ArticleJournal of the American Medical Informatics Association : JAMIA2021
Characterizing phenotypic abnormalities associated with high-risk individuals developing lung cancer using electronic health records from the All of Us researcher workbench.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.
What it found
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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.
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.
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Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- A scoping review of OMOP CDM adoption for cancer research using real world data.NPJ digital medicine · 2025Article
- Comprehensive analysis of genetic associations and single-cell expression profiles reveals potential links between migraine and multiple diseases: a phenome-wide association study.Frontiers in neurology · 2024Article
- Machine Learning for Lung Cancer Diagnosis, Treatment, and Prognosis.Genomics, proteomics & bioinformatics · 2022Review
Corrections and comments
- Erratum issued
Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveThe study sought to test the feasibility of conducting a phenome-wide association study to characterize phenotypic abnormalities associated with individuals at high risk for lung cancer using electronic health records. MATERIALS AND
methodsWe used the beta release of the All of Us Researcher Workbench with clinical and survey data from a population of 225 000 subjects. We identified 3 cohorts of individuals at high risk to develop lung cancer based on (1) the 2013 U.S. Preventive Services Task Force criteria, (2) the long-term quitters of cigarette smoking criteria, and (3) the younger age of onset criteria. We applied the logistic regression analysis to identify the significant associations between individuals' phenotypes and their risk categories. We validated our findings against a lung cancer cohort from the same population and conducted an expert review to understand whether these associations are known or potentially novel.
resultsWe found a total of 214 statistically significant associations (P < .05 with a Bonferroni correction and odds ratio > 1.5) enriched in the high-risk individuals from 3 cohorts, and 15 enriched in the low-risk individuals. Forty significant associations enriched in the high-risk individuals and 13 enriched in the low-risk individuals were validated in the cancer cohort. Expert review identified 15 potentially new associations enriched in the high-risk individuals.
conclusionsIt is feasible to conduct a phenome-wide association study to characterize phenotypic abnormalities associated in high-risk individuals developing lung cancer using electronic health records. The All of Us Research Workbench is a promising resource for the research studies to evaluate and optimize lung cancer screening criteria.
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Registered trials
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.