RESUMO
Clinical guidelines include monitoring blood test abnormalities to identify patients at increased risk of undiagnosed cancer. Noting blood test changes over time may improve cancer risk stratification by considering a patient's individual baseline and important changes within the normal range. We aimed to review the published literature to understand the association between blood test trends and undiagnosed cancer. MEDLINE and EMBASE were searched until 15 May 2023 for studies assessing the association between blood test trends and undiagnosed cancer. We used descriptive summaries and narratively synthesised studies. We included 29 articles. Common blood tests were haemoglobin (24%, n = 7), C-reactive protein (17%, n = 5), and fasting blood glucose (17%, n = 5), and common cancers were pancreatic (29%, n = 8) and colorectal (17%, n = 5). Of the 30 blood tests studied, an increasing trend in eight (27%) was associated with eight cancer types, and a decreasing trend in 17 (57%) with 10 cancer types. No association was reported between trends in 11 (37%) tests and breast, bile duct, glioma, haematological combined, liver, prostate, or thyroid cancers. Our review highlights trends in blood tests that could facilitate the identification of individuals at increased risk of undiagnosed cancer. For most possible combinations of tests and cancers, there was limited or no evidence.
RESUMO
OBJECTIVES: The aim of this systematic review was to summarize national and international guidelines that made recommendations for monitoring patients diagnosed with low-risk cancer. It appraised the quality of guidelines and determined whether the guidelines adequately identified patients for monitoring, specified which tests to use, defined monitoring intervals, and stated triggers for further intervention. It then assessed the evidence to support each recommendation. STUDY DESIGN AND SETTING: Following the Preferred Reporting Items for Systematic Reviews and Meta-analyses, we searched PubMed and Turning Research into Practice databases for national and international guidelines' that were written in English and developed or updated between 2012 and 2023. Quality of individual guidelines was assessed using the AGREE II tool. RESULTS: Across the 41 published guidelines, 48 different recommendations were identified: 15 (31%) for prostate cancer, 11 (23%) for renal cancer, 6 (12.5%) for thyroid cancer, and 10 (21%) for blood cancer. The remaining 6 (12.5%) were for brain, gastrointestinal, oral cavity, bone and pheochromocytoma and paraganglioma cancer. When combining all guidelines, 48 (100%) stated which patients qualify for monitoring, 31 (65%) specified which tests to use, 25 (52%) provided recommendations for surveillance intervals, and 23 (48%) outlined triggers to initiate intervention. Across all cancer sites, there was a strong positive trend with higher levels of evidence being associated with an increased likelihood of a recommendation being specific (P = 0.001) and the evidence for intervals was based on expert opinion or other guidance. CONCLUSION: With the exception of prostate cancer, the evidence base for monitoring low-risk cancer is weak and consequently recommendations in clinical guidelines are inconsistent. There is a lack of direct evidence to support monitoring recommendations in the literature making guideline developers reliant on expert opinion, alternative guidelines, or indirect or nonspecific evidence.
RESUMO
BACKGROUND: Simple blood tests can play an important role in identifying patients for cancer investigation. The current evidence base is limited almost entirely to tests used in isolation. However, recent evidence suggests combining multiple types of blood tests and investigating trends in blood test results over time could be more useful to select patients for further cancer investigation. Such trends could increase cancer yield and reduce unnecessary referrals. We aim to explore whether trends in blood test results are more useful than symptoms or single blood test results in selecting primary care patients for cancer investigation. We aim to develop clinical prediction models that incorporate trends in blood tests to identify the risk of cancer. METHODS: Primary care electronic health record data from the English Clinical Practice Research Datalink Aurum primary care database will be accessed and linked to cancer registrations and secondary care datasets. Using a cohort study design, we will describe patterns in blood testing (aim 1) and explore associations between covariates and trends in blood tests with cancer using mixed-effects, Cox, and dynamic models (aim 2). To build the predictive models for the risk of cancer, we will use dynamic risk modelling (such as multivariate joint modelling) and machine learning, incorporating simultaneous trends in multiple blood tests, together with other covariates (aim 3). Model performance will be assessed using various performance measures, including c-statistic and calibration plots. DISCUSSION: These models will form decision rules to help general practitioners find patients who need a referral for further investigation of cancer. This could increase cancer yield, reduce unnecessary referrals, and give more patients the opportunity for treatment and improved outcomes.
