Artificial Neural Network and Cox Regression Models for Predicting Mortality after Hip Fracture Surgery: A Population-Based Comparison
Artificial Neural Network and Cox Regression Models for Predicting Mortality after Hip Fracture Surgery: A Population-Based Comparison
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DOI：
10.3390/medicina56050243
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发表时间：
2020-05-01
影响因子：
2.6
通讯作者：
Shi, Hon-Yi
中科院分区：
文献类型：
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作者：
Chen, Cheng-Yen;Chen, Yu-Fu;Shi, Hon-Yi
本研究旨在验证人工神经网络(ANN)模型在研究期间预测髋部骨折术后死亡率的准确性，并比较ANN模型和COX回归模型的性能指标。1996-2010年间共有10,534名髋部骨折手术患者参加了这项研究。使用了三个数据集：训练数据集(n=7374)用于模型开发，测试数据集(n=1580)用于内部验证，验证数据集(1580)用于外部验证。此外，还进行了全局灵敏度分析，以评估输入预报器在神经网络模型中的相对重要性。髋部骨折术后死亡率与转诊系统、年龄、性别、居住地城市化、社会经济状况、Charlson共病指数(CCI)评分、囊内骨折、医院容量和外科医生数量显著相关(p<；0.05)。对于髋部骨折术后死亡率的预测，ANN模型比COX模型具有更高的预测精度和整体性能指数。神经网络模型的全局敏感性分析表明，转诊到较低级别的医疗机构是影响死亡率的最重要变量，其次是外科医生数量、医院数量和CCI评分。与COX回归模型相比，ANN模型在预测髋部骨折术后死亡率方面更为准确。这项研究中确定的与术后死亡率相关的预测因素也可以用于指导髋部骨折手术患者的康复过程和健康结果。
This study purposed to validate the accuracy of an artificial neural network (ANN) model for predicting the mortality after hip fracture surgery during the study period, and to compare performance indices between the ANN model and a Cox regression model. A total of 10,534 hip fracture surgery patients during 1996-2010 were recruited in the study. Three datasets were used: a training dataset (n = 7374) was used for model development, a testing dataset (n = 1580) was used for internal validation, and a validation dataset (1580) was used for external validation. Global sensitivity analysis also was performed to evaluate the relative importances of input predictors in the ANN model. Mortality after hip fracture surgery was significantly associated with referral system, age, gender, urbanization of residence area, socioeconomic status, Charlson comorbidity index (CCI) score, intracapsular fracture, hospital volume, and surgeon volume (p < 0.05). For predicting mortality after hip fracture surgery, the ANN model had higher prediction accuracy and overall performance indices compared to the Cox model. Global sensitivity analysis of the ANN model showed that the referral to lower-level medical institutions was the most important variable affecting mortality, followed by surgeon volume, hospital volume, and CCI score. Compared with the Cox regression model, the ANN model was more accurate in predicting postoperative mortality after a hip fracture. The forecasting predictors associated with postoperative mortality identified in this study can also bae used to educate candidates for hip fracture surgery with respect to the course of recovery and health outcomes.