Veeramani Ashwin, Zhang Andrew S, Blackburn Amy Z, Etzel Christine M, DiSilvestro Kevin J, McDonald Christopher L, Daniels Alan H
Department of Orthopedic Surgery, Rhode Island Hospital, Warren Alpert Medical School of Brown University, Providence, RI, USA.
Global Spine J. 2023 Sep;13(7):1849-1855. doi: 10.1177/21925682211053593. Epub 2022 Feb 8.
Level III retrospective database study.
The purpose of this study is to determine if machine learning algorithms are effective in predicting unplanned intubation following anterior cervical discectomy and fusion (ACDF).
The National Surgical Quality Initiative Program (NSQIP) was queried to select patients who had undergone ACDF. Machine learning analysis was conducted in Python and multivariate regression analysis was conducted in R. C-Statistics area under the curve (AUC) and prediction accuracy were used to measure the classifier's effectiveness in distinguishing cases.
In total, 54 502 patients met the study criteria. Of these patients, .51% underwent an unplanned re-intubation. Machine learning algorithms accurately classified between 72%-100% of the test cases with AUC values of between .52-.77. Multivariable regression indicated that the number of levels fused, male sex, COPD, American Society of Anesthesiologists (ASA) > 2, increased operating time, Age > 65, pre-operative weight loss, dialysis, and disseminated cancer were associated with increased risk of unplanned intubation.
The models presented here achieved high accuracy in predicting risk factors for re-intubation following ACDF surgery. Machine learning analysis may be useful in identifying patients who are at a higher risk of unplanned post-operative re-intubation and their treatment plans can be modified to prophylactically prevent respiratory compromise and consequently unplanned re-intubation.
III级回顾性数据库研究。
本研究旨在确定机器学习算法在预测颈椎前路椎间盘切除融合术（ACDF）后意外插管方面是否有效。
查询国家外科质量改进计划（NSQIP）以选择接受过ACDF手术的患者。在Python中进行机器学习分析，在R中进行多变量回归分析。使用曲线下C统计面积（AUC）和预测准确性来衡量分类器区分病例的有效性。
共有54502名患者符合研究标准。在这些患者中，0.51%接受了意外再次插管。机器学习算法准确分类了72%-100%的测试病例，AUC值在0.52-0.77之间。多变量回归表明，融合节段数、男性、慢性阻塞性肺疾病（COPD）、美国麻醉医师协会（ASA）分级>2、手术时间延长、年龄>65岁、术前体重减轻、透析和播散性癌症与意外插管风险增加相关。
本文提出的模型在预测ACDF手术后再次插管的风险因素方面具有较高的准确性。机器学习分析可能有助于识别术后意外再次插管风险较高的患者，并且可以修改他们的治疗计划以预防性地防止呼吸功能不全，从而避免意外再次插管。
