Prediction Model of Osteonecrosis of the Femoral Head After Femoral Neck Fracture: Machine Learning–Based Development and Validation Study

Wang, Huan; Wu, Wei; Han, Chunxia; Zheng, Jiaqi; Cai, Xinyu; Chang, Shi‐Min; Shi, Junlong; Xu, Nan; Ai, Zisheng

doi:10.2196/30079

Cited by 14 publications

(10 citation statements)

References 53 publications

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“…In the development and validation cohort, the concordance index was 0.96 and 0.94; and the discrimination slope was 0.51 and 0.47 [ 51 ]. Wang et al [ 52 ] proposed a six-variable XGBoost model by comparing different models, established by machine learning algorithms, in predicting ONFH after FNFs treated with internal fixation. They concluded that the six-variable XGBoost model with six predictors, including reduction quality, VAS score, Garden classification, operative time, cause of injury, and fracture location, could better predict the risk of ONFH.…”

Section: Resultsmentioning

confidence: 99%

Methods to predict osteonecrosis of femoral head after femoral neck fracture: a systematic review of the literature

Hu,

Yang,

Zhang

et al. 2023

J Orthop Surg Res

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Background Femoral neck fracture (FNF) is a very common traumatic disorder and a major cause of blood supply disruption to the femoral head, which may lead to a severe long-term complication, osteonecrosis of femoral head (ONFH). Early prediction and evaluation of ONFH after FNF could facilitate early treatment and may prevent or reverse the development of ONFH. In this review paper, we will review all the prediction methods reported in the previous literature. Methods Studies on the prediction of ONFH after FNF were included in PubMed and MEDLINE databases with articles published before October 2022. Further screening criteria were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. This study highlights all the advantages and disadvantages of the prediction methods. Results There were a total of 36 studies included, involving 11 methods to predict ONFH after FNF. Among radiographic imaging, superselective angiography could directly visualize the blood supply of the femoral head, but it is an invasive examination. As noninvasive detection methods, dynamic enhanced magnetic resonance imaging (MRI) and SPECT/CT are easy to operate, have a high sensitivity, and increase specificity. Though still at the early stage of development in clinical studies, micro-CT is a method of highly accurate quantification that can visualize femoral head intraosseous arteries. The prediction model relates to artificial intelligence and is easy to operate, but there is no consensus on the risk factors of ONFH. For the intraoperative methods, most of them are single studies and lack clinical evidence. Conclusion After reviewing all the prediction methods, we recommend using dynamic enhanced MRI or single photon emission computed tomography/computed tomography in combination with the intraoperative observation of bleeding from the holes of proximal cannulated screws to predict ONFH after FNF. Moreover, micro-CT is a promising imaging technique in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Methods to predict osteonecrosis of femoral head after femoral neck fracture: a systematic review of the literature

Hu,

Yang,

Zhang

et al. 2023

J Orthop Surg Res

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“…Pathophysiology and risk factors for these underlying mechanisms are partially overlapping but also differ. For example, the Garden classification is an important risk factor for avascular necrosis risk, but it does not necessarily predict the risk of screw cutout or implant loosening [ 20 , 21 , 52 , 53 ]. Ideally, subgroup analysis should have been performed to predict these specific outcomes.…”

Section: Discussionmentioning

confidence: 99%

Patients With Femoral Neck Fractures Are at Risk for Conversion to Arthroplasty After Internal Fixation: A Machine‐learning Algorithm

