A Study on the Evaluation of a Risk Score of Osteonecrosis of the Femoral Head Based on Survival Analysis

Zheng, Jiaqi; Wang, Huan; Gao, You-Shui; Ai, Zisheng

doi:10.1016/j.arth.2020.07.046

Cited by 10 publications

(11 citation statements)

References 20 publications

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“…The performance of our model on the validation set was better than that of the naive Bayesian prediction model proposed by Cui et al [ 24 ], whose accuracy, sensitivity, and AUC were 0.744, 0.742, and 0.746, respectively. The AUC of our model on the validation set was higher than that of the hybrid nomogram based on LR developed by Zhu et al (0.948) [ 26 ] and the nomogram based on Cox regression developed by Zheng et al (0.97) [ 25 ]. It also exhibited satisfactory generalization ability on external data, with accuracy, specificity, AUC, and log-loss values of 0.907, 0.935, 0.933, and 0.279, respectively.…”

Section: Discussionmentioning

confidence: 62%

“…After excluding patients who were lost to follow-up or who died, 259 patients with FNF and associated 43 variables were included in this study. The external data (n=376) were obtained from our previous retrospective cohort study [ 25 ], which collected data from a cohort of patients with FNFs from May 2013 to January 2017 at Shanghai Sixth Hospital, Tenth Hospital, and Tongji Hospital. Two patients aged >75 years were excluded.…”

Section: Methodsmentioning

confidence: 99%

“…However, the accuracy, sensitivity, and area under the receiver operating characteristic curve (AUC) of the model based on naive Bayes were all lower than 80%. Zheng et al [ 25 ] and Zhu et al [ 26 ] developed a nomogram for the risk assessment of femoral head necrosis based on a traditional regression analysis. The AUCs of the validation cohort were 0.94 and 0.95.…”

Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Wu²,

Han³

et al. 2021

JMIR Med Inform

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Background The absolute number of femoral neck fractures (FNFs) is increasing; however, the prediction of traumatic femoral head necrosis remains difficult. Machine learning algorithms have the potential to be superior to traditional prediction methods for the prediction of traumatic femoral head necrosis. Objective The aim of this study is to use machine learning to construct a model for the analysis of risk factors and prediction of osteonecrosis of the femoral head (ONFH) in patients with FNF after internal fixation. Methods We retrospectively collected preoperative, intraoperative, and postoperative clinical data of patients with FNF in 4 hospitals in Shanghai and followed up the patients for more than 2.5 years. A total of 259 patients with 43 variables were included in the study. The data were randomly divided into a training set (181/259, 69.8%) and a validation set (78/259, 30.1%). External data (n=376) were obtained from a retrospective cohort study of patients with FNF in 3 other hospitals. Least absolute shrinkage and selection operator regression and the support vector machine algorithm were used for variable selection. Logistic regression, random forest, support vector machine, and eXtreme Gradient Boosting (XGBoost) were used to develop the model on the training set. The validation set was used to tune the model hyperparameters to determine the final prediction model, and the external data were used to compare and evaluate the model performance. We compared the accuracy, discrimination, and calibration of the models to identify the best machine learning algorithm for predicting ONFH. Shapley additive explanations and local interpretable model-agnostic explanations were used to determine the interpretability of the black box model. Results A total of 11 variables were selected for the models. The XGBoost model performed best on the validation set and external data. The accuracy, sensitivity, and area under the receiver operating characteristic curve of the model on the validation set were 0.987, 0.929, and 0.992, respectively. The accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve of the model on the external data were 0.907, 0.807, 0.935, and 0.933, respectively, and the log-loss was 0.279. The calibration curve demonstrated good agreement between the predicted probability and actual risk. The interpretability of the features and individual predictions were realized using the Shapley additive explanations and local interpretable model-agnostic explanations algorithms. In addition, the XGBoost model was translated into a self-made web-based risk calculator to estimate an individual’s probability of ONFH. Conclusions Machine learning performs well in predicting ONFH after internal fixation of FNF. The 6-variable XGBoost model predicted the risk of ONFH well and had good generalization ability on the external data, which can be used for the clinical prediction of ONFH after internal fixation of FNF.

