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Int J Gynaecol Obstet. 2014 Jan;124(1):88-91. doi: 10.1016/j.ijgo.2013.06.036. Epub 2013 Oct 7.

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

Author information

1
Department of Obstetrics, Gynecology and Reproductive Sciences, Magee-Womens Hospital of University of Pittsburgh Medical Center, Pittsburgh, USA.
2
Division of Gynecologic Oncology, New York University School of Medicine, New York, USA.
3
Division of Gynecologic Oncology, New York University School of Medicine, New York, USA; Department of Obstetrics and Gynecology, New York Hospital Queens, New York, USA. Electronic address: jianqun.huang@nyumc.org.

Abstract

OBJECTIVE:

To review the first 100 cases of robotic-assisted hysterectomy performed by an individual surgeon.

METHODS:

A 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.

RESULTS:

Mean age was 46 years; mean body mass index was 27.8 kg/m(2). Median operative time was 120 minutes; 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.

CONCLUSION:

There 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.

KEYWORDS:

Hysterectomy; Learning analysis; Learning curve; Robotic

PMID:
24182553
DOI:
10.1016/j.ijgo.2013.06.036
[Indexed for MEDLINE]

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