External UK validation of the ENDPAC model to predict pancreatic cancer risk: A registered report protocol
Price, C. A.; Claridge, H.; de Lusignan, S.; Khalaf, N.; Mold, F.; Smith, N. A. S.; Winn, M.; Lemanska, A.
Show abstract
IntroductionOverall cancer survival has increased over recent decades, but the very low survival rates of pancreatic cancer have hardly changed in the last 50 years. This is attributed to late diagnosis. Pancreatic cancer symptoms are non-specific which makes early diagnosis challenging. Data-driven approaches, including algorithms using combinations of symptoms to predict cancer risk, can aid clinicians. A simple but effective algorithm called Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) has been developed in the United States (US). ENDPAC has not yet been used in the United Kingdom (UK), our aim is to translate ENDPAC into the UK setting. The objectives are to validate ENDPAC and report its predictive utility within primary care. MethodsA retrospective cohort study of people with new-onset diabetes using the nationally representative Oxford-Royal College of General Practitioners Clinical Informatics Digital Hub (ORCHID) database. ORCHID holds over 10 million primary care electronic healthcare records. ENDPAC scores will be calculated for eligible people along with positive predictive value, negative predictive value, sensitivity and specificity of the algorithm. We will evaluate the optimal cut-off for defining people with high-risk of having pancreatic cancer. DiscussionOnce validated within the UK, ENDPAC could be implemented in practice to improve early pancreatic cancer diagnosis by using routine data. ENDPAC is currently being tested in the US in a clinical trial to evaluate its effectiveness. ENDPAC offers an automatable and inexpensive way to improve early diagnosis as part of a sequential approach to identify individuals at high-risk of having undiagnosed pancreatic cancer. How this fits inPancreatic cancer is a devasting disease which is hard to diagnose. An algorithm called ENDPAC has been developed in the United States to help clinicians identify people at risk of having undiagnosed pancreatic cancer. These people can be referred for an imaging investigation to diagnose or rule out cancer. This protocol outlines a United Kingdom (UK) validation of ENDPAC so that it could be used in clinical practice in the UK.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Determining the feasibility of calculating pancreatic cancer risk scores for people with new-onset diabetes in primary care (DEFEND PRIME): study protocol 97%
- Replicating a COVID-19 study in a national England database to assess the generalisability of research with regional electronic health record data 95%
- The iDiabetes Platform: Enhanced Phenotyping of Patients with Diabetes for Precision Diagnosis, Prognosis and Treatment- study protocol for a cluster-randomised controlled study 95%
Similar papers in this journal
- The Impact of Clinical Audits on Improving the Effectiveness of Type 2 Diabetes Mellitus (T2DM) CARE in Primary Health Centers. A Comprehensive Pre-post analysis through Multi-layered Intervention: The ICAE-DM CARE study protocol 94%
- Type 1 and type 2 diabetes mellitus: Clinical outcomes due to COVID-19. Protocol of a systematic literature review 93%
- A cross-sectional questionnaire study: impaired awareness of hypoglycaemia remains prevalent in adults with type 1 diabetes and is associated with the risk of severe hypoglycaemia 93%
Similar papers in this journal
- Home monitoring of HbA1c in diabetes mellitus: A protocol for systematic review and narrative synthesis on reliability, accuracy, and patient acceptability 94%
- Patient characteristics associated with clinically coded long COVID: an OpenSAFELY study using electronic health records 87%
- A mixed-methods evaluation of patients’ views on primary care multi-disciplinary teams in Scotland 87%
Similar papers in this journal
- Construction of tongue image-based machine learning model for screening patients with gastric precancerous lesions 89%
- Dynamic Biobanking for Advancing Breast Cancer Research 89%
- Development and validation of decision rules models to stratify coronary artery disease, diabetes, and hypertension risk in preventive care: cohort study of returning UK Biobank participants 89%
Similar papers in this journal
- Development and validation of a modified Cambridge Multimorbidity Score for use with internationally recognized electronic health record clinical terms (SNOMED CT) 91%
- Characterisation of type 2 diabetes subgroups and their association with ethnicity and clinical outcomes: a UK real-world data study using the East London Database 91%
- OpenSAFELY NHS Service Restoration Observatory 1: describing trends and variation in primary care clinical activity for 23.3 million patients in England during the first wave of COVID-19 88%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.