ABSTRACT
Introduction:
Precise prediction of
hospital stay duration is essential for maximizing
resource utilization during
surgery. Existing lumbar
spinal stenosis (LSS)
surgery prediction models lack accuracy and generalizability.
Machine learning can improve accuracy by considering preoperative factors. This study aimed to develop and validate a
machine learning-based model for estimating
hospital stay duration following
decompression surgery for LSS.
Methods:
Data from 848
patients who underwent
decompression surgery for LSS at three
hospitals were examined. Twelve prediction models, using 79 preoperative variables, were developed for postoperative
hospital stay estimation. The top five models were chosen. Fourteen models predicted prolonged
hospital stay (≥14 days), and the most accurate model was chosen. Models were validated using a randomly divided
training sample (70%) and testing cohort (30%).
Results:
The top five models showed moderate linear correlations (0.576-0.624) between predicted and measured values in the testing sample. The ensemble of these models had moderate prediction accuracy for final
length of stay (linear correlation 0.626, absolute mean error 2.26 days, standard deviation 3.45 days). The c5.0
decision tree model was the top predictor for prolonged
hospital stay, with accuracies of 89.63% (
training) and 87.2% (testing). Key predictors for longer stay included JOABPEQ social
life domain, facility,
history of vertebral fracture,
diagnosis, and Visual Analogue Scale (VAS) of
low back pain.
Conclusions:
A
machine learning-based model was developed to predict postoperative
hospital stay after LSS
decompression surgery, using data from multiple
hospital settings. Numerical prediction of
length of stay was not very accurate, although favorable prediction of prolonged stay was accomplished using preoperative factors. The JOABPEQ social
life domain score was the most important predictor.
