Predicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal-external validation of a clinical risk prediction model
<p><strong>Background</p></strong> The National Institute for Health and Care Excellence (NICE) recommends that people aged 60+ years with newly diagnosed diabetes and weight loss undergo abdominal imaging to assess for pancreatic cancer. More nuanced stratification could lea...
| Main Authors: | , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Springer Nature
2024
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| _version_ | 1811140717734526976 |
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| author | Clift, AK Tan, PS Patone, M Liao, W Coupland, C Bashford-Rogers, R Sivakumar, S Hippisley-Cox, J |
| author_facet | Clift, AK Tan, PS Patone, M Liao, W Coupland, C Bashford-Rogers, R Sivakumar, S Hippisley-Cox, J |
| author_sort | Clift, AK |
| collection | OXFORD |
| description | <p><strong>Background</p></strong>
The National Institute for Health and Care Excellence (NICE) recommends that people aged 60+ years with newly diagnosed diabetes and weight loss undergo abdominal imaging to assess for pancreatic cancer. More nuanced stratification could lead to enrichment of these referral pathways.
<p><strong>
Methods</p></strong>
Population-based cohort study of adults aged 30–85 years at type 2 diabetes diagnosis (2010–2021) using the QResearch primary care database in England linked to secondary care data, the national cancer registry and mortality registers. Clinical prediction models were developed to estimate risks of pancreatic cancer diagnosis within 2 years and evaluated using internal–external cross-validation.
<p><strong>
Results</p></strong>
Seven hundred and sixty-seven of 253,766 individuals were diagnosed with pancreatic cancer within 2 years. Models included age, sex, BMI, prior venous thromboembolism, digoxin prescription, HbA1c, ALT, creatinine, haemoglobin, platelet count; and the presence of abdominal pain, weight loss, jaundice, heartburn, indigestion or nausea (previous 6 months). The Cox model had the highest discrimination (Harrell’s C-index 0.802 (95% CI: 0.797–0.817)), the highest clinical utility, and was well calibrated. The model’s highest 1% of predicted risks captured 12.51% of pancreatic cancer cases. NICE guidance had 3.95% sensitivity.
<p><strong>
Discussion</p></strong>
A new prediction model could have clinical utility in identifying individuals with recent onset diabetes suitable for fast-track abdominal imaging. |
| first_indexed | 2024-09-25T04:26:25Z |
| format | Journal article |
| id | oxford-uuid:ce1ced8f-3336-4641-bfe2-ef4b937720af |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:26:25Z |
| publishDate | 2024 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:ce1ced8f-3336-4641-bfe2-ef4b937720af2024-08-23T11:44:15ZPredicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal-external validation of a clinical risk prediction modelJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ce1ced8f-3336-4641-bfe2-ef4b937720afEnglishSymplectic ElementsSpringer Nature2024Clift, AKTan, PSPatone, MLiao, WCoupland, CBashford-Rogers, RSivakumar, SHippisley-Cox, J<p><strong>Background</p></strong> The National Institute for Health and Care Excellence (NICE) recommends that people aged 60+ years with newly diagnosed diabetes and weight loss undergo abdominal imaging to assess for pancreatic cancer. More nuanced stratification could lead to enrichment of these referral pathways. <p><strong> Methods</p></strong> Population-based cohort study of adults aged 30–85 years at type 2 diabetes diagnosis (2010–2021) using the QResearch primary care database in England linked to secondary care data, the national cancer registry and mortality registers. Clinical prediction models were developed to estimate risks of pancreatic cancer diagnosis within 2 years and evaluated using internal–external cross-validation. <p><strong> Results</p></strong> Seven hundred and sixty-seven of 253,766 individuals were diagnosed with pancreatic cancer within 2 years. Models included age, sex, BMI, prior venous thromboembolism, digoxin prescription, HbA1c, ALT, creatinine, haemoglobin, platelet count; and the presence of abdominal pain, weight loss, jaundice, heartburn, indigestion or nausea (previous 6 months). The Cox model had the highest discrimination (Harrell’s C-index 0.802 (95% CI: 0.797–0.817)), the highest clinical utility, and was well calibrated. The model’s highest 1% of predicted risks captured 12.51% of pancreatic cancer cases. NICE guidance had 3.95% sensitivity. <p><strong> Discussion</p></strong> A new prediction model could have clinical utility in identifying individuals with recent onset diabetes suitable for fast-track abdominal imaging. |
| spellingShingle | Clift, AK Tan, PS Patone, M Liao, W Coupland, C Bashford-Rogers, R Sivakumar, S Hippisley-Cox, J Predicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal-external validation of a clinical risk prediction model |
| title | Predicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal-external validation of a clinical risk prediction model |
| title_full | Predicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal-external validation of a clinical risk prediction model |
| title_fullStr | Predicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal-external validation of a clinical risk prediction model |
| title_full_unstemmed | Predicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal-external validation of a clinical risk prediction model |
| title_short | Predicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal-external validation of a clinical risk prediction model |
| title_sort | predicting the risk of pancreatic cancer in adults with new onset diabetes development and internal external validation of a clinical risk prediction model |
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