The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning Approach Article Swipe
Pedro Berjano
,
Francesco Langella
,
Luca Ventriglia
,
Domenico Compagnone
,
Paolo Barletta
,
David Huber
,
Francesca Mangili
,
Ginevra Licandro
,
Fabio Galbusera
,
Andrea Cina
,
Tito Bassani
,
Claudio Lamartina
,
Laura Scaramuzzo
,
Roberto Bassani
,
Marco Brayda‐Bruno
,
Jorge Hugo Villafañe
,
Lorenzo Monti
,
Laura Azzimonti
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/jpm11121377
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.3390/jpm11121377
The study aims to create a preoperative model from baseline demographic and health-related quality of life scores (HRQOL) to predict a good to excellent early clinical outcome using a machine learning (ML) approach. A single spine surgery center retrospective review of prospectively collected data from January 2016 to December 2020 from the institutional registry (SpineREG) was performed. The inclusion criteria were age ≥ 18 years, both sexes, lumbar arthrodesis procedure, a complete follow up assessment (Oswestry Disability Index—ODI, SF-36 and COMI back) and the capability to read and understand the Italian language. A delta of improvement of the ODI higher than 12.7/100 was considered a “good early outcome”. A combined target model of ODI (Δ ≥ 12.7/100), SF-36 PCS (Δ ≥ 6/100) and COMI back (Δ ≥ 2.2/10) was considered an “excellent early outcome”. The performance of the ML models was evaluated in terms of sensitivity, i.e., True Positive Rate (TPR), specificity, i.e., True Negative Rate (TNR), accuracy and area under the receiver operating characteristic curve (AUC ROC). A total of 1243 patients were included in this study. The model for predicting ODI at 6 months’ follow up showed a good balance between sensitivity (74.3%) and specificity (79.4%), while providing a good accuracy (75.8%) with ROC AUC = 0.842. The combined target model showed a sensitivity of 74.2% and specificity of 71.8%, with an accuracy of 72.8%, and an ROC AUC = 0.808. The results of our study suggest that a machine learning approach showed high performance in predicting early good to excellent clinical results.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jpm11121377
- https://www.mdpi.com/2075-4426/11/12/1377/pdf?version=1639647851
- OA Status
- gold
- Cited By
- 15
- References
- 39
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4225686436
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4225686436Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/jpm11121377Digital Object Identifier
- Title
-
The Influence of Baseline Clinical Status and Surgical Strategy on Early Good to Excellent Result in Spinal Lumbar Arthrodesis: A Machine Learning ApproachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-16Full publication date if available
- Authors
-
Pedro Berjano, Francesco Langella, Luca Ventriglia, Domenico Compagnone, Paolo Barletta, David Huber, Francesca Mangili, Ginevra Licandro, Fabio Galbusera, Andrea Cina, Tito Bassani, Claudio Lamartina, Laura Scaramuzzo, Roberto Bassani, Marco Brayda‐Bruno, Jorge Hugo Villafañe, Lorenzo Monti, Laura AzzimontiList of authors in order
- Landing page
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https://doi.org/10.3390/jpm11121377Publisher landing page
- PDF URL
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https://www.mdpi.com/2075-4426/11/12/1377/pdf?version=1639647851Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2075-4426/11/12/1377/pdf?version=1639647851Direct OA link when available
- Concepts
-
Oswestry Disability Index, Receiver operating characteristic, Medicine, Arthrodesis, Lumbar, Physical therapy, Area under the curve, Surgery, Internal medicine, Low back pain, Pathology, Alternative medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
15Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 3, 2024: 3, 2023: 6, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
39Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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