;\u003c/strong\u003e\u003cstrong\u003e16 years)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=746,254\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.9297124600639%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAge in years at presentation\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(median, IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.169329073482427%\" valign=\"top\"\u003e\n \u003cp\u003e3 (2-5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.41214057507987%\" valign=\"top\"\u003e\n \u003cp\u003e11 (9-13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.488817891373802%\" valign=\"top\"\u003e\n \u003cp\u003e53 (32-74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.9297124600639%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMale sex (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.169329073482427%\" valign=\"top\"\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.41214057507987%\" valign=\"top\"\u003e\n \u003cp\u003e54.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.488817891373802%\" valign=\"top\"\u003e\n \u003cp\u003e47.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.9297124600639%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eProportion admitted to hospital following emergency presentation (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.169329073482427%\" valign=\"top\"\u003e\n \u003cp\u003e17.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.41214057507987%\" valign=\"top\"\u003e\n \u003cp\u003e13.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.488817891373802%\" valign=\"top\"\u003e\n \u003cp\u003e34.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.9297124600639%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eProportion admitted to ICU (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.169329073482427%\" valign=\"top\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.41214057507987%\" valign=\"top\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.488817891373802%\" valign=\"top\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.9297124600639%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMortality during admission episode (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.169329073482427%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.41214057507987%\" valign=\"top\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.488817891373802%\" valign=\"top\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"43.9297124600639%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eAdmission duration in hours if admitted, median (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.169329073482427%\" valign=\"top\"\u003e\n \u003cp\u003e20 (12-42)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.41214057507987%\" valign=\"top\"\u003e\n \u003cp\u003e24 (14-49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.488817891373802%\" valign=\"top\"\u003e\n \u003cp\u003e56 (22-160)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eBiomarker\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRef. Range\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.169329073482427%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.41214057507987%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.488817891373802%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\" rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLiver biomarkers\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(median, IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\" valign=\"top\"\u003e\n \u003cp\u003eALT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003e10-45 IU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.169329073482427%\" valign=\"top\"\u003e\n \u003cp\u003e15 (12-21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.41214057507987%\" valign=\"top\"\u003e\n \u003cp\u003e14 (11-20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.488817891373802%\" valign=\"top\"\u003e\n \u003cp\u003e19 (13-30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.517241379310345%\" valign=\"top\"\u003e\n \u003cp\u003eAST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\" valign=\"top\"\u003e\n \u003cp\u003e15-42 IU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.988505747126435%\" valign=\"top\"\u003e\n \u003cp\u003e41 (31-144)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.881226053639846%\" valign=\"top\"\u003e\n \u003cp\u003e33 (21-119)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.371647509578544%\" valign=\"top\"\u003e\n \u003cp\u003e41 (22-110)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.517241379310345%\" valign=\"top\"\u003e\n \u003cp\u003eBilirubin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\" valign=\"top\"\u003e\n \u003cp\u003e0-21 \u0026micro;mol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.988505747126435%\" valign=\"top\"\u003e\n \u003cp\u003e5 (3-7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.881226053639846%\" valign=\"top\"\u003e\n \u003cp\u003e7 (5-11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.371647509578544%\" valign=\"top\"\u003e\n \u003cp\u003e9 (6-14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.517241379310345%\" valign=\"top\"\u003e\n \u003cp\u003eAlbumin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\" valign=\"top\"\u003e\n \u003cp\u003e32-50 g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.988505747126435%\" valign=\"top\"\u003e\n \u003cp\u003e38 (35-40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.881226053639846%\" valign=\"top\"\u003e\n \u003cp\u003e40 (38-43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.371647509578544%\" valign=\"top\"\u003e\n \u003cp\u003e37 (33-40)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.517241379310345%\" valign=\"top\"\u003e\n \u003cp\u003eALP\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\" valign=\"top\"\u003e\n \u003cp\u003e30-130 IU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.988505747126435%\" valign=\"top\"\u003e\n \u003cp\u003e195 (160-237)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.881226053639846%\" valign=\"top\"\u003e\n \u003cp\u003e181 (115-247)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.371647509578544%\" valign=\"top\"\u003e\n \u003cp\u003e79 (64-101)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.517241379310345%\" valign=\"top\"\u003e\n \u003cp\u003eGGT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\" valign=\"top\"\u003e\n \u003cp\u003e15-40 IU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.988505747126435%\" valign=\"top\"\u003e\n \u003cp\u003e17 (10-104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.881226053639846%\" valign=\"top\"\u003e\n \u003cp\u003e20 (13-62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.371647509578544%\" valign=\"top\"\u003e\n \u003cp\u003e85 (31-280)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.61341853035144%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInfection biomarkers\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(median, IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.939297124600639%\" valign=\"top\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.37699680511182%\" valign=\"top\"\u003e\n \u003cp\u003e0-5 mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.169329073482427%\" valign=\"top\"\u003e\n \u003cp\u003e10 (1.3-42.