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Section: Discussionmentioning

confidence: 62%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wang¹,

Wu²,

Han³

et al. 2021

JMIR Med Inform

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“…Femoral neck fractures (FNFs) are common in geriatric population, accounting for 48 to 54% of hip fractures and 3.6% of the total fractures in adults [ 1 ]. Nearly 20% of all the FNFs are nondisplaced and often require surgical treatment [ 2 , 3 ].…”

Section: Introductionmentioning

confidence: 99%

Nomogram for predicting reoperation following internal fixation of nondisplaced femoral neck fractures in elderly patients

Zhu

Deng

et al. 2021

J Orthop Surg Res

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Objective We aimed to evaluate risk factors and develop a nomogram for reoperation after internal fixation of nondisplaced femoral neck fractures (FNFs) in elderly patients. Methods We conducted a retrospective study involving a total of 255 elderly patients who underwent closed reduction and internal fixation with cannulated screw system for nondisplaced FNFs between January 2016 and January 2019. We collected data on demographics, preoperative radiological parameters, surgery, serum biochemical markers, and postoperative rehabilitation. In addition, we performed univariate and multivariate logistic regression analyses to determine independent risk factors for reoperation, and then developed a nomogram to assess the risks of reoperation. Besides, discriminative ability, calibration, and clinical usefulness of the nomogram were evaluated using the concordance index (C-index), the receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA), respectively. We employed bootstrap method to validate the performance of the developed nomogram. Results Our analysis showed that among the 255 patients, 28 (11.0%) underwent reoperation due to osteonecrosis of the femoral head (14 cases), mechanical failure (8 cases) or nonunion (6 cases). All of the 28 patients underwent conversion surgery to arthroplasty. The multivariate logistic regression analysis demonstrated that preoperative posterior tilt angle ≥ 20°, Pauwel’s III type, younger patients, preoperative elevated levels of alkaline phosphatase (ALP), preoperative hypoalbuminemia, and early postoperative weight-bearing were independent risk factors for reoperation. In addition, the C-index and the bootstrap value of the developed nomogram was 0.850 (95% CI = 0.803–0.913) and 0.811, respectively. Besides, the calibration curve showed good consistency between the actual diagnosed reoperation and the predicted probability, while the DCA indicated that the nomogram was clinically valuable. Conclusions Our analysis showed we successfully developed and validated a nomogram for personalized prediction of reoperation after internal fixation of nondisplaced FNFs in elderly patients. This model would help in individualized evaluation of the need for reoperation and inform strategies aimed at eliminating the need for the reoperation.

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“…A large number of studies have been performed using preoperative patient risk factors to predict ONFH after internal fixation (13)(14)(15). Although satisfactory results were obtained in these studies, intervention strategies could not be individualized to avoid or reduce the development of postoperative ONFH.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a predictive nomogram for postoperative osteonecrosis of the femoral head with cannulated screws fixation

Zhu¹,

Xie²,

Zhang³

et al. 2021

Ann Transl Med

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Background: Osteonecrosis of the femoral head (ONFH) remains a major complication of femoral neck fractures. Early interventions require preliminary prediction and detection. In this study, we aimed to evaluate the perioperative variables of postoperative ONFH in femoral neck fracture patients with closed reduction and cannulated screw fixation. We also established and validated an individualized nomogram for the prediction of postoperative ONFH. Methods: We included 470 patients with ONFH from two hospitals [First Affiliated Hospital of University of Science and Technology of China (n=360) and Southern Branch of the First Affiliated Hospital of the University of Science and Technology of China (n=110)]. We evaluated the prognostic value of multiple perioperative variables using a Cox regression model in the training cohort. We developed a nomogram for the prediction of ONFH using a logistic regression model. We assessed the performance of this nomogram in a validation cohort and evaluated its clinical value.Results: Of the 470 patients who met the inclusion criteria, 141 (30.0%) developed postoperative ONFH.

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A Study on the Evaluation of a Risk Score of Osteonecrosis of the Femoral Head Based on Survival Analysis

Cited by 10 publications

References 20 publications

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

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

Nomogram for predicting reoperation following internal fixation of nondisplaced femoral neck fractures in elderly patients

Development and validation of a predictive nomogram for postoperative osteonecrosis of the femoral head with cannulated screws fixation

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