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Reading: Development of an Algorithm to Classify Colonoscopy Indication from Coded Health Care Data

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Empirical research

Development of an Algorithm to Classify Colonoscopy Indication from Coded Health Care Data

Authors:

Kenneth F. Adams ,

Minnesota Department of Health, Saint Paul, MN
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Eric A. Johnson,

Group Health Research Institute, Seattle, WA
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Jessica Chubak,

Group Health Research Institute, Seattle, WA
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Aruna Kamineni,

Group Health Research Institute, Seattle, WA
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Chyke A. Doubeni,

Department of Family Medicine and Community Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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Diana S.M. Buist,

Group Health Research Institute, Seattle, WA
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Andrew E. Williams,

Maine Medical Center, Portland, ME
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Sheila Weinmann,

Center for Health Research, Kaiser Permanente Northwest, Portland, OR
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V. Paul Doria-Rose,

Applied Research Program, Health Services and Economics Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Bethesda, MD
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Carolyn M. Rutter

Group Health Research Institute, Seattle, WA (Current affiliation Rand Corp.)
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Abstract

Introduction: Electronic 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.

Methods: Data 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).

Results: The 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.

How to Cite: Adams KF, Johnson EA, Chubak J, Kamineni A, Doubeni CA, Buist DSM, et al.. Development of an Algorithm to Classify Colonoscopy Indication from Coded Health Care Data. eGEMs (Generating Evidence & Methods to improve patient outcomes). 2015;3(1):11. DOI: http://doi.org/10.13063/2327-9214.1171
Published on 18 May 2015.
Peer Reviewed

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