Learning curve analysis of the first 100 robotic-assisted laparoscopic hysterectomies performed by a single surgeon

Abstract

ObjectiveTo review the first 100 cases of robotic-assisted hysterectomy performed by an individual surgeon. MethodsA retrospective cohort study of the first 100 consecutive patients who underwent robotic-assisted hysterectomy by a newly trained minimally invasive gynecologic surgeon was conducted. Demographic factors and short-term surgical outcome variables were abstracted from medical records. We examined univariate associations and performed multivariable modeling with linear regression, and modeled the learning curve for total operative time using power-law function. ResultsMean age was 46years; mean body mass index was 27.8kg/m2. Median operative time was 120minutes; median estimated blood loss was 100mL. On multivariable analysis, case number (β –0.296; P<0.005) and uterine weight (β 0.330; P<0.005) independently predicted operative time, while uterine weight (β 0.387; P<0.005) independently predicted estimated blood loss. The point at which the slope of the case number–operative time curve crosses –1.0 is at case 28 when uncontrolled and at case 24 when controlled for other factors. ConclusionThere was a significantly decreased operative time for robotic-assisted hysterectomies performed later in the surgeon’s learning curve. Surgical proficiency, as measured by operative time, seemed to be attained after 20–30 cases.