Multi-institutional validation study of neural networks to predict duration of stay after laparoscopic radical/simple or partial nephrectomy

Abstract

Purpose: We developed models to predict post-laparoscopic radical or simple nephrectomy (LapNx) and post-laparoscopic partial nephrectomy (LapPNx) hospital duration of stay (DOS).

Materials and methods: We performed a retrospective review (design group) of all 726 patients (July 1997 to April 2004) who underwent LapNx or LapPNx at the Cleveland Clinic Foundation (CCF). Preoperative findings were recorded. Neural network algorithms were designed to predict the DOS before surgery. The models were then tested on a separate 252 patients from 6 different institutions, namely Tulane University Medical School, University of Arkansas for Medical Sciences, Cedars-Sinai Medical Center, University of Iowa, Mayo Clinic at Scottsdale and CCF.

Results: In the CCF design groups, the LapNx model accuracy was 73% to 74% and the LapPNx model 73% to 83%. Overall accuracy in the test groups at all 6 institutions was 72% (area under ROC 0.6 to 0.7) for the LapNx model and 52% to 81% (ROC 0.5 to 0.7) for the LapPNx model.

Conclusions: The LapNx model provides 72% accuracy in predicting the DOS at all 6 institutions. The LapPNx model provided fair accuracy only at CCF and Tulane University Medical School. These models may streamline the delivery of care and continued testing will allow for further refinement.