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Adam Wilkinson Wins Wellcome Career Development Award

Associate Professor Adam Wilkinson has won an eight-year Wellcome Career Development Award to further his research.

Novel pairing of integrins improves action of T cells on cancer cells to control tumour growth

New research published in Nature Immunology showcases the collaborative efforts of MRC TIDU researchers to describe a novel pairing of integrins which could improve T cell control of cancer.

Professor Graham Ogg Receives Wellcome Discovery Award with International Team

MRC TIDU Group Leader awarded funding for an international 8-year research programme from global charitable foundation Wellcome.

World Cancer Day 2024: Highlighting Cancer Research at the MRC WIMM

This World Cancer Day, we heard from four Group Leaders at the MRC Weatherall Institute of Molecular Medicine about their work and how their groups are helping in the fight against cancer.

New study provides framework for optimising the design of antibody therapeutics

New research published in Immunity by the Davis, Cornall and Klenerman groups has uncovered how antibodies trigger immune receptors in T cells.

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McMaster B. et al, (2024), Nat Methods

Walton I. et al, (2024), Journal of Anatomy

Suchy FP. et al, (2024), Nat Biotechnol

Milne TA., (2024), Curr Opin Genet Dev, 86

North D. and Chakraverty R., (2024), Journal of Clinical Investigation, 134

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Prostaglandin e2 hampers tumour-infiltrating lymphocyte expansion by collapsing interleukin-2 signalling and mitochondrial function.

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A phase i open-label, dose-escalation study of nuc-3373, a targeted thymidylate synthase inhibitor, in patients with advanced cancer (nutide:301)..

Spiliopoulou P. et al, (2024), J exp clin cancer res, 43

Cost-effectiveness analysis of a pragmatic randomized trial evaluating surgical reconstruction versus rehabilitation in patients with long-standing anterior cruciate ligament injury.

Leal J. et al, (2024), Bone joint j, 106-B, 38 - 45

Effect of a 2-week interruption in methotrexate treatment on COVID-19 vaccine response in people with immune-mediated inflammatory diseases (VROOM study): a randomised, open label, superiority trial.

Abhishek A. et al, (2024), Lancet rheumatol, 6, e92 - e104

Findings from a pilot randomized trial of spinal decompression alone or spinal decompression plus instrumented fusion.

Beresford-Cleary NJA. et al, (2023), Bone jt open, 4, 573 - 579

Effectiveness of nail bed repair in children with or without replacing the fingernail: NINJA multicentre randomized clinical trial.

Jain A. et al, (2023), Br j surg, 110, 432 - 438

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COVID-19 vaccine protects people of all body weights from hospitalisation and death

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Brain regions related to smell show decline following mild COVID-19

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RECOVERY trial shows Baricitinib reduces deaths in patients hospitalised with COVID-19

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The Medical Research Council Brain Network Dynamics Unit at the University of Oxford integrates exceptional research programmes covering clinical, experimental and computational neuroscience. Our collective goal is to understand and exploit the moment-to-moment interactions between nerve cells that are critical for brain function and behaviour, with a special focus on the brain circuits underlying movement and memory. In achieving this, we aim to develop and deliver novel therapies that specifically target the disturbed circuit interactions arising in disease.

Coordinating brain-distributed network activities in memory resistant to...

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From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation.

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Tumour Microenvironment

Understanding how the tumour microenvironment affects cancer cell behaviour and how it contributes to a cancer cell’s response to ionising radiation is essential in understanding how to prevent the establishment, growth and progression of cancers and how to exploit the tumour microenvironment for therapeutic gain.

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Welcome to the The Department of Oncology. The Department of Oncology's mission is to improve cancer care through research and teaching. There is strong emphasis on translation, with established infrastructure to develop scientific insights toward clinical appreciation.

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New funding for development of world's first lung cancer vaccine

22 March 2024

Oxford and UCL researchers seeking to create the world’s first vaccine to prevent lung cancer in people at high risk of the disease have been granted up to £1.7 million from Cancer Research UK and the CRIS Cancer Foundation.

Gulnar Abdullayeva receives an Azerbaijan Presidential Award for Youth 2024

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Prof. Ester Hammond and Dr Monica Olcina travel to Warsaw for the first meeting of the EU Horizon funded consortium – HIT-GLIO

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Researchers develop easy-to-deploy federated learning system that safeguards patient data

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The technique, which builds on recent advances in decentralised machine learning, uses inexpensive pre-programmed micro-computers, making it easy to deploy in hospitals and cheap to scale up.

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The Institute of Biomedical Engineering (IBME) is an interdisciplinary technology-focused research institute located at the heart of the University of Oxford’s Medical Sciences campus, adjacent to the Churchill Hospital.

Established in 2008, the IBME offers a world-class venue for cross-disciplinary biomedical engineering research and postgraduate training, where engineers, scientists and clinicians work together within a single ecosystem on addressing unmet needs in the prevention, early diagnosis and treatment of major diseases and conditions. The Institute’s core research missions are to develop novel medical devices, technology and systems capable of delivering substantial healthcare benefit, and to translate new engineering technologies into clinical practice.

A University of Oxford researcher is testing a revolutionary vaccine to prevent the onset of lung cancer

The fight against cancer has been decades in the works, killing millions of people every year as researchers struggle to find a cure for the disease’s various mutations.  

That might soon change, with researchers at the University of Oxford in the process of developing a potential cure for one of the world’s most prevalent and deadly forms of cancer.

Speaking to Spanish publication El País , Oxford researcher Sarah Blagden said she hoped a prospective vaccine to guard against lung cancer might be the first step in one injection, delivered to patients by the age of 40, being used to guard against all major cancers.

Blagden is part of the research team working on LungVax , a world-first attempt to prevent lung cancer with a vaccine, using technology first deployed for the COVID-19 vaccine and developed by Oxford University and AstraZeneca.

There are other attempts to prevent cancer with a shot—researchers in the U.S. are developing a vaccine to prevent colorectal cancer. The HPV vaccine also indirectly helps prevent the development of cervical cancer in women.

The LungVax vaccine seeks to stamp out one of the most deadly cancers, lung cancer, which is responsible for about 1.8 million deaths globally each year.

The group expects to start recruiting patients in 2026. If it proves a success, a vaccine to prevent lung cancer might be available for the public within 10 years.

“We think the vaccine could cover around 90% of all lung cancers, based on our computer models and previous research, and this funding will allow us to take the vital first steps towards trials in patients,” said Mariam Jamal-Hanjani, a professor at University College London and the lead on the LungVax clinical trial, as the group announced a £1.7 million ($2.1 million) grant.

“LungVax will not replace stopping smoking as the best way to reduce your risk of lung cancer. But it could offer a viable route to preventing some of the earliest-stage cancers from emerging in the first place,” Jamal-Hanjani noted.

Researchers appear to be in a race against time to more effectively treat and prevent cancer as global populations age, ramping up the pressure on health care services.    

In February, the World Health Organization warned global cancer rates were expected to rise by 77% by 2050. In addition to aging, obesity and alcohol and tobacco use are expected to play a role in the uptick in cases.

