Evidence map›Paper›PMID 39609923›Full record

ArticleJournal of orthopaedic surgery and research2024

Developing predictive models for residual back pain after percutaneous vertebral augmentation treatment for osteoporotic thoracolumbar compression fractures based on machine learning technique.

Hao Wu, Chao Li, Jiajun Song, Jiaming Zhou

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Article in Journal of orthopaedic surgery and research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 6 of them syntheses that pooled it.

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14citing papers in PubMed, 6 pooled it
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Hao Wu *Department of Anesthesiology, Tianjin Baodi Hospital, Baodi Clinical College of Tianjin Medical University, Tianjin, 301800, China.
Chao Li *Department of Orthopedics, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, 441000, China.
Jiajun Song *Department of Orthopedics, Tianjin Medical University General Hospital, Tianjin, 300052, China.
Jiaming ZhouDepartment of Orthopedics, Tianjin Medical University General Hospital, Tianjin, 300052, China. zhoujiamingtmu@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMachine learning (ML) has been widely applied to predict the outcomes of numerous diseases. The current study aimed to develop a prognostic prediction model using machine learning algorithms and identify risk factors associated with residual back pain in patients with osteoporotic vertebrae compression fracture (OVCF) following percutaneous vertebroplasty (PVP).

methodsA total of 863 OVCF patients who underwent PVP surgery were enrolled and analyzed. One month following surgery, a Visual Analog Scale (VAS) score of ≥ 4 was deemed to signify residual low back pain following the operation and patients were grouped into a residual pain group and pain-free group. The optimal feature set for both machine learning and statistical models was adjusted based on a 2000-resample bootstrap-based internal validation via an exhaustive search. The area under the curve (AUC), classification accuracy, sensitivity, and specificity of each model were then calculated to evaluate the predictive performance of each model.

resultsIn our current study, two main findings were observed: (1) Compared with statistical models, ML models exhibited superior predictive performance, with SVM demonstrating the highest prediction accuracy; (2) several variables were identified as the most predictive factors by both the machine learning and statistical models, including bone cement volume, number of fractured vertebrae, facet joint violation, paraspinal muscle degeneration, and intravertebral vacuum cleft.

conclusionOverall, the study demonstrated that machine learning classifiers such as SVM can effectively predict residual back pain for patients with OVCF following PVP while identifying associated predictors in a multivariate manner.

Indexed as

Fractures, CompressionLumbar VertebraeMachine LearningOsteoporotic FracturesSpinal FracturesThoracic VertebraeVertebroplastyAgedAged, 80 and overBack PainFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisMachine learningOsteoporotic vertebrae compression fracturePercutaneous vertebroplastyPrognostic predictionResidual back painVisual analog scale

Identifiers

PMID39609923
PMCPMC11603673

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.