CDSU Data Access Committee

MDS/24/ML/003: Developing and Validating a Paediatric Sepsis Prediction Tool Using Machine Learning

Principal Investigator: Dr David Kung

Summary: The AiSEPTRON Phase 2 study aims to create a tool to help clinicians detect sepsis in children more promptly. Sepsis can lead to life-threatening complications if not managed early. Using machine learning, the study will analyse anonymous hospital records from 140,000 children under 16 who visited Children’s Emergency Departments in England. The Machine Learning model will use the patients’ symptoms, test results, and other associated data to predict sepsis risk.

MDS/24/CR/001: Building a comprehensive tracheostomy dataset for audit, quality improvement and research

Principal Investigator: Prof. Brendan McGrath

Summary: Tracheostomies are small plastic tubes inserted into the necks of adult and paediatric patients. The tubes are usually used to help patients wake up and recover after Intensive Care Units (ICU) stays, or as part of head and neck surgical operations. Tracheostomies can stop patients eating, drinking or talking, and because patients need them to breathe, any difficulties can lead to rapid and life-threatening problems. The first step in improving care is to understand how many patients have had a tracheostomy and what problems they experienced. We can do this by linking together existing datasets that we collected at MFT for a few different reasons. We want to pull this information together into a standard format. We might be able new data directly from our electronic patient record system. We will use these data to improve care by trying out exploring what worked well in the past and trying different improvements and measuring their impact in the future

MDS/24/OB/001: An exploration of factors influencing healthy lifestyle, weight and family planning for postnatal women in Greater Manchester and Eastern Cheshire (GMEC)

Principal Investigator: Dr Kylie Watson:

Summary: In Greater Manchester, 63% of adults are either overweight or obese, and 25% of children in Year 6 are classified as obese. Over the past year, 9.4% of women who received maternity care at Saint Mary’s Hospital were obese, putting them at higher risk of complications. Pre-pregnancy obesity is strongly linked to negative health outcomes for both mothers and babies. Obesity during pregnancy can lead to serious complications. These include miscarriage, stillbirth, high blood pressure, diabetes, and problems with the baby’s growth. Pre-pregnancy obesity can also lead to increased weight gain during pregnancy and after birth. This can affect the long-term health of babies, increasing the likelihood of childhood obesity. Obesity is linked to social and health inequalities. Reducing rates of obesity requires a comprehensive approach. This should include access to high-quality maternity care that can support mothers and their families before, during, and after pregnancy. Research shows that targeted programmes can help women manage their weight and adopt healthier lifestyles. Family planning, access to contraception, and pre-pregnancy planning can reduce the risks associated with obesity in future pregnancies. This project aims to understand the characteristics and pregnancy outcomes of postnatal women who have maternity care across Saint Mary’s Hospital Managed Clinical Service. This is with a view to better understanding the characteristics that may be associated with a woman’s BMI. The long-term goal of this project is to inform the development of targeted interventions that promote a healthy lifestyle, weight and family planning for postnatal women in Greater Manchester.

MDS/24/RS/005: Establishing an interstitial lung abnormality registry

Principal Investigator: Dr Conal Hayton

Summary: Interstitial lung abnormalities (ILAs) are early lung changes which are commonly picked up on chest CT scans performed for other reasons. We know that some people with ILAs go on to be diagnosed with interstitial lung disease (ILD), a potentially fatal lung disease. It is important that we develop management strategies to follow them up efficiently. We have set up the first ILA clinic in the UK and have worked to develop a national follow-up pathway. We believe that a registry, which would allow us to collect information about people with ILA, would help us to learn more about the condition, improve follow-up and increase research opportunities. We plan to create an ILA registry database and collect health data from electronic records. Data would be collected from Manchester initially and then expanded to four other sites across the UK and eventually lead to a national registry.

