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The aim of the present study was to investigate the incidence and hallmarks of long-term survivors of recurrent ovarian carcinoma (LTSROC) in a large-scale retrospective cohort of patients from a multicenter study group.We performed a regional multicenter retrospective study between January 1986 and September 2021 using clinical data collected under the central pathological review system. Patients who underwent surgery for primary OC at diagnosis and developed recurrent tumors after the initial treatment were included. We defined LTSROC as patients who survived for 5 years or longer after initial tumor recurrence and examined factors affecting the long-term survival of ROC and outcomes of LTSROC.We collected information on patients with malignant ovarian tumors and finally 657 of them that developed ROC were included in the study population. Sixty-eight (10.4%) patients were LTSROC while 399 (60.7%) were short-term survivors of recurrent ovarian carcinoma. In a multivariate logistic regression analysis, negative ascites cytology [odds ratio (OR) 1.865; 95% CI 1.026-3.393; p = 0.041] and a recurrence-free interval (RFI) of 1 year or longer (OR 2.896; 95% CI 1.546-5.425; p 0.001) were identified as independent factors associated with LTSROC. Approximately 80% of LTSROC presented with solitary recurrent tumors. Furthermore, more than 50% of LTSROC underwent tumor debulking surgery for the first recurrent tumor with or without chemotherapy.RFI of 1 year or longer and negative ascites cytology in the initial surgery were identified as independent predictive factors for LTSROC.

Objective Complete-staging surgery is recommended for stage IA ovarian cancer, but may be omitted for various reasons, including the preservation of fertility and an advanced age. We herein investigated the prognostic impact of limited-staging surgery in patients with stage IA epithelial ovarian cancer. Methods We retrospectively collected data on 4730 patients with malignant ovarian tumors from the databases of multiple institutions and ultimately included 293 with stage IA epithelial ovarian cancer. Limited-staging surgery was defined as one that did not involve hysterectomy, systematic retroperitoneal lymphadenectomy or the collection of ascites cytology. We used an inverse probability of treatment weighting analysis with propensity scores and estimated the hazard ratios of recurrence and death with limited-staging surgery. Results In total, 176 out of 293 patients (39.9%) were assigned to the limited-staging surgery group. After propensity score adjustments, no significant differences were observed in recurrence-free survival or overall survival between the limited- and complete-staging surgery groups. Even in the subgroup analysis with age stratification, recurrence-free survival and overall survival were similar in the limited- and complete-staging surgery groups. Conclusions The present results indicate the limited prognostic impact of limited-staging surgery for stage IA epithelial ovarian cancer.

Introduction The objective of this cohort study was to investigate whether the revised post-Cardiac Arrest Syndrome for Therapeutic hypothermia score (rCAST), which we previously developed as a prognostic score for adult patients with post-cardiac arrest syndrome (PCAS), is also applicable to pediatric patients. Methods Pediatric PCAS patients were included from an out-of-hospital cardiac arrest (OHCA) registry of the Japanese Association for Acute Medicine (JAAM). We validated the predictive accuracy of the rCAST for the neurological outcomes at 30 and 90 days. We also evaluated the probability of a good neurological outcome in each of the three specified severity categories based on the rCAST (low severity: ≤5.5; moderate severity: 6.0–14.0; high severity: ≥14.5). Results Among the 737 pediatric patients with OHCA, the data of 179 pediatric PCAS patients in whom return of spontaneous circulation was achieved were analyzed. The areas under the curve (AUC) of the rCAST for predicting the neurological outcomes at 30 days and 90 days were 0.95 (95% CI: 0.90–0.99) and 0.96 (0.91–1.00), respectively. The proportions of patients with a good neurological outcome at 30 days were 100% (12/12) in the low severity group, 36.1% (13/36) in the moderate severity group, and 2.3% (3/131) in the high severity group. Conclusions The AUC of the rCAST for pediatric PCAS patients was found to be greater than 0.9 in the external validation, which corresponds to excellent predictive accuracy. There was no patient with good neurological outcome among the patients with more than 17.0 points (extremely high severity group).

