A Multidimensional Tool to Quantify Treatment Resistance in Depression: The Maudsley Staging Method
A Multidimensional Tool to Quantify Treatment Resistance in Depression: The Maudsley Staging Method
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DOI：
10.4088/jcp.08m04309
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发表时间：
2009-02-01
影响因子：
5.3
通讯作者：
Cleare, Anthony J.
中科院分区：
文献类型：
--
作者：
Fekadu, Abebaw;Wooderson, Sarah;Cleare, Anthony J.
目的：治疗抵抗是抑郁症的一种常见临床现象。然而，目前单一的分期模型未能反映其复杂性。我们的目的是设计一个模型来分期治疗抵抗性抑郁症，同时考虑到导致治疗失败的核心因素。方法：我们回顾了文献，以确定一致与治疗抵抗相关的因素。我们还分析了一个亚组的患者出院的专科住院单位，为他们提供足够的database.Results：我们提出了一个基于点的分期模型，将3个因素：治疗，疾病的严重程度，并提出发作的持续时间的数据。在该模型中，症状严重程度的评级范围从亚综合征抑郁症（评分1）到重度综合征抑郁症伴精神病（评分5）。抗癫痫治疗根据使用的药物数量在5分子量表上评定，而发作持续时间在3分子量表上评定。使用该模型估计的总体耐药水平从最低耐药（评分3）到严重耐药（评分15）不等。该评级系统允许将治疗抗性的总体严重程度总结为单个数字评分或单个描述性类别。也可以根据耐药的严重程度指定类别（轻度、中度和重度）。住院资料分析表明，纳入模型的因素和模型本身有一定的预测validity.Conclusion：该分期模型具有合理的面子和预测效度，可能有更好的效用分期治疗阻力比目前可用的方法。
Objective: Treatment resistance is a common clinical phenomenon in depression. However, current unitary models of staging fail to represent its complexity. We aimed to devise a model to stage treatment-resistant depression, taking into account the core factors contributing to treatment failure.Method: We reviewed the literature to identify factors consistently associated with treatment resistance. We also analyzed data from a subgroup of patients discharged from a specialist inpatient unit for whom adequate data were obtainable.Results: We present a points-based staging model incorporating 3 factors: treatment, severity of illness, and duration of presenting episode. In this model, the rating of symptom severity ranges from subsyndromal depression (score 1) to severe syndromal depression with psychosis (score 5). Antidepressant treatment is rated on a 5-point subscale based on number of medications used, While duration of the presenting episode is rated on a 3-point subscale. The overall level of resistance estimated using this model varies from minimal resistance (score of 3) to severe resistance (score of 15). The rating system allows the overall severity of treatment resistance to be summarized either as a single numeric score or under a single descriptive category. It may also be possible to specify categories (mild, moderate, and severe) based on severity of resistance. Analysis of inpatient data indicates that the factors incorporated in the model and the model itself have some predictive validity.Conclusion: This staging model has reasonable face and predictive validity and may have better utility in staging treatment resistance than currently available methods.