Blagden hopes that, eventually, major cancers will be prevented with a simple doctor’s appointment to receive a life-changing injection. “Ultimately, what I would love to see is a vaccine given to everyone at a certain age, around 40 or 50, to protect them from the major cancers later on in life. That’s where I think we should be, but we have to start somewhere,” Blagden told El País .

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StopCOVID cohort: An observational study of 3,480 patients admitted to the Sechenov University hospital network in Moscow city for suspected COVID-19 infection

Daniel munblit.

1 Department of Paediatrics and Paediatric Infectious Diseases, Institute of Child’s Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

2 Inflammation, Repair and Development Section, National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom

3 Research and Clinical Center for Neuropsychiatry, Moscow, Russia

Nikita A Nekliudov

Polina bugaeva, oleg blyuss.

4 School of Physics, Astronomy and Mathematics, University of Hertfordshire, College Lane, Hatfield, United Kingdom

Maria Kislova

Ekaterina listovskaya, aysylu gamirova, anastasia shikhaleva, vladimir belyaev.

5 Biobank, Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

Petr Timashev

6 Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

7 Chemistry Department, Lomonosov Moscow State University, Russia

8 Department of Polymers and Composites, N.N. Semenov Institute of Chemical Physics, Russia

John O Warner

Pasquale comberiati.

9 Department of Clinical and Experimental Medicine, Section of Pediatrics, University of Pisa, Pisa, Italy

Christian Apfelbacher

10 Institute of Social Medicine and Health Systems Research, Faculty of Medicine, Otto von Guericke University Magdeburg, Magdeburg, Germany

Evgenii Bezrukov

11 Institute for Urology and Reproductive Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

Mikhail E Politov

12 Department of Intensive Care, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

Andrey Yavorovskiy

Ekaterina bulanova, natalya tsareva.

13 Clinic of Pulmonology, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

Sergey Avdeev

Valentina a kapustina.

14 Department of Internal Medicine №1, Institute of Clinical Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

Yuri I Pigolkin

15 Department of Forensic Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

Emmanuelle A Dankwa

16 Department of Statistics, University of Oxford, United Kingdom

Christiana Kartsonaki

17 Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, United Kingdom

Mark G Pritchard

18 Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, United Kingdom

19 Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, United Kingdom

Fomin Victor

20 Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

Andrey A Svistunov

Denis butnaru, petr glybochko.

The epidemiology, clinical course, and outcomes of COVID-19 patients in the Russian population are unknown. Information on the differences between laboratory-confirmed and clinically-diagnosed COVID-19 in real-life settings is lacking.

We extracted data from the medical records of adult patients who were consecutively admitted for suspected COVID-19 infection in Moscow, between April 8 and May 28, 2020.

Of the 4261 patients hospitalised for suspected COVID-19, outcomes were available for 3480 patients (median age 56 years (interquartile range 45-66). The commonest comorbidities were hypertension, obesity, chronic cardiac disease and diabetes. Half of the patients (n=1728) had a positive RT-PCR while 1748 were negative on RT-PCR but had clinical symptoms and characteristic CT signs suggestive of COVID-19 infection.No significant differences in frequency of symptoms, laboratory test results and risk factors for in-hospital mortality were found between those exclusively clinically diagnosed or with positive SARS-CoV-2 RT-PCR.In a multivariable logistic regression model the following were associated with in-hospital mortality; older age (per 1 year increase) odds ratio [OR] 1.05 (95% confidence interval (CI) 1.03 - 1.06); male sex (OR 1.71, 1.24 - 2.37); chronic kidney disease (OR 2.99, 1.89 – 4.64); diabetes (OR 2.1, 1.46 - 2.99); chronic cardiac disease (OR 1.78, 1.24 - 2.57) and dementia (OR 2.73, 1.34 – 5.47).

Conclusions

Age, male sex, and chronic comorbidities were risk factors for in-hospital mortality. The combination of clinical features were sufficient to diagnoseCOVID-19 infection indicating that laboratory testing is not critical in real-life clinical practice.

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Article Contents

Supplementary data.

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Stop COVID Cohort: An Observational Study of 3480 Patients Admitted to the Sechenov University Hospital Network in Moscow City for Suspected Coronavirus Disease 2019 (COVID-19) Infection

D. M., N. A. N., P. B., D. B., and P. G. contributed equally.

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Daniel Munblit, Nikita A Nekliudov, Polina Bugaeva, Oleg Blyuss, Maria Kislova, Ekaterina Listovskaya, Aysylu Gamirova, Anastasia Shikhaleva, Vladimir Belyaev, Peter Timashev, John O Warner, Pasquale Comberiati, Christian Apfelbacher, Evgenii Bezrukov, Mikhail E Politov, Andrey Yavorovskiy, Ekaterina Bulanova, Natalya Tsareva, Sergey Avdeev, Valentina A Kapustina, Yuri I Pigolkin, Emmanuelle A Dankwa, Christiana Kartsonaki, Mark G Pritchard, Victor Fomin, Andrey A Svistunov, Denis Butnaru, Petr Glybochko, Sechenov StopCOVID Research Team , Stop COVID Cohort: An Observational Study of 3480 Patients Admitted to the Sechenov University Hospital Network in Moscow City for Suspected Coronavirus Disease 2019 (COVID-19) Infection, Clinical Infectious Diseases , Volume 73, Issue 1, 1 July 2021, Pages 1–11, https://doi.org/10.1093/cid/ciaa1535

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The epidemiology, clinical course, and outcomes of patients with coronavirus disease 2019 (COVID-19) in the Russian population are unknown. Information on the differences between laboratory-confirmed and clinically diagnosed COVID-19 in real-life settings is lacking.

We extracted data from the medical records of adult patients who were consecutively admitted for suspected COVID-19 infection in Moscow between 8 April and 28 May 2020.

Of the 4261 patients hospitalized for suspected COVID-19, outcomes were available for 3480 patients (median age, 56 years; interquartile range, 45–66). The most common comorbidities were hypertension, obesity, chronic cardiovascular disease, and diabetes. Half of the patients (n = 1728) had a positive reverse transcriptase–polymerase chain reaction (RT-PCR), while 1748 had a negative RT-PCR but had clinical symptoms and characteristic computed tomography signs suggestive of COVID-19. No significant differences in frequency of symptoms, laboratory test results, and risk factors for in-hospital mortality were found between those exclusively clinically diagnosed or with positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RT-PCR. In a multivariable logistic regression model the following were associated with in-hospital mortality: older age (per 1-year increase; odds ratio, 1.05; 95% confidence interval, 1.03–1.06), male sex (1.71; 1.24–2.37), chronic kidney disease (2.99; 1.89–4.64), diabetes (2.1; 1.46–2.99), chronic cardiovascular disease (1.78; 1.24–2.57), and dementia (2.73; 1.34–5.47).

Age, male sex, and chronic comorbidities were risk factors for in-hospital mortality. The combination of clinical features was sufficient to diagnose COVID-19 infection, indicating that laboratory testing is not critical in real-life clinical practice.

In Russia, the first confirmed cases of coronavirus disease 2019 (COVID-19) were reported by the state authorities in early March 2020 [ 1 ]. Since then, the Russian Federation climbed into the top 3 nations in the world affected by COVID-19, surpassing 400 000 cases by the end of May 2020.

