---
title: "Blood test trends versus single abnormal results to detect cancer in patients with unexpected weig"
id: "plos-medicine-1-blood-test-trend-versus-single-threshold-abnormality-to-discriminate-cancer"
canonical_url: "https://medichelpline.com/clinical-feed/plos-medicine-1-blood-test-trend-versus-single-threshold-abnormality-to-discriminate-cancer"
content_type: "clinical_feed_article"
specialty: "Oncology"
source_name: "PLOS Medicine"
source_url: "https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956"
published_at: "2026-09-17T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Blood test trends versus single abnormal results to detect cancer in patients with unexpected weig
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-medicine-1-blood-test-trend-versus-single-threshold-abnormality-to-discriminate-cancer
- **Specialty:** [Oncology](https://medichelpline.com/clinical-feed/oncology.md)
- **Primary Source:** PLOS Medicine
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956)
- **Published At:** 2026-09-17T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- This retrospective cohort study used English primary care electronic health records (Clinical Practice Research Datalink) to evaluate whether historical **blood test trends** discriminate undiagnosed cancer better than single-test abnormalities in patients presenting with **unexpected weight loss (UWL)**. - The cohort included 275,205 adults with UWL between 2000 and 2018; 5.0% (n = 13,798) were diagnosed with cancer after the UWL presentation. - Quantitative results for 26 blood tests were extracted for up to 10 years prior to UWL; testing frequency per person over 10 years had medians ranging mostly from 2 to 4 tests depending on the analyte. - Time between most recent blood test and UWL was short: median 0.1 years for cancer cases and 0.3 years for those without cancer. - The study compared discrimination using Cox models for single-test **abnormalities** and joint models for **trends** over 1-, 3-, 5-, and 10-year windows, reporting discrimination by area under the curve (AUC) with 95% confidence intervals. - Age- and sex-adjusted models improved discrimination compared with unadjusted analyses for both abnormalities and trends. - Adjusted trends outperformed equivalent adjusted single-test abnormalities on 34 test-cancer occasions, with the highest AUC observed at 0.82 (95% CI 0.81–0.83). - Examples where trend added discrimination included MCV trend for bowel cancer and lymphoma, WBC and neutrophil trends for lung cancer, and AST, RBC, haematocrit, and platelet-to-lymphocyte ratio trends for prostate cancer. - Key limitations: selection bias in who received testing was not modelled, and trend analyses required at least two tests within each trend window, restricting the sample for trend assessment. - Clinical implication: interpret blood test results adjusted for **age and sex**, and in some test–cancer pairs, consider longitudinal **blood test trend** to improve triage for cancer investigation in primary care patients with UWL.
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosmedicine%2Farticle%3Fid%3D10.1371%2Fjournal.pmed.1004956) * * About * Browse * Publish * [](https://journals.plos.org/plosmedicine/ "PLOS Medicine") * Search [advanced search](https://journals.plos.org/plosmedicine/search) * 0 [Save](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1004956#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1004956#savedHeader) * 0 [Citation](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1004956#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1004956#citedHeader) * 16 [View](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1004956#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1004956#viewedHeader) * 0 [Share](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1004956#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1004956#discussedHeader) Open Access Peer-reviewed Research Article # Blood test trend versus single threshold abnormality to discriminate cancer from non-cancer in patients with unexpected weight loss: A retrospective cohort study * Brian D. Nicholson , Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Visualization, Writing – original draft, Writing – review & editing * E-mail: brian.nicholson@phc.ox.ac.uk Affiliation Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0003-0661-7362 ](https://orcid.org/0000-0003-0661-7362 "ORCID Registry") ⨯ * Clare R. Bankhead, Roles Methodology, Supervision, Writing – review & editing Affiliation Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0003-1588-3849 ](https://orcid.org/0000-0003-1588-3849 "ORCID Registry") ⨯ * Sufen Zhu, Roles Investigation, Writing – review & editing Affiliation Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0009-0003-1298-3905 ](https://orcid.org/0009-0003-1298-3905 "ORCID Registry") ⨯ * Jason Lee Oke, Roles Conceptualization, Supervision, Writing – review & editing Affiliation Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom ⨯ * Cynthia Wright Drakesmith, Roles Data curation, Methodology, Project administration, Supervision, Writing – review & editing Affiliation Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-7196-1509 ](https://orcid.org/0000-0002-7196-1509 "ORCID Registry") ⨯ * Rafael Perera, Roles Conceptualization, Methodology, Supervision, Writing – review & editing Affiliation Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom ⨯ * Frederick D. Richard Hobbs, Roles Conceptualization, Resources, Supervision, Writing – review & editing Affiliations Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom, Oxford Institute of Digital Health, Oxford, United Kingdom [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0001-7976-7172 ](https://orcid.org/0000-0001-7976-7172 "ORCID Registry") ⨯ * Pradeep S. Virdee Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Visualization, Writing – original draft Affiliation Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-3006-8730 ](https://orcid.org/0000-0002-3006-8730 "ORCID Registry") ⨯ # Blood test trend versus single threshold abnormality to discriminate cancer from non-cancer in patients with unexpected weight loss: A retrospective cohort study * Brian D. Nicholson, * Clare R. Bankhead, * Sufen Zhu, * Jason Lee Oke, * Cynthia Wright Drakesmith, * Rafael Perera, * Frederick D. Richard Hobbs, … * Pradeep S. Virdee ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: September 17, 2026 * * [Article](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956) * [Authors](https://journals.plos.org/plosmedicine/article/authors?id=10.1371/journal.pmed.1004956) * [Metrics](https://journals.plos.org/plosmedicine/article/metrics?id=10.1371/journal.pmed.1004956) * [Comments](https://journals.plos.org/plosmedicine/article/comments?id=10.1371/journal.pmed.1004956) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pmed.1004956) * [Peer Review](https://journals.plos.org/plosmedicine/article/peerReview?id=10.1371/journal.pmed.1004956) * [Abstract](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#abstract0) * [Author summary](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#abstract1) * [Introduction](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#sec007) * [Methods](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#sec008) * [Results](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#sec017) * [Discussion](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#sec025) * [Supporting information](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#sec027) * [Acknowledgments](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#ack) * [References](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#references) * [Reader Comments](https://journals.plos.org/plosmedicine/article/comments?id=10.1371/journal.pmed.1004956) * [Figures](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956) ## Abstract ### Background Unexpected weight loss (UWL) is a non-specific cancer symptom. Abnormalities in the most recent blood test contribute to cancer referral decisions in primary care. Interpreting historical changes (trends) in blood tests could enhance discrimination for undiagnosed cancer. We aimed to assess the discrimination of blood test trend for undiagnosed cancer and compare this to that of blood test abnormality in patients presenting to primary care with UWL. ### Methods and findings We conducted a retrospective cohort study of patients presenting to English primary care with UWL aged ≥18 years between 01/01/2000 and 31/12/2018 in the Clinical Practice Research Datalink. We extracted all quantitative results for 26 blood tests prior to the date of UWL. Overall and site-specific cancer diagnosis was ascertained using linked National Cancer Registrations Data. Cox models, unadjusted and adjusted for age and sex, estimated cancer risk based on blood test abnormalities (such as low albumin or raised platelets) co-occurring with UWL and joint models assessed trends in each blood test over 1-, 3-, 5-, and 10-year periods prior to UWL. The area under the curve (AUC) was used to assess discrimination, with 95% confidence intervals (CIs). Amongst 275,205 UWL patients, 5.0% (_n_ = 13,798) with cancer, the median (interquartile range (IQR)) number of blood tests per person over the 10 years ranged from 2 (IQR [12–3]) for 3 types of tests to 4 (IQR [2–7]) for 12 types of tests. The median (IQR) time between the first and last blood test per person was 5.2 (IQR [1.5–8.2]) years cases and 4.3 (IQR [1.0–7.7]) years for those cancer-free. The median time between the last blood test and UWL per person was 0.1 (IQR [0.0–0.6]) years for cases and 0.3 (IQR [0.0–1.1]) years for those cancer-free. Adjusted blood test abnormalities