Temporality of clinical factors associated with pancreatic cancer: a case-control study using linked electronic health records

Ullah, Abu Z. Dayem; Stasinos, Konstantinos; Chelala, Claude; Kocher, Hemant M.

doi:10.1186/s12885-021-09014-w

Cited by 5 publications

(7 citation statements)

References 44 publications

(51 reference statements)

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“…Regarding other known risk factors, 2 smoking rates and alcohol use in our participants with type 2 diabetes were relatively low that may have masked an association. The lack of comorbidities in those developing pancreatic cancer in competing risk analysis is consistent with the observation that long‐standing cardiorespiratory or hepatobiliary conditions were associated with lower odds of pancreatic cancer than other pancreatic diseases or abdominal hernia in a recent large UK administrative database study 19 . Alternatively, there may have been a survivor effect in our cohort not fully captured by competing risk analysis.…”

Section: Discussionsupporting

confidence: 86%

The relationship between pancreatic cancer and type 2 diabetes: the Fremantle Diabetes Study Phase I

Davis

2022

Internal Medicine Journal

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Pancreatic cancer incidence was double (incidence rate ratio 2.06) in community‐based adults with (n = 1291) versus without (n = 5158) type 2 diabetes followed for up to 25 years in the Fremantle Diabetes Study Phase 1. Sustained higher fasting plasma glucose reflecting insulin resistance and fewer comorbidities were statistically significant risk factors in the cohort with diabetes. Past pancreatitis was an aetiologically significant determinant in the cohort as a whole.

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Section: Discussionsupporting

confidence: 86%

The relationship between pancreatic cancer and type 2 diabetes: the Fremantle Diabetes Study Phase I

Davis

2022

Internal Medicine Journal

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“…However, we found no association between higher BMI and PDAC, unlike what the literature suggests [3,8,11]. This could be due to the use of PnC patients as a comparison cohort, suggesting that obesity is likely to be associated with pancreatic conditions in general rather than disease severity [22]. We used patients' most recent pre-diagnostic BMI rather than historical trends, which could mask PDAC patients' transition from higher to lower BMI status.…”

Section: Discussionmentioning

confidence: 59%

Differentiating Ductal Adenocarcinoma of the Pancreas from Benign Conditions Using Routine Health Records: A Prospective Case-Control Study

Zardab

Balarajah

Banerjee

et al. 2022

Cancers

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The study aimed to develop a prediction model for differentiating suspected PDAC from benign conditions. We used a prospective cohort of patients with pancreatic disease (n = 762) enrolled at the Barts Pancreas Tissue Bank (2008-2021) and performed a case-control study examining the association of PDAC (n = 340) with predictor variables including demographics, comorbidities, lifestyle factors, presenting symptoms and commonly performed blood tests. Age (over 55), weight loss in hypertensive patients, recent symptoms of jaundice, high serum bilirubin, low serum creatinine, high serum alkaline phosphatase, low red blood cell count and low serum sodium were identified as the most important features. These predictors were then used for training several machine-learning-based risk-prediction models on 75% of the cohort. Models were assessed on the remaining 25%. A logistic regression-based model had the best overall performance in the validation cohort (area-under-the-curve = 0.90; Spiegelhalter’s z = −1·82, p = 0.07). Setting a probability threshold of 0.15 guided by the maximum F2-score of 0.855, 96.8% sensitivity was reached in the full cohort, which could lead to earlier detection of 84.7% of the PDAC patients. The prediction model has the potential to be applied in primary, secondary and emergency care settings for the early distinction of suspected PDAC patients and expedited referral to specialist hepato-pancreatico-biliary services.

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“…6,14,18 This could be due to the use of benign pancreatic disease patients as comparison cohort, suggesting that obesity is likely to be associated with pancreatic conditions in general rather than severity of the disease. 31 We used patients' most recent pre-diagnostic BMI rather than historical BMI trend, which could have also masked PDAC patients' transition from higher to lower BMI status. A further novel finding was the association of low creatinine with PDAC, this may be due to damage to the liver through obstructive jaundice or disease associated sarcopenia and cachexia.…”

Section: Discussionmentioning

confidence: 99%

Development of a Model for Differentiating PDAC from Benign Pancreatic Conditions: A Prospective Case-control Study

Zardab

Balarajah

Banerjee

et al. 2022

Preprint

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Background & aims Pancreatic ductal adenocarcinoma (PDAC) continues to be a devastating disease with late diagnosis and poor overall survival, complicated by clinical presentations similar to benign pancreatic diseases. We aimed to analyse clinical parameters with the goal of developing a prediction model for differentiating suspected PDAC from benign pancreatic conditions. Methods and results We used a prospectively recruited cohort of patients with pancreatic disease (n=762) enrolled at the Barts Pancreas Tissue Bank between January 1, 2008 and September 21, 2021 to perform a case-control study examining the association of PDAC (n=340) with predictor variables including demographics, comorbidities, lifestyle factors, presenting symptoms and commonly performed blood tests. Using a machine learning approach, candidate PDAC risk-prediction algorithms were trained on 75% of the cohort, using a subset of the predictor variables identified from a preliminary observational association study. Models were assessed on the remaining 25%. Multiple imputed datasets were used for both training and validation to accommodate for unknown data. Age (over 55), weight loss in hypertensive patients, recent symptom of jaundice, high serum bilirubin, low serum creatinine, high serum alkaline phosphatase, low lymphocyte count and low serum sodium were the most important features when separating putative PDAC cases from less severe pancreatic conditions. A simple logistic regression model had the best performance with an area under the curve (AUC) of 0.88. Setting a probability threshold of 0.17 guided by the maximum F2 score, a sensitivity of 95.6% was reached in the full cohort which could lead to early detection of around 84% of the PDAC patients. Conclusion The resultant prediction model significantly outperformed the current UK guidelines for suspected pancreatic cancer referral and could improve detection rates of PDAC in the community. After further work this approach could lead to an easy to understand, utilisable risk score to be applied in the primary and secondary care setting for referring patients to specialist hepato-pancreatico-biliary services.

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Temporality of clinical factors associated with pancreatic cancer: a case-control study using linked electronic health records

Cited by 5 publications

References 44 publications

The relationship between pancreatic cancer and type 2 diabetes: the Fremantle Diabetes Study Phase I

The relationship between pancreatic cancer and type 2 diabetes: the Fremantle Diabetes Study Phase I

Differentiating Ductal Adenocarcinoma of the Pancreas from Benign Conditions Using Routine Health Records: A Prospective Case-Control Study

Development of a Model for Differentiating PDAC from Benign Pancreatic Conditions: A Prospective Case-control Study

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