Use of random forest machine learning algorithm to predict short term outcomes following posterior cervical decompression with instrumented fusion

医学 围手术期 算法 随机森林 逻辑回归 红细胞压积 外科 机器学习 内科学 计算机科学
作者
Andrew Cabrera,Alexander Bouterse,Michael Nelson,Jacob Razzouk,Omar Ramos,David Chung,Wayne Cheng,Olumide Danisa
出处
期刊:Journal of Clinical Neuroscience [Elsevier BV]
卷期号:107: 167-171 被引量:16
标识
DOI:10.1016/j.jocn.2022.10.029
摘要

Random Forest (RF) is a widely used machine learning algorithm that can be utilized for identification of patient characteristics important for outcome prediction. Posterior cervical decompression with instrumented fusion (PCDF) is a procedure for the management of cervical spondylosis, cervical spinal stenosis, and degenerative disorders that can cause cervical myelopathy or radiculopathy. An RF algorithm was employed to predict and describe length of stay (LOS), readmission, reoperation, transfusion, and infection rates following elective PCDF using The American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database 2008 through 2018. The RF algorithm was tasked with determining the importance of independent clinical variables in predicting our outcomes of interest and importance of each variable based on the reduction in the Gini index. Application of an RF algorithm to the ACS-NSQIP database yielded a highly predictive set of patient characteristics and perioperative events for five outcomes of interest related to elective PCDF. These variables included postoperative infection, increased age, BMI, operative time, and LOS, and decreased preoperative hematocrit and white blood cell count. Risk factors that were predictive for rate of reoperation, readmission, hospital length of stay, transfusion requirement, and post-operative infection were identified with AUC values of 0.781, 0.791, 0.781, 0.902, and 0.724 respectively. Utilization of these findings may assist in risk analysis during the perioperative period and may influence clinical or surgical decision-making.
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