Establishment and validation of a nomogram and web calculator for the risk of new vertebral compression fractures and cement leakage after percutaneous vertebroplasty in patients with osteoporotic vertebral compression fractures

Li, Wenle; Wang, Haosheng; Dong, Shengtao; Tang, Zhi-Ri; Chen, Longhao; Cai, Xintian; Hu, Zhaohui; Yin, Chengliang

doi:10.1007/s00586-021-07064-z

Cited by 41 publications

(31 citation statements)

References 51 publications

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“…There were several studies on the latter shown that patient's age, associated comorbidities, functional status, etc. could lead the patient not being discharged as planned (26,27). Furthermore, as more data being obtained, models can be retrained over time to improve prediction accuracy.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty

Wang

Liu

et al. 2021

Front. Public Health

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Background: Bone cement leakage is a common complication of percutaneous vertebroplasty and it could be life-threatening to some extent. The aim of this study was to develop a machine learning model for predicting the risk of cement leakage in patients with osteoporotic vertebral compression fractures undergoing percutaneous vertebroplasty. Furthermore, we developed an online calculator for clinical application.Methods: This was a retrospective study including 385 patients, who had osteoporotic vertebral compression fracture disease and underwent surgery at the Department of Spine Surgery, Liuzhou People's Hospital from June 2016 to June 2018. Combing the patient's clinical characteristics variables, we applied six machine learning (ML) algorithms to develop the predictive models, including logistic regression (LR), Gradient boosting machine (GBM), Extreme gradient boosting (XGB), Random Forest (RF), Decision Tree (DT) and Multilayer perceptron (MLP), which could predict the risk of bone cement leakage. We tested the results with ten-fold cross-validation, which calculated the Area Under Curve (AUC) of the six models and selected the model with the highest AUC as the excellent performing model to build the web calculator.Results: The results showed that Injection volume of bone cement, Surgery time and Multiple vertebral fracture were all independent predictors of bone cement leakage by using multivariate logistic regression analysis in the 385 observation subjects. Furthermore, Heatmap revealed the relative proportions of the 15 clinical variables. In bone cement leakage prediction, the AUC of the six ML algorithms ranged from 0.633 to 0.898, while the RF model had an AUC of 0.898 and was used as the best performing ML Web calculator (https://share.streamlit.io/liuwencai0/pvp_leakage/main/pvp_leakage) was developed to estimate the risk of bone cement leakage that each patient undergoing vertebroplasty.Conclusion: It achieved a good prediction for the occurrence of bone cement leakage with our ML model. The Web calculator concluded based on RF model can help orthopedist to make more individual and rational clinical strategies.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty

Wang

Liu

et al. 2021

Front. Public Health

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“…It has been shown that prolonged menopause and low bone mineral density (BMD) may increase the risk of re-fracture after PVP [ 16 ]. Body mass index (BMI), preoperative multisegmental vertebral compression fractures, and dispersion degree of bone cement have also been reported to be related to re-fractures after PVP or PKP [ 17 – 19 ]. Overall, the relationship between the occurrence of vertebral fractures and PVP/PKP surgery remains controversial due to the varying results of clinical trials.…”

Section: Introductionmentioning

confidence: 99%

Risk factors of vertebral re-fracture after PVP or PKP for osteoporotic vertebral compression fractures, especially in Eastern Asia: a systematic review and meta-analysis

et al. 2022

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Objective Percutaneous vertebroplasty (PVP) and kyphoplasty (PKP) have been widely used to treat osteoporotic vertebral compression fractures (OVCF), but the risk of vertebral re-fracture after PVP/PKP remains controversial. This study aims to investigate the incidence and risk factors of vertebral re-fracture after PVP/PKP. Methods Relevant literatures published up to November 2021 were collected from PubMed, Embase and Web of Science. A meta-analysis was performed to extract data associated with risk factors of SVCF following the PRISMA guidelines. Also, pooled odds ratio (OR) or weighted mean difference (WMD) with 95% confidence interval (CI) was calculated. Results A total of 23 studies, encompassing 9372 patients with OVCF, met the inclusion criteria. 1255 patients (13.39%) suffered re-fracture after PVP/PKP surgery. A total of 22 studies were from Eastern Asia and only 1 study was from Europe. Female sex (OR = 1.34, 95%CI 1.09–1.64, P = 0.006), older age (WMD = 2.04, 95%CI 0.84–3.24, P = 0.001), lower bone mineral density (BMD, WMD = − 0.38, 95%CI − 0.49–0.26, P < 0.001) and bone cement leakages (OR = 2.05, 95% CI 1.40–3.00, P < 0.001) increased the risk of SVCF. The results of subgroup analysis showed the occurrence of re-fracture was significantly associated with gender (P = 0.002), age (P = 0.001) and BMD (P < 0.001) in Eastern Asia. Compared with the unfractured group, anterior-to-posterior vertebral body height ratio (AP ratio, WMD = 0.06, 95%CI 0.00–0.12, P = 0.037) and visual analog scale score (VAS, WMD = 0.62, 95%CI 0.09–1.15, P = 0.022) were higher in the refracture group, and kyphotic angle correction ratio (Cobb ratio, WMD = − 0.72, 95%CI − 1.26–0.18, P = 0.008) was smaller in Eastern Asia. In addition, anti-osteoporosis treatment (OR = 0.40, 95% CI 0.27–0.60, P < 0.001) could be a protective factor. Conclusion The main factors associated with re-fracture after PVP/PKP are sex, age, bone mineral density, AP ratio, Cobb ratio, VAS score, bone cement leakage and anti-osteoporosis treatment, especially in Eastern Asia.