The rate of infections in Moscow and the Moscow metropolitan area, with its high population density and number of inhabitants (20 million), has exceeded 180 000 confirmed cases, accounting for half of all the COVID-19 cases in Russia [ 2 ].

The clinical characteristics of COVID-19 have been described in studies from China [ 3 ], Italy [ 4 ], the United States [ 5–7 ], and the United Kingdom [ 8 ]. At present, no information on the clinical epidemiology, including clinical course, and outcomes of patients with COVID-19 in the Russian population is available. A recent editorial in The Lancet highlighted a surprisingly low mortality rate (~1%) in Russia [ 9 ]. With no academic data, perspectives on the COVID-19 pandemic in Russia are mainly based on media reports and briefs from Russian officials.

This study aimed to present demographic characteristics, symptoms, comorbidities, clinical test results, outcomes, and risk factors associated with mortality in a cohort of consecutively admitted patients with COVID-19 at the Sechenov University Hospital Network in Moscow. Secondarily, we aimed to test whether patients presenting with symptoms and radiological findings consistent with COVID-19 but without laboratory confirmation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have outcomes similar to those with positive reverse transcriptase–polymerase chain reaction (RT-PCR).

Study Design and Ethics

StopCOVID is an observational cohort study that took place at 4 large adult tertiary university hospitals in Moscow, Russia. All persons aged 18 years or olrder admitted to any of 4 Sechenov University Hospital Network hospitals between 8 April and 28 May 2020 with suspected COVID-19 infection were included in the study. RT-PCR to SARS-CoV-2 was the recommended mode of testing by the Russian Ministry of Health and was used throughout the study period in all the hospitals ( Supplementary Box 1 ). We enrolled all patients with confirmed or suspected COVID-19 infection, due to concerns of a high false-negative rate from RT-PCR results [ 10 ].

This study was approved by the Sechenov University Institutional Review Board on 22 April 2020 (protocol number 08–20).

Data Collection Process

The data were collected between 22 April and 6 June 2020. We reviewed electronic medical records for signs and symptoms on admission, baseline comorbidities, computed tomography (CT) imaging, and laboratory results for all admitted patients. Weight and height were self-reported by the patients to the clinical staff.

The data extraction was performed by a group of 40 medical students and resident doctors who went through personal protocol explanation webinars and data entry training prior to the beginning of the study. The team was supervised by senior academic staff members. The baseline characteristics were collected using the case report form (CRF) that was developed by the International Severe Acute Respiratory and Emerging Infection Consortium (ISARIC) and the World Health Organization (WHO) for use in outbreak investigations [ 11 ]. REDCap (Research Electronic Data Capture; Vanderbilt University, Nashville, TN, USA, hosted at Sechenov University) was used for data collection, storage, and management [ 12 , 13 ].

Study Definitions

Patients were defined as having confirmed COVID-19 if the diagnosis was confirmed by laboratory testing (at least 1 SARS-CoV-2 RT-PCR positive result).

Patients were defined as having “clinically diagnosed COVID-19” if laboratory confirmation was inconclusive or not available. Details of COVID-19 case definitions, criteria for hospitalization, grading of severity, and recommended treatment approaches are presented in Supplementary Box 1 .

We reviewed radiology reports of chest CT imaging during hospitalization. The data on the presence/absence of ground-glass opacities, consolidation, and severity of radiologic changes were retrieved. Incomplete reports containing no information on severity were excluded from the analysis. The severity of changes was graded by radiologists as per national COVID-19 guidelines using the modified visual assessment scale by Inui et al [ 14 ] ( Supplementary Table 1 ). The primary outcome in this study was in-hospital mortality.

Statistical Analysis

Descriptive statistics were calculated for baseline characteristics. Continuous variables were summarized as medians (interquartile range) and categorical variables as frequencies (percentage). The chi-square test or Fisher’s exact test was used for testing differences in proportions between individuals. The Wilcoxon rank-sum test was used to test for differences in laboratory test results between the groups.

We first ran univariate analysis to investigate associations between demographic characteristics and comorbidities with mortality. Then, we performed a multivariable logistic regression model, which included all statistically significant (at P  = .001) potential predictors from the univariate analysis.

A Bonferroni correction was used to adjust for multiple comparisons, such that P values less than or equal to .001 were considered statistically significant for the analysis of symptoms and comorbidities and P values less than .001 were considered statistically significant for laboratory markers. All routine clinical laboratory measurements were used in the analysis, except the ones which were available for less than 10 deceased patients. Statistical analysis was performed using R version 3.5.1 (R Core Team).

A total of 4261 adults with suspected COVID-19 infection were admitted to the hospitals. Primary outcome data were available for 3535 patients who were discharged, died, or transferred to another hospital. The study primary endpoint was available for all but 55 individuals transferred to other hospitals; thus, 3480 (82%) individuals were included in the statistical analysis.

Half of the patients (n = 1728) had positive RT-PCR results, while the second half (n = 1748) were negative on RT-PCR but had clinical symptoms and CT signs suggestive of COVID-19. No differences were noted in the baseline demographic and clinical characteristics and laboratory and radiologic findings of those with RT-PCR–confirmed versus clinically diagnosed COVID-19 ( Table 1 , Supplementary Tables 2, 4, 5, 7 ).

Laboratory Test Results (Median [IQR]) in Patients With Clinically Diagnosed COVID-19 Infection (RT-PCR Negative) and Patients With RT-PCR–Confirmed COVID-19 Infection

Statistically significant results at P values <.001 are presented in bold. The number of patients is presented for each parameter.

Abbreviations: COVID-19, coronavirus disease 2019; IQR, interquartile range; RT-PCR, reverse transcriptase–polymerase chain reaction.

Baseline Characteristics

Table 2 and Supplementary Table 2 present an overview of baseline characteristics, stratified by the primary outcome and the RT-PCT result, respectively. The median age of all patients at admission was 56 years (interquartile range, 45–66; range, 18–100 years). Similar numbers of men (50.5%, n = 1758) and women (49.5%, n = 1722) were admitted to the hospitals ( P  = .55). The median age of patients who died in the hospital was higher, 72 (61.5–81) years compared with 55 (44–65) years in survivors. Time from hospitalization to discharge/death was 14.5 (11.8–17.7) days, with shorter hospital stay in patients who died. Severity at admission was recorded as mild in 632 (18.2%), moderate in 2634 (75.7%), severe in 204 (5.9%), and critical in 7 (0.2%) patients, respectively.

Baseline Characteristics of Patients Admitted to Sechenov University Hospitals, Stratified by Outcome

Abbreviations: COVID-19, coronavirus disease 2019; ICU, intensive care unit; IQR, interquartile range; RT-PCR, reverse transcriptase–polymerase chain reaction; PT, Prothrombin.

a The proportion of patients in each subgroup is calculated from the total number of patients receiving a particular type of care (ICU, noninvasive ventilation, and invasive mechanical ventilation). Calculations were performed for each type of care, regardless of whether patients were discharged/died within the ICU facilities or were transferred to the ward and were discharged/died there.

