Importance of endoscopist quality metrics for findings at surveillance colonoscopy: The detection-surveillance paradox

Background

Guidelines recommend surveillance colonoscopies based exclusively on findings at baseline colonoscopy. This recommendation leads to the paradox that the higher the baseline colonoscopy quality, the more surveillance colonoscopies will be indicated according to current guidelines.

Objective

The aim of this study was to evaluate the effect on follow-up findings of different quality metrics of the endoscopist performing the baseline colonoscopy.

Methods

This retrospective cohort study included individuals with advanced adenomas at baseline colonoscopy. Adenoma detection rate (ADR) and adenomas per colonoscopy rate (APCR) were determined for 44 endoscopists. Surveillance colonoscopies were checked after systematic tracking.

Results

A total of 574 individuals were diagnosed with advanced adenomas, of whom 270 received a surveillance colonoscopy. Patients whose baseline colonoscopy endoscopist had an ADR lower than the median of 33.8% had significantly higher rates of advanced neoplasia at follow-up (13.1% vs 4.0%; p = 0.001). On univariate analysis, high-risk advanced adenomas at baseline (HR 0.43; 95% CI 0.19-0.97) and ADR (HR 0.94; 95% CI 0.89-0.99) showed a significant relationship with advanced neoplasia at surveillance. In a multivariate Cox model, the ADR of the endoscopist who performed the baseline colonoscopy was the only independent predictor of risk for developing advanced neoplasia at follow-up (HR 0.94; 95% CI 0.89-0.99).

Conclusions

Our results suggest that the risk of identifying advanced adenomas at follow-up is closely related to the quality metrics of the endoscopist who performs the baseline colonoscopy.

Natural language processing as an alternative to manual reporting of colonoscopy quality metrics

BACKGROUND AND AIMS

The adenoma detection rate (ADR) is a quality metric tied to interval colon cancer occurrence. However, manual extraction of data to calculate and track the ADR in clinical practice is labor-intensive. To overcome this difficulty, we developed a natural language processing (NLP) method to identify adenomas and sessile serrated adenomas (SSAs) in patients undergoing their first screening colonoscopy. We compared the NLP-generated results with that of manual data extraction to test the accuracy of NLP and report on colonoscopy quality metrics using NLP.

METHODS

Identification of screening colonoscopies using NLP was compared with that using the manual method for 12,748 patients who underwent colonoscopies from July 2010 to February 2013. Also, identification of adenomas and SSAs using NLP was compared with that using the manual method with 2259 matched patient records. Colonoscopy ADRs using these methods were generated for each physician.

RESULTS

NLP correctly identified 91.3% of the screening examinations, whereas the manual method identified 87.8% of them. Both the manual method and NLP correctly identified examinations of patients with adenomas and SSAs in the matched records almost perfectly. Both NLP and the manual method produced comparable values for ADRs for each endoscopist and for the group as a whole.

CONCLUSIONS

NLP can correctly identify screening colonoscopies, accurately identify adenomas and SSAs in a pathology database, and provide real-time quality metrics for colonoscopy.