OBJECTIVE: To identify core symptoms that discriminate premenstrual syndrome (PMS) in prospective daily diary ratings and determine the association of these symptoms with functional impairment. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVE: To identify core symptoms that discriminate premenstrual syndrome (PMS) in prospective daily diary ratings and determine the association of these symptoms with functional impairment. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
Authors: E W Steyerberg; F E Harrell; G J Borsboom; M J Eijkemans; Y Vergouwe; J D Habbema Journal: J Clin Epidemiol Date: 2001-08 Impact factor: 6.437
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