预测纯种马掌骨/跖骨髁骨折修复后重返赛场。

Natalie Young, F. Corletto, I. Wright
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摘要

目的探讨髁突骨折拉力螺钉修复术后重返赛场的影响因素,并建立重返赛场的预测模型。研究设计回顾性队列研究。1999年1月至2018年12月期间,英国一家转诊医院共有356匹马出现第三次掌骨/跖骨髁骨折。方法从病例记录中检索患者的性别、骨折部位、骨折特征、手术相关变量及并发症。将数据分成两组进行模型训练和模型验证。进行单变量分析,并以逐步方式选择预测因子,以纳入多变量逻辑回归模型。使用第二个数据集评估敏感性和特异性。结果马、母马、前肢骨折、复杂性、完全性、移位或扩展性骨折以及并发近端籽骨骨折与重返赛场呈负相关。小马和公马的参赛几率分别是母马的3倍和4倍。后肢骨折、不完全性骨折和非延伸性骨折的马比前肢骨折、完全性骨折和延伸性骨折的马参赛的可能性分别高出4、5和4倍。使用0.5的预测概率截止阈值,在一个数据集(灵敏度= 84%,特异性= 50.5%)中创建预测模型,并应用于另一个数据集(灵敏度= 83.1%,特异性= 24.0%)。结论确定了阴性预后因素,并建立了一个敏感性和特异性可接受的预测模型。临床意义:研究结果为该模型在报告种群中的概念提供了证明,并证明了在不同种群中的进一步验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting return to racing after repair of fractures of the metacarpal/metatarsal condyles in Thoroughbred racehorses.
OBJECTIVE To identify prognostic factors for return to racing after lag screw repair of condylar fractures and develop a predictive model for return to racing. STUDY DESIGN Retrospective cohort study. ANIMALS A total of 356 horses referred to a single referral hospital in the UK with a third metacarpal/metatarsal condylar fracture between January 1999 and December 2018. METHODS Age, sex, fracture site, fracture characteristics, surgery related variables and complications were retrieved from case records. Data were divided into two sets for model training and model validation. Univariable analyses were performed, and predictors were selected in a stepwise fashion for inclusion in the multivariable logistic regression model. Sensitivity and specificity were evaluated using the second dataset. RESULTS Older horses, fillies, fractures of forelimbs, complex, complete, displaced or propagating fractures and concurrent proximal sesamoid bone fracture were negatively associated with return to racing. Colts and geldings were 3 and 4 times more likely to race than fillies, respectively. Horses with hindlimb, incomplete or nonpropagating fractures were 4, 5 and 4 times more likely to race than those with a forelimb, complete or propagating fracture, respectively. Using a predicted probability cut-off threshold of 0.5, a predictive model was created within one dataset (sensitivity = 84%, specificity = 50.5%) and applied to another (sensitivity = 83.1%, specificity = 24.0%). CONCLUSION Negative prognostic factors were identified and led to a predictive model with acceptable sensitivity and specificity in the tested population. CLINICAL SIGNIFICANCE The results provide proof of concept for the model in the reported population and justify further validation in different populations of horses.
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