PDF(Pubmed)
Abstract：
UNASSIGNED: The Spasticity-Plus Syndrome (SPS) in multiple sclerosis (MS) refers to a combination of spasticity and other signs/symptoms such as spasms, cramps, bladder dysfunction, tremor, sleep disorder, pain, and fatigue. The main purpose is to develop a user-friendly tool that could help neurologists to detect SPS in MS patients as soon as possible.
UNASSIGNED: A survey research based on a conjoint analysis approach was used. An orthogonal factorial design was employed to form 12 patient profiles combining, at random, the eight principal SPS signs/symptoms. Expert neurologists evaluated in a survey and a logistic regression model determined the weight of each SPS sign/symptom, classifying profiles as SPS or not.
UNASSIGNED: 72 neurologists participated in the survey answering the conjoint exercise. Logistic regression results of the survey showed the relative contribution of each sign/symptom to the classification as SPS. Spasticity was the most influential sign, followed by spasms, tremor, cramps, and bladder dysfunction. The goodness of fit of the model was appropriate (AUC = 0.816). Concordance between the experts\' evaluation vs. model estimation showed strong Pearson\'s (r = 0.936) and Spearman\'s (r = 0.893) correlation coefficients. The application of the algorithm provides with a probability of showing SPS and the following ranges are proposed to interpret the results: high (> 60%), moderate (30-60%), or low (< 30%) probability of SPS.
UNASSIGNED: This study offers an algorithmic tool to help healthcare professionals to identify SPS in MS patients. The use of this tool could simplify the management of SPS, reducing side effects related with polypharmacotherapy.
UNASSIGNED: A survey research based on a conjoint analysis approach was used. An orthogonal factorial design was employed to form 12 patient profiles combining, at random, the eight principal SPS signs/symptoms. Expert neurologists evaluated in a survey and a logistic regression model determined the weight of each SPS sign/symptom, classifying profiles as SPS or not.
UNASSIGNED: 72 neurologists participated in the survey answering the conjoint exercise. Logistic regression results of the survey showed the relative contribution of each sign/symptom to the classification as SPS. Spasticity was the most influential sign, followed by spasms, tremor, cramps, and bladder dysfunction. The goodness of fit of the model was appropriate (AUC = 0.816). Concordance between the experts\' evaluation vs. model estimation showed strong Pearson\'s (r = 0.936) and Spearman\'s (r = 0.893) correlation coefficients. The application of the algorithm provides with a probability of showing SPS and the following ranges are proposed to interpret the results: high (> 60%), moderate (30-60%), or low (< 30%) probability of SPS.
UNASSIGNED: This study offers an algorithmic tool to help healthcare professionals to identify SPS in MS patients. The use of this tool could simplify the management of SPS, reducing side effects related with polypharmacotherapy.
摘要：
■多发性硬化症（MS）中的痉挛综合征（SPS）是指痉挛和其他体征/症状（例如痉挛）的组合，抽筋,膀胱功能障碍,震颤,睡眠障碍,疼痛,和疲劳。主要目的是开发一种用户友好的工具，可以帮助神经科医生尽快检测MS患者的SPS。
■使用了基于联合分析方法的调查研究。采用正交阶乘设计组合形成12个患者资料，随机的,八种主要的SPS体征/症状。在调查中评估的专家神经学家和逻辑回归模型确定了每个SPS体征/症状的重量，将配置文件分类为SPS与否。
■72位神经科医生参与了这项联合运动的调查。调查的Logistic回归结果显示了每个体征/症状对SPS分类的相对贡献。痉挛是最具影响力的征兆,接着是痉挛,震颤,抽筋,和膀胱功能障碍。模型的拟合优度是适当的(AUC=0.816)。专家评估与评估之间的一致性模型估计显示出较强的皮尔逊(r=0.936)和斯皮尔曼(r=0.893)相关系数。该算法的应用提供了显示SPS的概率，并提出了以下范围来解释结果：高（>60％），中等（30-60%），或SPS的低(<30%)概率。
■这项研究提供了一种算法工具来帮助医疗保健专业人员识别MS患者的SPS。使用该工具可以简化SPS的管理，减少与多药物治疗相关的副作用。
■使用了基于联合分析方法的调查研究。采用正交阶乘设计组合形成12个患者资料，随机的,八种主要的SPS体征/症状。在调查中评估的专家神经学家和逻辑回归模型确定了每个SPS体征/症状的重量，将配置文件分类为SPS与否。
■72位神经科医生参与了这项联合运动的调查。调查的Logistic回归结果显示了每个体征/症状对SPS分类的相对贡献。痉挛是最具影响力的征兆,接着是痉挛,震颤,抽筋,和膀胱功能障碍。模型的拟合优度是适当的(AUC=0.816)。专家评估与评估之间的一致性模型估计显示出较强的皮尔逊(r=0.936)和斯皮尔曼(r=0.893)相关系数。该算法的应用提供了显示SPS的概率，并提出了以下范围来解释结果：高（>60％），中等（30-60%），或SPS的低(<30%)概率。
■这项研究提供了一种算法工具来帮助医疗保健专业人员识别MS患者的SPS。使用该工具可以简化SPS的管理，减少与多药物治疗相关的副作用。
