Evidence map›Paper›PMID 37080616›Full record

ArticleBMJ open2023

Symptoms and signs of lung cancer prior to diagnosis: case-control study using electronic health records from ambulatory care within a large US-based tertiary care centre.

Maria G Prado, Larry G Kessler, Margaret A Au, Hannah A Burkhardt, Monica Zigman Suchsland, Lesleigh Kowalski, Kari A Stephens, Meliha Yetisgen, Fiona M Walter, Richard D Neal and 7 more

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
3.9field-weighted citation impact, top 6% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.

  1. Life Course Trajectories of Body Mass Index and Risk of Cancer in Adulthood: Systematic Review and Meta-Analysis.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026
    Pooled it
  2. Using large language models to identify prediagnostic clinical features of ovarian cancer from healthcare records: a population-based case-control study.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026
    Article
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  10. Predicting the Future by Studying the Past for Patients With Cancer Diagnosed in the Emergency Department.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors at 7 institutions in 2 countries.

Maria G PradoDepartment of Family Medicine, University of Washington, Seattle, Washington, USA.
Larry G KesslerHealth Services, University of Washington, Seattle, Washington, USA.
Margaret A AuDepartment of Family Medicine, University of Washington, Seattle, Washington, USA.
Hannah A BurkhardtDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA.
Monica Zigman SuchslandDepartment of Family Medicine, University of Washington, Seattle, Washington, USA.ORCID 0000-0001-7007-6973
Lesleigh KowalskiDepartment of Family Medicine, University of Washington, Seattle, Washington, USA.
Kari A StephensDepartment of Family Medicine, University of Washington, Seattle, Washington, USA.
Meliha YetisgenDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA.
Fiona M WalterWolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Richard D NealUniversity of Exeter, Exeter, UK.
Kevin LybargerDepartment of Information Sciences and Technology, George Mason University, Fairfax, Virginia, USA.
Caroline A ThompsonDepartment of Epidemiology, The University of North Carolina, Chapel Hill, North Carolina, USA.
Morhaf Al AchkarDepartment of Family Medicine, University of Washington, Seattle, Washington, USA.ORCID 0000-0002-4160-0550
Elizabeth A SarmaNational Cancer Institute, NIH, Bethesda, Maryland, USA.
Grace TurnerDepartment of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA.
Farhood FarjahDepartment of Surgery, University of Washington, Seattle, Washington, USA.
Matthew J ThompsonDepartment of Family Medicine, University of Washington, Seattle, Washington, USA mjt@uw.edu.ORCID 0000-0003-0256-8444
University of Washington · USUniversity of Washington Medical Center · USGeorge Mason University · USNational Cancer Institute · USQueen Mary University of London · GBUniversity of Exeter · GBUniversity of North Carolina at Chapel Hill · US

Funding

Transform Dissemination and Implementation Science in CTSA ProgramsUL1TR002319 · NCATS · UNIVERSITY OF WASHINGTON · PI John K. Amory · 2017 to 2026
$100.0M
University of Washington Biomedical Informatics and Data Science Research Training ProgramT15LM007442 · NLM · UNIVERSITY OF WASHINGTON · PI JOHN H. GENNARI, Andrea L. Hartzler · 2002 to 2026
$19.6M
NCATS NIH HHS UL1 TR002319NCI NIH HHS HHSN261201000040INCI NIH HHS HHSN261201800004CNLM NIH HHS T15 LM007442
6 · The paper itself

Abstract

objectiveLung cancer is the most common cause of cancer-related death in the USA. While most patients are diagnosed following symptomatic presentation, no studies have compared symptoms and physical examination signs at or prior to diagnosis from electronic health records (EHRs) in the USA. We aimed to identify symptoms and signs in patients prior to diagnosis in EHR data.

designCase-control study.

settingAmbulatory care clinics at a large tertiary care academic health centre in the USA. PARTICIPANTS, OUTCOMES: We studied 698 primary lung cancer cases in adults diagnosed between 1 January 2012 and 31 December 2019, and 6841 controls matched by age, sex, smoking status and type of clinic. Coded and free-text data from the EHR were extracted from 2 years prior to diagnosis date for cases and index date for controls. Univariate and multivariable conditional logistic regression were used to identify symptoms and signs associated with lung cancer at time of diagnosis, and 1, 3, 6 and 12 months before the diagnosis/index dates.

resultsEleven symptoms and signs recorded during the study period were associated with a significantly higher chance of being a lung cancer case in multivariable analyses. Of these, seven were significantly associated with lung cancer 6 months prior to diagnosis: haemoptysis (OR 3.2, 95% CI 1.9 to 5.3), cough (OR 3.1, 95% CI 2.4 to 4.0), chest crackles or wheeze (OR 3.1, 95% CI 2.3 to 4.1), bone pain (OR 2.7, 95% CI 2.1 to 3.6), back pain (OR 2.5, 95% CI 1.9 to 3.2), weight loss (OR 2.1, 95% CI 1.5 to 2.8) and fatigue (OR 1.6, 95% CI 1.3 to 2.1).

conclusionsPatients diagnosed with lung cancer appear to have symptoms and signs recorded in the EHR that distinguish them from similar matched patients in ambulatory care, often 6 months or more before diagnosis. These findings suggest opportunities to improve the diagnostic process for lung cancer.

Indexed as

Electronic Health RecordsLung NeoplasmsAdultAmbulatory CareCase-Control StudiesHumansTertiary Care CentersHealth informaticsPRIMARY CARERespiratory tract tumoursThoracic medicine

Identifiers

PMID37080616
PMCPMC10124310
OpenAlexW4366587704

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.