Journal of robotic surgery · 2026
The importance of procedural indication in predicting operative time for elective robotic colon surgery.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Ma Rachel, Nasseri Yosef, Wu George, Zeller Matthew, Solis-Pazmino Paola, Wetherell Joseph, Barnajian Moshe, Feinman Marcie, Felton Jessica, Wolf Joshua
Аннотация
Background: Accurate prediction of operative time in robotic surgery is essential for efficient operating room scheduling and resource utilization. Most current estimation methods rely primarily on Current Procedural Terminology (CPT) codes, which do not account for the clinical indication that could influence surgical complexity. We assessed if surgical indication independently contributes to variation in operative duration for robotic colon procedures. Methods: We conducted a retrospective cohort study using the ACS-NSQIP from 2018 to 2023 of adults undergoing elective robotic colectomy. Cases were limited to the four most common CPT codes and four most common surgical indications: colon cancer, benign neoplasm, uncomplicated diverticulitis, and complicated diverticulitis. Operative time, defined as minutes from incision to closure, was compared across indications within each CPT category. Multivariable linear regression adjusting for demographic and clinical factors assessed whether indication independently predicted operative duration. Results: Among 29,090 cases, median operative time differed significantly across indications within every CPT category. Cancer and complicated diverticulitis were consistently associated with the longest operative durations (292.0 min, 283.0 min), while benign neoplasm had the shortest (172.0 min). In adjusted models, indication remained an independent predictor of operative time, adding up to 43 min depending on CPT and indication (β:42.77 min, 95%CI:36.14–49.40 min, p < 0.001). Conclusions: Surgical indication significantly influences operative time in robotic colon surgery beyond the information captured by CPT codes. Incorporating diagnosis-specific data into time estimation models may improve operating room scheduling accuracy and overall resource utilization.