Only 218 (6.3%) patients required admission and/or transfer to the intensive care unit (ICU), with some patients requiring noninvasive ventilation and/or invasive mechanical ventilation: 80 (2.3%) and 171 (5.0%), respectively. Although the proportion discharged alive from the ICU facilities was 42.5%, among all patients who received care in the ICU during the hospital stay, 57 (26.1%) were discharged from the hospital alive. Eight (4.7%) patients who received invasive mechanical ventilation during the hospital stay were discharged alive.

Data on symptoms and comorbidities at the time of hospital admission were available in 3382 (97%) patients. The most common symptoms in the medical records were fever (3157, 93.3%), fatigue/malaise (2684, 79.4%), cough (2476, 73.2%), and shortness of breath (2013, 59.5%). We also found a significant overlap between the top 3 most common symptoms, with 1912 (56.5%) patients having all 3 symptoms ( Figure 1 ). Shortness of breath, altered consciousness, and inability to walk were present significantly more often in patients who died, while anosmia, sore throat, fever, and muscle pain were found more frequently in those discharged alive ( Supplementary Table 3 ). Symptoms at admission did not differ significantly between the patients with laboratory-confirmed and clinically diagnosed COVID-19 ( Supplementary Table 4 ).

Stacked bar charts presenting the ( A ) top 10 most common symptoms and ( B ) most common comorbidities. Venn diagrams showing the coexistence of the ( C ) top 3 symptoms and ( D ) top 3 comorbidities at the time of hospital admission.

Detailed information on comorbidities in our cohort is presented in Table 3 , Supplementary Table 5 , and Figure 1 . The most common comorbidities were hypertension (1539, 45.5%), obesity (1129, 33.4%), chronic cardiovascular disease (621, 18.4%), and diabetes (predominantly type 2; 459, 13.6%). One in 10 patients reported current (139, 4.1%) or former (235, 6.9%) smoking. There was little overlap between the top 3 most common comorbidities, with only 145 (4%) patients having all 3, while 965 (28.5%) did not report any comorbidities.

Patient-reported Comorbidities at the Time of Hospital Admission and Chest Computed Tomography Imaging Stratified by Outcome

Statistically significant results at P values ≤.001 are presented in bold.

Abbreviations: ART, antiretroviral therapy; CT, computed tomography; HIV, human immunodeficiency virus.

aExcluding asthma.

b Obesity defined as body mass index based on electronic medical records data, and if data on height and weight were missing, records were screened for obesity definition by clinical staff.

Clinical Investigations

Most patients (71.6%) had significant changes on chest CT, equivalent to CT-2–CT-3 severity grade. Ground-glass opacity was found in over 95% of the patients and 77.95% had lung consolidation in accordance with the radiologist’s reports.

We reviewed routine clinical test measurements at admission and found abnormal changes to the coagulation profile, greater median levels of C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), aspartate aminotransferase (AST), and lactate dehydrogenase and decreased iron levels. Those patients who died in the hospital had more abnormal changes to their coagulation profile (D-dimer, international normalized ratio, prothrombin time, ferritin, fibrinogen), lymphocytopenia, and neutrophilia, and much higher levels of CRP and ESR, high blood urea nitrogen, AST, and γ-glutamyltransferase when compared with survivors ( Table 4 ). Platelet to lymphocyte ratio was associated with a higher in-hospital mortality odds ratio (1.003; 95% confidence interval, 1.002–1.004) adjusted for age and sex.

Laboratory Test Results (Median [IQR]), Stratified by Outcome

Statistically significant results at P values <.001 and parameters with levels higher/lower than the reference range are presented in bold. The number of patients is presented for each variable.

Abbreviations: IQR, interquartile range; PT, Prothrombin.

Results of the laboratory tests routinely performed in the clinical setting did not differ significantly between patients with confirmed and clinically diagnosed COVID-19 for 48 out of 51 parameters ( Table 1 ). Platelets, leukocytes, and neutrophil count were significantly lower in patients with confirmed COVID-19, but the differences were unlikely to be relevant, being within the normal reference ranges for both groups.

Patient Outcomes and Risk Factors

Among the 3480 patients who were discharged or died during hospitalization, the overall mortality was 5.5%, with a total number of 191 people who died.

In a univariate analysis, chronic cardiovascular disease, hypertension, chronic pulmonary disease, chronic kidney disease, chronic neurological disorder, malignant neoplasm, diabetes, and dementia significantly differed between survivors and patients who died ( Table 3 ). In multivariable analysis, older age was a predictor of in-hospital mortality with an odds ratio (per 1-year increase) of 1.05 (95% confidence interval, 1.03–1.06). Other predictors associated with in-hospital mortality were male sex (1.71; 1.24–2.37), chronic kidney disease (2.99; 1.89–4.64), diabetes (2.1; 1.46–2.99), chronic cardiovascular disease (1.78; 1.24–2.57), and dementia (2.73; 1.34–5.47) ( Figure 2 ). The same risk factors were significantly associated with the admission/transfer to the ICU, with only dementia not reaching statistical significance ( Supplementary Figure 1 ).

Odds ratios and 95% CIs for in-hospital mortality from a multivariable logistic regression model. Abbreviation: CI, confidence interval.

When including COVID-19 laboratory-confirmed/suspected status as a covariate in the multivariable logistic regression model we found no evidence that it was associated with mortality (odds ratio, 1.22; 95% confidence interval, .89–1.69) and it did not have major impact on the effect size and significance of other predictors ( Supplementary Figure 2 ).

We did not find any statistically significant association of CT severity grade with in-hospital mortality, adjusting for age and sex ( Supplementary Table 6 ). With respect to CT imaging, no evidence of difference was found between the patients with confirmed and clinically diagnosed COVID-19 ( Supplementary Table 7 ).

Hydroxychloroquine was the most frequently used (84%) medication, followed by antibiotics (azithromycin [77.7%] and ceftriaxone [30.3%]), heparin (56.4%), paracetamol (34.4%), mucolytics (25.4%), lopinavir/ritonavir (16.2%), and systemic corticosteroids (10.4%), respectively ( Supplementary Table 8 ). There was a significant overlap between the top 3 most commonly used medications, with hydroxychloroquine, azithromycin, and heparin used in 1322 patients ( Supplementary Figure 3 ).

To our knowledge, StopCOVID cohort is the first large-scale study of consecutively hospitalized patients with COVID-19 in Russia assessing clinical characteristics and risk factors for in-hospital mortality. This is also the first large cohort, including both RT-PCR–confirmed COVID-19 cases and patients, diagnosed with COVID-19 based on clinical and radiological presentation in the absence of the SARS-CoV-2 RT-PCR confirmation. We found that older age and male sex as well as existing comorbidities were associated with in-hospital mortality. We found no significant difference between patients with clinical COVID-19 and laboratory-confirmed COVID-19, either in clinical presentation or in clinical measurements and risk factors for in-hospital mortality. We feel it is entirely appropriate to treat patients with clinical and radiological signs of COVID-19 who do not have an alternative diagnosis to explain their symptoms equivalently to PCR-confirmed cases. Sequential RT-PCR testing can identify patients with COVID-19 whose initial result was false-negative [ 15 ]. In settings where repeat testing is not performed, it can also be appropriate to include patients with clinical and radiological COVID-19 alongside those with laboratory-confirmed disease.

