Development of a prediction model to aid primary care physicians in early identification of women at high risk of developing endometriosis: cross-sectional study

Nina Julie Verket, Ragnhild Sørum Falk, Erik Qvigstad, Tom Gunnar Tanbo, Tom Tanbo, Leiv Sandvik
BMJ open · 2019 · vol. 9(12) , pp. e030346 · doi:10.1136/bmjopen-2019-030346 · PMID:31806607 · W2991742128
article OA: gold CC0 ⤵ 6 in-corpus citations
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This study developed and validated a prediction model using logistic and LASSO regression to identify women at high risk for endometriosis based on dysmenorrhea-related absenteeism, family history, and adolescent pain experiences.

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Abstract

Objectives To identify predictors of disease among a few factors commonly associated with endometriosis and if successful, to combine these to develop a prediction model to aid primary care physicians in early identification of women at high risk of developing endometriosis. Design Cross-sectional anonymous postal questionnaire study. Setting Women aged 18–45 years recruited from the Norwegian Endometriosis Association and a random sample of women residing in Oslo, Norway. Participants 157 women with and 156 women without endometriosis. Main outcome measures Logistic and least absolute shrinkage and selection operator (LASSO) regression analyses were performed with endometriosis as dependent variable. Predictors were identified and combined to develop a prediction model. The predictive ability of the model was evaluated by calculating the area under the receiver operating characteristic curve (AUC) and positive predictive values (PPVs) and negative predictive values (NPVs). To take into account the likelihood of skewed representativeness of the patient sample towards high symptom burden, we considered the hypothetical prevalences of endometriosis in the general population 0.1%, 0.5%, 1% and 2%. Results The predictors absenteeism from school due to dysmenorrhea and family history of endometriosis demonstrated the strongest association with disease. The model based on logistic regression (AUC 0.83) included these two predictors only, while the model based on LASSO regression (AUC 0.85) included two more: severe dysmenorrhea in adolescence and use of painkillers due to dysmenorrhea in adolescence . For the prevalences 0.1%, 0.5%, 1% and 2%, both models ascertained endometriosis with PPV equal to 2.0%, 9.4%, 17.2% and 29.6%, respectively. NPV was at least 98% for all values considered. Conclusions External validation is needed before model implementation. Meanwhile, endometriosis should be considered a differential diagnosis in women with frequent absenteeism from school or work due to painful menstruations and positive family history of endometriosis.

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Condition tags

mesh:D004715endometriosisdysmenorrhea

MeSH descriptors

Endometriosis Models, Statistical Primary Health Care Adolescent Adult Cross-Sectional Studies Early Diagnosis Endometriosis Endometriosis Female Humans Middle Aged Prevalence Risk Assessment Young Adult

Citation neighborhood (2-hop)

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. Outer rings show 2-hop neighbours — papers reached through the immediate citers/citees. [ collapse to 1-hop ]

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Cited by (7)

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