and trends were more discriminative than the unadjusted equivalent. Adjustment resulted in higher AUCs for trend compared to the equivalent blood test abnormality on 34 occasions, with highest AUC of 0.82 (95% CI [0.81, 0.83]). This included 26 trends for cancer overall, mean cell volume trend for bowel cancer and lymphoma, white blood count and neutrophil trend for lung cancer, and aspartate aminotransferase, red blood cell count, haematocrit, and platelet-to-lymphocyte ratio trend for prostate cancer. A limitation of our modelling strategy is that it did not take into account the bias in who was tested and our trend analysis was also restricted to patients with 2 or more tests in each trend window. ### Conclusions Blood test abnormalities should be interpreted after age and sex adjustment to improve triage for cancer investigation in patients attending primary care with UWL. Evaluating historical trend offers further discrimination after adjustment for some test-cancer combinations. ## Author summary ### Why was this study done? * Abnormal blood test results, such as low haemoglobin or raised platelets, support cancer risk assessment in primary care. * Monitoring trends (changes over time) in blood test results could enhance cancer risk assessment. * No study has assessed the potential of blood test trends in comparison to blood test abnormalities in patients who present with non-specific symptoms, such as unexpected weight loss (UWL). ### What did the researchers do and find? * We conducted a cohort study using electronic health records data for 275,205 patients with UWL attending English primary care. * We compared the discrimination of trends in 26 blood tests prior to UWL for a new cancer diagnosis with the abnormality for the same blood test that is used in clinical practice. * We found that age- and sex-adjusted blood test abnormalities and trends were more discriminative than unadjusted blood test trends and abnormalities, and for 34 test-cancer combinations adjusted trends were more discriminative than the equivalent adjusted blood test abnormality, including 26 trends for cancer overall, mean cell volume trend for bowel cancer and lymphoma, white blood count and neutrophil trend for lung cancer, and aspartate aminotransferase, red blood cell count, haematocrit, and platelet-to-lymphocyte ratio trend for prostate cancer. ### What do these findings mean? * Blood test results should be adjusted by age and sex to improve patient selection for cancer investigation in patients attending primary care with UWL. * Blood test trends over repeat tests could further enhance patient selection in some test-cancer combinations. * Our analysis uses routinely collected data so is limited to when patients had blood tests in clinical practice and to patients who had blood tests. ## Figures ![Fig 4](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1004956.g004) ![Table 1](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1004956.t001) ![Fig 1](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1004956.g001) ![Table 2](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1004956.t002) ![Fig 2](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1004956.g002) ![Table 3](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1004956.t003) ![Fig 3](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1004956.g003) ![Fig 4](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1004956.g004) ![Table 1](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1004956.t001) ![Fig 1](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1004956.g001) ![Table 2](https://journals.plos.org/plosmedicine/article/figure/image?size=inline&id=10.1371/journal.pmed.1004956.t002) **Citation:** Nicholson BD, Bankhead CR, Zhu S, Oke JL, Wright Drakesmith C, Perera R, et al. (2026) Blood test trend versus single threshold abnormality to discriminate cancer from non-cancer in patients with unexpected weight loss: A retrospective cohort study. PLoS Med 23(9): e1004956. https://doi.org/10.1371/journal.pmed.1004956 **Academic Editor:** Eleanor L. Watts, undefined, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND **Received:** February 4, 2026; **Accepted:** June 15, 2026; **Published:** September 17, 2026 **Copyright:** © 2026 Nicholson et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. **Data Availability:** The data used in this study is not freely or publicly available. The data is owned and available from the CPRD. Due to privacy laws and the data user agreement between Oxford and CPRD, researchers are not authorised to share individual patient data. Data access is subject to approval via CPRD’s Research Data Governance Team, who, under the Health Research Authority Review Board, are required to review and approve access to the patient data before it can be shared. A data request application for protocol number 22_001798 can be made on the CPRD website: [[31](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#pmed.1004956.ref031)] or by email: enquiries@cprd.com. The R code used in the analysis is available from GitHub: (DOI: ) [[14](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#pmed.1004956.ref014)]. **Funding:** BDN was funded by the NIHR Policy Research Programme on Cancer Awareness, Screening and Early Diagnosis (Reference PR-PRU-NIHR206132). