Li Qiyi, Zhong Haoyan, Girardi Federico P, Poeran Jashvant, Wilson Lauren A, Memtsoudis Stavros G, Liu Jiabin
Department of Orthopaedics, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Peking, China.
Department of Anesthesiology, Critical Care, and Pain Management, Hospital for Special Surgery, New York, NY, USA.
Global Spine J. 2022 Sep;12(7):1363-1368. doi: 10.1177/2192568220979835. Epub 2021 Jan 7.
retrospective cohort study.
To test and compare 2 machine learning algorithms to define characteristics associated with candidates for ambulatory same day laminectomy surgery.
The American College of Surgeons National Surgical Quality Improvement Program database was queried for patients who underwent single level laminectomy in 2017 and 2018. The main outcome was ambulatory same day discharge. Study variables of interest included demographic information, comorbidities, preoperative laboratory values, and intra-operative information. Two machine learning predictive modeling algorithms, artificial neural network (ANN) and random forest, were trained to predict same day discharge. The quality of models was evaluated with area under the curve (AUC), accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) measures.
Among 35,644 patients, 13,230 (37.1%) were discharged on the day of surgery. Both ANN and RF demonstrated a satisfactory model quality in terms of AUC (0.77 and 0.77), accuracy (0.69 and 0.70), sensitivity (0.83 and 0.58), specificity (0.55 and 0.80), PPV (0.77 and 0.69), and NPV (0.64 and 0.70). Both models highlighted several important predictive variables, including age, duration of operation, body mass index and preoperative laboratory values including, hematocrit, platelets, white blood cells, and alkaline phosphatase.
Machine learning approaches provide a promising tool to identify candidates for ambulatory laminectomy surgery. Both machine learning algorithms highlighted the as yet unrecognized importance of preoperative laboratory testing on patient pathway design.
回顾性队列研究。
测试并比较两种机器学习算法，以确定与门诊当日腰椎切除术手术候选人相关的特征。
查询美国外科医师学会国家外科质量改进计划数据库中2017年和2018年接受单节段腰椎切除术的患者。主要结局是门诊当日出院。感兴趣的研究变量包括人口统计学信息、合并症、术前实验室检查值和术中信息。训练两种机器学习预测建模算法，即人工神经网络（ANN）和随机森林，以预测当日出院情况。用曲线下面积（AUC）、准确率、敏感性、特异性、阳性预测值（PPV）和阴性预测值（NPV）指标评估模型质量。
在35644例患者中，13230例（37.1%）在手术当日出院。ANN和随机森林在AUC（分别为0.77和0.77）、准确率（分别为0.69和0.70）、敏感性（分别为0.83和0.58）、特异性（分别为0.55和0.80）、PPV（分别为0.77和0.69）以及NPV（分别为0.64和0.70）方面均显示出令人满意的模型质量。两种模型都突出了几个重要的预测变量，包括年龄、手术时长、体重指数以及术前实验室检查值，包括血细胞比容、血小板、白细胞和碱性磷酸酶。
机器学习方法为识别门诊腰椎切除术手术候选人提供了一个有前景的工具。两种机器学习算法都凸显了术前实验室检查在患者路径设计中尚未被认识到的重要性。
