ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2026
Development and validation of machine learning models for predicting the risk of refracture after percutaneous kyphoplasty in OVCF patients.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aims to identify the risk factors for adjacent vertebral refracture after PKP (Percutoneous Kyphoplasty) and to develop a predictive model using the multiple machine learning models to evaluate the impact of various clinical features on postoperative outcomes.
methodsA retrospective analysis was conducted on 3,942 OVCF patients who underwent PKP between 2018 and 2023. Patients were classified into non-refracture (Group A) and refracture (Group B) groups. Univariate and multivariate logistic regression analyses were used to identify independent risk factors. The multiple machine learning models were constructed and validated to predict refracture risk.
resultsAmong the patients, 424 (10.75%) experienced adjacent vertebral refracture. Independent risk factors include bone mineral density (BMD), preoperative AVH, the anterior vertebral height restoration rate (AVHRR), and osteoporosis treatment (P < 0.05). The Balanced Bagging model achieved an accuracy of 96.58%, sensitivity of 94.12%, specificity of 96.88%, and an F1 score of approximately 0.8556 in predicting postoperative refracture.
conclusionThe risk of adjacent vertebral refracture after KP is associated with AVHRR, BMD, and osteoporosis treatment. Through a comparison of multiple models, the optimal predictive model, the Balanced Bagging model, was identified. This will aid clinicians in implementing personalized postoperative management, improving patient outcomes, and reducing the likelihood of subsequent surgeries.
Indexed as
Identifiers
40952502What OpenQuestion holds
Registered trials
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.