MDS/25/CA/001: Lipoprotein (a) testing in atherosclerotic cardiovascular patients in UK: A multicentric observational study using a secondary care data network across UK (Lp(a) Data Network)

Principal Investigator: Madelina Placido

Summary: Lipoprotein(a) (Lp(a)) is a type of cholesterol linked to heart disease that can be tested in clinical practice. This study aims to understand how (Lp(a)) testing is used in UK healthcare and its impact on patient care. By analysing anonymized health records from eight NHS Trusts, the researchers will track how often Lp(a) tests are done, who gets tested, and how test results influence treatment. They will also explore links between high Lp(a) levels, healthcare usage, costs, and heart health risks. This work will help improve the knowledge on Lp(a) testing in routine care, ensuring better informed treatment for people at risk of heart disease. The study will look at data from 2021 to 2026 and use a secure data system to protect privacy. Ultimately, the findings aim to support healthcare providers in using Lp(a) testing to improve heart health outcomes across the UK

MDS/22/ON/007: Investigation of Prehab4Cancers long term impacts on Lung and Colorectal cancer patients in Greater Manchester

Principal Investigator: John Moore

Summary: Prehabilitation (prehab) is a means of improving patients’ physical, nutritional and psychological wellbeing to enable them to better tolerate major treatments (surgery, chemotherapy and radiotherapy) with better resilience and less complications. Prehab has mostly focussed on cancer related treatments and Greater Manchester (GM) has developed a programme for patients preparing and recovering from cancer surgery. This programme called Prehab4Cancer has helped support almost 6000 patients across GM since its introduction in 2019 and is offered to all patients undergoing lung and colorectal cancer surgery in GM. This project wishes to understand the impact Prehab4Cancer has had on patients’ recovery and longer-term outcomes for those who have had major lung or colorectal cancer surgery at Manchester University NHS Hospitals. Patients entering the programme give their permission for their prehab data to be shared with their provider hospital and we would like to compare this information with patient related outcomes from MFT. Patients who have not been in the programme but have had similar surgery will act as control group for comparison.

MDS/25/DM/001: Understanding inclusive participation in research studies of rheumatoid arthritis, asthma and heart failure in the Manchester Biomedical Research Centre

Principal Investigator: Andrea Murray

Summary: One of our key goals in the Manchester Biomedical Research Centre is to increase the diversity of our participation in research studies. To do this we need to understand our current recruitment. In the first instance we would like to look at 3 common diseases across our themes; rheumatoid arthritis, asthma and heart failure. This project will enable us to determine the population that takes part in our studies and how this compares to the local population attending our clinics. This can then also be compared to the census data to give us an overview of how these data compare to the Manchester population. If given permission via this process, we will publish this anonymised data to make the data available to other researchers and improve understanding of this important issue. This will enable us to work towards improving participation and involvement in research studies taking place at MFT and more widely.

MDP/24/GV/001: Evaluation of wearable vital signs data collected during the MFT-Corsano pilot

Principal Investigator: Dr Anthony Wilson

Summary: Wearable sensors (patches or smart watches) are a new way to monitor patients’ vitals in hospital. These sensors could help us to spot patients who are becoming unwell sooner and they could reduce the workload on our nurses. Before we can roll out these sensors we need to check if the readings they give agree with ones made by our nurses. In Nov-Feb, MFT took part in a project to test one of these sensors (Corsano CardioWatch). About 100 patients wore the sensor on our haematology ward. In this project we want to analyse the data that was recorded to see if the readings agree with our nurses and to see if the wearable sensors were able to detect patients who were deteriorating sooner. Computers can sometimes help us to analyse data like this. We also want to see if a computer model could use this data to predict deterioration before it even happens.

MDS/24/IM/002: Epidemiology of Infections in Manchester Hospitals: A Comprehensive Analysis

Principal Investigator: Dr Tim Felton

Summary: Antibiotic resistance is a threat to the health of individuals across the world. Antibiotic resistance develops in bacteria and stops antibiotics from working making, even simple infections, difficult to treat. Our project will use routinely collected data within the patient record (HIVE) in Manchester University NHS Foundation Trust. We will look at how antibiotic resistance affects patients with different types of infections (such as pneumonia, urinary tract infection and meningitis). We will measure the impact of antibiotic resistance on patients and we will look at whether they get better after antibiotic treatment, how long they are in hospital for and whether they die from the infection.