Patients in our study were of an age very similar to the New York cohort [ 6 ] and of a much lower median age than similar cohorts in Italy [ 4 ] and the United Kingdom [ 8 ]. This may be partly explained by a lack of a clear message from the authorities to the public with regard to whom should present to a hospital. Healthcare-seeking behavior may further explain a younger age at admission, which differs between the countries. Russian people are known for active specialist-seeking behavior [ 16 ], particularly in the presence of distrust of media sources [ 17 ] and easy access to free healthcare. It is, however, more likely to be a reflection of varying approaches from health services in different countries.

Patients in Moscow typically presented with fever, fatigue, cough, and shortness of breath, which is in agreement with the previously reported symptom patterns in other countries [ 5 , 8 , 18 ]. Among symptoms, anosmia was associated with a more favorable outcome, which is similar to the data from Hopkins et al [ 19 ], which showed rapid improvement in patients with COVID-19 presenting with a loss of smell.

Similar to other cohorts, cardiological conditions, hypertension, obesity, and diabetes were common problems in the hospitalized population. The lower median age of the patients in our cohort may explain the lower comorbidity rate when compared with some other studies [ 6 , 8 ]. We recorded a much lower number of patients with chronic pulmonary diseases, which is in agreement with data from Richardson et al [ 7 ] but in contrast to other US [ 6 ] and particularly UK [ 8 ] cohorts. We also found low rates of asthma in our cohort, which did not exceed the prevalence in the general population, which has been reported previously [ 20 ].

Patient age, male sex, and the presence of major comorbidities were all predictors of in-hospital mortality. These findings are in line with other international cohorts [ 6 , 21 ], including a UK ISARIC study using a similar data-collection protocol [ 8 ]. We also found common changes in the coagulation profile [ 6 ] and previously reported clinical patterns, such as lymphocytopenia, neutrophilia, and very high levels of CRP and ESR in patients who subsequently died from COVID-19. The platelet to lymphocyte ratio has been previously reported to be associated with higher severity and mortality in patients with COVID-19 [ 22 ]. Our findings agree with previous research but require further validation.

The proportion of patients admitted to the ICU in our cohort study was much lower than in the similar cohorts from the United Kingdom (17%) [ 8 ] and the United States (14.2%) [ 7 ], but similar to published data from China [ 18 ]. The decision for ICU admission within the Sechenov University Hospital Network is normally based on a joint opinion of a multidisciplinary team of respiratory physicians and intensivists. Due to good access to high-flow oxygen and noninvasive ventilation within the COVID-19 wards, only critical patients were transferred into the ICU, which may explain the lesser need for ICU admission in our cohort. Active use of noninvasive ventilation on the wards may explain the low in-hospital mortality in this group of patients. As only the most severely unwell patients were admitted for invasive mechanical ventilation, this may explain the high mortality recorded in ICU patients. The overall mortality rate in our cohort was similar to the average worldwide estimate [ 23 ] but much lower than in other international cohorts of hospitalized individuals, which may be a direct reflection of their much younger age and moderate state of disease at the time of admission in most of the patients.

Half of the patients admitted to the Sechenov University Hospital Network did not have positive RT-PCR test results, despite having clinical features of COVID-19 infection. Our findings are similar to the US data, with 42% [ 5 ] to 51.8% [ 6 ] of individuals having negative RT-PCR test results. The false-negative rate of the RT-PCR tests varies between 20% and 66% depending on the day since symptom onset [ 10 ], meaning that results must be cautiously interpreted [ 24 ], which represents a major concern related to control of the pandemic [ 25 ]. Previous research suggests that a negative RT-PCR test result does not exclude the possibility of COVID-19. Repeated testing and sampling were shown to improve the sensitivity of RT-PCR [ 15 ]. To our knowledge, previous studies of patients with COVID-19 excluded those with suspected COVID-19 infection in the absence of a positive test result [ 3–8 ]. However, this approach differs from pragmatic clinical practice, in which, in the absence of an alternative diagnosis, patients with a clinical diagnosis of COVID-19 are treated equally to laboratory-confirmed cases. When evaluating radiological findings in COVID-19, it must be born in mind that some patients may present with clinical symptoms or extrapulmonary manifestations, such as hepatic, cardiovascular, or kidney injury, but initially will have normal CT findings [ 26 ]. In our study we did not solely rely on CT findings for clinical diagnosis of COVID-19. However, new approaches to minimize the exclusion of patients with false-negative RT-PCR results should be sought, as highlighted in a recent report suggesting real-time lung ultrasound as an auxiliary method to rule-in COVID-19 during screening [ 27 ].

Limitations

This cohort study has some limitations. First, the study population only included patients within Moscow. Second, the data were collected retrospectively from the electronic medical records with no access to additional information that could be potentially retrieved from the medical notes. Third, half of the patients in our cohort did not have RT-PCR–confirmed COVID-19 infection, although this is unlikely to affect the outcomes as we failed to find any significant differences between clinically diagnosed and laboratory-confirmed cases. Fourth, endpoint outcome data were available for 83% of admitted patients. Patients admitted and/or transferred to the ICU and receiving invasive mechanical ventilation can spend a significant amount of time attached to the machine [ 7 , 8 ]. The absence of data on patients (18%) who remained in the hospital at the time of data analysis completion may lead to bias and may influence overall mortality calculations. Fifth, morbidity related to invasive procedures or sequelae in clinically suspected and/or laboratory-confirmed cases has not been recorded. Sixth, the definition of “clinically diagnosed COVID-19” implies changes on chest CT and nonspecific signs and symptoms, which may be present in other respiratory viral illnesses. The scoring system used for radiological signs is able to differentiate between symptomatic and asymptomatic cases of COVID-19 but is not fully able to differentiate between COVID-19 from other similar conditions.

Conclusions

The clinical features, chest CT, and blood test results did not differ between test-confirmed and clinically diagnosed patients. Furthermore, clinical outcomes were also identical. Our study results suggest that in order to assess the full impact of this pandemic on populations, all clinically diagnosed patients should be included. Comorbidities associated with death were similar to other published studies on COVID-19. Mortality in our cohort was low, which may have been due to the mean age of patients being lower than in some other published studies. Anosmia was associated with milder disease while asthma did not appear to pose an increased risk of adverse outcome. As with other studies, manifestations of nonrespiratory problems including coagulopathy, immune deficiency, hyperinflammation and renal deficits were associated with higher risks of death. The data collection within StopCOVID cohort is continuing and further analysis focused on predictive models of adverse outcomes for routine clinical practice is in progress.

Supplementary materials are available at Clinical Infectious Diseases online. Consisting of data provided by the authors to benefit the reader, the posted materials are not copyedited and are the sole responsibility of the authors, so questions or comments should be addressed to the corresponding author.

