Endometriosis is increasingly collecting worldwide attention due to its medical complexity and social impact. The European community has identified this as a “social disease”. A large amount of information comes from scientists, yet several aspects of this pathology and staging criteria need to be clearly defined on a suitable number of individuals. In fact, available studies on endometriosis are not easily comparable due to a lack of standardized criteria to collect patients’ informations and scarce definitions of symptoms. Currently, only retrospective surgical stadiation is used to measure pathology intensity, while the Evidence Based Medicine (EBM) requires shareable methods and correct statistical models for disease classification and prognosis. We addressed this issue by setting up a unified evaluation model designated “Endometriosis Index” (EI), obtained from a real-time software using 32 clinical indicators after homotetic transformation. The indicators, collected by the gynecologists are expressed as normalised scores. Normalised variables are cumulated in order to obtain the EI value. The entire panel of variables is then expressed by a unique number to possibly suggest a) grade of the disease, b) indication to surgery, c) trend of disease recurrence and d) prognostic indications . The model of the EI construction has been conceived to be easily applicable and interpretable by all doctors under different clinical protocols. Moreover, all variables were considered as discrete scores, computed to reliably and simultaneously express three concurrent elements: a) patient pain self-assessment, b) physician examination and 3) laboratory diagnostics. This work briefly explains the mathematical model, describes its software functional features and reports its practical application in a group of patients with endometriosis. A summary of the statistics of an observational study is also cited in order to explain the multi-centre consensus validation of the model.


Bioinformatics | Categorical Data Analysis | Clinical Epidemiology | Computational Biology | Disease Modeling | Epidemiology | Vital and Health Statistics