BMI and HbA1c are metabolic markers for pancreatic cancer: Matched case-control study using a UK primary care database

Lemańska, Agnieszka; Price, Claire A.; Jeffreys, Nathan; Byford, Rachel; Dambha‐Miller, Hajira; Fan, Xuejuan; Hinton, William; Otter, Sophie; Rice, Rebecca; Stunt, Ali; Whyte, Martin; Faithfull, Sara; Lusignan, Simon de

doi:10.1371/journal.pone.0275369

Cited by 20 publications

(17 citation statements)

References 30 publications

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“…We expect further increases in prediction accuracy with the real-world availability of data beyond disease codes, such as medication, laboratory values, observations in clinical notes, abdominal imaging (computerized tomography, magnetic resonance imaging), treatment records from general practitioners (Malhotra et al 2021; Lemanska et al 2022) as well as germline genetic profiles. To achieve a globally useful set of prediction rules, access to large data sets of disease histories aggregated nationally or internationally will be extremely valuable, with careful assessment of the accuracy of clinical records.…”

Section: Discussionmentioning

confidence: 99%

Pancreatic cancer risk predicted from disease trajectories using deep learning

Yuan

et al. 2021

Preprint

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Pancreatic cancer is an aggressive disease that typically presents late with poor patient outcomes. There is a pronounced medical need for early detection of pancreatic cancer, which can be facilitated by identifying high-risk populations. Here we apply artificial intelligence (AI) methods to a large corpus of more than 6 million patient records spanning 40 years with 24,000 pancreatic cancer cases in the Danish National Patient Registry. In contrast to existing methods that do not use temporal information, we explicitly train machine learning models on the time sequence of diseases in patient clinical histories. In addition, the models predict the risk of cancer occurrence in time intervals of 3 to 60 months duration after risk assessment. For cancer occurrence within 12 months, the performance of the best model trained on full trajectories (AUROC=0.91) substantially exceeds that of a model without time information (AUROC=0.81). For the best model, lower performance (AUROC=0.86) results when disease events within a 3 month window before cancer diagnosis are excluded from training, reflecting the decreasing information value of earlier disease events. These results raise the state-of-the-art level of performance of cancer risk prediction on real-world data sets and provide support for the design of real-world population-wide clinical screening trials, in which high risk patients are assigned to serial imaging and measurement of blood-based markers to facilitate earlier cancer detection. AI on real-world clinical records has the potential to shift focus from treatment of late- to early-stage cancer, benefiting patients by improving lifespan and quality of life.

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Section: Discussionmentioning

confidence: 99%

Pancreatic cancer risk predicted from disease trajectories using deep learning

Yuan

et al. 2021

Preprint

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“…However, a new study suggests that pancreatic cancer could be detected earlier by looking for simple signs such as dramatic weight loss or new onset diabetes 1 . The team analysed data from 8777 patients diagnosed with pancreatic cancer compared with a control group of 34,979.…”

Section: Earlier Diagnosis Of Pancreatic Cancermentioning

confidence: 99%

Update on technologies, medicines and treatments

O’Neill¹

2023

Diabet Med

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From the beginning of November, General Practitioner (GP)s are able to offer people with common mental health problems, such as stress, anxiety and mild-to-moderate depression, a digital tool to support them while they await referral to mental health services such as Improving Access to Psychological Therapies (IAPT) and Child and Adolescent Mental Health Services (CAMHS).Wysa Premium is an Artificial Intelligence (AI)enabled mental health app which has been approved by the National Health Service (NHS). The app is clinically safe, adhering to strict Digital Technology Assessment Criteria (DTAC), ensuring that it meets NHS and social care clinical safety, data protection, technical security, interoperability and usability and accessibility standards. Access to the app is via a mobile phone or tablet.The app provides an 'emotionally intelligent' service which responds to the emotions you express and uses evidence-based cognitive-behavioural techniques such as meditation, breathing, yoga, motivational interviewing and micro-actions to help you build mental resilience skills and feel better. It was developed by a team of 15 people, consisting of psychologists, designers, developers, and over 500,000 users to understand how AI chat can help us learn skills to build emotional resilience. Wysa has already been rolled out across a number of NHS IAPT and CAMHS talking therapy services.