Sechenov StopCOVID Research Team . Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia:Anna Berbenyuk, Polina Bobkova, Semyon Bordyugov, Aleksandra Borisenko, Ekaterina Bugaiskaya, Olesya Druzhkova, Dmitry Eliseev, Yasmin El-Taravi, Natalia Gorbova, Elizaveta Gribaleva, Rina Grigoryan, Shabnam Ibragimova, Khadizhat Kabieva, Alena Khrapkova, Natalia Kogut, Karina Kovygina, Margaret Kvaratskheliya, Maria Lobova, Anna Lunicheva, Anastasia Maystrenko, Daria Nikolaeva, Anna Pavlenko, Olga Perekosova, Olga Romanova, Olga Sokova, Veronika Solovieva, Olga Spasskaya, Ekaterina Spiridonova, Olga Sukhodolskaya, Shakir Suleimanov, Nailya Urmantaeva, Olga Usalka, Margarita Zaikina, Anastasia Zorina; 1C First Bit, Moscow, Russia:Nadezhda Khitrina.

Author contributions. D. M.: Conceptualization, methodology, validation, formal analysis, resources, data curation, writing (original draft, review, and editing), supervision, project administration. N. A. N.: Conceptualization, methodology, formal analysis, investigation, writing (original draft, review, and editing), visualization, project administration. P. B.: Conceptualization, methodology, investigation, writing (original draft, review, editing), project administration. O. B.: Conceptualization, methodology, software, validation, formal analysis, data curation, writing (original draft, review, and editing), visualization. M. K.: Formal analysis, investigation, writing (original draft, review, and editing), visualization. E. L.: Investigation, writing (original draft, review, and editing), project administration. A. G.: Investigation, writing (original draft, review, and editing), project administration. A. S.: Investigation, project administration. V. B.: Resources, writing (review and editing). P. T.: Resources, project administration, writing (review and editing). J. O. W., P. C., and C. A.: Writing (original draft, review, and editing). E. Bezrukov: Funding acquisition, writing (review and editing). M. E. P., A. Y., E. Bulanova, and N. T.: Writing (review and editing). S. A.: Writing (review and editing), investigation. V. K. and Y. P.: Writing (review and editing). E. A. D., C. K., and M. P.: Methodology, writing (review and editing). V. F.: Writing (review and editing). A. A. S.: Funding acquisition, writing (review and editing). D. B.: Conceptualization, methodology, resources, writing (review and editing), project administration, funding acquisition. P. G.: Project administration, funding acquisition, writing (review and editing), supervision. StopCOVID Research Team: Investigation, writing (review and editing).

Acknowledgments. The authors are very grateful to the Sechenov University Hospital Network clinical staff and to the patients, carers, and families for their kindness and understanding during these difficult times of the COVID-19 pandemic. We thank Dr Inna Tulina, Dr Yuri Kitsenko, Mrs Ekaterina Rebrova, and Mr Maksim Kholopov for providing technical support in data collection and database administration. We are grateful to Ms Olga Burencheva, Dr Daria Levina, Ms Olga Sokova, Ms Natalia Chepelova, and Ms Elizaveta Mikhsin for assistance in data extraction. We highly appreciate the kind expert advice from Professor Gareth Tudor-Williams, Dr Jethro Herberg, Dr Nikita Sushentsev, and Dr Anna Pokshubina for assistance in data interpretation. Finally, we extend our gratitude to Laura Merson and the entire ISARIC team for their continuous support and expertise and for providing access to the REDCap CRF module.

Financial support. This work was supported by the Russian Academic Excellence Project “5–100” and Russian Foundation for Basic Research (RFBR) (grant number 20-04-60063).

Potential conflicts of interest. J. W. reports grants and personal fees from Danone/Nutricia and Airsonnet, nonfinancial support from Anaphylaxis Campaign, and lecture fees from Friesland Campina, outside the submitted work. All other authors report no potential conflicts. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.

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Author notes

• comorbidity
• hospital mortality
• hospitals, university
• laboratory techniques and procedures
• reverse transcriptase polymerase chain reaction
• signs and symptoms

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Stop COVID Cohort: An Observational Study of 3480 Patients Admitted to the Sechenov University Hospital Network in Moscow City for Suspected Coronavirus Disease 2019 (COVID-19) Infection

Collaborators.

• Sechenov StopCOVID Research Team : Anna Berbenyuk ,  Polina Bobkova ,  Semyon Bordyugov ,  Aleksandra Borisenko ,  Ekaterina Bugaiskaya ,  Olesya Druzhkova ,  Dmitry Eliseev ,  Yasmin El-Taravi ,  Natalia Gorbova ,  Elizaveta Gribaleva ,  Rina Grigoryan ,  Shabnam Ibragimova ,  Khadizhat Kabieva ,  Alena Khrapkova ,  Natalia Kogut ,  Karina Kovygina ,  Margaret Kvaratskheliya ,  Maria Lobova ,  Anna Lunicheva ,  Anastasia Maystrenko ,  Daria Nikolaeva ,  Anna Pavlenko ,  Olga Perekosova ,  Olga Romanova ,  Olga Sokova ,  Veronika Solovieva ,  Olga Spasskaya ,  Ekaterina Spiridonova ,  Olga Sukhodolskaya ,  Shakir Suleimanov ,  Nailya Urmantaeva ,  Olga Usalka ,  Margarita Zaikina ,  Anastasia Zorina ,  Nadezhda Khitrina

Affiliations

• 1 Department of Pediatrics and Pediatric Infectious Diseases, Institute of Child's Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
• 2 Inflammation, Repair, and Development Section, National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom.
• 3 Soloviev Research and Clinical Center for Neuropsychiatry, Moscow, Russia.
• 4 School of Physics, Astronomy, and Mathematics, University of Hertfordshire, Hatfield, United Kingdom.
• 5 Biobank, Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
• 6 Institute for Regenerative Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
• 7 Chemistry Department, Lomonosov Moscow State University, Moscow, Russia.
• 8 Department of Polymers and Composites, N. N. Semenov Institute of Chemical Physics, Moscow, Russia.
• 9 Department of Clinical and Experimental Medicine, Section of Pediatrics, University of Pisa, Pisa, Italy.
• 10 Institute of Social Medicine and Health Systems Research, Faculty of Medicine, Otto von Guericke University Magdeburg, Magdeburg, Germany.
• 11 Institute for Urology and Reproductive Health, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
• 12 Department of Intensive Care, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
• 13 Clinic of Pulmonology, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
• 14 Department of Internal Medicine No. 1, Institute of Clinical Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
• 15 Department of Forensic Medicine, Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
• 16 Department of Statistics, University of Oxford, Oxford, United Kingdom.
• 17 Medical Research Council Population Health Research Unit, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
• 18 Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom.
• 19 Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom.
• 20 Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
• PMID: 33035307
• PMCID: PMC7665333
• DOI: 10.1093/cid/ciaa1535

Background: The epidemiology, clinical course, and outcomes of patients with coronavirus disease 2019 (COVID-19) in the Russian population are unknown. Information on the differences between laboratory-confirmed and clinically diagnosed COVID-19 in real-life settings is lacking.

Methods: We extracted data from the medical records of adult patients who were consecutively admitted for suspected COVID-19 infection in Moscow between 8 April and 28 May 2020.

