目的:目前尚无预测中风后髋部骨折的模型。本研究旨在调查导致脑卒中患者髋部骨折的危险因素,并建立风险预测模型以可视化该风险。
方法:回顾性分析徐州医科大学附属医院2014年6月至2017年6月收治的439例脑卒中伴或不伴髋部骨折患者作为训练组。并收集2020年6月至2023年6月哈尔滨医科大学附属第一医院和徐州医科大学附属医院同类型患者83例作为检测集。根据是否存在髋部骨折将患者分为骨折组和非骨折组。采用多因素logistic回归分析筛选有意义的因素。基于多因素分析建立预测髋部骨折发生风险的列线图,并使用受试者工作特性曲线(ROC)评估性能,校正曲线,和决策曲线分析(DCA)。创建了一个网络计算器,以促进临床医生更方便的互动体验。
结果:在训练集中,脑卒中后髋部骨折35例(7.9%),在测试集中,该数据为13例(15.6%)。在训练集中,单因素分析显示两组跌倒次数有显著差异,吸烟,高血压,糖皮质激素,笔划数,小型精神状态检查(MMSE),视敏度水平,美国国立卫生研究院卒中量表(NIHSS),伯格平衡量表(BBS),说话时停止行走(SWWT)(P<0.05)。多因素分析显示跌倒次数[OR=17.104,95%CI(3.727-78.489),P=0.000],NIHSS[OR=1.565,95%CI(1.193-2.052),P=0.001],SWWT[OR=12.080,95%CI(2.398-60.851),P=0.003]是与新发骨折呈正相关的独立危险因素。骨密度[OR=0.155,95%CI(0.044-0.546),P=0.012]和BBS[OR=0.840,95%CI(0.739-0.954),P=0.007]与新骨折呈负相关。训练和测试集列线图曲线下面积(AUC)分别为0.939(95%CI:0.748-0.943)和0.980(95%CI:0.886-1.000),分别,校准曲线显示预测和实际状态之间的高度一致性,决策曲线下的面积分别为0.034和0.109。
结论:跌倒的次数,骨折史,BBS得分低,NIHSS得分高,和阳性SWWT是脑卒中后髋部骨折的危险因素。基于此,开发了一个高精度的列线图和一个网络计算器(https://stroke。shinyapps.io/DynNomapp/)已创建。
OBJECTIVE: There are currently no models for predicting hip fractures after stroke. This
study wanted to investigate the risk factors leading to hip fracture in stroke patients and to establish a risk prediction model to visualize this risk.
METHODS: We reviewed 439 stroke patients with or without hip fractures admitted to the Affiliated Hospital of Xuzhou Medical University from June 2014 to June 2017 as the training set, and collected 83 patients of the same type from the First Affiliated Hospital of Harbin Medical University and the Affiliated Hospital of Xuzhou Medical University from June 2020 to June 2023 as the testing set. Patients were divided into fracture group and non-fracture group based on the presence of hip fractures. Multivariate logistic regression analysis was used to screen for meaningful factors. Nomogram predicting the risk of hip fracture occurrence were created based on the multifactor analysis, and performance was evaluated using receiver operating characteristic curve (ROC), calibration curves, and decision curve analysis (DCA). A web calculator was created to facilitate a more convenient interactive experience for clinicians.
RESULTS: In training set, there were 35 cases (7.9 %) of hip fractures after stroke, while in testing set, this data was 13 cases (15.6 %). In training set, univariate analysis showed significant differences between the two groups in the number of falls, smoking, hypertension, glucocorticoids, number of strokes, Mini-Mental State Examination (MMSE), visual acuity level, National Institute of Health stroke scale (NIHSS), Berg Balance Scale (BBS), and Stop Walking When Talking (SWWT) (P<0.05). Multivariate analysis showed that number of falls [OR=17.104, 95 % CI (3.727-78.489), P = 0.000], NIHSS [OR=1.565, 95 % CI (1.193-2.052), P = 0.001], SWWT [OR=12.080, 95 % CI (2.398-60.851), P = 0.003] were independent risk factors positively associated with new fractures. BMD [OR = 0.155, 95 % CI (0.044-0.546), P = 0.012] and BBS [OR = 0.840, 95 % CI (0.739-0.954), P = 0.007] were negatively associated with new fractures. The area under the curve (AUC) of nomogram were 0.939 (95 % CI: 0.748-0.943) and 0.980 (95 % CI: 0.886-1.000) in training and testing sets, respectively, and the calibration curves showed a high agreement between predicted and actual status with an area under the decision curve of 0.034 and 0.109, respectively.
CONCLUSIONS: The number of falls, fracture history, low BBS score, high NIHSS score, and positive SWWT are risk factors for hip fracture after stroke. Based on this, a nomogram with high accuracy was developed and a web calculator (https://stroke.shinyapps.io/DynNomapp/) was created.