MDS/24/CA/004:  Detecting EARLY Heart Failure in Greater Manchester

Principal Investigator: Prof. Christopher Miller

Summary: Heart failure is a disease in which the heart is unable to pump blood around the body effectively. As people age, heart failure becomes increasingly common, and it is a major cause of death and disability in middle-aged and older people. Currently, heart failure is usually diagnosed too late, after people have already developed severe symptoms or need to come to hospital. It is important to identify people with heart failure much earlier in the hope that we can start treatments earlier and prevent progression to more severe disease. We have developed a method that provides an indication of a person developing heart failure or becoming unwell with it. It currently includes measurements of heart structure and function made from cardiac MRI scanning, blood levels of a marker of fluid retention and medical history factors. This study aims to evaluate this in people living in Greater Manchester. We hope that many people will take part in the study. We are particularly keen to include people from ethnic minorities and people registered at GP practices serving more deprived areas of the City

MDS/23/OB/001: Disparities In Access to the Northwest Ambulance Service during pregnancy, birth and postpartum period and its association with neonatal and maternal outcomes

Principal Investigator: Dr Stephanie Heys

Summary: This study aims to understand and compare the reasons for using ambulance services during pregnancy or after childbirth with women from different backgrounds. We will also compare maternal and neonatal outcomes between different groups of women using ambulance services. This knowledge will help healthcare services become more aware of what care and support is needed for women who use ambulance services to improve outcomes for women and babies.

MDS/24/RE/002: Optimised Medical Care in Nephrology

Principal Investigator: Dr John Hartemink

Summary: We have new life-saving medicines recommended by NICE and funded by NHS England available for kidney patients but the number of patients on these treatments are depressingly low (5-10%) in Manchester and UK. We wish to explore methods to maximise uptake of these medications in patients under our care, to prevent huge numbers of patients ending up on dialysis. We would like to develop a clinical registry of all kidney patients from the information within our EPR health record system (EPIC) to implement and study effects of these interventions. Kidney patient waiting lists are long, many deteriorate significantly while awaiting our input. The risk is often clear from existing EPR information. This information could be compiled within a comprehensive registry, to design smarter methods of implementing evidence. The project, if successful, could expand to other specialties for multimorbidity management and potentially extend to other UK EPIC sites for wider impact.

MDS/22/CA/002: AI-based Natural Language Processing models to support care delivery in Adult Congenital Heart Disease

Principal Investigator: Bernard Keavney

Summary: There is a national shortage of specialist cardiologists looking after the continuously increasing population of patients who are Adult survivors of childhood Congenital Heart Disease (ACHD patients). This runs the risk of compromising the quality of patient care in the UK. The focus of this project is therefore to derive an Artificial Intelligence (AI) system, trained on large amounts of ACHD patient data derived from electronic health records (EHR) at MFT, that has the capacity to identify patients requiring timely specialist review, and predict adverse events. Our ultimate aim is to provide an AI “co-pilot” system that has the capacity to work effectively alongside cardiologists with a general or a specialist training, to enhance the quality of care provided in both specialist ACHD units and general cardiology units. Such an AI system could significantly impact projected supply/demand imbalance in NHS ACHD care, increase the accessibility of care, and enable patients to be treated closer to their own homes.

MDS/24/CA/003: Predicting myocardial infarction through retinal scans and minimal personal information: a validation study in a diverse, real-world cohort

Principal Investigator: Dr Thomas Julian

Summary:  Heart attacks are the leading cause of death in the developed world. If those at risk can be identified, then most early heart attacks could be prevented. In previous research, we have shown that we can predict who is likely to have a heart attack using pictures of the inside lining of the eye known as “retinal photographs”. We made our predictions using artificial intelligence (AI) technology and found we could predict disease more accurately than the tools currently used in general practice. However, our AI tool was developed in a group of largely healthy European ethnicity individuals that do not reflect the general population. Before our tool can be used in the healthcare system, we must explore how accurately it works in a group that better represents the general public. Therefore, in this project we intend to test the accuracy of our tool in a cohort of NHS patients.

