Department of Obstetrics/Gynecology, University of Pennsylvania, Philadelphia, PA 19104, USA.
J Womens Health (Larchmt). 2011 Jan;20(1):29-35. doi: 10.1089/jwh.2010.2161. Epub 2010 Dec 3.
To identify core symptoms that discriminate premenstrual syndrome (PMS) in prospective daily diary ratings and determine the association of these symptoms with functional impairment.
The study analyzed prospective daily symptom ratings and functional impairment data provided by 1081 women who requested PMS treatment at an academic medical center. The data were obtained before any treatment procedures. A random-split sample design provided separate developmental and validation datasets. Logistic regression was used to identify a reduced set of symptoms that best discriminated PMS. The results were validated in a separate dataset. Optimal cutoff points in the symptom scores were identified for clinical use.
Statistical modeling identified 6 symptoms that discriminated PMS and not PMS as well as 17 symptoms in daily diary ratings. The identified core symptoms included anxiety/tension, mood swings, aches, appetite/food cravings, cramps, and decreased interest in activities. The area under the curve (AUC) was 0.84 in both models. The sums of the premenstrual symptom scores also discriminated PMS and not PMS and correctly classified 84%-86% of the cases.
Six symptoms rated in daily diaries discriminate between PMS and not PMS among women seeking treatment and are significantly associated with functional impairment. The findings suggest that the burden of daily diaries to confirm PMS can be reduced to a smaller number of symptoms that distinguish the patients who meet this requirement. Results also support the concept that a clinical diagnosis of PMS can be developed around a core symptom group.
在前瞻性日常日记评定中识别出区分经前期综合征(PMS)的核心症状,并确定这些症状与功能障碍的相关性。
该研究分析了在学术医疗中心寻求 PMS 治疗的 1081 名女性提供的前瞻性日常症状评分和功能障碍数据。这些数据是在任何治疗程序之前获得的。随机分割样本设计提供了单独的开发和验证数据集。逻辑回归用于识别最佳区分 PMS 的症状的减少集合。结果在单独的数据集中进行了验证。为临床应用确定了症状评分的最佳截断点。
统计模型确定了 6 种可区分 PMS 和非 PMS 的症状,以及日常日记评定中的 17 种症状。确定的核心症状包括焦虑/紧张、情绪波动、疼痛、食欲/食物渴望、痉挛和活动兴趣减退。两个模型的曲线下面积(AUC)均为 0.84。经前期症状评分总和也可区分 PMS 和非 PMS,并正确分类了 84%-86%的病例。
在寻求治疗的女性中,日常日记中评定的 6 种症状可区分 PMS 和非 PMS,与功能障碍显著相关。这些发现表明,为了确认 PMS,日常日记的负担可以减少到可以区分符合这一要求的患者的少数症状。结果还支持这样的概念,即可以围绕核心症状群开发 PMS 的临床诊断。