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.41214057507987%\" valign=\"top\"\u003e\n \u003cp\u003e2.5 (0.4-20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.488817891373802%\" valign=\"top\"\u003e\n \u003cp\u003e8.6 (2.1-43.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.517241379310345%\" valign=\"top\"\u003e\n \u003cp\u003eWBC count\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.24137931034483%\" valign=\"top\"\u003e\n \u003cp\u003e4-11 x10^9/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.988505747126435%\" valign=\"top\"\u003e\n \u003cp\u003e10 (7.51-13.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.881226053639846%\" valign=\"top\"\u003e\n \u003cp\u003e8.53 (6.58-11.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.371647509578544%\" valign=\"top\"\u003e\n \u003cp\u003e8.87 (6.9-11.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2:\u0026nbsp;\u003c/strong\u003eDiagnostic codes representing Acute Hepatitis of Unknown Aetiology (AHUA) and confirmed viral hepatitis A-E infection assigned to patients presenting as an emergency to Oxford University Hospitals NHS Foundation Trust (UK) between 2016 and 2022. In order to protect against the risk of identification of individual cases, any subgroups numbering fewer than 5 individuals are not numerated. \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"620\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eICD10 code\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCondition(s) coded\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYounger children\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(18 months-6 years)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.838709677419356%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOlder children (7-15 years)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.93548387096774%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdults\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026ge;16 years)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003en=3702\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCodes representing acute hepatitis of unknown aetiology (AHUA; \u003cem\u003en\u003c/em\u003e=1005)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eK716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eToxic liver disease with hepatitis, not elsewhere classified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.838709677419356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.93548387096774%\" valign=\"top\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eK720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eAcute and subacute hepatic failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.838709677419356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.93548387096774%\" valign=\"top\"\u003e\n \u003cp\u003e534\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eK752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eNonspecific reactive hepatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.838709677419356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.93548387096774%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eK759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eInflammatory liver disease, unspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.838709677419356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.93548387096774%\" valign=\"top\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eB178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eOther specified acute viral hepatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.838709677419356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.93548387096774%\" valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eB179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eAcute viral hepatitis, unspecified\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\" valign=\"top\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.838709677419356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.93548387096774%\" valign=\"top\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eB199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eUnspecified viral hepatitis without hepatic coma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.838709677419356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.93548387096774%\" valign=\"top\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCodes representing viral hepatitis A-E (\u003cem\u003en\u003c/em\u003e=2724)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eB159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eHepatitis A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.838709677419356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.93548387096774%\" valign=\"top\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eB162, B169, B180, B181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eAcute or chronic viral hepatitis B \u0026plusmn; delta virus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.838709677419356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.93548387096774%\" valign=\"top\"\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eB171, B182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eAcute or chronic viral hepatitis C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.838709677419356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.93548387096774%\" valign=\"top\"\u003e\n \u003cp\u003e2057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.67741935483871%\" valign=\"top\"\u003e\n \u003cp\u003eB172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.322580645161292%\" valign=\"top\"\u003e\n \u003cp\u003eAcute viral hepatitis E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.225806451612904%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.838709677419356%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.93548387096774%\" valign=\"top\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: Outcomes of presentation to hospital among individuals presenting\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eas an emergency to Oxford University Hospitals NHS Foundation Trust (UK) between 2016 and 2022\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.471910112359552%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePopulation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.903691813804173%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHospital admission rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780096308186195%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eICU admission rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of hospital admission in hours\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(median, IQR)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality rate (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.471910112359552%\" valign=\"top\"\u003e\n \u003cp\u003eAll patients (n=903,433)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.903691813804173%\" valign=\"top\"\u003e\n \u003cp\u003e30.