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Is symptom-based diagnosis of lung cancer possible? A systematic review and meta-analysis of symptomatic lung cancer prior to diagnosis for comparison with real-time data from routine general practice

Title: Is symptom-based diagnosis of lung cancer possible? A systematic review and meta-analysis of symptomatic lung cancer prior to diagnosis for comparison with real-time data from routine general practice
Authors: Okoli, GN
Kostopoulou, O
Delaney, BC
Item Type: Journal Article
Abstract: Background Lung cancer is a good example of the potential benefit of symptom-based diagnosis, as it is the commonest cancer worldwide, with the highest mortality from late diagnosis and poor symptom recognition. The diagnosis and risk assessment tools currently available have been shown to require further validation. In this study, we determine the symptoms associated with lung cancer prior to diagnosis and demonstrate that by separating prior risk based on factors such as smoking history and age, from presenting symptoms and combining them at the individual patient level, we can make greater use of this knowledge to create a practical framework for the symptomatic diagnosis of individual patients presenting in primary care. Aim To provide an evidence-based analysis of symptoms observed in lung cancer patients prior to diagnosis. Design and setting Systematic review and meta-analysis of primary and secondary care data. Method Seven databases were searched (MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature, Health Management Information Consortium, Web of Science, British Nursing Index and Cochrane Library). Thirteen studies were selected based on predetermined eligibility and quality criteria for diagnostic assessment to establish the value of symptom-based diagnosis using diagnosistic odds ratio (DOR) and summary receiver operating characteristic (SROC) curve. In addition, routinely collated real-time data from primary care electronic health records (EHR), TransHis, was analysed to compare with our findings. Results Haemoptysis was found to have the greatest diagnostic value for lung cancer, diagnostic odds ratio (DOR) 6.39 (3.32–12.28), followed by dyspnoea 2.73 (1.54–4.85) then cough 2.64 (1.24–5.64) and lastly chest pain 2.02 (0.88–4.60). The use of symptom-based diagnosis to accurately diagnose lung cancer cases from non-cases was determined using the summary receiver operating characteristic (SROC) curve, the area under the curve (AUC) was consistently above 0.6 for each of the symptoms described, indicating reasonable discriminatory power. The positive predictive value (PPV) of diagnostic symptoms depends on an individual’s prior risk of lung cancer, as well as their presenting symptom pattern. For at risk individuals we calculated prior risk using validated epidemiological models for risk factors such as age and smoking history, then combined with the calculated likelihood ratios for each symptom to establish posterior risk or positive predictive value (PPV). Conclusion Our findings show that there is diagnostic value in the clinical symptoms associated with lung cancer and the potential benefit of characterising these symptoms using routine data studies to identify high-risk patients.
Issue Date: 21-Nov-2018
Date of Acceptance: 5-Nov-2018
URI: http://hdl.handle.net/10044/1/65409
DOI: https://dx.doi.org/10.1371/journal.pone.0207686
ISSN: 1932-6203
Publisher: Public Library of Science (PLoS)
Journal / Book Title: PLoS ONE
Volume: 13
Issue: 11
Copyright Statement: © 2018 Okoli et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Sponsor/Funder: Cancer Research UK
Funder's Grant Number: C33754/A17871
Keywords: Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
PRIMARY-CARE
CLINICAL-FEATURES
RISK
BENEFITS
MODEL
STAGE
MD Multidisciplinary
General Science & Technology
Publication Status: Published
Article Number: e0207686
Online Publication Date: 2018-11-21
Appears in Collections:Division of Surgery
Faculty of Medicine



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