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“…Healthcare assessments, such as BMI, HbA1c and liver function, were delivered to fewer people than would be expected if the pandemic had not occurred. This could impair not only the early diagnosis of pancreatic cancer, but also diagnosis of other diseases such as diabetes [29]. In addition, this could have implications for the quality of routine data for research [29,35,36].…”

Section: Summary and Findings In Contextmentioning

confidence: 99%

“…This could impair not only the early diagnosis of pancreatic cancer, but also diagnosis of other diseases such as diabetes [29]. In addition, this could have implications for the quality of routine data for research [29,35,36].…”

Section: Summary and Findings In Contextmentioning

confidence: 99%

“…Weight loss, hyperglycaemia, diabetes, and bile duct obstruction often occur as complications of pancreatic cancer [28]. Therefore, timely assessments of body mass index (BMI), glycated haemoglobin (HbA1c) and liver function can support early diagnosis [29] and monitoring of the progression [30]. However, the negative effect of the COVID-19 pandemic resulted in delays and missed opportunities throughout the cancer pathway, which in turn affected patient outcomes including survival [6-9, 11-13, 20, 31].…”

Section: Introductionmentioning

confidence: 99%

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Healthcare in England was affected by the COVID-19 pandemic across the pancreatic cancer pathway: a cohort study using OpenSAFELY-TPP

Lemańska

Andrews

Fisher

et al. 2022

Preprint

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Background Healthcare across all sectors, in the UK and globally, was negatively affected by the COVID-19 pandemic. We investigated the effect of the COVID-19 pandemic on the quantity of healthcare services delivered to people with pancreatic cancer. Methods With the approval of NHS England, and drawing from a nationally representative OpenSAFELY-TPP dataset of 24 million patients (over 40% of the English population), we undertook a cohort study of people diagnosed with pancreatic cancer. We queried electronic healthcare records for information on the provision of healthcare services across the pancreatic cancer pathway. To estimate the effect of the COVID-19 pandemic, we predicted the rates of healthcare services if the pandemic had not happened. We used generalised linear models (GLM) and the pre-pandemic data from January 2015 to February 2020 to predict rates in March 2020 to September 2022. The 95% confidence intervals of the predicted values were used to estimate the significance of the difference between the predicted and observed rates. Results The rate of pancreatic cancer and diabetes diagnoses in the cohort was not affected by the pandemic. There were 24,500 people diagnosed with pancreatic cancer from January 2015 to September 2022. The mean age at diagnosis was 72 (SD 11), 48% of people were female, 95% were of White ethnicity and 39% were diagnosed with diabetes. We found a reduction in surgical resections by nearly 25% during the pandemic. In addition, 20%, 10% and 5% fewer people received BMI, HbA1c and liver function tests respectively before they were diagnosed with pancreatic cancer. There was no impact of the pandemic on the number of people making contact with primary care, but the number of contacts increased on average by 1 to 2 per person amongst those who made contact. Abdominal scans decreased by 7% and reporting of jaundice decreased by 20%, but recovered within six months into the pandemic. Emergency department visits, hospital admissions and deaths were not affected. Conclusions The pandemic affected healthcare in England across the pancreatic cancer pathway. Positive lessons could be learnt from services that recovered quickly. The reductions in healthcare experienced by people with cancer have the potential to lead to worse outcomes. Current efforts should focus on addressing the unmet needs of people with cancer.

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BMI and HbA1c are metabolic markers for pancreatic cancer: Matched case-control study using a UK primary care database

Cited by 20 publications

References 30 publications

Pancreatic cancer risk predicted from disease trajectories using deep learning

Pancreatic cancer risk predicted from disease trajectories using deep learning

Update on technologies, medicines and treatments

Healthcare in England was affected by the COVID-19 pandemic across the pancreatic cancer pathway: a cohort study using OpenSAFELY-TPP

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