Artificial intelligence in predicting early-onset adjacent segment degeneration following anterior cervical discectomy and fusion

Purpose Anterior cervical discectomy and fusion (ACDF) is a common surgical treatment for degenerative disease in the cervical spine. However, resultant biomechanical alterations may predispose to early-onset adjacent segment degeneration (EO-ASD), which may become symptomatic and require reoperation. This study aimed to develop and validate a machine learning (ML) model to predict EO-ASD following ACDF. Methods Retrospective review of prospectively collected data of patients undergoing ACDF at a quaternary referral medical center was performed. Patients > 18 years of age with > 6 months of follow-up and complete pre- and postoperative X-ray and MRI imaging were included. An ML-based algorithm was developed to predict EO-ASD based on preoperative demographic, clinical, and radiographic parameters, and model performance was evaluated according to discrimination and overall performance. Results In total, 366 ACDF patients were included (50.8% male, mean age 51.4 ± 11.1 years). Over 18.7 ± 20.9 months of follow-up, 97 (26.5%) patients developed EO-ASD. The model demonstrated good discrimination and overall performance according to precision (EO-ASD: 0.70, non-ASD: 0.88), recall (EO-ASD: 0.73, non-ASD: 0.87), accuracy (0.82), F1-score (0.79), Brier score (0.203), and AUC (0.794), with C4/C5 posterior disc bulge, C4/C5 anterior disc bulge, C6 posterior superior osteophyte, presence of osteophytes, and C6/C7 anterior disc bulge identified as the most important predictive features. Conclusions Through an ML approach, the model identified risk factors and predicted development of EO-ASD following ACDF with good discrimination and overall performance. By addressing the shortcomings of traditional statistics, ML techniques can support discovery, clinical decision-making, and precision-based spine care..

Medienart:

E-Artikel

Erscheinungsjahr:

2022

Erschienen:

2022

Enthalten in:

Zur Gesamtaufnahme - volume:31

Enthalten in:

European spine journal - 31(2022), 8 vom: 11. Mai, Seite 2104-2114

Sprache:

Englisch

Beteiligte Personen:

Rudisill, Samuel S. [VerfasserIn]
Hornung, Alexander L. [VerfasserIn]
Barajas, J. Nicolás [VerfasserIn]
Bridge, Jack J. [VerfasserIn]
Mallow, G. Michael [VerfasserIn]
Lopez, Wylie [VerfasserIn]
Sayari, Arash J. [VerfasserIn]
Louie, Philip K. [VerfasserIn]
Harada, Garrett K. [VerfasserIn]
Tao, Youping [VerfasserIn]
Wilke, Hans-Joachim [VerfasserIn]
Colman, Matthew W. [VerfasserIn]
Phillips, Frank M. [VerfasserIn]
An, Howard S. [VerfasserIn]
Samartzis, Dino [VerfasserIn]

Links:

Volltext [lizenzpflichtig]

Themen:

Adjacent segment
Anterior cervical discectomy
Artificial intelligence
Cervical spine
Degeneration
Disc
Fusion
Machine learning
Outcomes
Predictive modeling

RVK:

RVK Klassifikation

Anmerkungen:

© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022

doi:

10.1007/s00586-022-07238-3

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

OLC2131520395

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