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780096308186195%\" valign=\"top\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e51 (21-146)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.471910112359552%\" valign=\"top\"\u003e\n \u003cp\u003eHAdV tested\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=3707)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.903691813804173%\" valign=\"top\"\u003e\n \u003cp\u003e78.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780096308186195%\" valign=\"top\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e101 (45-221)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.471910112359552%\" valign=\"top\"\u003e\n \u003cp\u003eHAdV untested\u003c/p\u003e\n \u003cp\u003e(n=899,726)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.903691813804173%\" valign=\"top\"\u003e\n \u003cp\u003e30.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780096308186195%\" valign=\"top\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e50 (21-145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.471910112359552%\" valign=\"top\"\u003e\n \u003cp\u003eHAdV tested and positive (n=179)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.903691813804173%\" valign=\"top\"\u003e\n \u003cp\u003e60.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780096308186195%\" valign=\"top\"\u003e\n \u003cp\u003e11.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e65 (31-139)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.471910112359552%\" valign=\"top\"\u003e\n \u003cp\u003eHAdV tested and negative (n=3528)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.903691813804173%\" valign=\"top\"\u003e\n \u003cp\u003e79.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780096308186195%\" valign=\"top\"\u003e\n \u003cp\u003e18.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e102 (46-224)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.471910112359552%\" valign=\"top\"\u003e\n \u003cp\u003ePatients with diagnostic codes relevant to the study\u003c/p\u003e\n \u003cp\u003e(n=1531)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.903691813804173%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780096308186195%\" valign=\"top\"\u003e\n \u003cp\u003e12.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e93 (35-234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.471910112359552%\" valign=\"top\"\u003e\n \u003cp\u003ePatients with only diagnostic codes indicative of AHUA (n=391)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.903691813804173%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780096308186195%\" valign=\"top\"\u003e\n \u003cp\u003e26.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e146 (57-333)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e3.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.471910112359552%\" valign=\"top\"\u003e\n \u003cp\u003ePatients with diagnostic codes indicating viral hepatitis A-E\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(n=1140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.903691813804173%\" valign=\"top\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780096308186195%\" valign=\"top\"\u003e\n \u003cp\u003e7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e79 (29-199)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.471910112359552%\" valign=\"top\"\u003e\n \u003cp\u003ePatients without AHUA (903,042)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.903691813804173%\" valign=\"top\"\u003e\n \u003cp\u003e30.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.780096308186195%\" valign=\"top\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e50 (21-145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.42215088282504%\" valign=\"top\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4:\u0026nbsp;\u003c/strong\u003eCharacteristics of 179 patient episodes with HAdV-positive microbiology tests among children and adults presenting as an emergency to Oxford University Hospitals NHS Foundation Trust (UK) between 2016 and 2022.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"624\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"40%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eYounger children (18 months-6 years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOlder children (7-15 years)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdults\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(\u0026ge;16 years)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal number tested for HAdV infection\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e2486\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal number positive for HAdV infection (% of those tested)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e124/781 (15.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e9/440 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e46/2486 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" rowspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eEpoch of presentation\u003csup\u003ea\u003c/sup\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003ePre-COVID-19 (n=79 HAdV-positive)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e45/79 (57.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e4/79 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e30/79 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eCOVID-19 pandemic (n=100 HAdV-positive)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e79/100 (79.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e5/100 (5.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e16/100 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eAS-Hep-UA (n=60 HAdV-positive)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e51/60 (85.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e2/60 (3.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e7/60 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLevels of derangement for ALT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eNot tested\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eNormal\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eMild\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(Up to 2x ULN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eModerate\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(2-5x ULN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eSevere\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(\u0026gt;5x ULN)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eInflammatory markers (median, IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eCRP (mg/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e47.2 (10.6-73.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e24.0 (11.8-88.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e81.9 (23.3-143.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eWhite cells (x10^9/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e11.6 (8.53-17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e9.1 (6.60-9.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e10.4 (6.73-14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmission rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e74/124 (59.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e5/9 (55.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e30/46 (65.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eICU admission rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e9/124 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0/9 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e12/46 (26.