̽��ֱ�� of Cambridge - hospitals

A very healthy relationship: the ̽��ֱ�� and the NHS

cjb250 — Mon, 03 Jul 2023 15:40:21 +0000

As the NHS celebrates its 75th anniversary, we look at how the close relationship between the ̽��ֱ�� and the hospitals on its doorstep is driving major improvements in how we care for patients.

Minority ethnic doctors less likely to get specialty NHS training posts while some specialties show gender bias

cjb250 — Wed, 19 Apr 2023 23:01:16 +0000

Their analysis, published today in BMJ Open, also found that while female applicants are more successful overall, particular specialities tend to appeal to different genders.

Researchers from the ̽��ֱ�� of Cambridge and Cambridge ̽��ֱ�� Hospitals NHS Foundation Trust examined data from applicants to Specialty Training Posts through Health Education England for the recruitment cycle 2021-22 to look at potential disparities in the success of applicants according to gender, ethnicity and disability.

During this period, there were just under 12,500 successful applicants to Health Education England for training posts – a success rate of one in three (32.7%). Overall, females were more successful than males (37.0% versus 29.1%).

̽��ֱ��researchers found clear evidence that certain specialities were more attractive to females or to males. Of note, surgical specialities and radiology had the highest proportion of male applicants (65.3% and 64.3% respectively), while obstetrics and gynaecology and public health had the highest proportion of female applicants (72.4% and 67.2% respectively).

Senior author Professor Sharon Peacock, from the Department of Medicine at the ̽��ֱ�� of Cambridge, said: “ ̽��ֱ��success by female applicants in many specialties is a positive step towards gender balance, and perhaps reflects existing efforts to address disparities. But the skew in applications and subsequent recruitment by gender, particularly amongst surgical specialities, is concerning.”

Gender disparities are known to have knock-on effects. For example, a lack of female representation contributes towards a male dominated culture, which can then result in fewer female role models to inspire and encourage aspiring female doctors.

̽��ֱ��researchers say there are several reasons for these disparities. In surgical specialities, for example, a male-dominated workplace culture, bullying and harassment, few female role models, and career inflexibility, have been suggested as factors that deter females from applying. Female surgeons have reported quality of life and fewer unsocial hours as explanations of why women prefer other clinical specialities, in addition to the fear that working less-than-full-time or taking career breaks is perceived negatively.

Approximately half (50.2%) of the applicants were non-UK graduates. ̽��ֱ��overall success rate of UK graduates was 44.5%, compared with 22.8% for non-UK graduates.

When it came to minority ethnic groups, after adjusting for country of graduation, applicants from eleven out of fifteen groups (73.3%) were significantly less likely to be successful compared to White British. Those who fared worst were those of Mixed White and Black African ethnicity, who were only half as likely (52%) to be successful as White British applicants.

Dr Dinesh Aggarwal, the study’s first author, also from the Department of Medicine, said: “ ̽��ֱ��data suggests there’s a need to review recruitment policies and processes from a diversity and inclusion perspective. But the issues extend beyond recruitment – doctors from minority ethnic groups can struggle to progress within the NHS and report disproportionately high levels of discrimination from colleagues.

“More than four in ten of the medical and dental workforce in NHS trusts and clinical commissioning groups in England are from a minority ethnic group, and ensuring that they are able to work within an inclusive environment, that allows them to thrive and progress, should be a priority.”

Although only a very small proportion of successful applicants (1.4%) declared a disability, they were more likely to be successful (38.6% compared with 32.8% of non-disabled applicants). However, there were no disabled applicants to 22.4% of the specialities, and for a further 36.2% of specialities, no disabled applicants were accepted.

Dr Dinesh Aggarwal added: “It’s encouraging to see a high proportion of acceptances among individuals disclosing a disability. ̽��ֱ��NHS needs to ensure that application and recruitment processes are accessible and open to adjustments for all disabilities, eliminate any fear of discrimination, and provide assurance that all NHS workplaces will accommodate reasonable adjustments to ensure that disabled doctors can carry out their work. This will not only help to encourage more disabled applicants, but also allow disabled clinicians to feel more comfortable disclosing this information.”

