Exploring heterogeneity of perimenopause with application of multivariable analysis approaches

Abstract Objectives Heterogeneity of clinical appearance had made it a challenge to make individualized and comprehensive management of perimenopause. This study aimed to estimate the profiles over heterogenous appearances of perimenopause with application of latent variable analysis methods over an optimized multidimensional assessing framework. Methods A two-phase clinical study was designed and advanced in the research center in Guangzhou, China. The assessing framework was developed over the initial item pool as integration of 4 scales including Insomnia severity index, Modified Kupperman index, Self-rating anxiety scale, and Self-rating depression scale. Validity and reliability of the instrument were evaluated and the psychometric properties of the items were estimated with multidimensional item response theory(MIRT). And then computer adaptive testing(CAT) was developed with the estimated model. We used latent profile analysis (LPA) to cluster patients into subgroups as patterns characterized by multidimensional latent trait scores. Finally, interpretability and efficiency were analyzed via comparison between the two assessing strategies. Result There were in total 336 patients diagnosed with perimenopause enrolled for the assessment. A conceptual framework was estimated consisting of 6 factors including sleep disturbance, mood swings, vasomotor symptoms, positive attitude towards life, multisystem abnormality, and fatigue. The construct validity was evaluated as optimized with CMIN/df = 1.814, GFI = 0.619, CFI = 0.721, TLI = 0.707 and RMSEA = 0.075. With scores in the simulated CAT, the 4 latent profiles model was estimated indicating the heterogeneity of perimenopause characterized by different severity of psychological and physical discomforts in the LPA. Conclusion The quantitative paradigm raised in this study revealed the potential patterns presenting heterogeneity of perimenopause offering better interpretation for clinical assessment.