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Development and validation of an algorithm for classifying colonoscopy indication
Jeffrey K Lee1, Christopher D Jensen2, Alexander Lee1
1Department of Medicine, Division of Gastroenterology, University of California San Francisco, San Francisco, California, USA.
Background:
Accurate determination of colonoscopy indication is required for managing clinical programs and performing research; however, existing algorithms that use available electronic databases (eg, diagnostic and procedure codes) have yielded limited accuracy.
Objective:
To develop and validate an algorithm for classifying colonoscopy indication that uses comprehensive electronic medical data sources.
Design:
We developed an algorithm for classifying colonoscopy indication by using commonly available electronic diagnostic, pathology, cancer, and laboratory test databases and validated its performance characteristics in comparison with a comprehensive review of patient medical records. We also evaluated the influence of each data source on the algorithm's performance characteristics.
Setting:
Kaiser Permanente Northern California healthcare system.
Patients:
A total of 300 patients who underwent colonoscopy between 2007 and 2010.
Interventions:
Colonoscopy.
Main Outcome Measurements:
Algorithm's sensitivity, specificity, and positive predictive value (PPV) for classifying screening, surveillance, and diagnostic colonoscopies. The reference standard was the indication assigned after comprehensive medical record review.
Results:
For screening indications, the algorithm's sensitivity was 88.5% (95% confidence interval [CI], 80.4%-91.7%), specificity was 91.7% (95% CI, 87.0%-95.1%), and PPV was 83.3% (95% CI, 74.7%-90.0%). For surveillance indications, the algorithm's sensitivity was 93.4% (95% CI, 86.2%-97.5%), specificity was 92.8% (95% CI, 88.4%-95.9%), and PPV was 85.0% (95% CI, 76.5%-91.4%). The algorithm's sensitivity, specificity, and PPV for diagnostic indications were 81.4% (95% CI, 73.0%-88.1%), 96.8% (95% CI, 93.2%-98.8%), and 93.9% (95% CI, 87.2%-97.7%), respectively.
Limitations:
Validation was confined to a single healthcare system.
Conclusion:
An algorithm that uses commonly available modern electronic medical data sources yielded a high sensitivity, specificity, and PPV for classifying screening, surveillance, and diagnostic colonoscopy indications. This algorithm had greater accuracy than the indication listed on the colonoscopy report.
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