Prediction of Major Complications and Readmission After Lumbar Spinal Fusion: A Machine Learning-Driven Approach.

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Abstract

Given the significant cost and morbidity of patients undergoing lumbar fusion, accurate preoperative risk-stratification would be of great utility. We aim to develop a machine learning model for prediction of major complications and readmission after lumbar fusion. We also aim to identify the factors most important to performance of each tested model.

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Author and article information

Journal

Journal ID (iso-abbrev): World Neurosurg

Title: World neurosurgery

Publisher: Elsevier BV

ISSN (Electronic): 1878-8769

ISSN (Print): 1878-8750

Publication date (Electronic): August 2021

Volume: 152

Affiliations

[1 ] Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA. Electronic address: aashah@mednet.ucla.edu.

[2 ] Department of Orthopaedic Surgery, David Geffen School of Medicine at UCLA, Los Angeles, California, USA.

[3 ] Department of Electrical and Computer Engineering, University of California, Los Angeles, California, USA.

[4 ] Department of Electrical and Computer Engineering, University of California, Los Angeles, California, USA; Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom.

Article

Publisher Item ID: S1878-8750(21)00775-0 Mid ID: NIHMS1709719

DOI: 10.1016/j.wneu.2021.05.080

PMC ID: 8338911

PubMed ID: 34058366

SO-VID: fcd30d66-614d-4efa-8af2-ad6f2a843ad5

History

Keywords: Complications,Lumbar fusion,Machine learning,Outcomes,Readmission

Data availability:

Keywords: Complications, Lumbar fusion, Machine learning, Outcomes, Readmission

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