Literature DB >> 34483054

A decision support tool for the detection of pancreatic cancer in general practice: A modified Delphi consensus.

B Thompson1, S Philcox2, B Devereaux3, A Metz4, D Croagh5, J Windsor6, A Davaris7, S Gupta8, J Barlow9, J Rhee10, P Tagkalidis11, A Zimet12, A Sharma13, R Manocha14, R E Neale15.   

Abstract

BACKGROUND/
OBJECTIVES: Diagnosis of pancreatic cancer is often delayed, contributing to patient and family distress and leading to worse survival. We aimed to develop a decision support tool to support primary care providers to identify patients that should undergo investigations for pancreatic cancer, and to recommend initial diagnostic pathways.
METHODS: A modified Delphi process, including a series of three surveys, was undertaken to ascertain clinical expert opinion on which combinations of signs, symptoms and risk factors should be included in a tool for the early identification of pancreatic cancer. A group of clinical specialists finalised the development of the tool during a focus group meeting.
RESULTS: The tool presents individual or combinations of signs, symptoms, and risk factors in three tiers which direct the urgency of investigation. Tier 1 includes 5 clinical presentation and risk factors clusters that indicate the need for urgent investigation of the pancreas. A further five clusters are included as Tier 2 aiming to elimate other causes and reduce the time to investigating the pancreas. Tier 3 includes a list of non-specific signs, symptoms and risk factors that indicate the need to consider pancreatic cancer as a potential diagnosis, but without specific recommendations for investigation.
CONCLUSIONS: Prospective validation studies are now required prior to implementation in the primary care setting. Implementation into primary care practice and as an educational resource may facilitate rapid diagnosis and improve outcomes such as distress and survival.
Copyright © 2021 IAP and EPC. Published by Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Delayed diagnosis; Early detection of cancer; Pancreatic neoplasm; Primary health care

Mesh:

Year:  2021        PMID: 34483054     DOI: 10.1016/j.pan.2021.08.007

Source DB:  PubMed          Journal:  Pancreatology        ISSN: 1424-3903            Impact factor:   3.996


  1 in total

1.  Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images.

Authors:  Thavavel Vaiyapuri; Ashit Kumar Dutta; I S Hephzi Punithavathi; P Duraipandy; Saud S Alotaibi; Hadeel Alsolai; Abdullah Mohamed; Hany Mahgoub
Journal:  Healthcare (Basel)       Date:  2022-04-03
  1 in total

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