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

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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A case of multilevel percutaneous vertebroplasty for vertebral metastases resulting in temporary paraparesis

Journal of International Medical Research ◽

10.1177/0300060519835084 ◽

2019 ◽

Vol 48 (2) ◽

pp. 030006051983508

Author(s):

Guan Shi ◽

Fei Feng ◽

Chen Hao ◽

Jia Pu ◽

Bao Li ◽

...

Keyword(s):

Bone Cement ◽

Percutaneous Vertebroplasty ◽

Cement Leakage ◽

Spinal Nerve ◽

Vertebral Compression Fractures ◽

Vertebral Metastases ◽

Osteoporotic Vertebral Compression Fractures ◽

Invasive Treatment ◽

Compression Fractures ◽

Cord Injury

Percutaneous vertebroplasty (PVP) is a minimally invasive treatment that has been widely used for the treatment of osteoporotic vertebral compression fractures and vertebral tumors. However, the maximum number of vertebral segments treated in a single PVP remains controversial. Furthermore, PVP may cause complications, including cement leakage, pulmonary embolism, bone cement toxicity, and spinal nerve-puncture injury. We report the rare case of a patient who underwent multilevel PVP for vertebral metastases, with no bone cement leakage or spinal cord injury, but who developed temporary paraparesis.

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Using machine learning to improve risk prediction in durable left ventricular assist devices

PLoS ONE ◽

10.1371/journal.pone.0247866 ◽

2021 ◽

Vol 16 (3) ◽

pp. e0247866

Author(s):

Arman Kilic ◽

Daniel Dochtermann ◽

Rema Padman ◽

James K. Miller ◽

Artur Dubrawski

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Left Ventricular ◽

Ventricular Assist Devices ◽

Gradient Boosting ◽

Left Ventricular Assist Devices ◽

Ventricular Assist ◽

Assist Devices ◽

Extreme Gradient Boosting ◽

Left Ventricular Assist

Risk models have historically displayed only moderate predictive performance in estimating mortality risk in left ventricular assist device therapy. This study evaluated whether machine learning can improve risk prediction for left ventricular assist devices. Primary durable left ventricular assist devices reported in the Interagency Registry for Mechanically Assisted Circulatory Support between March 1, 2006 and December 31, 2016 were included. The study cohort was randomly divided 3:1 into training and testing sets. Logistic regression and machine learning models (extreme gradient boosting) were created in the training set for 90-day and 1-year mortality and their performance was evaluated after bootstrapping with 1000 replications in the testing set. Differences in model performance were also evaluated in cases of concordance versus discordance in predicted risk between logistic regression and extreme gradient boosting as defined by equal size patient tertiles. A total of 16,120 patients were included. Calibration metrics were comparable between logistic regression and extreme gradient boosting. C-index was improved with extreme gradient boosting (90-day: 0.707 [0.683–0.730] versus 0.740 [0.717–0.762] and 1-year: 0.691 [0.673–0.710] versus 0.714 [0.695–0.734]; each p<0.001). Net reclassification index analysis similarly demonstrated an improvement of 48.8% and 36.9% for 90-day and 1-year mortality, respectively, with extreme gradient boosting (each p<0.001). Concordance in predicted risk between logistic regression and extreme gradient boosting resulted in substantially improved c-index for both logistic regression and extreme gradient boosting (90-day logistic regression 0.536 versus 0.752, 1-year logistic regression 0.555 versus 0.726, 90-day extreme gradient boosting 0.623 versus 0.772, 1-year extreme gradient boosting 0.613 versus 0.742, each p<0.001). These results demonstrate that machine learning can improve risk model performance for durable left ventricular assist devices, both independently and as an adjunct to logistic regression.

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Machine Learning-Based Three-Month Outcome Prediction in Acute Ischemic Stroke: A Single Cerebrovascular-Specialty Hospital Study in South Korea

Diagnostics ◽

10.3390/diagnostics11101909 ◽

2021 ◽

Vol 11 (10) ◽

pp. 1909

Author(s):

Dougho Park ◽

Eunhwan Jeong ◽

Haejong Kim ◽

Hae Wook Pyun ◽

Haemin Kim ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Ischemic Stroke ◽