Results: Of the 4261 patients hospitalized for suspected COVID-19, outcomes were available for 3480 patients (median age, 56 years; interquartile range, 45-66). The most common comorbidities were hypertension, obesity, chronic cardiovascular disease, and diabetes. Half of the patients (n = 1728) had a positive reverse transcriptase-polymerase chain reaction (RT-PCR), while 1748 had a negative RT-PCR but had clinical symptoms and characteristic computed tomography signs suggestive of COVID-19. No significant differences in frequency of symptoms, laboratory test results, and risk factors for in-hospital mortality were found between those exclusively clinically diagnosed or with positive severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RT-PCR. In a multivariable logistic regression model the following were associated with in-hospital mortality: older age (per 1-year increase; odds ratio, 1.05; 95% confidence interval, 1.03-1.06), male sex (1.71; 1.24-2.37), chronic kidney disease (2.99; 1.89-4.64), diabetes (2.1; 1.46-2.99), chronic cardiovascular disease (1.78; 1.24-2.57), and dementia (2.73; 1.34-5.47).

Conclusions: Age, male sex, and chronic comorbidities were risk factors for in-hospital mortality. The combination of clinical features was sufficient to diagnose COVID-19 infection, indicating that laboratory testing is not critical in real-life clinical practice.

Keywords: COVID-19; Russia; SARS-CoV-2; cohort; mortality risk factors.

© The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: [email protected].

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A university of oxford researcher is testing a revolutionary vaccine to prevent the onset of lung cancer.

The fight against cancer has been decades in the works, killing millions of people every year as researchers struggle to find a cure for the disease’s various mutations.

That might soon change, with researchers at the University of Oxford in the process of developing a potential cure for one of the world’s most prevalent and deadly forms of cancer.

Speaking to Spanish publication El País , Oxford researcher Sarah Blagden said she hoped a prospective vaccine to guard against lung cancer might be the first step in one injection, delivered to patients by the age of 40, being used to guard against all major cancers.

Blagden is part of the research team working on LungVax , a world-first attempt to prevent lung cancer with a vaccine, using technology first deployed for the COVID-19 vaccine and developed by Oxford University and AstraZeneca.

There are other attempts to prevent cancer with a shot—researchers in the U.S. are developing a vaccine to prevent colorectal cancer. The HPV vaccine also indirectly helps prevent the development of cervical cancer in women.

The LungVax vaccine seeks to stamp out one of the most deadly cancers, lung cancer, which is responsible for about 1.8 million deaths globally each year.

The group expects to start recruiting patients in 2026. If it proves a success, a vaccine to prevent lung cancer might be available for the public within 10 years.

“We think the vaccine could cover around 90% of all lung cancers, based on our computer models and previous research, and this funding will allow us to take the vital first steps towards trials in patients,” said Mariam Jamal-Hanjani, a professor at University College London and the lead on the LungVax clinical trial, as the group announced a £1.7 million ($2.1 million) grant.

“LungVax will not replace stopping smoking as the best way to reduce your risk of lung cancer. But it could offer a viable route to preventing some of the earliest-stage cancers from emerging in the first place,” Jamal-Hanjani noted.

Researchers appear to be in a race against time to more effectively treat and prevent cancer as global populations age, ramping up the pressure on health care services.

In February, the World Health Organization warned global cancer rates were expected to rise by 77% by 2050. In addition to aging, obesity and alcohol and tobacco use are expected to play a role in the uptick in cases.

Blagden hopes that, eventually, major cancers will be prevented with a simple doctor’s appointment to receive a life-changing injection. “Ultimately, what I would love to see is a vaccine given to everyone at a certain age, around 40 or 50, to protect them from the major cancers later on in life. That’s where I think we should be, but we have to start somewhere,” Blagden told El País .

This story was originally featured on Fortune.com

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NHS baby check may miss dislocated hips in newborn babies

by University of Oxford

A collaboration between the University of Oxford and University of Leeds, it shows that the tests currently used by doctors can be unreliable. Using data on 27,000 babies, their research , published in JAMA , found the best tests miss over half of abnormal hips—and indicates that for every 1,000 hips screened, four dislocations will be identified, but five will be missed.

Developmental dysplasia of the hip (DDH) is a condition where the "ball and socket" joint of the hip does not properly form in babies and young children . DDH is one of the most common musculoskeletal conditions in infants and around one in every 100 babies are born with the ball of the hip bone dislocated from the socket.

If a dislocated hip is diagnosed early, it can usually be treated with a removable brace that the baby wears over their clothes for a couple of months. However, delayed diagnosis often requires complex surgery, and can lead to early arthritis requiring a hip replacement when the children become young adults.

The NHS baby hip screening checks involve several clinical tests , performed within 72 hours of birth and again between 6-8 weeks. However, researchers highlight there has been some doubt concerning the reliability of these tests, so the study set out to evaluate the diagnostic accuracy of clinical examination in identifying dislocated hips in infants.

Professor Daniel Perry, NIHR Professor at the University of Liverpool and a children's orthopaedic surgeon at Alder Hey Children's Hospital said, "Doctors have known for some time that there are challenges with the newborn hip examination and this study confirms the problem. What brings additional concern is that all hip examinations in this study were conducted by experts in hip disease—which is not usually the case in the NHS—so the true problem may be bigger. Nevertheless, we're determined to make things better.

"NHS England has recently started working with the National Institute for Health and Care Research (NIHR) to begin research to improve hip screening among babies. Earlier this month NHS England launched a change in the hip screening pathway for babies, which will enable the UK to be world leaders in research this area.

"We hope that this could soon mean new tests on the hips of babies, such as artificial intelligence enabled ultrasound. It's heartening to see a new collaboration between NHS England and NIHR which I hope will translate into better care for children and young people."

Dr. Abhinav Singh, Orthopaedic Surgeon and Researcher at NDORMS said, "It is important that doctors are well trained in the hip examination and know which tests can help identify hip abnormalities. However, our findings clearly highlight that examining babies' hips will not identify all dislocations. Emphasizing certain examination maneuvers that can assist in early diagnosis of DDH is of clear benefit to infants, their families and the clinician, but our results also provide evidence of the fallibility of the examination."

The study pooled data from 50, 579 hip joints in 27, 175 infants examined between 1992 to 2021 in screening programs run by experts. Statistical models were used to calculate the sensitivity, specificity and likelihood ratios of different examination maneuvers in identifying a dislocated hip in infants aged three months or younger.

While useful in some children, researchers identified that the clinical examination missed more dislocated hips than it was able to identify. The only part of the test that the authors identified to be useful is called the "Barlow/Ortolani maneuver." The authors concluded that other parts of the test, including limited hip abduction and a clicking sound had no clear diagnostic utility.

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Artificial intelligence beats doctors in accurately assessing eye problems

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A study has found that the AI model GPT-4 significantly exceeds the ability of non-specialist doctors to assess eye problems and provide advice.

We could realistically deploy AI in triaging patients with eye issues to decide which cases are emergencies. Arun Thirunavukarasu

The clinical knowledge and reasoning skills of GPT-4 are approaching the level of specialist eye doctors, a study led by the University of Cambridge has found.