Bayes factor analysis has the attractive property of accommodating the risks of both false negatives and false positives when identifying susceptibility gene variants in genome-wide association studies (GWASs). For a particular SNP, the critical aspect of this analysis is that it incorporates the probability of obtaining the observed value of a statistic on disease association under the alternative hypotheses of non-null association. An approximate Bayes factor (ABF) was proposed by Wakefield (Genetic Epidemiology 2009;33:79–86) based on a normal prior for the underlying effect-size distribution. However, misspecification of the prior can lead to failure in incorporating the probability under the alternative hypothesis. In this paper, we propose a semi-parametric, empirical Bayes factor (SP-EBF) based on a nonparametric effect-size distribution estimated from the data. Analysis of several GWAS datasets revealed the presence of substantial numbers of SNPs with small effect sizes, and the SP-EBF attributed much greater significance to such SNPs than the ABF. Overall, the SP-EBF incorporates an effect-size distribution that is estimated from the data, and it has the potential to improve the accuracy of Bayes factor analysis in GWASs.

Stroke and neurological outcomes in the early phase following revascularization for moyamoya disease (MMD) may depend on the patient's age. In this study, an age-stratified comparative analysis was performed to clarify this issue. We reviewed 105 MMD patients who underwent 179 revascularization surgeries. The demographic characteristics were collected in four age groups (≤ 5 and 6-17 years for pediatric patients and 18-49 and ≥ 50 years for adults). Additionally, we assessed the incidence of subsequent stroke and deterioration of modified Rankin Scale (mRS) score. Then, we evaluated predictors of postoperative stroke and mRS deterioration using logistic regression. The mean patient age was 26.2 ± 18.5 years. No significant difference in the incidence of postoperative stroke was observed between age groups; however, the incidence tended to be increased among patients aged ≤ 5 years (17.9%) and patients aged ≥ 50 years (16.7%). Deterioration of mRS scores was significantly associated with ages ≤ 5 years (17.9%) and ≥ 50 years (11.1%). Logistic regression showed that posterior cerebral artery involvement (odds ratio [OR], 4.6) and postoperative transient neurological events (TNEs) (OR, 5.93) were risk factors for postoperative stroke. Age ≤ 5 years (OR, 9.73), postoperative TNEs (OR, 7.38), and postoperative stroke (OR, 49) were identified as predictors of unfavorable neurological outcomes. The novel feature of this comparative analysis by age group is that membership in the early-childhood MMD patient group (under 5 years old) was an independent risk factor for unfavorable short-term neurological outcomes and was mainly associated with the incidence of postoperative severe cerebral infarction.

The clinical significance of a higher BMI on the prognosis of ovarian cancer remains controversial; therefore, a more detailed analysis is demanded. This study investigated the impact of BMI on peritoneum-specific recurrence to clarify the involvement of adipose tissue in the proliferation of cancer cells at sites of peritoneal dissemination.Among 4,730 patients with malignant ovarian tumors, 280 diagnosed with International Federation of Gynecology and Obstetrics (FIGO) stage IIB to IIIC epithelial ovarian cancer and who underwent complete resection in the primary surgery were included in the present study.There were 42, 201, and 37 women in the low, normal, and high BMI groups, respectively. Peritoneum-specific recurrence-free survival and overall survival were both significantly shorter in patients with a high BMI than in those with a normal BMI (p = 0.028 and 0.018, respectively). No significant differences were observed in the distribution of sites of recurrence between these two groups. A multivariate analysis identified obesity as an independent prognostic factor in addition to pT3 tumor staging and positive ascites cytology.Patients with a high BMI had a significantly worse prognosis than those with a normal BMI, and peritoneal adipose tissue may have contributed to this difference.

