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Simplifying Measurement of Adenoma Detection Rates for Colonoscopy

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Abstract

Background

Adenoma detection rate (ADR) is the colonoscopy quality metric with the strongest association to interval or “missed” cancer. Accurate measurement of ADR can be laborious and costly.

Aims

Our aim was to determine if administrative procedure codes for colonoscopy and text searches of pathology results for adenoma mentions could estimate ADR.

Methods

We identified US Veterans with a colonoscopy using Current Procedure Terminology (CPT) codes between January 2013 and December 2016 at ten Veterans Affairs sites. We applied simple text searches using Microsoft SQL Server full-text searches to query all pathology notes for “adenoma(s)” or “adenomatous” text mentions to calculate ADRs. To validate our identification of colonoscopy procedures, endoscopists of record, and adenoma detection from the electronic health record, we manually reviewed a random sample of 2000 procedure and pathology notes from the 10 sites.

Results

Structured data fields were accurate in identification of colonoscopies being performed (PPV = 0.99; 95% CI 0.99–1.00) and identifying the endoscopist of record (PPV of 0.95; 95% CI 0.94–0.96) for ADR measurement. Simple text searches of pathology notes for adenoma mentions had excellent performance statistics as follows: sensitivity 0.99 (95% CI 0.98–1.00), specificity 0.93 (95% CI 0.92–0.95), NPV 0.99 (95% CI 0.98–1.00), and PPV 0.93 (0.91–0.94) for measurement of ADR. There was no clinically significant difference in the estimates of overall ADR vs. screening ADR (p > 0.05).

Conclusions

Measuring ADR using administrative codes and text searches from pathology results is an efficient method to broadly survey colonoscopy quality.

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Abbreviations

CRC:

Colorectal cancer

ADR:

Adenoma detection rate

VA:

Veterans affairs

NLP:

Natural language processing

EHR:

Electronic health record

CDW:

Corporate data warehouse

CPT:

Current procedure terminology

ICD:

International classification of disease

PPV:

Positive predictive value

NPV:

Negative predictive value

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Acknowledgment

The contents of this work do not represent the views of the Department of Veterans Affairs or the United States Government.

Funding

This work was partially supported by the following: T. Kaltenbach and A. Gawron: Department of Veterans Affairs Quality Enhancement Research Initiative (Measurement Science QUERI (15-283), Project PI: Tonya Kaltenbach). A. Gawron and P. Lawrence: Salt Lake City Specialty Care Center of Innovation (Regional Director Grant Cannon). A. Gawron: Salt Lake City IDEAS COIN which is funded by Department of Veterans Affairs HSR&D Grant I50HX001240.

Author information

Authors and Affiliations

  1. VA Salt Lake City Health Care System, Salt Lake City, UT, USA

    Andrew J. Gawron

  2. Informatics, Decision-Enhancement, and Analytic Sciences Center, VA Salt Lake City Health Care System, Salt Lake City, UT, USA

    Andrew J. Gawron, Yiwen Yao & Garrett Cole

  3. Department of Internal Medicine, Division of Gastroenterology and Hepatology, University of Utah School of Medicine, Salt Lake City, UT, USA

    Andrew J. Gawron

  4. Department of Internal Medicine, Division of Epidemiology, University of Utah School of Medicine, Salt Lake City, UT, USA

    Yiwen Yao & Garrett Cole

  5. San Diego Veterans Affairs Health Care System, San Diego, CA, USA

    Samir Gupta

  6. Division of Gastroenterology and the Moores Cancer Center, University of California San Diego, La Jolla, CA, USA

    Samir Gupta

  7. Measurement Science QUERI, San Francisco, CA, USA

    Mary A. Whooley & Tonya Kaltenbach

  8. University of California San Francisco, San Francisco, CA, USA

    Mary A. Whooley & Tonya Kaltenbach

  9. VA Puget Sound Health Care System, Seattle, WA, USA

    Jason A. Dominitz

  10. Division of Gastroenterology, University of Washington School of Medicine, Seattle, WA, USA

    Jason A. Dominitz

Authors
  1. Andrew J. Gawron
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  2. Yiwen Yao
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  3. Samir Gupta
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  4. Garrett Cole
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  5. Mary A. Whooley
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  6. Jason A. Dominitz
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  7. Tonya Kaltenbach
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Contributions

AJG: planning study design, collection and interpretation of data, manual chart review, data analysis, drafting and revising the manuscript. YY: data management, collection and interpretation of data, manual chart review, data analysis. SG: planning study design, interpretation of data, drafting and revising the manuscript. GC: data management, manual chart review, collection and interpretation of data, data analysis. MW: planning study, drafting and revising the manuscript. JAD: planning study design, interpretation of data, drafting and revising the manuscript. TK: planning study design, collection and interpretation of data, manual chart review, data analysis, drafting and revising the manuscript.

Corresponding author

Correspondence to Andrew J. Gawron.

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Gawron, A.J., Yao, Y., Gupta, S. et al. Simplifying Measurement of Adenoma Detection Rates for Colonoscopy. Dig Dis Sci 66, 3149–3155 (2021). https://doi.org/10.1007/s10620-020-06627-2

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  • DOI: https://doi.org/10.1007/s10620-020-06627-2

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