New Model May Help Predict Endometriosis Risk in Individuals, Study Reports

New Model May Help Predict Endometriosis Risk in Individuals, Study Reports

A family history of endometriosis and missing school due to menstrual cramps are predictive factors of endometriosis, a recent study suggests.

According to the researchers, a newly developed prediction model based on these findings may help identify individuals who are at high risk of developing endometriosis.

The study, published in BMJ Open, was titled “Development of a prediction model to aid primary care physicians in early identification of women at high risk of developing endometriosis: cross-sectional study.”

The process of diagnosing endometriosis is particularly difficult and, in some cases, can take a long time because symptoms, such as painful periods and painful intercourse, are not very specific, being common among many people without the condition.

There is no cure for endometriosis, but a diagnosis is necessary for proper care and treatment of symptoms. As a result, there is a need for screening tools that can identify endometriosis. Some exist, but these tend to require specialized equipment like ultrasounds that can’t necessarily be done as part of a visit to a primary care doctor.

To address this, researchers at Oslo University Hospital in Norway set out to determine predictors of endometriosis and then to use these identified factors to build a model for identifying the disease in a primary care setting.

They sent a questionnaire to a group of people with endometriosis and to a random sampling of females in the general population. They received replies and analyzed information from 157 endometriosis patients and 156 people in the general population group. All participants were 18–45 years old.

The questionnaires asked respondents about background information such as age, weight, etc. They also asked a number of questions that might be relevant for endometriosis, which were designed these questions to fulfill three criteria: They had to be, first “applicable to most, if not all, female adolescents”; second, “simple and comprehensible to young adolescents”; and third, “be available from early stages of the disease and reasonably frequent.”

Ultimately, this consisted of about half a dozen questions covering age at first period, or menarche, family history of endometriosis, school attendance, personal history of painful periods, and use of medications for painful periods. Some of these questions included numeric ranges — for example, response options ranging from zero, for “never,” to four, for “always”).

The researchers then used statistical analyses to determine which responses were best at differentiating between people with endometriosis and those without.

One analysis suggested that having a family history of endometriosis and missing school due to menstrual cramps, known as dysmenorrhea, were the best indicators of endometriosis. A second analysis also identified these two factors and added two more: severe cramps in adolescence and the use of painkillers due to dysmenorrhea during adolescence.

These analyses were used to construct models that basically took responses from the questionnaire and used them to calculate a numerical score, and the researchers determined that particular cutoffs, which varied depending on the model, could identify people with endometriosis with high specificity in a particular group, meaning the test is very accurate in identifying those without the disease.

This finding is based on a re-analysis of the original data, so it’s probable that any specificity calculated would be very high. However, the researchers emphasized, this model will need to be tested in other settings to determine how it can be most effective — but they are hopeful that it could allow for faster diagnosis of endometriosis, particularly in a primary care setting. And the information gleaned may in itself help healthcare providers make diagnoses.

“The developed prediction models need to be validated in future studies before use. Meanwhile, endometriosis should be considered a differential diagnosis in women with frequent absenteeism from school or work due to dysmenorrhea and positive family history of endometriosis,” the authors concluded.

Marisa holds an MS in Cellular and Molecular Pathology from the University of Pittsburgh, where she studied novel genetic drivers of ovarian cancer. She specializes in cancer biology, immunology, and genetics. Marisa began working with BioNews in 2018, and has written about science and health for SelfHacked and the Genetics Society of America. She also writes/composes musicals and coaches the University of Pittsburgh fencing club.
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Margarida graduated with a BS in Health Sciences from the University of Lisbon and a MSc in Biotechnology from Instituto Superior Técnico (IST-UL). She worked as a molecular biologist research associate at a Cambridge UK-based biotech company that discovers and develops therapeutic, fully human monoclonal antibodies.
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Marisa holds an MS in Cellular and Molecular Pathology from the University of Pittsburgh, where she studied novel genetic drivers of ovarian cancer. She specializes in cancer biology, immunology, and genetics. Marisa began working with BioNews in 2018, and has written about science and health for SelfHacked and the Genetics Society of America. She also writes/composes musicals and coaches the University of Pittsburgh fencing club.
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