ArticleEGEMS (Washington, DC)2015
Development of an Algorithm to Classify Colonoscopy Indication from Coded Health Care Data.
Article in EGEMS (Washington, DC), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed, 14 citations in OpenAlex.
- The Impact and Interactions of Race and Gender on Healthcare Use and Spending in Irritable Bowel Syndrome.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2025Article
- Predictors of mobility status one year post hip fracture among community-dwelling older adults prior to fracture: A prospective cohort study.Journal of the American Geriatrics Society · 2023Article
- Risk for Post-Colonoscopy Irritable Bowel Syndrome in Patients With and Without Antibiotic Exposure: A Retrospective Cohort Study.Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association · 2022Article
- Colonoscopy Indication Algorithm Performance Across Diverse Health Care Systems in the PROSPR Consortium.EGEMS (Washington, DC) · 2019Article
- Changes in screening colonoscopy following Medicare reimbursement and cost-sharing changes.Health services research · 2019Article
- Case-Control Studies of the Efficacy of Screening Tests That Seek to Prevent Cancer Incidence: Results of an Approach That Utilizes Administrative Claims Data That Do Not Provide Information Regarding Test Indication.American journal of epidemiology · 2019Article
- Predicting Future Elective Colon Resection for Diverticulitis Using Patterns of Health Care Utilization.EGEMS (Washington, DC) · 2018Article
- Observational
- Racial and Ethnic Disparities in Interval Colorectal Cancer Incidence: A Population-Based Cohort Study.Annals of internal medicine · 2017Article
- Accounting for misclassification in electronic health records-derived exposures using generalized linear finite mixture models.Health services & outcomes research methodology · 2017Article
- Association Between Primary Care Visits and Colorectal Cancer Screening Outcomes in the Era of Population Health Outreach.Journal of general internal medicine · 2016Article
- Patterns of colorectal cancer screening uptake in newly eligible men and women.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 6 institutions in 1 country.
Funding
Abstract
introductionElectronic health data are potentially valuable resources for evaluating colonoscopy screening utilization and effectiveness. The ability to distinguish screening colonoscopies from exams performed for other purposes is critical for research that examines factors related to screening uptake and adherence, and the impact of screening on patient outcomes, but distinguishing between these indications in secondary health data proves challenging. The objective of this study is to develop a new and more accurate algorithm for identification of screening colonoscopies using electronic health data.
methodsData from a case-control study of colorectal cancer with adjudicated colonoscopy indication was used to develop logistic regression-based algorithms. The proposed algorithms predict the probability that a colonoscopy was indicated for screening, with variables selected for inclusion in the models using the Least Absolute Shrinkage and Selection Operator (LASSO).
resultsThe algorithms had excellent classification accuracy in internal validation. The primary, restricted model had AUC= 0.94, sensitivity=0.91, and specificity=0.82. The secondary, extended model had AUC=0.96, sensitivity=0.88, and specificity=0.90. DISCUSSION: The LASSO approach enabled estimation of parsimonious algorithms that identified screening colonoscopies with high accuracy in our study population. External validation is needed to replicate these results and to explore the performance of these algorithms in other settings.
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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.