Machine Learning Approaches for Predicting Bisphosphonate-Related Osteonecrosis in Women with Osteoporosis Using VEGFA Gene Polymorphisms

Kim, Jin Woo; Yee, Jeong; Oh, Sang Hyeon; Kim, Sun Hyun; Kim, Sun Jong; Chung, Jee-Eun; Gwak, Hye Sun

doi:10.3390/jpm11060541

Cited by 10 publications

(8 citation statements)

References 56 publications

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“…ML algorithms have made a profound contribution to solve intractable problems in the medical field ( 11 – 13 ). As one of the classical ML algorithms, support vector machines (SVMs) exhibit excellent classification capabilities and play an irreplaceable role in the osteoporosis–AI intersection ( 14 , 15 ). Given the remarkable contribution of ML algorithms in the field of medical big data, this work attempts to develop a predictive model for assessing recollapse based on routine clinical data.…”

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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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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“…This study was an analysis of prospectively collected saliva samples from January 2014 to December 2018 at EWHA Womans University Mokdong Hospital. The detailed explanation of the study patients have already been provided in our previous paper ( 29 ). Briefly, all participants with current or previous BP use were diagnosed with osteoporosis by a physician.…”

Section: Methodsmentioning

confidence: 99%

Prediction of medication-related osteonecrosis of the jaws using machine learning methods from estrogen receptor 1 polymorphisms and clinical information

Choi

Kim

et al. 2023

Front. Med.

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ObjectiveThe purpose of this study was to evaluate the effect of estrogen receptor 1 (ESR1) polymorphisms on the development of medication-related osteonecrosis of the jaws (MRONJ) in women with osteoporosis.MethodsA total of 125 patients taking bisphosphonates was evaluated the relationship between MRONJ occurrence and single nucleotide polymorphisms (SNPs) of ESR1. Clinical information was collected, including current age, treatment duration, and comorbidity. Univariate and Multivariable regression analyzes were performed to evaluate the independent predictive factors for MRONJ occurrence. Predictive models were constructed using machine learning methods such as Lasso regression, Random forest (RF), and Support vector machine (SVM). The area under the receiver-operating curve (AUROC) was used to evaluate the performance of a binary classifier.ResultTwo SNPs of ESR1 (rs4870056 and rs78177662) were significantly associated with MRONJ development. Patients with variant allele (A) of rs4870056 showed 2.45 times (95% CI, 1.03–5.87) the odds of MRONJ occurrence compared to those with wild-type homozygote (GG) after adjusting covariates. Additionally, carriers with variant allele (T) of rs78177662 had higher odds than those with wild-type homozygote (CC) (adjusted odds ratio (aOR), 2.64, 95% CI, 1.00–6.94). Among demographic variables, age ≥ 72 years (aOR, 3.98, 95% CI, 1.60–9.87) and bisphosphonate exposure ≥48 months (aOR, 3.16, 95% CI, 1.26–7.93) were also significant risk factors for MRONJ occurrence. AUROC values of machine learning methods ranged between 0.756–0.806 in the study.ConclusionOur study showed that the MRONJ occurrence was associated with ESR1 polymorphisms in osteoporotic women.

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“…Machine learning algorithms were developed to predict risk factors for AKI occurrence ( Kim et al, 2021 ). Five-fold cross-validated multivariate logistic regression, elastic net, random forest (RF), and support vector machine (SVM) classification models were utilized.…”

Section: Methodsmentioning

confidence: 99%

Risk Scoring System for Vancomycin-Associated Acute Kidney Injury

Kim

Yee

et al. 2022

Front. Pharmacol.

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Vancomycin-associated acute kidney injury (AKI) remains a major challenge for patients and clinicians. This study aimed to construct a risk scoring system for vancomycin-associated AKI. We retrospectively reviewed medical records of patients who underwent therapeutic drug monitoring for vancomycin from June 2018 to July 2019. We selected possible risk factors for AKI by univariate and multivariable logistic regression analyses and developed a scoring system for vancomycin-associated AKI. Machine learning methods were utilized to predict risk factors for the occurrence of AKI. The incidence of vancomycin-associated AKI was 31.7% among 104 patients included in this study. A bodyweight ≤60 kg (two points), a Charlson comorbidity index ≥3 (two points), a vancomycin trough serum level >15 μg/ml (one point), and concomitant use of ≥6 nephrotoxic agents (two points) were included to construct a risk scoring system based on the coefficient from the logistic regression model. The area under the receiver operating characteristic curve (AUROC) (mean, 95% confidence interval (CI)) across 10 random iterations using five-fold cross-validated multivariate logistic regression, elastic net, random forest, support vector machine (SVM)-linear kernel, and SVM-radial kernel models was 0.735 (0.638–0.833), 0.737 (0.638–0.835), 0.721 (0.610–0.833), 0.739 (0.648–0.829), and 0.733 (0.640–0.826), respectively. For total scores of 0–1, 2–3, 4–5, 6–7, the risk of vancomycin-associated AKI was 5, 25, 45, and 65%, respectively. Our scoring system can be applied to clinical settings in which several nephrotoxic agents are used along with vancomycin therapy.

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Machine Learning Approaches for Predicting Bisphosphonate-Related Osteonecrosis in Women with Osteoporosis Using VEGFA Gene Polymorphisms

Cited by 10 publications

References 56 publications

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

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

Prediction of medication-related osteonecrosis of the jaws using machine learning methods from estrogen receptor 1 polymorphisms and clinical information

Risk Scoring System for Vancomycin-Associated Acute Kidney Injury

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