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

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 charac-teristics 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 peri-operative period and may influence clinical or surgical decision-making.

Automated summary

Random Forest algorithm was used to predict and describe patient outcomes after posterior cervical decompression with instrumented fusion (PCDF), including length of stay, readmission rates, reoperation rates, transfusion requirements, and infection rates. The algorithm analyzed data from The American College of Surgeons National Quality Improvement Program (ACS-NSQIP) database from 2008 to 2018, and identified the importance of independent clinical variables in predicting these outcomes. These findings can aid in perioperative risk analysis and clinical decision-making.