RESUMO
BACKGROUND: Polycystic ovary syndrome (PCOS) is an endocrine disrupting disorder affecting about 10% of reproductive-aged women. PCOS diagnosis may be delayed several years and may require multiple physicians, resulting in lost time for risk-reducing interventions. Menstrual tracking apps are a potential tool to alert women of their risk while also prompting evaluation from a medical professional. OBJECTIVE: The primary objective of this study was to develop and pilot test the irregular cycle feature, a predictive model that generated a PCOS risk score, in the menstrual tracking app, Clue. The secondary objectives were to run the model using virtual test subjects, create a quantitative risk score, compare the feature's risk score with that of a physician, and determine the sensitivity and specificity of the model before empirical testing on human subjects. METHODS: A literature review was conducted to generate a list of signs and symptoms of PCOS, termed variables. Variables were then assigned a probability and built into a Bayesian network. Questions were created based on these variables. A total of 9 virtual test subjects were identified using self-reported menstrual cycles and answers to the feature's questions. Upon completion of the questionnaire, a Result Screen and Doctor's Report summarizing the probability of having PCOS was displayed. This provided information about PCOS and data to facilitate diagnosis by a medical professional. To assess the accuracy of the feature, the same set of 9 virtual test subjects was assigned probabilities by the feature and the physician, who served as the gold standard. The feature recommended individuals with a score greater than or equal to 25% to follow-up with a physician. Differences between the feature and physician scores were evaluated using a t test and a Pearson correlation coefficient in 8 of the 9 virtual test subjects. A second iteration was conducted to assess the feature's probability capabilities. RESULTS: The irregular cycle feature's first iteration produced 1 false-positive compared with the physician score and had an absolute mean difference of 15.5% (SD 15.1%) among the virtual test subjects. The second iteration had 2 false positives compared with the physician score and had an absolute mean difference of 18.8% (SD 13.6%). The feature overpredicted the virtual test subjects' risk of PCOS compared with the physician. However, a significant positive correlation existed between the feature and physician score (Pearson correlation coefficient=0.82; P=.01). The second iteration performed worse, with a Pearson correlation coefficient of 0.73 (P=.03). CONCLUSIONS: The first iteration of the feature outperformed the second and better predicted the probability of PCOS. Although further research is needed with a more robust sample size, this pilot study indicates the potential value for developing a screening tool to prompt high-risk subjects to seek evaluation by a medical professional.
RESUMO
BACKGROUND AND OBJECTIVES: The underlying reasons why some women experience debilitating premenstrual symptoms and others do not are largely unknown. Here, we test the evolutionary ecological hypothesis that some negative premenstrual symptoms may be exacerbated by the presence of chronic sexually transmitted infections (STIs). METHODOLOGY: 34 511 women were recruited through a digital period-tracker app. Participants were asked: (i) Have you ever been diagnosed with a STI? (ii) If yes, when was it, and were you given treatment? Those data were combined with longitudinal cycle data on menstrual bleeding patterns, the experience of pain and emotions and hormonal contraceptive use. RESULTS: 865 women had at least two complete menstrual cycle data and were eligible for analysis. Before diagnosis, the presence of an infection predicts a ca. 2-fold increase in the odds of reporting both headache, cramps and sadness during the late luteal phase and sensitive emotions during the wider luteal phase. After diagnosis, the odds of reporting negative symptoms pre-menstrually remain unchanged among STI negative individuals, but the odds of reporting sensitive emotions decrease among STI positive individuals receiving a treatment. No relationships between STIs, pain and emotions are observed among hormonal contraceptive users. CONCLUSIONS AND IMPLICATIONS: The results support the idea that a negative premenstrual experience might be aggravated by the presence of undiagnosed STIs, a leading cause of infertility worldwide. Caution is warranted in extrapolating the results as the data are self-reported, inflammatory levels are unknown and the tracker is biased towards recording negative premenstrual symptoms among Western individuals.