Literature DB >> 33252328

Cancer diagnostic tools to aid decision-making in primary care: mixed-methods systematic reviews and cost-effectiveness analysis.

Antonieta Medina-Lara1, Bogdan Grigore2, Ruth Lewis3, Jaime Peters2, Sarah Price4, Paolo Landa1, Sophie Robinson5, Richard Neal6, William Hamilton4, Anne E Spencer1.   

Abstract

BACKGROUND: Tools based on diagnostic prediction models are available to help general practitioners diagnose cancer. It is unclear whether or not tools expedite diagnosis or affect patient quality of life and/or survival.
OBJECTIVES: The objectives were to evaluate the evidence on the validation, clinical effectiveness, cost-effectiveness, and availability and use of cancer diagnostic tools in primary care.
METHODS: Two systematic reviews were conducted to examine the clinical effectiveness (review 1) and the development, validation and accuracy (review 2) of diagnostic prediction models for aiding general practitioners in cancer diagnosis. Bibliographic searches were conducted on MEDLINE, MEDLINE In-Process, EMBASE, Cochrane Library and Web of Science) in May 2017, with updated searches conducted in November 2018. A decision-analytic model explored the tools' clinical effectiveness and cost-effectiveness in colorectal cancer. The model compared patient outcomes and costs between strategies that included the use of the tools and those that did not, using the NHS perspective. We surveyed 4600 general practitioners in randomly selected UK practices to determine the proportions of general practices and general practitioners with access to, and using, cancer decision support tools. Association between access to these tools and practice-level cancer diagnostic indicators was explored.
RESULTS: Systematic review 1 - five studies, of different design and quality, reporting on three diagnostic tools, were included. We found no evidence that using the tools was associated with better outcomes. Systematic review 2 - 43 studies were included, reporting on prediction models, in various stages of development, for 14 cancer sites (including multiple cancers). Most studies relate to QCancer® (ClinRisk Ltd, Leeds, UK) and risk assessment tools. DECISION MODEL: In the absence of studies reporting their clinical outcomes, QCancer and risk assessment tools were evaluated against faecal immunochemical testing. A linked data approach was used, which translates diagnostic accuracy into time to diagnosis and treatment, and stage at diagnosis. Given the current lack of evidence, the model showed that the cost-effectiveness of diagnostic tools in colorectal cancer relies on demonstrating patient survival benefits. Sensitivity of faecal immunochemical testing and specificity of QCancer and risk assessment tools in a low-risk population were the key uncertain parameters. SURVEY: Practitioner- and practice-level response rates were 10.3% (476/4600) and 23.3% (227/975), respectively. Cancer decision support tools were available in 83 out of 227 practices (36.6%, 95% confidence interval 30.3% to 43.1%), and were likely to be used in 38 out of 227 practices (16.7%, 95% confidence interval 12.1% to 22.2%). The mean 2-week-wait referral rate did not differ between practices that do and practices that do not have access to QCancer or risk assessment tools (mean difference of 1.8 referrals per 100,000 referrals, 95% confidence interval -6.7 to 10.3 referrals per 100,000 referrals). LIMITATIONS: There is little good-quality evidence on the clinical effectiveness and cost-effectiveness of diagnostic tools. Many diagnostic prediction models are limited by a lack of external validation. There are limited data on current UK practice and clinical outcomes of diagnostic strategies, and there is no evidence on the quality-of-life outcomes of diagnostic results. The survey was limited by low response rates.
CONCLUSION: The evidence base on the tools is limited. Research on how general practitioners interact with the tools may help to identify barriers to implementation and uptake, and the potential for clinical effectiveness. FUTURE WORK: Continued model validation is recommended, especially for risk assessment tools. Assessment of the tools' impact on time to diagnosis and treatment, stage at diagnosis, and health outcomes is also recommended, as is further work to understand how tools are used in general practitioner consultations. STUDY REGISTRATION: This study is registered as PROSPERO CRD42017068373 and CRD42017068375. FUNDING: This project was funded by the National Institute for Health Research (NIHR) Health Technology programme and will be published in full in Health Technology Assessment; Vol. 24, No. 66. See the NIHR Journals Library website for further project information.

Entities:  

Keywords:  COLONOSCOPY; COLORECTAL CANCER; COST-EFFECTIVENESS; DECISION ANALYSIS; DECISION-MAKING; DIAGNOSTIC INTERVAL; DIAGNOSTIC PREDICTION TOOLS; GP SURVEY; REFERRAL INTERVAL; SUSPECTED CANCERS

Year:  2020        PMID: 33252328      PMCID: PMC7768788          DOI: 10.3310/hta24660

Source DB:  PubMed          Journal:  Health Technol Assess        ISSN: 1366-5278            Impact factor:   4.014


  368 in total

1.  Predictive modeling of colorectal cancer using a dedicated pre-processing pipeline on routine electronic medical records.