Acute Ischemic Stroke ◽

Functional Outcome ◽

Outcome Prediction ◽

Prediction Models ◽

Gradient Boosting ◽

Support Vector ◽

Extreme Gradient Boosting

Background: Functional outcomes after acute ischemic stroke are of great concern to patients and their families, as well as physicians and surgeons who make the clinical decisions. We developed machine learning (ML)-based functional outcome prediction models in acute ischemic stroke. Methods: This retrospective study used a prospective cohort database. A total of 1066 patients with acute ischemic stroke between January 2019 and March 2021 were included. Variables such as demographic factors, stroke-related factors, laboratory findings, and comorbidities were utilized at the time of admission. Five ML algorithms were applied to predict a favorable functional outcome (modified Rankin Scale 0 or 1) at 3 months after stroke onset. Results: Regularized logistic regression showed the best performance with an area under the receiver operating characteristic curve (AUC) of 0.86. Support vector machines represented the second-highest AUC of 0.85 with the highest F1-score of 0.86, and finally, all ML models applied achieved an AUC > 0.8. The National Institute of Health Stroke Scale at admission and age were consistently the top two important variables for generalized logistic regression, random forest, and extreme gradient boosting models. Conclusions: ML-based functional outcome prediction models for acute ischemic stroke were validated and proven to be readily applicable and useful.

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Machine Learning for the Prediction of Progression in Patients with Acute Kidney Injury in Critical Care

10.21203/rs.3.rs-412422/v1 ◽

2021 ◽

Author(s):

Lifan Zhang ◽

Canzheng Wei ◽

Yunxia Feng ◽

Aijia Ma ◽

Yan Kang

Keyword(s):

Machine Learning ◽

Acute Kidney Injury ◽

Logistic Regression ◽

Critical Care ◽

Intensive Care ◽

Logistic Regression Model ◽

Kidney Injury ◽

Gradient Boosting ◽

Extreme Gradient Boosting ◽

Stage 1

Abstract Background: Acute kidney injury (AKI) is a serve and harmful syndrome in the intensive care unit. The purpose of this study is to develop a prediction model that predict whether patients with AKI stage 1/2 will progress to AKI stage 3. Methods: Patients with AKI stage 1/2, when they were ﬁrst diagnosed with AKI in the Medical Information Mart for Intensive Care (MIMIC-III), were included. We excluded patients who had underwent RRT or progressed to AKI stage 3 within 72 hours of the ﬁrst AKI diagnosis. We also excluded patients with chronic kidney disease (CKD). We used the Logistic regression and machine learning extreme gradient boosting (XGBoost) to build two models which can predict patients who will progress to AKI stage 3. Established models were evaluated by cross-validation, receiver operating characteristic curve (ROC), and precision-recall curves (PRC). Results: We included 25711 patients, of whom 2130 (8.3%) progressed to AKI stage 3. Creatinine, multiple organ failure syndromes (MODS), blood urea nitrogen (BUN), sepsis, and respiratory failure were the most important in AKI progression prediction. The XGBoost model has a better performance than the Logistic regression model on predicting AKI stage 3 progression (AU-ROC, 0.926; 95%CI, 0.917 to 0.931 vs. 0.784; 95%CI, 0.771 to 0.796, respectively). Conclusions: The XGboost model can better identify patients with AKI progression than Logistic regression model. Machine learning techniques may improve predictive modeling in medical research. Keywords: Acute kidney injury; Critical care; Logistic Models; Extreme gradient boosting

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Machine Learning Models of Acute Kidney Injury Prediction in Acute Pancreatitis Patients

Gastroenterology Research and Practice ◽

10.1155/2020/3431290 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Cheng Qu ◽

Lin Gao ◽

Xian-qiang Yu ◽

Mei Wei ◽

Guo-quan Fang ◽

...

Keyword(s):

Machine Learning ◽

Acute Kidney Injury ◽

Acute Pancreatitis ◽

Logistic Regression ◽

Kidney Injury ◽

Gradient Boosting ◽

Support Vector ◽

Learning Models ◽

Extreme Gradient Boosting ◽

Machine Learning Models

Background. Acute kidney injury (AKI) has long been recognized as a common and important complication of acute pancreatitis (AP). In the study, machine learning (ML) techniques were used to establish predictive models for AKI in AP patients during hospitalization. This is a retrospective review of prospectively collected data of AP patients admitted within one week after the onset of abdominal pain to our department from January 2014 to January 2019. Eighty patients developed AKI after admission (AKI group) and 254 patients did not (non-AKI group) in the hospital. With the provision of additional information such as demographic characteristics or laboratory data, support vector machine (SVM), random forest (RF), classification and regression tree (CART), and extreme gradient boosting (XGBoost) were used to build models of AKI prediction and compared to the predictive performance of the classic model using logistic regression (LR). XGBoost performed best in predicting AKI with an AUC of 91.93% among the machine learning models. The AUC of logistic regression analysis was 87.28%. Present findings suggest that compared to the classical logistic regression model, machine learning models using features that can be easily obtained at admission had a better performance in predicting AKI in the AP patients.

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A Review of PMMA Bone Cement and Intra-Cardiac Embolism

10.20944/preprints201609.0092.v1 ◽

2016 ◽

Author(s):

Puneeth Shridhar ◽

Yanfei Chen ◽

Ramzi Khalil ◽

Anton Plakseyhuk ◽

Sung Kwon Cho ◽

...