MDS/24/HE/002: Hyperglycaemia and inflammation in pancreas transplant

Principal Investigator: Dr Rory Brown

Summary: Pancreas transplant is a high-risk operation often transplanted together with a kidney to treat young people with severe diabetes and kidney failure. The complication rates are high and 1 in 6 transplants will fail within 5 years. High blood sugars after the operation are linked to early transplant failure but we don’t know why. High levels of pancreas inflammation are also linked to early complications. We want to find out whether the high blood sugar causes inflammation in the pancreas which leads to transplant failure. We want to study the pancreas transplants we have already performed, in detail, to see whether patients who have high-blood sugars after the operation have more pancreas inflammation. By understanding how high-blood sugars are linked to pancreas transplants which fail, we can then explore ways in which we can treat it with the aim of improving outcomes for this vulnerable patient group.

MDS/24/CA/001: Development of Virtual Cardiovascular Patient Populations using Deidentified Patient Data, and Validation against Real Patient Data

Principal Investigator: Dr Nishant Ravikumar

Summary: Our project aims to create virtual representations of heart blood vessel structures using deidentified medical imaging from Manchester hospitals. We will compare these virtual representations to real patients’ data to ensure they’re accurate and representative. These virtual models should help researchers and medical device

companies test new treatments and technologies without putting real patients at risk. This could speed up the development of new medical devices, reduce animal testing, and make clinical trials safer and more efficient. Our goal is to improve patient care by providing tools that make medical innovation faster and safer.

MDS/24/RE/001: Living Kidney Donor CT Imaging Analysis Using an Automated Algorithm

Principal Investigator: Prof. Rachel Lennon

Summary: Living kidney donation is the best treatment for people with severe kidney disease, but it requires careful checks to ensure donor safety. Currently, these checks include multiple scans and tests, which are time-consuming and costly. Research suggests that measuring kidney size from a CT scan might be a good alternative to some of these tests. This would reduce the number of hospital visits and speed up the process for donors, while also saving money for the healthcare system. We have developed a computer program that can automatically measure kidney size from scans. In this study, we want to test this program on real patient scans and see if it can accurately predict the how much the left kidney and right kidney are contributing separately to the overall kidney function. If successful, this could lead to a simpler and faster way to assess living kidney donors.

MDS/24/GA/001: Novel Artificial Intelligence Methods for the Early diagnosis of Acute Bowel Ischaemia

Principal Investigator: Mr Anthony Chan

Summary: Acute mesenteric ischaemia (AMI) is a life-threatening emergency where bowel is starved of oxygen due to a blockage in their blood supply. AMI kills 50-90% of patients unless emergency surgery is performed. Diagnosing AMI is difficult as symptoms are often vague and not always obvious on a CT scan even to an experienced radiologist. There is a need to explore better ways to diagnose AMI earlier to improve outcomes for these patients.

Artificial intelligence (AI) learns by using lots of information to recognise certain patterns. One example of this might be an AI program looking at a chest x-ray and predicting if that patient has COVID.

We want to train an AI program to use a patient’s blood results, clinical observations such as blood pressure and temperature, together with CT scan images to predict whether that patient has AMI. If successful, this could lead to an earlier diagnosis.

MDS/24/RS/003: Creating a Virtual Cohort for Chronic Obstructive Pulmonary Disease (COPD): Combining Data Exploration, Clinical Coding Systems, and Natural Language Processing for Enhanced Monitoring and Insights.

Principal Investigator: Anuoluwapo Adetayo

Summary: The study goal is to create a virtual group of people who have (COPD). To do this, there is a need to first analyse a large amount of data and understand how doctors and hospitals code information on COPD. Once this information is gathered, it will be organized to build a clear image of the COPD community, emphasising key health data. To gain deeper understanding of the group, advanced computer techniques will be used to extract additional information from textual records. 

The goal is to have detailed understanding of this group’s health, which will benefit doctors and researchers in the future. A mechanism to monitor changes in the group’s health will be developed over time, which will help review how the disease progresses and how well treatments work.

MDS/24/RS/004:: Derivation and validation of a novel model incorporating PET-CT for predicting malignancy in screen detected lung nodules – The PRECISE Study

Principal Investigator: Dr Haval Balata

Summary: Screening CT scans are used to look for early stage lung cancers in healthy people without any lung symptoms. This is predicted to pick up thousands of patients with lung nodules (spots on the lungs), some of which will be benign but some will be lung cancers. Once we see a nodule, we undertake further tests, one of which tests is a PET-CT scan. Once we have done the PET-CT scan, we apply a cancer risk prediction to help decide what is the appropriate next step.