GPT-4 - a ‘large language model’ - was tested against doctors at different stages in their careers, including unspecialised junior doctors, and trainee and expert eye doctors. Each was presented with a series of 87 patient scenarios involving a specific eye problem, and asked to give a diagnosis or advise on treatment by selecting from four options.

GPT-4 scored significantly better in the test than unspecialised junior doctors, who are comparable to general practitioners in their level of specialist eye knowledge.

GPT-4 gained similar scores to trainee and expert eye doctors - although the top performing doctors scored higher.

The researchers say that large language models aren’t likely to replace healthcare professionals, but have the potential to improve healthcare as part of the clinical workflow.

They say state-of-the-art large language models like GPT-4 could be useful for providing eye-related advice, diagnosis, and management suggestions in well-controlled contexts, like triaging patients, or where access to specialist healthcare professionals is limited.

“We could realistically deploy AI in triaging patients with eye issues to decide which cases are emergencies that need to be seen by a specialist immediately, which can be seen by a GP, and which don’t need treatment,” said Dr Arun Thirunavukarasu, lead author of the study, which he carried out while a student at the University of Cambridge’s School of Clinical Medicine.

He added: “The models could follow clear algorithms already in use, and we’ve found that GPT-4 is as good as expert clinicians at processing eye symptoms and signs to answer more complicated questions.

“With further development, large language models could also advise GPs who are struggling to get prompt advice from eye doctors. People in the UK are waiting longer than ever for eye care.

Large volumes of clinical text are needed to help fine-tune and develop these models, and work is ongoing around the world to facilitate this.

The researchers say that their study is superior to similar, previous studies because they compared the abilities of AI to practicing doctors, rather than to sets of examination results.

“Doctors aren't revising for exams for their whole career. We wanted to see how AI fared when pitted against to the on-the-spot knowledge and abilities of practicing doctors, to provide a fair comparison,” said Thirunavukarasu, who is now an Academic Foundation Doctor at Oxford University Hospitals NHS Foundation Trust.

He added: “We also need to characterise the capabilities and limitations of commercially available models, as patients may already be using them - rather than the internet - for advice.”

The test included questions about a huge range of eye problems, including extreme light sensitivity, decreased vision, lesions, itchy and painful eyes, taken from a textbook used to test trainee eye doctors. This textbook is not freely available on the internet, making it unlikely that its content was included in GPT-4’s training datasets.

The results are published today in the journal PLOS Digital Health .

“Even taking the future use of AI into account, I think doctors will continue to be in charge of patient care. The most important thing is to empower patients to decide whether they want computer systems to be involved or not. That will be an individual decision for each patient to make,” said Thirunavukarasu.

GPT-4 and GPT-3.5 – or ‘Generative Pre-trained Transformers’ - are trained on datasets containing hundreds of billions of words from articles, books, and other internet sources. These are two examples of large language models; others in wide use include Pathways Language Model 2 (PaLM 2) and Large Language Model Meta AI 2 (LLaMA 2).

The study also tested GPT-3.5, PaLM2, and LLaMA with the same set of questions. GPT-4 gave more accurate responses than all of them.

GPT-4 powers the online chatbot ChatGPT to provide bespoke responses to human queries. In recent months, ChatGPT has attracted significant attention in medicine for attaining passing level performance in medical school examinations, and providing more accurate and empathetic messages than human doctors in response to patient queries.

The field of artificially intelligent large language models is moving very rapidly. Since the study was conducted, more advanced models have been released - which may be even closer to the level of expert eye doctors.

Reference: Thirunavukarasu, A J et al: ‘ Large language models approach expert-level clinical knowledge and reasoning in ophthalmology: A head-to-head cross-sectional study .’ PLOS Digital Health, April 2024. DOI: 10.1371/journal.pdig.0000341

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Researchers find oldest undisputed evidence of Earth's magnetic field

by University of Oxford

A new study, led by the University of Oxford and MIT, has recovered a 3.7-billion-year-old record of Earth's magnetic field, and found that it appears remarkably similar to the field surrounding Earth today. The findings have been published in the Journal of Geophysical Research .

Without its magnetic field, life on Earth would not be possible since this shields us from harmful cosmic radiation and charged particles emitted by the sun (the ' solar wind '). But up to now, there has been no reliable date for when the modern magnetic field was first established.

In the study, the researchers examined an ancient sequence of iron-containing rocks from Isua, Greenland. Iron particles effectively act as tiny magnets that can record both magnetic field strength and direction when the process of crystallization locks them in place. The researchers found that rocks dating from 3.7 billion years ago captured a magnetic field strength of at least 15 microtesla comparable to the modern magnetic field (30 microtesla).

These results provide the oldest estimate of the strength of Earth's magnetic field derived from whole rock samples, which provide a more accurate and reliable assessment than previous studies which used individual crystals.

Lead researcher Professor Claire Nichols (Department of Earth Sciences, University of Oxford) said, "Extracting reliable records from rocks this old is extremely challenging, and it was really exciting to see primary magnetic signals begin to emerge when we analyzed these samples in the lab. This is a really important step forward as we try and determine the role of the ancient magnetic field when life on Earth was first emerging."

While the magnetic field strength appears to have remained relatively constant, the solar wind is known to have been significantly stronger in the past. This suggests that the protection of Earth's surface from the solar wind has increased over time, which may have allowed life to move onto the continents and leave the protection of the oceans.

Earth's magnetic field is generated by mixing of the molten iron in the fluid outer core, driven by buoyancy forces as the inner core solidifies, which create a dynamo. During Earth's early formation, the solid inner core had not yet formed, leaving open questions about how the early magnetic field was sustained.

These new results suggest the mechanism driving Earth's early dynamo was similarly efficient to the solidification process that generates Earth's magnetic field today.

Understanding how Earth's magnetic field strength has varied over time is also key for determining when Earth's inner, solid core began to form. This will help us to understand how rapidly heat is escaping from Earth's deep interior, which is key for understanding processes such as plate tectonics.

A significant challenge in reconstructing Earth's magnetic field so far back in time is that any event which heats the rock can alter preserved signals. Rocks in the Earth's crust often have long and complex geological histories which erase previous magnetic field information.

However, the Isua Supracrustal Belt has a unique geology, sitting on top of thick continental crust which protects it from extensive tectonic activity and deformation. This allowed the researchers to build a clear body of evidence supporting the existence of the magnetic field 3.7 billion years ago.

The results may also provide new insights into the role of our magnetic field in shaping the development of Earth's atmosphere as we know it, particularly regarding atmospheric escape of gases.

A currently unexplained phenomenon is the loss of the unreactive gas xenon from our atmosphere more than 2.5 billion years ago. Xenon is relatively heavy and therefore unlikely to have simply drifted out of our atmosphere. Recently, scientists have begun to investigate the possibility that charged xenon particles were removed from the atmosphere by the magnetic field.

In the future, researchers hope to expand our knowledge of Earth's magnetic field prior to the rise of oxygen in Earth's atmosphere around 2.5 billion years ago by examining other ancient rock sequences in Canada, Australia, and South Africa.

A better understanding of the ancient strength and variability of Earth's magnetic field will help us to determine whether planetary magnetic fields are critical for hosting life on a planetary surface and their role in atmospheric evolution.

Provided by University of Oxford

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