Keyword(s):

Bone Cement ◽

Percutaneous Vertebroplasty ◽

Cardiac Injury ◽

Cement Leakage ◽

Orthopedic Procedures ◽

Cardiac Complications ◽

Vertebral Compression Fractures ◽

Mechanical Stabilization ◽

Pmma Bone Cement ◽

Life Threatening

Percutaneous vertebroplasty procedure is of major importance, given the significant increasing aging population and higher number of orthopedic procedures related to vertebral compression fractures. Vertebroplasty is a complex technique involving injection of polymethylmethacrylate (PMMA) into the compressed vertebral body for mechanical stabilization of the fracture. Our understanding and ability to modify these mechanisms through alterations in cement material is rapidly evolving. However, the rate of cardiac complications secondary to PMMA injection and subsequent cement leakage has increased with time. The following review considers the main features of PMMA bone cement on the heart, and the extent of influence of materials on cardiac embolism. Clinically, cement leakage results in life-threatening cardiac injury. The convolution of this outcome through an appropriate balance of complex material properties is highlighted via clinical case report.

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A machine learning-based prediction of hospital mortality in patients with postoperative sepsis

10.21203/rs.2.24188/v1 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ren-qi Yao ◽

Xin Jin ◽

Guo-wei Wang ◽

Yue Yu ◽

Guo-sheng Wu ◽

...

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Hospital Mortality ◽

Regression Model ◽

Logistic Regression Model ◽

Gradient Boosting ◽

Stepwise Logistic Regression ◽

Postoperative Sepsis ◽

Extreme Gradient Boosting ◽

Stepwise Logistic Regression Model

Abstract Background: The incidence of postoperative sepsis is continually increased, while few studies have specifically focused on the risk factors and clinical outcomes associated with the development of sepsis after surgical procedures. The present study aimed to develop a mathematical model for predicting the in-hospital mortality among patients with postoperative sepsis.Methods: Surgical patients in Medical Information Mart for Intensive Care (MIMIC-III) database who simultaneously fulfilled Sepsis 3.0 as well as Agency for Healthcare Research and Quality (AHRQ) criteria during ICU admission were incorporated. We employed both extreme gradient boosting (XGBoost) and stepwise logistic regression model to predict in-hospital mortality among included patients with postoperative sepsis. Consequently, model performance was assessed from the angles of discrimination and calibration.Results: We included 3713 patients who fulfilled our inclusion criteria, in which 397 (10.7%) patients died during hospitalization, while 3316 (89.3%) of them survived through discharge. Fluid-electrolyte disturbance, coagulopathy, renal replacement therapy (RRT), urine output, and cardiovascular surgery were important features related to the in-hospital mortality. The XGBoost model had a better performance in both discriminatory ability (c-statistics, 0.835 [95% CI, 0.786 to 0.877] vs. c-statistics, 0.737 [95% CI, 0.688 to 0.786]) and goodness of fit (visualized by calibration curve) compared to the stepwise logistic regression model. Conclusion: XGBoost model appears to be a better performance in predicting hospital mortality among postoperative septic patients compared to the conventional stepwise logistic regression model. Machine learning-based algorithm might have significant application in the development of early warning system for septic patients following major operations.

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Comparison of early results of Vesselplasty and percutaneous vertebroplasty in the treatment of elderly patients with osteoporotic vertebral compression fracture

10.21203/rs.3.rs-17452/v1 ◽

2020 ◽

Author(s):

jun mei ◽

Dou Wu ◽

Xu Xiao Song ◽

Qiang Liu

Keyword(s):