Professor Peacock added: “ ̽��ֱ��NHS is the largest employer in the UK and it’s vital that it nurtures diverse talent to benefit patient care. People from diverse backgrounds bring different lived experiences and perspectives, which in turn strengthens the pool of knowledge and skills within the NHS. A lack of workforce diversity can be detrimental to patient care, and research shows that inherent biases can influence how clinicians treat patients.”

Dr Aggarwal is a PhD student at Churchill College. Professor Peacock is a Fellow at St John’s College.

Reference
Aggarwal, D et al. Applications to medical and surgical specialist training in the UK National Health Service, 2021-22: a cross-sectional observational study to characterise the diversity of successful applicants. BMJ Open; 20 April 2023; DOI: 10.1136/bmjopen-2022-069846

Most minority ethnic groups are less successful than their White British counterparts when applying to specialty training programmes in the NHS, Cambridge researchers have shown.

̽��ֱ��NHS is the largest employer in the UK and it’s vital that it nurtures diverse talent to benefit patient care

Sharon Peacock

DarkoStojanovic

Doctor's white coat with stethoscope and pens

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Partnering with local hospitals during COVID-19

lw355 — Tue, 13 Dec 2022 09:20:13 +0000

From developing intensive care equipment to tackling supplies of PPE and oxygen, here's how a group of Cambridge researchers refocused and partnered with local hospitals during COVID-19.

Air filter significantly reduces presence of airborne SARS-CoV-2 in COVID-19 wards

cjb250 — Tue, 16 Nov 2021 08:36:58 +0000

While the discovery could have implications for improving the safety of repurposed ‘surge wards’, the researchers say it also opens up the possibility of being able to set standards for cleaner air to reduce the risk of airborne transmission of infections.

Over the duration of the pandemic there has been a steady rise in the evidence that the SARS-CoV-2 virus can be transmitted through the air in small droplets (aerosols). But as hospitals have seen their capacity overwhelmed, they have been forced to manage many of their COVID-19 patients in repurposed ‘surge’ wards, which often lack the ability to change the air with a high frequency. While the use of appropriate personal protective equipment (PPE) protects staff and patients significantly reduces the risk of transmission, there are still reports of patient-to-healthcare worker transmission of the virus, potentially through the inhalation of viral particles.

A team at the ̽��ֱ�� of Cambridge and Cambridge ̽��ֱ�� Hospitals (CUH) NHS Foundation Trust investigated whether portable air filtration/UV sterilisation devices could reduce airborne SARS- CoV-2 in general wards that had been repurposed as a COVID ward and a COVID Intensive Care Unit (ICU). ̽��ֱ��results are published in Clinical Infectious Diseases.

Dr Vilas Navapurkar, a Consultant in Intensive Care Medicine at CUH, who led the study, said: “Reducing airborne transmission of the coronavirus is extremely important for the safety of both patients and staff. Effective PPE has made a huge difference, but anything we can do to reduce the risk further is important.”

“Because of the numbers of patients being admitted with COVID-19, hospitals have had to use wards not designed for managing respiratory infections. During an intensely busy time, we were able to pull together a team from across the hospital and ̽��ֱ�� to test whether portable air filtration devices, which are relatively inexpensive, might remove airborne SARS-CoV-2 and make these wards safer.”

̽��ֱ��team performed their study in two repurposed COVID-19 units in Addenbrooke’s Hospital. One area was a surge ward managing patients who required simple oxygen treatment or no respiratory support; the second was a surge ICU managing patients who required ventilation either through non-invasive mask ventilation or invasive respiratory support, such as involving the use of an invasive tube and tracheostomy.

̽��ֱ��team installed a High Efficiency Particulate Air (HEPA) air filter/UV steriliser. HEPA filters are made up of thousands of fibres knitted together to form a material that filters out particles above a certain size. ̽��ֱ��machines were placed in fixed positions and operated continuously for seven days, filtering the full volume of air in each room between five and ten times per hour.

In the surge ward, during the first week prior to the air filter being activated, the researchers were able to detect SARS-CoV-2 on all sampling days. Once the air filter was switched on and run continuously, the team were unable to detect SARS-CoV-2 on any of the five testing days. They then switched off the machine and repeated the sampling – once again, they were able to detect SARS-CoV-2 on three of the five sampling days.