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“…Osteoporotic vertebral compression fractures (OVCFs) have become a frequent low-energy injury with the accelerated aging of the population worldwide ( 1 , 2 ). Approximately 1.4 million patients worldwide are diagnosed with fresh OVCF each year ( 3 ). Patients suffer from chronic back pain, reduced mobility and daily activities, poor quality of life, and the accompanying heavy financial burden.…”

Section: Introductionmentioning

confidence: 99%

Development and Internal Validation of Supervised Machine Learning Algorithm for Predicting the Risk of Recollapse Following Minimally Invasive Kyphoplasty in Osteoporotic Vertebral Compression Fractures

Dong

Zhu

Yang

et al. 2022

Front. Public Health

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BackgroundThe published literatures indicate that patients with osteoporotic vertebral compression fractures (OVCFs) benefit significantly from percutaneous kyphoplasty (PKP), but this surgical technique is associated with frequent postoperative recollapse, a complication that severely limits long-term postoperative functional recovery.MethodsThis study retrospectively analyzed single-segment OVCF patients who underwent bilateral PKP at our academic center from January 1, 2017 to September 30, 2019. Comparing the plain films of patients within 3 days after surgery and at the final follow-up, we classified patients with more than 10% loss of sagittal anterior height as the recollapse group. Univariate and multivariate logistic regression analyses were performed to determine the risk factors affecting recollapse after PKP. Based on the logistic regression results, we constructed one support vector machine (SVM) classifier to predict recollapse using machine learning (ML) algorithm. The predictive performance of this prediction model was validated by the receiver operating characteristic (ROC) curve, 10-fold cross validation, and confusion matrix.ResultsAmong the 346 consecutive patients (346 vertebral bodies in total), postoperative recollapse was observed in 40 patients (11.56%). The results of the multivariate logistical regression analysis showed that high body mass index (BMI) (Odds ratio [OR]: 2.08, 95% confidence interval [CI]: 1.58–2.72, p < 0.001), low bone mineral density (BMD) T-scores (OR: 4.27, 95% CI: 1.55–11.75, p = 0.005), presence of intravertebral vacuum cleft (IVC) (OR: 3.10, 95% CI: 1.21–7.99, p = 0.019), separated cement masses (OR: 3.10, 95% CI: 1.21–7.99, p = 0.019), cranial endplate or anterior cortical wall violation (OR: 0.17, 95% CI: 0.04–0.79, p = 0.024), cement-contacted upper endplate alone (OR: 4.39, 95% CI: 1.20–16.08, p = 0.025), and thoracolumbar fracture (OR: 6.17, 95% CI: 1.04–36.71, p = 0.045) were identified as independent risk factors for recollapse after a kyphoplasty surgery. Furthermore, the evaluation indices demonstrated a superior predictive performance of the constructed SVM model, including mean area under receiver operating characteristic curve (AUC) of 0.81, maximum AUC of 0.85, accuracy of 0.81, precision of 0.89, and sensitivity of 0.98.ConclusionsFor patients with OVCFs, the risk factors leading to postoperative recollapse were multidimensional. The predictive model we constructed provided insights into treatment strategies targeting secondary recollapse prevention.

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Establishment and validation of a nomogram and web calculator for the risk of new vertebral compression fractures and cement leakage after percutaneous vertebroplasty in patients with osteoporotic vertebral compression fractures

Cited by 41 publications

References 51 publications

Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty

Machine Learning Applications for the Prediction of Bone Cement Leakage in Percutaneous Vertebroplasty

Risk factors of vertebral re-fracture after PVP or PKP for osteoporotic vertebral compression fractures, especially in Eastern Asia: a systematic review and meta-analysis

Development and Internal Validation of Supervised Machine Learning Algorithm for Predicting the Risk of Recollapse Following Minimally Invasive Kyphoplasty in Osteoporotic Vertebral Compression Fractures

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