Early identification of bipolar disorder among young adults - a 22-year community birth cohort.

OBJECTIVE: We set forth to build a prediction model of individuals who would develop bipolar disorder (BD) using machine learning techniques in a large birth cohort. METHODS: A total of 3748 subjects were studied at birth, 11, 15, 18, and 22 years of age in a community birth cohort. We used the elastic net algorithm with 10-fold cross-validation to predict which individuals would develop BD at endpoint (22 years) at each follow-up visit before diagnosis (from birth up to 18 years). Afterward, we used the best model to calculate the subgroups of subjects at higher and lower risk of developing BD and analyzed the clinical differences among them. RESULTS: A total of 107 (2.8%) individuals within the cohort presented with BD type I, 26 (0.6%) with BD type II, and 87 (2.3%) with BD not otherwise specified. Frequency of female individuals was 58.82% (n = 150) in the BD sample and 53.02% (n = 1868) among the unaffected population. The model with variables assessed at the 18-year follow-up visit achieved the best performance: AUC 0.82 (CI 0.75-0.88), balanced accuracy 0.75, sensitivity 0.72, and specificity 0.77. The most important variables to detect BD at the 18-year follow-up visit were suicide risk, generalized anxiety disorder, parental physical abuse, and financial problems. Additionally, the high-risk subgroup of BD showed a high frequency of drug use and depressive symptoms. CONCLUSIONS: We developed a risk calculator for BD incorporating both demographic and clinical variables from a 22-year birth cohort. Our findings support previous studies in high-risk samples showing the significance of suicide risk and generalized anxiety disorder prior to the onset of BD, and highlight the role of social factors and adverse life events.

Analytical reliability of four oral fluid point-of-collection testing devices for drug detection in drivers.

BACKGROUND: Point-of-collection testing (POCT) devices for psychoactive substance detection through oral fluid samples are used in several countries for traffic enforcement. However, the reported reliability of such devices is quite heterogeneous among studies, and evaluating and comparing their analytical performance is of paramount importance to guide enforcement policies. AIM: To evaluate the analytical reliability of four POCT devices for the detection of cocaine and cannabinoids using oral fluid samples of Brazilian drivers. METHOD: A total of 168 drivers were recruited during standard roadblockfI procedures in Southern Brazil. Subjects were screened using one of the following POCT devices: the DDS2™, the DOA MultiScreen™, the Dräger Drug Test 5000™ and the Multi-Drug Multi-Line Twist Screen Device™ (MDML). Results of the screening tests were compared with chromatographic analyses in order to obtain the reliability parameters. RESULTS: The prevalence of confirmed positive samples for cocaine and cannabinoids were 9 % and 4.4 %, respectively. For cocaine, three POCT devices (MDML™, Dräger DrugTest 5000™, DOA MultiScreen™) showed good reliability, greater than 80 % of performance measures, using guidelines for research on drugged driving published by Walsh et al. (cutoff 10ng/mL). However, for cannabinoids, the devices had low reliability-only Dräger DrugTest 5000™ had good performance using cut-offs proposed by Walsh et al. (cutoff 2ng/mL). CONCLUSION: We observed a high prevalence of drivers testing positive for cocaine and cannabinoids. Most devices achieved good reliability performance for cocaine detection using cutoffs proposed by Walsh et al. or using the device's own cutoff. Instead, the reliability for cannabinoid detection obtained the desired parameters in just one device using cut-offs proposed by Walsh et al. and its own cutoff. Difficulties in detecting cannabinoids at the roadside should be better evaluated before the implementation of such tests.