On the ICU, the team found limited evidence of airborne SARS-CoV-2 in the weeks when the machine was switched off and traces of the virus on one sampling day when the machine was active.

Additionally, the air filters significantly reduced levels of bacterial, fungal and other viral bioaerosols on the both the surge ward and the ICU, highlighting an added benefit of the system.

First author Dr Andrew Conway Morris, from the Department of Medicine at the ̽��ֱ�� of Cambridge, said: “We were really surprised by quite how effect air filters were at removing airborne SARS-CoV-2 on the wards. Although it was only a small study, it highlights their potential to improve the safety of wards, particularly in areas not designed for managing highly infectious diseases such as COVID-19.”

Crucially, the research team developed a robust technique for assessing the quality of air, involving placing air samplers at various points in the room and then testing the samples using PCR assays similar those used in the ‘gold standard’ COVID-19 tests.

Professor Stephen Baker, from the Cambridge Institute of Therapeutic Immunology and Infectious Disease at the ̽��ֱ�� of Cambridge, said: “Cleaner air will reduce the risk of airborne disease transmission, but it’s unlikely to be the case that just installing an air filter will be enough to guarantee the air is clean enough. Every room and every situation will be different. A key part of our work has been developing a robust way of measuring air quality.”

Dr Navapurkar added: “We’re all familiar with the idea of having standards for clean water and of hygiene standards for food. We need now to agree standards for what is acceptable air quality and how we meet and monitor those standards.”

̽��ֱ��research was supported by Wellcome, the Medical Research Council and the National Institute for Health Research Cambridge Biomedical Research Centre.

Reference
Conway Morris, A, et al. ̽��ֱ��removal of airborne SARS-CoV-2 and other microbial bioaerosols by air filtration on COVID-19 surge units. Clin Inf Dis; 30 Oct 2021; DOI: 10.1093/cid/ciab933

Image: Dr Vilas Navapurkar in ICU beside an air filter

When a team of doctors, scientists and engineers at Addenbrooke’s Hospital and the ̽��ֱ�� of Cambridge placed an air filtration machine in COVID-19 wards, they found that it removed almost all traces of airborne SARS-CoV-2.

Reducing airborne transmission of the coronavirus is extremely important for the safety of both patients and staff

Vilas Navapurkar

Medi2Go

Coronavirus

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. Images, including our videos, are Copyright © ̽��ֱ�� of Cambridge and licensors/contributors as identified. All rights reserved. We make our image and video content available in a number of ways – as here, on our main website under its Terms and conditions, and on a range of channels including social media that permit your use and sharing of our content under their respective Terms.

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Proper fit of face masks is more important than material, study suggests

sc604 — Thu, 11 Feb 2021 11:23:02 +0000

̽��ֱ��researchers, from the ̽��ֱ�� of Cambridge, carried out a series of different fit tests, and found that when a high-performance mask – such as an N95, KN95 or FFP2 mask – is not properly fitted, it performs no better than a cloth mask. Minor differences in facial features, such as the amount of fat under the skin, make significant differences in how well a mask fits.

̽��ֱ��results, published in the journal PLoS ONE, also suggest that the fit-check routine used in many healthcare settings has high failure rates, as minor leaks may be difficult or impossible to detect by the wearer. While the sample size was small, the researchers hope their findings will help develop new fit tests that are quick and reliable, in the case of future public health emergencies. ̽��ֱ��current study only evaluated the impact of fit on the wearer of the mask – the team will evaluate how fit impacts the protection of others in future research.

̽��ֱ��COVID-19 pandemic has made well-fitting face masks a vital piece of protective equipment for healthcare workers and civilians. While the importance of wearing face masks in slowing the spread of the virus has been demonstrated, there remains a lack of understanding about the role that good fit plays in ensuring their effectiveness.

“We know that unless there is a good seal between the mask and the wearer’s face, many aerosols and droplets will leak through the top and sides of the mask, as many people who wear glasses will be well aware of,” said Eugenia O’Kelly from Cambridge’s Department of Engineering, the paper’s first author. “We wanted to quantitatively evaluate the level of fit offered by various types of masks, and most importantly, assess the accuracy of implementing fit-checks by comparing fit-check results to quantitative fit testing results.”

