Yagi Mitsuru, Yamamoto Tatsuya, Iga Takahito, Ogura Yoji, Suzuki Satoshi, Ozaki Masahiro, Takahashi Yohei, Tsuji Osahiko, Nagoshi Narihito, Kono Hitoshi, Ogawa Jun, Matsumoto Morio, Nakamura Masaya, Watanabe Kota
Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo, Japan.
Department of Orthopedic Surgery, International University of Health and Welfare, School of Medicine, Chiba, Japan.
Spine Surg Relat Res. 2024 Feb 14;8(3):315-321. doi: 10.22603/ssrr.2023-0255. eCollection 2024 May 27.
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.
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%).
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.
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.
准确预测住院时间对于手术期间最大限度地利用资源至关重要。现有的腰椎管狭窄症（LSS）手术预测模型缺乏准确性和通用性。机器学习可以通过考虑术前因素来提高准确性。本研究旨在开发并验证一种基于机器学习的模型，用于估计LSS减压手术后的住院时间。
研究了三家医院848例接受LSS减压手术患者的数据。使用79个术前变量开发了12个预测模型，用于术后住院时间估计。选择了前五个模型。14个模型预测住院时间延长（≥14天），并选择了最准确的模型。使用随机划分的训练样本（70%）和测试队列（30%）对模型进行验证。
前五个模型在测试样本中的预测值与测量值之间显示出中等程度的线性相关性（0.576 - 0.624）。这些模型的组合对最终住院时间具有中等预测准确性（线性相关性0.626，绝对平均误差2.26天，标准差3.45天）。c5.0决策树模型是预测住院时间延长的最佳模型，训练准确率为89.63%，测试准确率为87.2%。住院时间较长的关键预测因素包括JOABPEQ社会生活领域、医疗机构、椎体骨折史、诊断以及腰痛视觉模拟量表（VAS）。
利用多家医院的数据开发了一种基于机器学习的模型，用于预测LSS减压手术后的住院时间。住院时间的数值预测不太准确，尽管使用术前因素对住院时间延长进行了良好的预测。JOABPEQ社会生活领域评分是最重要的预测因素。