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. BDN and PSV were also funded by a Cancer Research UK Clinical Careers Committee Postdoctoral Fellowship (RCCPDF\100005). FDRH and CWD were supported by the NIHR ARC OTV ( ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The views expressed are those of the authors and not necessarily those of these funders. **Competing interests:** The authors have declared that no competing interests exist. **Abbreviations:** AIC, Akaike Information Criterion; ALT, alanine transaminase; AUC, area under the curve; BIC, Bayesian Information Criterion; BMI, body mass index; CIs, confidence intervals; CPRD, Clinical Practice Research Datalink; CRP, C-reactive protein; ESR, erythrocyte sedimentation rate; FIT, Faecal Immunochemical Test; GPs, general practitioners; HES, Hospital Episode Statistics; HRs, hazard ratios; ICD10, International Classification of Diseases, 10th Revision; IMD, Index of Multiple Deprivation; IQR, interquartile range; LOWESS, Locally Weighted Scatterplot Smoothing; MCV, mean cell volume; MCH, mean cell haemoglobin; MCHC, mean cell haemoglobin concentration; mGPR, modified Glasgow Prognostic ratio; mGPS, modified Glasgow Prognostic Score; MPV, mean platelet volume; NCRAS, National Cancer Registration and Analysis Service; NICE, National Institute for Health and Care Excellence; NLR, neutrophil-to-lymphocyte ratio; NLS, neutrophil-to-lymphocyte score; ONS, Office of National Statistics; PLR, platelet-to-lymphocyte ratio; PLS, platelet-to-lymphocyte score; PSA, Prostate Specific Antigen; RBC, red blood cell count; RDW, red cell distribution width; RECORD, REporting of studies Conducted using Observational Routinely-collected health Data; SD, standard deviation; SNOMED-CT, Systematized Nomenclature of Medicine-Clinical Terms; STROBE, STrengthening the Reporting of OBservational studies in Epidemiology; UWL, unexpected weight loss; WBC, white blood cell count ## Introduction The current evidence base for using non-specific blood tests to risk stratify for cancer in primary care is almost entirely limited to unadjusted blood test abnormalities of single tests [[1](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#pmed.1004956.ref001)–[3](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#pmed.1004956.ref003)]. For most blood test abnormalities, the associated risk of a single cancer is too low to warrant urgent cancer investigation. Low albumin, raised platelets, raised calcium, and raised inflammatory markers increase the risk of cancer overall above the 3% threshold recommended by the National Institute for Health and Care Excellence (NICE) for urgent investigation for cancer in England and Wales [[1](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#pmed.1004956.ref001)]. Our recent work identified the blood test abnormalities associated with increased risk of cancer overall in patients with unexpected weight loss (UWL) [[4](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#pmed.1004956.ref004),[5](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#pmed.1004956.ref005)], who often experience diagnostic challenges [[6](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#pmed.1004956.ref006)–[8](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#pmed.1004956.ref008)], but blood tests are common [[9](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#pmed.1004956.ref009)]. For non-specific symptoms such as UWL, these abnormalities give general practitioners (GPs) an indication that cancer may be present, but uncertainty remains over which cancer(s) should be investigated. Current primary care clinical guidelines focus on selecting patients for cancer investigation based on combinations of risk factors, signs, symptoms, and blood test abnormalities [[1](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#pmed.1004956.ref001)]. Monitoring temporal changes (or trends) in blood tests may enhance cancer risk stratification over and above acting on the only most recent blood test result [[10](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#pmed.1004956.ref010)–[12](https://journals.plos.org/plosmedicine/article?id=10.1371/journal.pmed.1004956#pmed.1004956.ref012)]. A patient with a low-normal haemoglobin may not be considered high-risk if the haemoglobin result is interpreted in relation to a binary threshold, but a low-normal resu
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