A decision tree analysis to predict clinical outcome of minimally invasive lumbar decompression surgery for lumbar spinal stenosis with and without coexisting spondylolisthesis and scoliosis.
Chi-square automatic interaction detection
Clinical outcomes
Decision tree analysis
Lumbar spinal stenosis
Machine learning
Minimally invasive
Risk factors
Surgery
Journal
The spine journal : official journal of the North American Spine Society
ISSN: 1878-1632
Titre abrégé: Spine J
Pays: United States
ID NLM: 101130732
Informations de publication
Date de publication:
Jul 2023
Jul 2023
Historique:
received:
25
09
2022
revised:
22
01
2023
accepted:
30
01
2023
medline:
26
6
2023
pubmed:
6
2
2023
entrez:
5
2
2023
Statut:
ppublish
Résumé
Implementing machine learning techniques, such as decision trees, known as prediction models that use logical construction diagrams, are rarely used to predict clinical outcomes. To develop a clinical prediction rule to predict clinical outcomes in patients who undergo minimally invasive lumbar decompression surgery for lumbar spinal stenosis with and without coexisting spondylolisthesis and scoliosis using a decision tree model. A retrospective analysis of prospectively collected data. This study included 331 patients who underwent minimally invasive surgery for lumbar spinal stenosis and were followed up for ≥2 years at 1 institution. Self-report measures: The Japanese Orthopedic Association (JOA) scores and low back pain (LBP)/leg pain/leg numbness visual analog scale (VAS) scores. Physiologic measures: Standing sagittal spinopelvic alignment, computed tomography, and magnetic resonance imaging results. Low achievement in clinical outcomes were defined as the postoperative JOA score at the 2-year follow-up <25 points. Univariate and multiple logistic regression analysis and chi-square automatic interaction detection (CHAID) were used for analysis. The CHAID model for JOA score <25 points showed spontaneous numbness/pain as the first decision node. For the presence of spontaneous numbness/pain, sagittal vertical axis ≥70 mm was selected as the second decision node. Then lateral wedging, ≥6° and pelvic incidence minus lumbar lordosis (PI-LL) ≥30° followed as the third decision node. For the absence of spontaneous numbness/pain, sex and lateral olisthesis, ≥3mm and American Society of Anesthesiologists physical status classification system score were selected as the second and third decision nodes. The sensitivity, specificity, and the positive predictive value of this CHAID model was 65.1, 69.8, and 64.7% respectively. The CHAID model incorporating basic information and functional and radiologic factors is useful for predicting surgical outcomes.
Sections du résumé
BACKGROUND CONTEXT
BACKGROUND
Implementing machine learning techniques, such as decision trees, known as prediction models that use logical construction diagrams, are rarely used to predict clinical outcomes.
PURPOSE
OBJECTIVE
To develop a clinical prediction rule to predict clinical outcomes in patients who undergo minimally invasive lumbar decompression surgery for lumbar spinal stenosis with and without coexisting spondylolisthesis and scoliosis using a decision tree model.
STUDY DESIGN/SETTING
METHODS
A retrospective analysis of prospectively collected data.
PATIENT SAMPLE
METHODS
This study included 331 patients who underwent minimally invasive surgery for lumbar spinal stenosis and were followed up for ≥2 years at 1 institution.
OUTCOME MEASURES
METHODS
Self-report measures: The Japanese Orthopedic Association (JOA) scores and low back pain (LBP)/leg pain/leg numbness visual analog scale (VAS) scores. Physiologic measures: Standing sagittal spinopelvic alignment, computed tomography, and magnetic resonance imaging results.
METHODS
METHODS
Low achievement in clinical outcomes were defined as the postoperative JOA score at the 2-year follow-up <25 points. Univariate and multiple logistic regression analysis and chi-square automatic interaction detection (CHAID) were used for analysis.
RESULTS
RESULTS
The CHAID model for JOA score <25 points showed spontaneous numbness/pain as the first decision node. For the presence of spontaneous numbness/pain, sagittal vertical axis ≥70 mm was selected as the second decision node. Then lateral wedging, ≥6° and pelvic incidence minus lumbar lordosis (PI-LL) ≥30° followed as the third decision node. For the absence of spontaneous numbness/pain, sex and lateral olisthesis, ≥3mm and American Society of Anesthesiologists physical status classification system score were selected as the second and third decision nodes. The sensitivity, specificity, and the positive predictive value of this CHAID model was 65.1, 69.8, and 64.7% respectively.
CONCLUSIONS
CONCLUSIONS
The CHAID model incorporating basic information and functional and radiologic factors is useful for predicting surgical outcomes.
Identifiants
pubmed: 36739978
pii: S1529-9430(23)00047-5
doi: 10.1016/j.spinee.2023.01.023
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
973-981Informations de copyright
Copyright © 2023 Elsevier Inc. All rights reserved.