Kuit

Oosterhoff

Dijkstra

et al. 2022

Clin Orthop Relat Res

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Background Femoral neck fractures are common and are frequently treated with internal fixation. A major disadvantage of internal fixation is the substantially high number of conversions to arthroplasty because of nonunion, malunion, avascular necrosis, or implant failure. A clinical prediction model identifying patients at high risk of conversion to arthroplasty may help clinicians in selecting patients who could have benefited from arthroplasty initially. Question/purpose What is the predictive performance of a machine-learning (ML) algorithm to predict conversion to arthroplasty within 24 months after internal fixation in patients with femoral neck fractures? Methods We included 875 patients from the Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) trial. The FAITH trial consisted of patients with low-energy femoral neck fractures who were randomly assigned to receive a sliding hip screw or cancellous screws for internal fixation. Of these patients, 18% (155 of 875) underwent conversion to THA or hemiarthroplasty within the first 24 months. All patients were randomly divided into a training set (80%) and test set (20%). First, we identified 27 potential patient and fracture characteristics that may have been associated with our primary outcome, based on biomechanical rationale and previous studies. Then, random forest algorithms (an ML learning, decision tree-based algorithm that selects variables) identified 10 predictors of conversion: BMI, cardiac disease, Garden classification, use of cardiac medication, use of pulmonary medication, age, a Members of the Machine Learning Consortium and Fixation using Alternative Implants for the Treatment of Hip fractures (FAITH) Investigators group are listed in an Appendix at the end of this article.

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“…Model performance was assessed on a validation set using AUC, accuracy, recall, specificity, F1-Scores and probability calibration curves (Moons et al, 2019). SHAP (Lundberg et al, 2020;Wang et al, 2021b) and LIME (Molnar, 2020) were used to explainably analyze the optimal model. All data analysis and construction were conducted using Python 3.10.9.…”

Section: Development Of Predictive Modelsmentioning

confidence: 99%

Predictive modeling of perioperative blood transfusion in lumbar posterior interbody fusion using machine learning

Lang,

Liu,

Wang

2023

Front. Physiol.

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Background: Accurate estimation of perioperative blood transfusion risk in lumbar posterior interbody fusion is essential to reduce the number, cost, and complications associated with blood transfusions. Machine learning algorithms have the potential to outperform traditional prediction methods in predicting perioperative blood transfusion. This study aimed to construct a machine learning-based perioperative transfusion risk prediction model for lumbar posterior interbody fusion in order to improve the efficacy of surgical decision-making.Methods: We retrospectively collected clinical data on 1905 patients who underwent lumbar posterior interbody fusion surgery at the Second Hospital of Shanxi Medical University between January 2021 and March 2023. All the data was randomly divided into a training set and a validation set, and the “feature_importances” method provided by eXtreme Gradient Boosting (XGBoost) algorithm was applied to select statistically significant features on the training set to establish five machine learning prediction models. The optimal model was identified by utilizing the area under the curve (AUC) and the probability calibration curve on the validation set. Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) were employed for interpretable analysis of the optimal model.Results: In the postoperative outcomes of patients, the number of hospital days in the transfusion group was longer than that in the non-transfusion group. Additionally, the transfusion group experienced higher total hospital costs, 90-day readmission rates, and complication rates within 90 days after surgery than the non-transfusion group. A total of 9 features were selected for the models. The XGBoost model performed best with an AUC value of 0.958. The SHAP values showed that intraoperative blood loss, intraoperative fluid infusion, and number of fused segments were the top 3 most important features affecting perioperative blood transfusion in lumbar posterior interbody fusion. The LIME algorithm was used to interpret the individualized prediction.Conclusion: Surgery, ASA class, levels fused, total intraoperative blood loss, operative time, and preoperative Hb are viable predictors of perioperative blood transfusion in lumbar posterior interbody fusion. The XGBoost model has demonstrated superior predictive efficacy compared to the traditional logistic regression model, making it a more effective decision-making tool for perioperative blood transfusion.

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Prediction Model of Osteonecrosis of the Femoral Head After Femoral Neck Fracture: Machine Learning–Based Development and Validation Study

Cited by 14 publications

References 53 publications

Methods to predict osteonecrosis of femoral head after femoral neck fracture: a systematic review of the literature

Methods to predict osteonecrosis of femoral head after femoral neck fracture: a systematic review of the literature

Patients With Femoral Neck Fractures Are at Risk for Conversion to Arthroplasty After Internal Fixation: A Machine‐learning Algorithm

Predictive modeling of perioperative blood transfusion in lumbar posterior interbody fusion using machine learning

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