Insertion time as a proxy for procedural complexity: Refining its role in predictive models of adenoma detection

Abstract

Existing literature indicates that prolonged insertion time is associated with procedural complexity and may influence adenoma detection. Xu et al recently reported that longer insertion time correlates with lower adenoma detection, but this effect can be mitigated by sufficient withdrawal duration. Insertion time should not be regarded merely as a numeric variable but rather as a multidimensional marker of technical difficulty. Integrating the insertion-to-withdrawal ratio with composite indicators such as looping or bowel preparation quality may enhance predictive models of colonoscopy performance. Conceptualizing insertion time in this way provides a more nuanced understanding of its role in adenoma detection and highlights the need for improved frameworks that link procedural complexity with quality outcomes.

Key Words: Adenoma detection rate; Colonoscopy; Cecal intubation time; Withdrawal time; Procedural complexity; Machine learning; Quality indicators

Core Tip: Insertion time is not merely duration but a marker of procedural complexity. Clinicians should view prolonged insertion time as a potential risk factor for reduced adenoma detection, unless offset by adequate withdrawal. Predictive models that incorporate insertion-to-withdrawal ratios and established quality benchmarks may better guide colonoscopy performance and improve outcomes in colorectal cancer prevention.

TO THE EDITOR

We have read with great interest the study by Xu et al[1], who developed and validated a hybrid machine-learning model to personalize colonoscopy withdrawal time by incorporating insertion time (IT) together with patient and endoscopist-related factors, implemented in a user-friendly Shiny application. Their approach represents an innovative step toward individualized quality assurance in colonoscopy. While this work is important, we believe that the specific role of IT in predictive models deserves further critical consideration.

IT is more than a continuous variable that can be directly entered into a statistical or machine-learning model. It is widely recognized as a proxy for procedural complexity. Prolonged cecal intubation often reflects challenging anatomy, looping, poor patient tolerance, or technical difficulty. These elements do not simply add minutes to the insertion phase but may influence vigilance and mucosal inspection during withdrawal. The literature provides consistent signals that IT conveys clinically relevant information which should be interpreted more carefully than as an isolated measure.

von Renteln et al[2] evaluated 1043 patients undergoing colonoscopy and found that longer cecal IT was significantly associated with reduced adenoma detection, including advanced adenomas. Choi et al[3] confirmed this association in a cohort of 12402 colonoscopies, where longer intubation was independently linked with lower adenoma detection and higher miss rates, particularly for serrated lesions in the right colon. By contrast, Fritz et al[4] reported that IT alone was not associated with detection rates when withdrawal was sufficiently prolonged, demonstrating that the insertion-to-withdrawal ratio is a more meaningful determinant.

Taken together, these studies suggest that IT is not a simple linear predictor but a multidimensional marker of complexity, whose impact depends on withdrawal context. A model that treats IT only as a continuous covariate may risk oversimplification. We believe future predictive models should explicitly incorporate the insertion-to-withdrawal ratio and conceptualize IT as part of a composite construct of procedural difficulty, integrating bowel preparation, looping, sedation, and operator maneuvers. Such refinements may enhance the performance of predictive algorithms and more closely reflect clinical reality.

Operationalization of IT could involve categorical thresholds or, more robustly, a composite index that integrates IT with routinely documented factors such as bowel preparation quality or loop formation. Future studies should also clarify whether prolonged IT reflects procedural complexity or deliberate caution by accounting for operator-level variables and prospective video review. Structured reporting standards and emerging artificial intelligence (AI)-based video analysis may further enable objective capture of technical events, while direct real-time indicators of mucosal visualization quality or view stability may ultimately surpass IT as predictive inputs.

The work aligns with current quality standards but could be further strengthened by integrating recommendations from the American College of Gastroenterology guidelines, which reaffirm adenoma detection rate and withdrawal time as key quality indicators, with a minimum withdrawal of six minutes[5]. Personalized withdrawal time is promising, but it should complement, not replace, established benchmarks. Models that merge personalization with established quality metrics may achieve the highest clinical utility.

CONCLUSION

In conclusion, Xu et al[1] have provided a valuable proof of concept for AI-supported personalization of colonoscopy quality indicators. We suggest that IT, rather than being used solely as a numeric variable, may be better conceptualized as a proxy for procedural complexity and interpreted in relation to withdrawal time. Integrating the insertion-to-withdrawal ratio and other markers of technical difficulty may enhance predictive accuracy and practical application in colorectal cancer prevention.