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of hospital admission in hours (median, IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e56 (26-95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e118 (33-309)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e125 (49-450)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"40%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeath rate during admission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0/124 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0/9 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0/46 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVital signs at presentation (median, IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003eHeart rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e134 (112-145)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e111 (109-123)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e92 (84-106)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eDiastolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e61 (54.0-71.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e81.5 (74.0, 86.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e73.0 (61.3-82.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eSystolic blood pressure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e104.0 (94.5-110.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e109.0 (105.0-116.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e123.5 (113.5-135.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eTympanic temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e37.5 (36.7-38.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e37.3 (36.9-37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e36.9 (36.4-37.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eOxygen saturation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e98.0 (96.0-99.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e98.0 (98.0-100.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e96.0 (95.0-98.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e28.0 (24.0-35.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e27.5 (22.5-30.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e19.0 (17.0-21.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003ea\u0026nbsp;\u003c/sup\u003eAS-Hep-UA epoch is nested within the Covid-19 pandemic period. Epochs were considered as pre-COVID-19 (1st March 2016 - 10 March 2020), COVID-19 pandemic period (11th March 2020 - 31st December 2022), and the AS-Hep-UA outbreak (1st Oct 2021 - 31 Aug 2022).\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"acute hepatitis, adenovirus, outbreak, AAV, epidemiology, electronic health records, surveillance","lastPublishedDoi":"10.21203/rs.3.rs-3591392/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3591392/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e An outbreak of acute severe hepatitis of unknown aetiology (AS-Hep-UA) in children during 2022 has subsequently been linked to infections by adenovirus-associated virus 2 and other ‘helper viruses’, including human adenovirus. It is possible that evidence of such an outbreak could be identified at a population level based on routine data captured by electronic health records (EHR).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We used anonymised EHR to collate retrospective data for all emergency presentations to Oxford University Hospitals NHS Foundation Trust in the UK, between 2016-2022, for all ages from 18 months and older. We investigated clinical characteristics and temporal distribution of presentations of acute hepatitis and of adenovirus infections based on laboratory data and clinical coding. We relaxed the stringent case definition adopted during the AS-Hep-UA to identify all cases of acute hepatitis with unknown aetiology (termed AHUA). We compared events within the outbreak period (defined as 1st Oct 2021 - 31 Aug 2022) to the rest of our study period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Over the study period, there were 903,433 acute presentations overall, of which 391 (0.04%) were classified as AHUA. AHUA episodes had significantly higher critical care admission rates (p\u0026lt;0.0001, OR=41.7, 95% CI:26.3-65.0) and longer inpatient admissions (p\u0026lt;0.0001) compared with the rest of the patient population. During the outbreak period, significantly more adults (≥16 years) were diagnosed with AHUA (p\u0026lt;0.0001, OR=3.01, 95% CI: 2.20-4.12), and there were significantly more human adenovirus (HadV) infections in children (p\u0026lt;0.001, OR=1.78, 95% CI:1.27-2.47). There were also more HAdV tests performed during the outbreak (p\u0026lt;0.0001, OR=1.27, 95% CI:1.17-1.37).\u003cstrong\u003e \u003c/strong\u003eAmong 3,707 individuals who were tested for HAdV, 179 (4.8%) were positive. However, there was no evidence of more acute hepatitis or increased severity of illness in HadV-positive compared to negative cases.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eOur results highlight an increase in AHUA in adults coinciding with the period of the outbreak in children, but not linked to documented HAdV infection. Tracking changes in routinely collected clinical data through EHR could be used to support outbreak surveillance.\u003c/p\u003e","manuscriptTitle":"Retrospective analysis of hospital electronic health records reveals unseen cases of acute hepatitis with unknown aetiology in adults in Oxfordshire","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-13 19:14:00","doi":"10.21203/rs.3.rs-3591392/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-04-15T06:25:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-03-27T17:34:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"f5110f8d-5c2a-42d9-8675-3c7fe1f38ba5","date":"2024-02-21T03:40:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-02-19T05:39:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"5d3afad7-dafa-4ddd-9292-351fbba3f381","date":"2024-02-11T23:37:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2023-12-28T08:33:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2023-12-19T14:31:37+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2023-12-12T04:55:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2023-12-12T04:54:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2023-11-10T14:24:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"219c74b6-63db-4e58-b644-d5dda6a770d3","owner":[],"postedDate":"December 13th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-08-01T16:20:46+00:00","versionOfRecord":{"articleIdentity":"rs-3591392","link":"https://doi.org/10.1186/s12889-024-19292-1","journal":{"identity":"bmc-public-health","isVorOnly":false,"title":"BMC Public Health"},"publishedOn":"2024-07-15 16:13:19","publishedOnDateReadable":"July 15th, 2024"},"versionCreatedAt":"2023-12-13 19:14:00","video":"","vorDoi":"10.1186/s12889-024-19292-1","vorDoiUrl":"https://doi.org/10.1186/s12889-024-19292-1","workflowStages":[]},"version":"v1","identity":"rs-3591392","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3591392","identity":"rs-3591392","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}