Category: Laparoscopic/ Robotic: Other

MP25-2 - Developing a Predictive Nomogram for Operative Duration in Minimally Invasive Partial Neprectomy

Sun, Sep 23
10:00 AM - 12:00 PM

Introduction & Objective : Minimal data exsits on appropiate or suggested operative duration (OD) in minimally invasive partial nephrectomy (MIPN). Existing data focuses only on a small number of variables. Such nomograms may allow further understanding and examination of value.
Objective: To establish a predictive nomogram for OD in MIPN through examination of 31 variables.


Methods : Our institution’s National Surgical Quality Improvement Program (NSQIP) database was queried using CPT codes for MIPN performed from 2012-2017.  209 MIPN were identfied for analysis. An AIC-based, stepwise elimination procedure was used to find a regression model most predictive of operative duration. The following were included as predictors in the full regression model: sodium, BUN, creatinine, WBC, HCT, platelet count, age, BMI, inpatient/outpatient, surgeon, diabetes, smoker, dyspnea, functional status, COPD, hypertension, cancer, open wound, steroid, preoperative weight loss, bleeding, transfusion, sepsis, wound class, sex, cT, pT, grade, robot assistance, renal score, and ASA class ≥ III. The stepwise elimination procedure resulted in the following predictors of operative duration: BMI, surgeon, dyspnea, COPD, sex, cT, robot assistance, and ASA class ≥ III. The model estimates were rounded to the nearest minute to create a nomogram.


Results : The nomogram is depicted in table 1. Suppose we have a patient with the following characteristics: BMI of 28, being operated on by Surgeon C, no preop dyspnea, COPD, male, cT1a, robot assisted, and an ASA class of II. Then we predict the following operative duration:  100 + 14 + 51 + 23 + 40 + 22 + 0 + 46 + 0 = 296 minutes. Comparison of actual to predicted times is shown in figure 1. 


Conclusions : The potential use for such nomograms is vast including scheduling, benchmarking, credentialing, and inclusion in various payment models. 

Andrew M. Harris

Endourology/Robotics Fellow
University of Kentucky
Lexington, Kentucky

My name is Andrew M. Harris, MD and I'm currently the endourology and robotics fellow at the University of Kentucky. I finished my residency training at the University of Pennsylvania in 2012 followed by a brief period in private practice prior to matriculation to fellowship. My reserach efforts focus on health care economics/cost/safety/qi/lean implementation/resident education and am currently in classes to receive a certificate in improving health care value with an emphasis in safety and quality improvement.

Adam Dugan

University of Kentucky
Lexington, Kentucky

Adam J. Dugan, MS, University of Kentucky Department of Surgery

Jason R. Bylund

Associate Professor
University of Kentucky
Lexington, Kentucky