Anonymous records describing patient features, scan findings, and cancers that are found will be analysed to develop an improved cancer risk model that is developed specifically for people who have nodules that are found during screening.

Overall, this work should help make us better at knowing which lung nodules might be cancer and which can be left alone.

MDS/24/CA/002: Pre-hospital point of care cardiac troponin testing to maximise efficiency for patients calling 999 with chest pain

Principal Investigator: Professor Rick Body

Summary: Currently it can be difficult for paramedics to accurately determine whether a patient suffering from chest pain is having a heart attack. The Chest Pain Diagnosis project aims to improve emergency ambulance efficiency for acute chest-pain patients by utilising a point-of-care test within ambulances that identifies the presence of a biomarker called Troponin that is released when a patient is having a heart attack. The results of this test (and other clinical observations) will be input by paramedics into a computerised decision aid called T-MACS which calculates an individual patient’s probability that they are having a heart attack. This innovative way of working will enable paramedics to accurately identify patients that are suffering from a heart attack and transport them immediately to the most appropriate location for further treatment and care.

MDS/24/NO/001: Fetal scalp blood sampling during labour: real-world data for improved safety evaluation

Principal Investigator: Dr Victoria Palin

Summary: Fetal scalp blood sampling (FSBS) is a test used during labour to measure whether a baby is receiving enough oxygen. The test consists of a small scratch made on the baby’s scalp to draw blood, which is quickly analysed. If a test result is positive then birth is expedited, for example via emergency caesarean section, to prevent harm to the baby.  Studies have suggested that FSBS can lead to harm to mother and baby, such as infection from the scratch. However, the frequency of these harms is unclear and the accuracy of the test for predicting poor pregnancy outcomes is unknown but could be improved. This study will analyse historic hospital data to determine the safety of FSBS in labour, and how often it leads to harm for mother and/or baby. We will also see if the accuracy of the test can be improved for future clinical use.

MDS/22/IF/001: The impact of introducing a digital technology intervention on outpatient appointment no-shows

Principal Investigator: Professor Dawn Dowding

Summary: Outpatient appointment no-shows pose a significant challenge to the NHS and are associated with inefficiencies in the delivery of healthcare services, as well as substantial financial cost.

In September 2022, MFT implemented a new Trust-wide Electronic Health Record (EHR), known as Hive, which is expected to bring efficiencies in the scheduling and management of outpatient appointments.

Our main aim is to assess the impact of the implementation of Hive on the proportion and management of outpatient appointment no-shows at MFT.

We will also consider whether there was any variation in the impact of Hive across different patient groups, including level of deprivation, age, sex, and ethnicity.

MDS/22/ON/008: Machine Learning Models in the Assessment of an Acute Abdomen

Principal Investigator: Mr Anthony Chan

Summary: Acute abdominal pain (AAP) accounts for about 10% of visits to A&E, and the diagnosis can be challenging as

pain can be caused by a broad range of conditions, such as acute appendicitis. A missed diagnosis or misdiagnosis can result in delays in treatment, worse outcomes or, in some cases, permanent impairment or death.

This study uses artificial intelligence to develop a machine learning model to predict a patient’s diagnosis and anticipated outcomes in hospital based on routine measurements such as blood pressure, heart rate, temperature, and blood results. Data from previous patient admissions will be used to train this model, and it is hoped that the model can be integrated into MFT’s Hive Electronic Patient Record system to automatically predict and help clinicians determine their patient’s diagnosis as well as other metrics such as predicted length of stay in hospital.

MDS/23/IN/002: Measures of shock reversal and clinical outcomes in critically ill patients with septic shock: An observational cohort study

Principal Investigator: Dr Jonathan Bannard-Smith

Summary: Sepsis is a life-threatening infection, with septic shock being the most severe form. In septic shock, the infection is so severe that drugs (vasopressors) are needed to maintain blood pressure in order to maintain flow to the body’s organs.

We think that reversing septic shock quickly is a good thing that will help patients to get better quicker and suffer less complications. However, no one knows the best way to measure when septic shock has been reversed.

