Why the study?
Lumbar spinal stenosis causes significant socioeconomic burdens and hospital length of stay evaluates postoperative complications and socioeconomic effects, motivating a machine learning-based tool to predict and interpret the risk of prolonged length of stay after surgery.
Can a machine learning model accurately predict prolonged length of stay after lumbar spinal stenosis surgery?
Population
540 patients registered from the spine surgery department in one hospital
Design
Retrospective cohort study
Key result
The optimal random forest model achieved an area under the curve of 0.83 on the test set for predicting prolonged length of stay after lumbar spinal stenosis surgery, identifying intraoperative blood loss as the most significant contributor.
Authors
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May support preoperative risk stratification after LSS surgery; hypothesis-generating and requires prospective validation.
Cohort (n=540)
No
Can a machine learning model accurately predict prolonged length of stay after lumbar spinal stenosis surgery?
Effect estimate: AUC 0.83
A random forest machine learning model combined with SHAP and LIME can accurately predict and interpret the risk of prolonged length of stay after lumbar spinal stenosis surgery.
Yasheng et al. (2025) conducted a cohort in Lumbar spinal stenosis (n=540). Random Forest machine learning model vs. Other machine learning models was evaluated on Prediction of prolonged length of stay (PLOS) ≥ 75th percentile (AUC 0.83). The optimal random forest model achieved an area under the curve of 0.83 on the test set for predicting prolonged length of stay after lumbar spinal stenosis surgery, identifying intraoperative blood loss as the most significant contributor.
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