背景:准确估计手术时间是一项重要的手术室效率指标。脊柱手术中的当前预测技术包括不太复杂的方法,例如经典的多变量统计模型。机器学习方法已用于预测结果,例如停留时间和恢复正常工作的时间。但没有集中在案件的持续时间。
目标:这4年的主要目标,单一学术中心,回顾性研究是使用集成学习方法,该方法可能会提高脊柱手术预定病例持续时间的准确性。主要结果指标是病例持续时间。
方法:我们将使用手术和患者特征的机器学习模型与我们的机构方法进行了比较,根据需要使用历史平均值和外科医生调整。我们实施了多元线性回归,随机森林,装袋,和XGBoost(极限梯度提升),并计算平均R2,均方根误差(RMSE),解释方差,和使用k折交叉验证的平均绝对误差(MAE)。然后,我们使用SHAP(Shapley加法解释)解释器模型来确定特征重要性。
结果:共纳入3189例接受脊柱手术的患者。机构当前预测病例次数的方法与实际次数的确定系数非常差(R2=0.213)。在k折交叉验证中,线性回归模型的解释方差得分为0.345,R2为0.34,RMSE为162.84分钟,MAE为127.22分钟。在所有型号中,XGBoost回归函数表现最好,解释方差分数为0.778,R2为0.770,RMSE为92.95分钟,MAE为44.31分钟。基于XGBoost回归的SHAP分析,身体质量指数,脊柱融合,外科手术,涉及的脊柱水平数量是对模型影响最大的特征。
结论:使用基于集成学习的预测模型,特别是XGBoost回归,可以提高脊柱手术次数估计的准确性。
BACKGROUND: Estimating surgical
case duration accurately is an important operating room efficiency metric. Current predictive techniques in spine surgery include less sophisticated approaches such as classical multivariable statistical models. Machine learning approaches have been used to predict outcomes such as length of stay and time returning to normal work, but have not been focused on
case duration.
OBJECTIVE: The primary objective of this 4-year, single-academic-center, retrospective study was to use an ensemble learning approach that may improve the accuracy of scheduled
case duration for spine surgery. The primary outcome measure was
case duration.
METHODS: We compared machine learning models using surgical and patient features to our institutional method, which used historic averages and surgeon adjustments as needed. We implemented multivariable linear regression, random forest, bagging, and XGBoost (Extreme Gradient Boosting) and calculated the average R2, root-mean-square error (RMSE), explained variance, and mean absolute error (MAE) using k-fold cross-validation. We then used the SHAP (Shapley Additive Explanations) explainer model to determine feature importance.
RESULTS: A total of 3189 patients who underwent spine surgery were included. The institution\'s current method of predicting
case times has a very poor coefficient of determination with actual times (R2=0.213). On k-fold cross-validation, the linear regression model had an explained variance score of 0.345, an R2 of 0.34, an RMSE of 162.84 minutes, and an MAE of 127.22 minutes. Among all models, the XGBoost regressor performed the best with an explained variance score of 0.778, an R2 of 0.770, an RMSE of 92.95 minutes, and an MAE of 44.31 minutes. Based on SHAP analysis of the XGBoost regression, body mass index, spinal fusions, surgical procedure, and number of spine levels involved were the features with the most impact on the model.
CONCLUSIONS: Using ensemble learning-based predictive models, specifically XGBoost regression, can improve the accuracy of the estimation of spine surgery times.