Authors:  Reinier Kop; Mark Hoogendoorn; Annette Ten Teije; Frederike L Büchner; Pauline Slottje; Leon M G Moons; Mattijs E Numans
Journal:  Comput Biol Med       Date:  2016-06-22       Impact factor: 4.589

2.  Effect on survival of longer intervals between confirmed diagnosis and treatment initiation among low-income women with breast cancer.

Authors:  John M McLaughlin; Roger T Anderson; Amy K Ferketich; Eric E Seiber; Rajesh Balkrishnan; Electra D Paskett
Journal:  J Clin Oncol       Date:  2012-11-19       Impact factor: 44.544

3.  Delays in diagnosis and bladder cancer mortality.

Authors:  Brent K Hollenbeck; Rodney L Dunn; Zaojun Ye; John M Hollingsworth; Ted A Skolarus; Simon P Kim; James E Montie; Cheryl T Lee; David P Wood; David C Miller
Journal:  Cancer       Date:  2010-11-15       Impact factor: 6.860

4.  Delays in the diagnosis and treatment of primary lung cancer: are longer delays associated with advanced pathological stage?

Authors:  Adnan Yilmaz; Ebru Damadoglu; Cuneyt Salturk; Erdal Okur; Leyla Yagci Tuncer; Semih Halezeroglu
Journal:  Ups J Med Sci       Date:  2008       Impact factor: 2.384

5.  Does delay in diagnosing colorectal cancer in symptomatic patients affect tumor stage and survival? A population-based observational study.

Authors:  Jochim S Terhaar sive Droste; Frank A Oort; René W M van der Hulst; Veerle M H Coupé; Mike E Craanen; Gerrit A Meijer; Linde M Morsink; Otto Visser; Roy L J van Wanrooij; Chris J J Mulder
Journal:  BMC Cancer       Date:  2010-06-28       Impact factor: 4.430

6.  Time delay and its effect on survival in malaysian patients with non-small cell lung carcinoma.

Authors:  Li-Cher Loh; Li-Yen Chan; Ru-Yu Tan; Selvaratnam Govindaraju; Kananathan Ratnavelu; Shalini Kumar; Sree Raman; Pillai Vijayasingham; Tamizi Thayaparan
Journal:  Malays J Med Sci       Date:  2006-01

7.  Effect of adding a diagnostic aid to best practice to manage suspicious pigmented lesions in primary care: randomised controlled trial.

Authors:  Fiona M Walter; Helen C Morris; Elka Humphrys; Per N Hall; A Toby Prevost; Nigel Burrows; Lucy Bradshaw; Edward C F Wilson; Paul Norris; Joe Walls; Margaret Johnson; Ann Louise Kinmonth; Jon D Emery
Journal:  BMJ       Date:  2012-07-04

8.  Variation in 'fast-track' referrals for suspected cancer by patient characteristic and cancer diagnosis: evidence from 670 000 patients with cancers of 35 different sites.

Authors:  Y Zhou; S C Mendonca; G A Abel; W Hamilton; F M Walter; S Johnson; J Shelton; L Elliss-Brookes; S McPhail; G Lyratzopoulos
Journal:  Br J Cancer       Date:  2017-11-28       Impact factor: 7.640

9.  Clinical features of bowel disease in patients aged <50 years in primary care: a large case-control study.

Authors:  Sally A Stapley; Greg P Rubin; Deborah Alsina; Elizabeth A Shephard; Matthew D Rutter; William T Hamilton
Journal:  Br J Gen Pract       Date:  2017-03-27       Impact factor: 5.386

10.  Risk factors and negative consequences of patient's delay for penile carcinoma.

Authors:  Wen Gao; Le-bin Song; Jie Yang; Ning-hong Song; Xin-feng Wu; Ning-jing Song; Di Qiao; Chen Chen; Jia-yi Zhang; Zeng-jun Wang
Journal:  World J Surg Oncol       Date:  2016-04-27       Impact factor: 2.754

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  2 in total

1.  Lung cancer prediction using machine learning on data from a symptom e-questionnaire for never smokers, formers smokers and current smokers.

Authors:  Elinor Nemlander; Andreas Rosenblad; Eliya Abedi; Simon Ekman; Jan Hasselström; Lars E Eriksson; Axel C Carlsson
Journal:  PLoS One       Date:  2022-10-21       Impact factor: 3.752

2.  Recognising Colorectal Cancer in Primary Care.

Authors:  Natalia Calanzani; Aina Chang; Marije Van Melle; Merel M Pannebakker; Garth Funston; Fiona M Walter
Journal:  Adv Ther       Date:  2021-04-17       Impact factor: 3.845

  2 in total

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