Lung cancer in symptomatic patients presenting in primary care: a systematic review of risk prediction tools

Schmidt‐Hansen, Mia; Berendse, Sabine; Hamilton, William; Baldwin, David R

doi:10.3399/bjgp17x690917

Cited by 16 publications

(12 citation statements)

References 22 publications

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“…In this study, we aim to determine the validity of symptom-based lung cancer diagnosis, using published studies, routine data from electronic health records and published prior risk models. A recent review of lung cancer diagnosis using ‘Risk Assessment Tools’ (RATs) found that there was insufficient validation, and that the inclusion of ‘epidemiological risk factors’ in the models, along with symptoms, created confounders [ 5 ]. In this review, we specifically assess symptoms associated with lung cancer diagnosis without epidemiological factors, to avoid confounding.…”

Section: Introductionmentioning

confidence: 99%

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

Okoli¹,

Kostopoulou²,

Delaney³

2018

PLoS ONE

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BackgroundLung 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.AimTo provide an evidence-based analysis of symptoms observed in lung cancer patients prior to diagnosis.Design and settingSystematic review and meta-analysis of primary and secondary care data.MethodSeven 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.ResultsHaemoptysis 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).ConclusionOur 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.

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Section: Introductionmentioning

confidence: 99%

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

Okoli¹,

Kostopoulou²,

Delaney³

2018

PLoS ONE

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“…In a similar review, looking at risk prediction models for screening, Usher-Smith and colleagues (2015) [ 53 ] concluded that, even though some of the colorectal cancer prediction models had potential for clinical application, there remains considerable uncertainty about their clinical utility. Similarly, Schmidt-Hansen and colleagues (2017) [ 58 ] conducted a review of lung cancer tools and found limited evidence to support the recommendation of any of the identified risk prediction tools, due to lack of external validation or cost impact assessment.…”

Section: Discussionmentioning

confidence: 99%

Development, validation and effectiveness of diagnostic prediction tools for colorectal cancer in primary care: a systematic review

et al. 2020

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Background Tools based on diagnostic prediction models are available to help general practitioners (GP) diagnose colorectal cancer. It is unclear how well they perform and whether they lead to increased or quicker diagnoses and ultimately impact on patient quality of life and/or survival. The aim of this systematic review is to evaluate the development, validation, effectiveness, and cost-effectiveness, of cancer diagnostic tools for colorectal cancer in primary care. Methods Electronic databases including Medline and Web of Science were searched in May 2017 (updated October 2019). Two reviewers independently screened titles, abstracts and full-texts. Studies were included if they reported the development, validation or accuracy of a prediction model, or assessed the effectiveness or cost-effectiveness of diagnostic tools based on prediction models to aid GP decision-making for symptomatic patients presenting with features potentially indicative of colorectal cancer. Data extraction and risk of bias were completed by one reviewer and checked by a second. A narrative synthesis was conducted. Results Eleven thousand one hundred thirteen records were screened and 23 studies met the inclusion criteria. Twenty-studies reported on the development, validation and/or accuracy of 13 prediction models: eight for colorectal cancer, five for cancer areas/types that include colorectal cancer. The Qcancer models were generally the best performing. Three impact studies met the inclusion criteria. Two (an RCT and a pre-post study) assessed tools based on the RAT prediction model. The third study looked at the impact of GP practices having access to RAT or Qcancer. Although the pre-post study reported a positive impact of the tools on outcomes, the results of the RCT and cross-sectional survey found no evidence that use of, or access to, the tools was associated with better outcomes. No study evaluated cost effectiveness. Conclusions Many prediction models have been developed but none have been fully validated. Evidence demonstrating improved patient outcome of introducing the tools is the main deficiency and is essential given the imperfect classification achieved by all tools. This need is emphasised by the equivocal results of the small number of impact studies done so far.

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“…A recently published systematic review of risk prediction tools for patients with LC based on UK primary care data compared five different tools. 38 There was not sufficient evidence to recommend any of them because of the lack of external validation, evaluation in clinical practice, and cost impact. Also, none of the tools differentiated between symptoms depending on tumour stage.…”

Section: Discussionmentioning

confidence: 99%

Clinical features of patients with non-metastatic lung cancer in primary care: a case-control study

et al. 2018

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Lung cancer in symptomatic patients presenting in primary care: a systematic review of risk prediction tools

Cited by 16 publications

References 22 publications

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

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

Development, validation and effectiveness of diagnostic prediction tools for colorectal cancer in primary care: a systematic review

Clinical features of patients with non-metastatic lung cancer in primary care: a case-control study

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