We will look at three ways to measure this: ‘time on vasopressors’, ‘time vasopressor free’, and ‘average vasopressor dose over time’. We will work this out in a group of patients with septic shock and see which measure best describes when septic shock has reversed.  We will explore which measure is best linked to important outcomes for patients.

We will use data from patients who have been cared for in the intensive care unit at Manchester Royal Infirmary.  Only data which has already been collected will be used.  We will look at other characteristics of the patients (age, gender, other health problems) and their illness to see if any other factors affect how long it takes for septic shock to reverse.

MDS/23/CA/002: ECG-X – Explainable automated ECG interpretation for Long QT Syndrome – validation with further data

Principal Investigator: Dr Alaa Alahmadi

Summary: Every year about 100,000 people in the UK die of sudden cardiac death, often without having had any recognisable symptoms. Some of these deaths are caused by a condition called Long QT Syndrome (LQTS) which can lead to the heart beating irregularly and subsequently failing. LQTS can be acquired (e.g., caused by certain medications or having other cardiac and non-cardiac conditions including diabetes) or congenital (when someone was born with this syndrome caused by certain genetic mutation) and is often not discovered on routine electrocardiograms (ECG) as it is hard to spot visually and need careful manual measurement of QT-interval. We have now developed an algorithm that highlights LQTS through applying colour to the ECG waves and therefore make it easier to interpret and diagnose. Our algorithm was tested successfully as a proof of concept, but we now need further clinical data to test, verify, and optimise our algorithm. Hence, we’d like to access MFT’s data to develop a clinically sound and useful algorithm that can reliable detect LQTS and save lives. On top of that, our algorithms are explainable which means we always how the algorithm came to that decision (e.g. patient is at risk because of very prolonged QT) and how it came to its decision based on reading the visualised coloured ECG. The explainability is based on clinical rules and the project included important stakeholders from early design to implementation

MDS/23/IF/034 Improving Transparency of Processes for Accessing Health Data for Research Purposes

Principal Investigator: Claire MacDonald

Summary: CDSU will work with our Research Communications department to develop a public-facing website on already existing site building and hosting infrastructure. The website will meet the Data Transparency Standards as set out by Health Data Research UK (HDRUK) working groups and will display information on how researchers can apply for access to MFT data. There will be brief descriptions of the role of our Data Access Committee and our security and governance approvals, along with a list of projects approved through the Committee.

MDS/23/HO/001: Optimal Blood Culture Timing in the ICU

Principal Investigator: Mr Kenny Wong

Summary: Blood cultures are a test used to check for bacteria in the blood and confirm their type if they exist, however, they are very prone to error. Although many papers have looked at what factors could possibly influence the result, there has been little research into how the timing of taking these tests affects the result. In this project, I aim to look at how timing affects blood cultures and, if possible, find the best time to take samples to consistently produce correct results.

MDS/23/PR/005: Investigating The Potential Value of Pre-Emptive Pharmacogenomic Testing Through a Trust-Wide Assessment of Prescribing Practice

Principal Investigator: Dr John McDermott

Summary: Medicines play a crucial role in healthcare, but their effectiveness and safety vary between individuals. Some people receive ineffective medication while others experience adverse reactions. This has negative effects on individuals and society. One solution is to use a person’s genetic information to improve medicine selection and dosing, known as pharmacogenetics. The aim is to gather data to understand the benefits of this approach on a larger scale. However, it is challenging to measure the long-term impact of this approach using standard data capture methodologies. To address this, the proposal suggests using a clinical dataset from the

Manchester University NHS Foundation Trust to assess the value of pharmacogenetic testing.

MDS/23/OB/002: Routine data to investigate risk prediction and implementation science in maternity

Principal Investigator: Professor Jenny Myers

Summary: Routine pregnancy care includes a number of detailed risk assessments which are carried out a different times in pregnancy. Whilst some risk assessments are supported by high quality evidence, some assessments could be improved by testing how they perform in routine care and adjusting them where necessary to improve accuracy. In addition, we know from our previous analyses that some adverse pregnancy outcomes are more common in some of the communities we serve, but we don’t fully understand why. This project will use routinely recorded pregnancy information to refine risk assessments in pregnancy and to understand how we can improve care and outcomes for women across our diverse communities.