Machine learning-driven identification of novel patient factors for prediction of major complications after posterior cervical spinal fusion.

ABSTRACT:

Purpose

Posterior cervical fusion is associated with increased rates of complications and readmission when compared to anterior fusion. Machine learning (ML) models for risk stratification of patients undergoing posterior cervical fusion remain limited. We aim to develop a novel ensemble ML algorithm for prediction of major perioperative complications and readmission after posterior cervical fusion and identify factors important to model performance.

Methods

This is a retrospective cohort study of adults who underwent posterior cervical fusion at non-federal California hospitals between 2015 and 2017. The primary outcome was readmission or major complication. We developed an ensemble model predicting complication risk using an automated ML framework. We compared performance

SUBMITTER: Shah AA 

PROVIDER: S-EPMC8844303 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature