Cao Yang, Forssten Maximilian Peter, Mohammad Ismail Ahmad, Borg Tomas, Ioannidis Ioannis, Montgomery Scott, Mohseni Shahin
Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, 70182 Örebro, Sweden.
Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institute, 17177 Stockholm, Sweden.
J Pers Med. 2021 Apr 28;11(5):353. doi: 10.3390/jpm11050353.
Hip fracture patients have a high risk of mortality after surgery, with 30-day postoperative rates as high as 10%. This study aimed to explore the predictive ability of preoperative characteristics in traumatic hip fracture patients as they relate to 30-day postoperative mortality using readily available variables in clinical practice. All adult patients who underwent primary emergency hip fracture surgery in Sweden between 2008 and 2017 were included in the analysis. Associations between the possible predictors and 30-day mortality was performed using a multivariate logistic regression (LR) model; the bidirectional stepwise method was used for variable selection. An LR model and convolutional neural network (CNN) were then fitted for prediction. The relative importance of individual predictors was evaluated using the permutation importance and Gini importance. A total of 134,915 traumatic hip fracture patients were included in the study. The CNN and LR models displayed an acceptable predictive ability for predicting 30-day postoperative mortality using a test dataset, displaying an area under the ROC curve (AUC) of as high as 0.76. The variables with the highest importance in prediction were age, sex, hypertension, dementia, American Society of Anesthesiologists (ASA) classification, and the Revised Cardiac Risk Index (RCRI). Both the CNN and LR models achieved an acceptable performance in identifying patients at risk of mortality 30 days after hip fracture surgery. The most important variables for prediction, based on the variables used in the current study are age, hypertension, dementia, sex, ASA classification, and RCRI.
髋部骨折患者术后死亡风险较高，术后30天死亡率高达10%。本研究旨在利用临床实践中易于获得的变量，探讨创伤性髋部骨折患者术前特征与术后30天死亡率之间的预测能力。分析纳入了2008年至2017年间在瑞典接受初次急诊髋部骨折手术的所有成年患者。使用多变量逻辑回归（LR）模型分析可能的预测因素与30天死亡率之间的关联；采用双向逐步法进行变量选择。然后拟合LR模型和卷积神经网络（CNN）进行预测。使用排列重要性和基尼重要性评估各个预测因素的相对重要性。本研究共纳入134915例创伤性髋部骨折患者。CNN和LR模型在使用测试数据集预测术后30天死亡率方面显示出可接受的预测能力，受试者工作特征曲线下面积（AUC）高达0.76。预测中最重要的变量是年龄、性别、高血压、痴呆、美国麻醉医师协会（ASA）分级和修订心脏风险指数（RCRI）。CNN和LR模型在识别髋部骨折手术后30天有死亡风险的患者方面均表现出可接受的性能。基于本研究中使用的变量，预测最重要的变量是年龄、高血压、痴呆、性别、ASA分级和RCRI。