Bone Cement ◽

Operative Time ◽

Percutaneous Vertebroplasty ◽

Cement Leakage ◽

Postoperative Recovery ◽

Leakage Rate ◽

Vertebral Compression Fractures ◽

Vertebral Height ◽

Compression Fractures ◽

Vas Score

Abstract Objective To investigate the early clinical effect of vesselplasty and percutaneous vertebroplasty in the treatment of elderly patients osteoporotic vertebral compression fractures. MethodsA retrospective analysis was performed on 22 patients (10 males and 12 females, aged 60~85 years old （73.1±9.6）with osteoporosis fracture treated by vesselplasty in Shanxi Bethune Hospital from March 2017 to December 2018..During the same period, 56 patients (20 males and 36 females, aged 60-80 years (70.3±9.4) were treated with percutaneous vertebroplasty (PVP) for osteoporosis fractures.Preoperative and postoperative Visual Analogue Score (VAS), operative time, intraoperative bone cement leakage, preoperative and postoperative vertebral body anterior margin height were recorded to evaluate the clinical efficacy.ResultsIn the vesselplasty group, preoperative VAS score was 7.9±0.9, postoperative VAS score was 3.8±0.8, postoperative recovery rate of vertebral height was 19.9%±19.1%, operative time (33.6±6.2)min, and bone cement leakage was observed in 3 cases.In the PVP group,preoperative VAS score was 7.9±0.9, postoperative VAS score was 3.7±0.8, postoperative recovery rate of vertebral height was 18.8%±18.2%, operative time (35.8±6.6)min, and bone cement leakage was observed in 15 cases.Compared with the PVP group（26.8%, the bone cement leakage rate of the vesselplasty group （13.6%）was significantly reduced, and the difference was statistically significant, but there was no significant difference in other data. ConclusionBoth vesselplasty and percutaneous vertebroplasty can achieve satisfactory early clinical efficacy in the treatment of senile osteoporosis vertebral compression fractures. Bone cement leakage rate of vesselplasty is smaller and it is safer.

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Comparison of unilateral and bilateral percutaneous vertebroplasty in the treatment of severe vertebral compression fractures

10.21203/rs.3.rs-22865/v1 ◽

2020 ◽

Author(s):

Juan Long ◽

Chun Jing He ◽

Zikun Duan ◽

Xinguo Kang ◽

Jinfeng Zou

Keyword(s):

Bone Cement ◽

Percutaneous Vertebroplasty ◽

Operation Time ◽

Cement Leakage ◽

Vertebral Compression Fractures ◽

Compression Fractures ◽

Needle Position ◽

Relieve Pain ◽

Group A ◽

Group B

Abstract BACKGROUND The purpose of this study was to comparison of unilateral and bilateral percutaneous vertebroplasty in the treatment of severe vertebral compression fractures. METHODS Sixty-four severe vertebral compression fractures patients were treated in our hospital were randomly divided into group A and group B(n = 32). Group A received Percutaneous vertebroplasty (PVP) treatment by unilateral vertebral pedicle approach. Group B received PVP treatment by bilateral vertebralpedicle approach. Visual Analogue scale (VAS) score and Oswesty Disability Index (ODI) were recorded before surgery, and at 1d,1 month, and 6 months after operation. Also, the puncture path, needle position, intraoperative bone cement injection volume, bone cement dispersion, intra-operative and postoperative complications were observed. RESULTS Sixty-four vertebrae were successfully punctured.The postoperative VAS,ODI were lower than preoperative, showed statistical difference compared with the preoperative VAS, ODI, but there are no difference between Group A and Group B.The cement leakage and operation time is lower in group A than that in Group B. CONCLUSION PVP by unilateral vertebral pedicle approach in treating severe vertebral compression fractures can significantly relieve pain and promote functional recovery, which has advantages such as convenient operation and low complication rate.

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Comparison of percutaneous vertebroplasty with a side-hole push rod and conventional percutaneous vertebroplasty for the treatment of OVCFs

10.21203/rs.3.rs-32093/v1 ◽

2020 ◽

Author(s):

Zhengshuai Jin ◽

Hailong Zhou ◽

Xuefei Yan ◽

Chunming Wang ◽

Yuanqing Mao ◽

...

Keyword(s):

Bone Cement ◽

Operative Time ◽

Percutaneous Vertebroplasty ◽

Cement Leakage ◽

Vertebral Compression Fractures ◽

Postoperative Results ◽

Injection Volume ◽

Side Hole ◽

Cement Injection

Abstract ObjectiveThe aim of this study was to compare the clinical efficacy of percutaneous vertebroplasty with a side-hole push rod (modified PVP) and conventional PVP in treating osteoporotic vertebral compression fractures (OVCFs).Materials and methodsThis study included 69 patients with 1-level OVCFs. Perioperative indicators, including the operative time, cement injection volume, cement leakage rate, and distribution of cement in the fractured area, were analysed. The visual analogue scale (VAS) and Oswestry disability index (ODI) were administered pre- and postoperatively.ResultsNo significant differences were observed in the operative time or cement injection volume between the two groups (p > 0.05). The total diffusion score of bone cement in the PVP group was significantly lower than that in the modified PVP group (p < 0.05). Compared with the conventional PVP group, the modified PVP group had a significantly lower VAS score at 3 days postoperatively (p < 0.05). There were no differences in the VAS or ODI scores between the two groups at the last follow-up (p > 0.05). Bone cement leakage was observed in 15 cases in the conventional PVP group (15/32) and in 9 cases in the modified PVP group (9/37).ConclusionThe modified version of PVP provides sufficient cement to fill the fractured area and is associated with a lower incidence of cement leakage and undesired postoperative results than is conventional PVP, indicating that modified PVP is a safe and effective new technique for the treatment of OVCFs.

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