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建立一种列线图以预测3型脊髓小脑共济失调患者的跌倒情况。

Establish a Nomogram to Predict Falls in Spinocerebellar Ataxia Type 3.

作者信息

Lin Junyu, Zhang Lingyu, Cao Bei, Wei Qianqian, Ou Ruwei, Hou Yanbing, Xu Xinran, Liu Kuncheng, Gu Xiaojing, Shang Huifang

机构信息

Department of Neurology, Laboratory of Neurodegenerative Disorders, Rare Diseases Center, West China Hospital, Sichuan University, Chengdu, China.

出版信息

Front Neurol. 2021 Jan 27;11:602003. doi: 10.3389/fneur.2020.602003. eCollection 2020.

Abstract

Falls are common and are frequently accompanied by injuries in patients with spinocerebellar ataxias type 3 (SCA3). We explored which factors could predict falls in a cohort of patients with SCA3 and developed a nomogram model to predict the first fall in non-fallen patients with SCA3. We conducted a prospective cohort study. Forty-four non-fallen patients with SCA3 were followed up until the first fall or November 5, 2020, whichever came first. Univariate and multivariate Cox proportional hazard regression analyses were applied to explore the predictive factors of falls in patients with SCA3. A nomogram model predicting the no-fall probabilities at 3, 6, 12, and 24 months was formulated based on the results of the multivariate Cox analysis. Internal validation was conducted to assess the discrimination and calibration of the final model using bootstrapping with 1,000 resamples. Multivariate Cox proportional hazard regression showed that the presence of dystonia, hyperreflexia, urinary incontinence, and hidrosis and the number of abnormal eye movements predicted a more rapid progression to falls in patients with SCA3. The nomogram model showed good discrimination with a concordance index of 0.83 and good calibration. Patients with dystonia, hyperreflexia, urinary incontinence, and hidrosis, and more types of abnormal eye movement had a more rapid progression to falls in SCA3.

摘要

跌倒在3型脊髓小脑共济失调(SCA3)患者中很常见,且常常伴有损伤。我们探究了哪些因素可预测SCA3患者的跌倒情况,并开发了一种列线图模型来预测未发生跌倒的SCA3患者首次跌倒的情况。我们进行了一项前瞻性队列研究。对44例未发生跌倒的SCA3患者进行随访,直至首次跌倒或2020年11月5日(以先到者为准)。应用单因素和多因素Cox比例风险回归分析来探究SCA3患者跌倒的预测因素。基于多因素Cox分析结果,制定了一个预测3个月、6个月、12个月和24个月无跌倒概率的列线图模型。使用1000次重复抽样的自举法进行内部验证,以评估最终模型的区分度和校准度。多因素Cox比例风险回归分析显示,肌张力障碍、反射亢进、尿失禁、多汗的存在以及异常眼动的数量可预测SCA3患者更快地进展为跌倒。列线图模型显示出良好的区分度,一致性指数为0.83,且校准良好。患有肌张力障碍、反射亢进、尿失禁和多汗以及更多类型异常眼动的SCA3患者跌倒进展更快。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/562c/7873475/3288017fc4d0/fneur-11-602003-g0001.jpg

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