For the study, seven participants first evaluated N95 and KN95 masks by performing a fit check, according to NHS guidelines. Participants then underwent quantitative fit testing – which uses a particle counter to measure the concentration of particles inside and outside the mask – while wearing N95 and KN95 masks, surgical masks, and fabric masks. ̽��ֱ��results assessed the protection to the mask wearer, which is important in clinical settings.

N95 masks – which are a similar standard to the FFP3 masks available in the UK and the rest of Europe – offered higher degrees of protection than the other categories of masks tested; however, most N95 masks failed to fit the participants adequately.

In their study, the researchers found that when fitted properly, N95 masks filtered more than 95% of airborne particles, offering superior protection. However, in some cases, poorly-fitted N95 masks were only comparable with surgical or cloth masks.

“It’s not enough to assume that any single N95 model will fit the majority of a population,” said O’Kelly. “ ̽��ֱ��most widely-fitting mask we looked at, the 8511 N95, fit only three out of the seven participants in our study.”

One observation the researchers made during their study was the width of the flange of the mask - the area of the material which comes in contact with the skin – may be a critical feature to fit. Masks which fit the greatest number of participants tended to have wider, more flexible flanges around the border.

In addition, small facial differences were observed to have a significant impact on quantitative fit. “Fitting the face perfectly is a difficult technical challenge and, as our research showed, small differences such as a centimetre wider nose or slightly fuller cheeks can make or break the fit of a mask,” said O’Kelly.

Self-performed fit-checks are attractive because they save on time and resources, and are often the only method of fit testing available. However, this study, and studies of fit-check systems in other countries, indicate that such fit-check systems are not reliable.

̽��ֱ��researchers hope that their results will be of use for those who are working on new technologies and programmes to assess fit, so that healthcare and other frontline workers are adequately protected in the case of any future pandemics. Additionally, they hope these results will bring attention to the importance of fit in clinical-grade masks, especially if such masks are to be widely used by the public. This study did not evaluate the impact of fit on protecting others, which is a future area of research.

Reference:
Eugenia O’Kelly et al. ‘Comparing the fit of N95, KN95, surgical, and cloth face masks and assessing the accuracy of fit checking.’ PLoS ONE (2021). DOI: 10.1371/journal.pone.0245688

A team of researchers studying the effectiveness of different types of face masks has found that in order to provide the best protection against COVID-19, the fit of a mask is as important, or more important, than the material it is made of.

Fitting the face perfectly is a difficult technical challenge and small differences, such as a centimetre wider nose or slightly fuller cheeks, can make or break the fit of a mask

Eugenia O'Kelly

Defence Images

Doctor in the Fight Against Ebola in Sierra Leone

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DNA test can quickly identify pneumonia in patients with severe COVID-19, aiding faster treatment

sc604 — Fri, 15 Jan 2021 06:00:00 +0000

For patients with the most severe forms of COVID-19, mechanical ventilation is often the only way to keep them alive, as doctors use anti-inflammatory therapies to treat their inflamed lungs. However, these patients are susceptible to further infections from bacteria and fungi that they may acquire while in hospital – so called ‘ventilator-associated pneumonia’.

Now, a team of scientists and doctors at the ̽��ֱ�� of Cambridge and Cambridge ̽��ֱ�� Hospitals NHS Foundation Trust, led by Professor Gordon Dougan, Dr Vilas Navapurkar and Dr Andrew Conway Morris, have developed a simple DNA test to quickly identify these infections and target antibiotic treatment as needed.

̽��ֱ��test, developed at Addenbrooke’s hospital in collaboration with Public Health England, gives doctors the information they need to start treatment within hours rather than days, fine-tuning treatment as required and reducing the inappropriate use of antibiotics. This approach, based on higher throughput DNA testing, is being rolled out at Cambridge ̽��ֱ�� Hospitals and offers a route towards better treatments for infection more generally. ̽��ֱ��results are reported in the journal Critical Care.

