Dysmenorrhea is the most common gynecological disorder for women in the reproductive age. Study has indicated that dysmenorrhea might be a general risk factor of chronic pelvic pain and even chronic non-pelvic pain, such as fibromyalgia. We used the Longitudinal Health Insurance Database 2000 from the Taiwan National Health Research Institutes Database to investigate whether women with dysmenorrhea have a higher risk of fibromyalgia and whether treatment of dysmenorrhea reduced the risk of fibromyalgia. The dysmenorrhea cohort was matched with a non-dysmenorrhea cohort at a 1:1 ratio based on gender, age, and the year of entry study by frequency matching. Multivariable Cox proportional hazard regression models were used to assess the risk of fibromyalgia, with controlling for potential confounding variables such as age, comorbidities, and medication use. After controlling confounding variables, results revealed that women with dysmenorrhea have a significantly higher risk of fibromyalgia than women without dysmenorrhea. However, only treatment of dysmenorrhea with hormonal contraceptives reduce the risk of fibromyalgia. These results indicated that dysmenorrhea may be a risk factor of fibromyalgia, whereas personalized medicine for treatment of dysmenorrhea may be the key to reduce the risk of fibromyalgia. Future studies are needed to identify the causes and prevention strategies in detail.

Objective: To determine the differences in the incidences and risks of suicide attempt (SA) and suicidal drug overdose (SDO) between patients with epilepsy with and without comorbid depression by using data from Taiwan’s National Health Insurance Research Database. Methods: We analyzed data of patients (≥20 years) who had received epilepsy diagnoses between 2000 and 2012; the diagnosis date of epilepsy was defined as the index date. The epilepsy patients were divided into the cohorts, with and without comorbid depression, and compared against a cohort from the non-affected population. We calculated adjusted hazard ratios and the corresponding 95% confidence intervals for SA and SDO in the three cohorts after adjustment for age, sex, and comorbidities. Results: The incidences of SA and SDO in the cohort with epilepsy and depression were 42.9 and 97.4 per 10,000 person-years, respectively. The epilepsy with depression cohort had 21.3 times of SA risk; and 22.9 times of SDO risk than did the comparison cohort had a 6.03-fold increased risk of SA and a 2.56-fold increased risk of SDO than did the epilepsy patients without depression. Moreover, patients’ age <65 years, and female sex would further increase the risk of SA in patients with epilepsy and comorbid depression. Conclusion: Risks of SA and SDO in patients with epilepsy are proportionally increased when depression is coexisted. Our findings provide crucial information for clinicians and the government for suicide prevention and to question whether prescribing a large number of medications to patients with epilepsy and depression is safe.

Aims: To determine the risks of suicide attempt (SA) and suicidal drug overdose (SDO) following the admission for head injury of patients with depression. Design: We analyzed the NHIRD data of patients aged ≥20 years who had received depression diagnoses between 2000 and 2010. They were divided into cohorts of those with admission for head injury (DHI) and those without it (DWI) during the follow-up period and compared against a sex-, age-, comorbidity-, and index-date-matched cohort from the general population. Setting: The Taiwan National Health Insurance Research Database (NHIRD). Participants/Cases: We analyzed the NHIRD data of patients (≥20 years) who had received depression diagnoses between 2000 and 2010. Intervention(s): Regular interventions. Measurements: We calculated the adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) of SA and SDO in these cohorts after adjustment for age, sex, and comorbidities. Findings: Up to the end of 2011, our results revealed extremely high incidences of SA and SDO with 63.3 and 88.6 per 10,000 person-years, respectively, in the DHI cohort. The DHI cohort had a 37.4-times higher risk for SA and a 17.1-times higher risk for SDO compared with the comparison group and had aHRs of 14.4 and 16.3, respectively, for poisoning by medicinal substances and poisoning by tranquilizers compared with patients in the DWI cohort. Patients with DHI aged <50 years, of female sex, with high incomes, living in more urbanized areas, and without other comorbidities had extraordinarily higher risks for SA. Conclusions: The risks of SA and SDO were proportionally increased by head injury in patients with depression in Taiwan. Our findings provide crucial information to implement efficient suicide prevention strategies in the future.

Background/Objectives: The duration of the response to radiotherapy-related treatment is a critical prognostic indicator for patients with nasopharyngeal carcinoma (NPC). Persistent tumor status, including residual tumor presence and early recurrence, is associated with poorer survival outcomes. To address this, we developed a prediction model to identify patients at a high risk of persistent tumor status prior to initiating treatment. Methods: This retrospective study included 104 patients with NPC receiving radiotherapy-related treatment who had completed a 3-year follow-up period; 29 were classified into the persistent tumor status group and 75 into the disease-free group. Radiomic features were extracted from pretreatment positron emission tomography (PET) images and used to construct a prediction model by employing machine learning algorithms. The model’s diagnostic performance was assessed using the area under the receiver operating characteristic curve (AUC), whereas SHapley Additive exPlanations (SHAP) analysis was conducted to determine the contribution of individual features to the model. Results: The prediction model developed using the AdaBoost algorithm and validated through five-fold cross-validation achieved the highest AUC of 0.934. Its sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 89.66%, 86.67%, 72.22%, 95.59%, and 87.5%, respectively. SHAP analysis revealed that the feature of high dependence low metabolic uptake emphasis₅₀ had the greatest impact on model predictions. Furthermore, patients classified as disease-free exhibited markedly higher overall survival rates compared with those with persistent tumor status. Conclusions: In conclusion, the proposed prediction model efficiently identified patients with NPC at a high risk of persistent tumor status by using radiomic features extracted from pretreatment PET images.