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Background Little is known regarding the association between adverse events (AEs) and psychiatric medications administered to pregnant women in clinical trials during the pre-marketing period. This study analyzes reports of AE association with psychiatric medication administrated during pregnancy using post-marketing spontaneous reports of AE from the Japanese Adverse Drug Event Report (JADER) database and Food and Drug Administration Adverse Event Reporting System in the United States (FAERS-US). Methods We summarized AE reports of psychiatric medication administrated during pregnancy by comparing data obtained from JADER and FAERS-US databases with medication patterns determined as classes via latent class analysis. The odds ratios (ORs) of AE reports categorized into system organ classes in which each class was compared with those without psychiatric medications. Results The proportions of AE reports under psychiatric medication in pregnancy among all AE reports were 22.0% and 16.6% in JADER and FAERS-US, respectively. The 10,389 reports of psychiatric medication during pregnancy were classified into 11 classes. The proportion of patients receiving four or more psychiatric drugs in JADER was larger than that in FAERS-US. The maximum number of reports in combinations of AE and medication pattern in JADER was 169, for ‘general disorders and administration site conditions’ from the class of four or more medications (OR = 9.1), while that in FAERS-US was 1,654, for ‘injury, poisoning, and procedural complications’ from the class of single psychiatric medication (OR = 2.8). Conclusions The main AE reports and associated AE differed depending on medication patterns in pregnant women taking psychiatric medication. This study may provide a prediction of AEs that are likely to be reported with each medication pattern. Our findings of the association between AE reports and medication patterns could help improve the administration of psychiatric medications during pregnancy, though further research on additional datasets is needed to clarify these results.

Background The aim of this study is to investigate the association between treatment in a closed ICU and survival at discharge in patients with sepsis. Methods This is a post hoc analysis utilizing data from the Japan Septic Disseminated Intravascular Coagulation study, including data from patients with sepsis from 2011 to 2013. Multiple logistic regression analysis was used to estimate the association between ICU policy and survival at discharge, and propensity score matching analysis was performed including the same covariates as a sensitivity analysis. Multiple linear regression analysis for the length of ICU stay in surviving patients was also performed with adjustments for the same covariates. Results Two thousand four hundred ninety-five patients were analyzed. The median Acute Physiology and Chronic Health Evaluation (APACHE) II score was 22 [17–29], the median Sequential Organ Failure Assessment (SOFA) score was 9 [7–12], and the overall mortality was 33%. There were 979 patients treated in 17 open ICUs and 1516 patients in 18 closed ICUs. In comparison, the APACHE II score and SOFA scores were significantly higher in patients in closed ICUs (closed vs open = 23 [18–29] vs 21 [16–28]; p

Background The spatial scan statistic proposed by Kulldorff has been applied to a wide variety of epidemiological studies for cluster detection. This scan statistic, however, uses a circular window to define the potential cluster areas and thus has difficulty in correctly detecting actual noncircular clusters. A recent proposal by Duczmal and Assunção for detecting noncircular clusters is shown to detect a cluster of very irregular shape that is much larger than the true cluster in our experiences. Methods We propose a flexibly shaped spatial scan statistic that can detect irregular shaped clusters within relatively small neighborhoods of each region. The performance of the proposed spatial scan statistic is compared to that of Kulldorff's circular spatial scan statistic with Monte Carlo simulation by considering several circular and noncircular hot-spot cluster models. For comparison, we also propose a new bivariate power distribution classified by the number of regions detected as the most likely cluster and the number of hot-spot regions included in the most likely cluster. Results The circular spatial scan statistics shows a high level of accuracy in detecting circular clusters exactly. The proposed spatial scan statistic is shown to have good usual powers plus the ability to detect the noncircular hot-spot clusters more accurately than the circular one. Conclusion The proposed spatial scan statistic is shown to work well for small to moderate cluster size, up to say 30. For larger cluster sizes, the method is not practically feasible and a more efficient algorithm is needed.