Patients who need mechanical ventilation are at significant risk of developing secondary pneumonia while they are in intensive care. These infections are often caused by antibiotic-resistant bacteria, and are hard to diagnose and need targeted treatment.

“Early on in the pandemic we noticed that COVID-19 patients appeared to be particularly at risk of developing secondary pneumonia, and started using a rapid diagnostic test that we had developed for just such a situation,” said co-author Dr Andrew Conway Morris from Cambridge’s Department of Medicine and an intensive care consultant. “Using this test, we found that patients with COVID-19 were twice as likely to develop secondary pneumonia as other patients in the same intensive care unit.”

COVID-19 patients are thought to be at increased risk of infection for several reasons. Due to the amount of lung damage, these severe COVID-19 cases tend to spend more time on a ventilator than patients without COVID-19. In addition, many of these patients also have a poorly-regulated immune system, where the immune cells damage the organs, but also have impaired anti-microbial functions, increasing the risk of infection.

Normally, confirming a pneumonia diagnosis is challenging, as bacterial samples from patients need to be cultured and grown in a lab, which is time-consuming. ̽��ֱ��Cambridge test takes an alternative approach by detecting the DNA of different pathogens, which allows for faster and more accurate testing.

̽��ֱ��test uses multiple polymerase chain reaction (PCR) which detects the DNA of the bacteria and can be done in around four hours, meaning there is no need to wait for the bacteria to grow. “Often, patients have already started to receive antobiotics before the bacteria have had time to grow in the lab,” said Morris. “This means that results from cultures are often negative, whereas PCR doesn’t need viable bacteria to detect – making this a more accurate test.”

̽��ֱ��test – which was developed with Dr Martin Curran, a specialist in PCR diagnostics from Public Health England’s Cambridge laboratory – runs multiple PCR reactions in parallel, and can simultaneously pick up 52 different pathogens, which often infect the lungs of patients in intensive care. At the same time, it can also test for antibiotic resistance.

“We found that although patients with COVID-19 were more likely to develop secondary pneumonia, the bacteria that caused these infections were similar to those in ICU patients without COVID-19,” said lead author Mailis Maes, also from the Department of Medicine. “This means that standard antibiotic protocols can be applied to COVID-19 patients.”

This is one of the first times that this technology has been used in routine clinical practice and has now been approved by the hospital. ̽��ֱ��researchers anticipate that similar approaches would benefit patients if used more broadly.

This study was funded by the National Institute for Health Research Cambridge Biomedical Research Centre.

Reference:
Mailis Maes et al. ‘Ventilator-associated pneumonia in critically ill patients with COVID-19.’ Critical Care (2021). DOI: 10.1186/s13054-021-03460-5

Researchers have developed a DNA test to quickly identify secondary infections in COVID-19 patients, who have double the risk of developing pneumonia while on ventilation than non-COVID-19 patients.

Using this test, we found that patients with COVID-19 were twice as likely to develop secondary pneumonia as other patients in the same intensive care unit

Andrew Conway Morris

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Lt. Cmdr. Michael Heimes checks on a patient connected to a ventilator at Baton Rouge General Mid City campus

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AI could help cut waiting times for cancer by automating mark-up of patient scans prior to radiotherapy

cjb250 — Wed, 16 Dec 2020 14:08:25 +0000

̽��ֱ��AI technology, known as InnerEye, is the result of an eight-year collaboration between researchers at Cambridge-based Microsoft Research, Addenbrooke’s Hospital and the ̽��ֱ�� of Cambridge.

InnerEye aims to save clinicians many hours of time laboriously marking up patient scans prior to radiotherapy. ̽��ֱ��team has demonstrated how machine learning (ML) models built using the InnerEye open-source technology can cut this preparation time by up to 90% - meaning that waiting times for starting potentially life-saving radiotherapy treatment can be dramatically reduced.

Health and Social Care Secretary Matt Hancock said: “New innovations like this can make all the difference to patients and I am proud to see we are once again leading the way in new cancer treatments.

“Helping people receive treatment faster is incredibly important and will not only improve recovery rates but will save clinicians precious time so they can focus on caring for patients.

“Embracing new technologies will help save lives and is vital for the sustainability of the NHS, and our NHS Long Term Plan will continue to deliver the best possible care for patients so that we can offer faster, more personalised and effective cancer treatment for all.”

Dr Raj Jena from the Department of Oncology at the ̽��ֱ�� of Cambridge and an oncologist at Addenbrooke’s, who co-leads InnerEye, said: “These results are a game-changer. To be diagnosed with a tumour of any kind is an incredibly traumatic experience for patients. So as clinicians we want to start radiotherapy promptly to improve survival rates and reduce anxiety. Using machine learning tools can save time for busy clinicians and help get our patients onto treatment as quickly as possible.”

Dr Yvonne Rimmer, also from at Addenbrooke's, said: “There is no doubt that InnerEye is saving me time. It’s very good at understanding where tumours and healthy organs are. It’s speeding up the process so I can concentrate on looking at a patient’s diagnostic images and tailoring treatment to them.

“But it’s important for patients to know that the AI is helping me do my job; it’s not replacing me in the process. I double check everything the AI does and can change it if I need to. ̽��ֱ��key thing is that most of the time, I don’t need to change anything.”

Up to half of the population in the UK will be diagnosed with cancer at some point in their lives. Of those, half will be treated with radiotherapy, often in combination with other treatments such as surgery, chemotherapy, and increasingly immunotherapy.

Radiotherapy involves focusing high-intensity radiation beams to damage the DNA of hard cancerous tumours while avoiding surrounding healthy organs. This is a critical tool, with around 40% of successfully treated patients undergoing some form of radiotherapy.

Planning radiotherapy treatment can be a lengthy process. It starts with a 3D CT (Computed Tomography) imaging scan of the part of the body to be targeted. These CT images come in the form of stacks of 2D images, dozens of images deep, each of which must be examined and marked up by a radiation oncologist or specialist technician. This process is called contouring. In each image, an expert must manually draw a contour line around the tumours and key healthy organs at risk in the target area using dedicated computer software. For complex cases, this can take several hours in the planning of a single patient’s treatment.

This image segmentation task is a rate-limiting factor in the cancer treatment pathway for radiotherapy, which increases the burden of time on clinicians and the financial cost to hospitals. As this task is subjective, there can be significant variability across experts and institutions where acquisition protocols and patient demographics vary. This is a limitation to the use of imaging in clinical trials and can introduce variability in patient care.

Research published by the team in JAMA Network Open confirms that the InnerEye ML models can accurately and rapidly carry out the otherwise lengthy ‘image segmentation’ requiring hours of expert clinicians’ time.

Head of Health Intelligence at Microsoft Research, Aditya Nori, said: “This is the first time, we believe, that an NHS Trust has implemented its own deep learning solution trained on their own data, so it can be used on their patients. It paves the way for more NHS Trusts to take advantage of open-source AI tools to help reduce cancer treatment times.”

̽��ֱ��InnerEye Deep Learning Toolkit has been made freely available as open-source software by Microsoft.

While ML models developed using the tool need to be tested and validated in each individual healthcare setting, doctors at Cambridge ̽��ֱ�� Hospitals (CUH) have demonstrated how the technology can be applied in clinical settings.

Reference
Ozan Oktay, et al. Evaluation of Deep Learning to Augment Image-Guided Radiotherapy for Head and Neck and Prostate Cancers. JAMA Network Open; 30 Nov 2020; DOI: 10.1001/jamanetworkopen.2020.27426

Adapted from press releases from Microsoft and Cambridge ̽��ֱ�� Hospitals NHS Foundation Trust.

Doctors at Addenbrooke’s Hospital in Cambridge aim to drastically cut cancer waiting times by using artificial intelligence (AI) to automate lengthy radiotherapy preparations.

As clinicians we want to start radiotherapy promptly to improve survival rates and reduce anxiety. Using machine learning tools can save time for busy clinicians and help get our patients onto treatment as quickly as possible

Raj Jena

People Who Change ̽��ֱ��World | Dr Rajesh Jena

Dr Raj Jena and Yvonne Rimmer look at scans

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Surviving birth

lw355 — Thu, 10 Dec 2020 09:00:28 +0000

Researchers at one of the busiest maternity hospitals in the world aim to help more women survive complications giving birth.