Postpartum depression (PPD) and perinatal mental health care are of growing importance worldwide. Here we aimed to develop and validate machine learning models for the prediction of PPD, and to evaluate the usefulness of the recently adopted 2-week postpartum checkup in some parts of Japan for the identification of women at high risk of PPD.A multicenter retrospective study was conducted using the clinical data of 10 013 women who delivered at ≥35 weeks of gestation at 12 maternity care hospitals in Japan. PPD was defined as an Edinburgh Postnatal Depression Scale score of ≥9 points at 4 weeks postpartum. We developed prediction models using conventional logistic regression and four machine learning algorithms based on the information that can be routinely collected in daily clinical practice. The model performance was evaluated using the area under the receiver operating characteristic curve (AUROC).In the machine learning models developed using clinical data before discharge, the AUROCs were similar to those in the conventional logistic regression models (AUROC, 0.569-0.630 vs. 0.626). The incorporation of additional 2-week postpartum checkup data into the model significantly improved the predictive performance for PPD compared to that without in the Ridge regression and Elastic net (AUROC, 0.702 vs. 0.630 [p 0.01] and 0.701 vs. 0.628 [p 0.01], respectively).Our machine learning models did not achieve better predictive performance for PPD than conventional logistic regression models. However, we demonstrated the usefulness of the 2-week postpartum checkup for the identification of women at high risk of PPD.

Low‐dose methotrexate (MTX) plus vinblastine (VBL) chemotherapy is an effective treatment for desmoid‐type fibromatosis (DF). However, previous reports have described a weekly regimen, with no reports available on a biweekly one. The aim of this study was to determine the clinical outcomes of a biweekly regimen in a cohort prospectively treated in our single institution. Since 2010, we have prospectively treated refractory DF patients with biweekly MTX (30 mg/m2) + VBL (6 mg/m2). Efficacy, progression‐free survival (PFS), and correlating factors were analyzed. Adverse events (AEs) were recorded. In total, 38 patients received low‐dose MTX + VBL therapy, and its efficacy was assessed in 37 of them. Nineteen (51%) patients showed partial response (PR). Clinical benefit rate was 95%. PFS at 5 y was 80.8%. In PR cases, median time to response was 10 mo. Longer duration of therapy was significantly associated with the response of PR (P = .007) by univariate analysis. There was no clear association between various clinicopathological factors, including tumor size, location, catenin beta‐1 (CTNNB1) mutation status with effect. Only 3 AEs of grade 3/4 were observed. Tumor regrowth after MTX + VBL discontinuation was observed in 5 (20%) of 25 patients. Biweekly administration of MTX + VBL chemotherapy was well tolerated compared with weekly administration, and its efficacy was anticipated in DF patents, although the time needed to achieve a response may be relatively long. The treatment interval should be determined taking into account both the condition of the tumor and the patient's preference., Biweekly administration of MTX + VBL chemotherapy for desmoid patients was well tolerated compared with weekly administration, and its efficacy was similar. The time needed to achieve a response may be relatively long with a biweekly regimen.

Positive ascites cytology is a strong prognostic factor in patients with early-stage ovarian cancer (OvCa). However, limited information is currently available on the impact of positive ascites cytology on patient prognoses under each clinical background. We herein investigated the comprehensive impact of positive ascites cytology on patients with epithelial OvCa and the effectiveness of additional therapeutic interventions, including complete staging surgery and chemotherapy. Among 4730 patients with malignant ovarian neoplasms, retrospectively identified in multiple institutions, 1906 with epithelial OvCa were included. In the investigation of its effects on clinical factors using a multivariate analysis, positive ascites cytology correlated with a poor prognosis. Positive ascites cytology had a significantly worse prognosis than those with negative cytology in all subgroups except for patients with stage IV tumors and a mucinous histology. Chemotherapy may be effective in reducing the negative impact of positive ascites cytology on the prognosis of patients in terms of progression-free and overall survivals, while complete staging surgery did not improve the prognosis of patients with positive ascites cytology. Collectively, our findings suggested that positive ascites cytology had a negative impact on the prognosis of patients with epithelial OvCa, but not those with stage IV tumors or a mucinous histology.

Objectives The optimal target temperature during targeted temperature management for patients after cardiac arrest remains under debate. The aim of this study was to evaluate the association between targeted temperature management at lower target temperatures and the neurologic outcomes among patients classified by the severity of postcardiac arrest syndrome. Design A multicenter observational study from the out-of-hospital cardiac arrest registry of the Japanese Association for Acute Medicine, which is a nationwide prospective registry of out-of-hospital cardiac arrest patients. Setting A total of 125 critical care medical centers or hospitals with an emergency care department across Japan. Patients A total of 1,111 out-of-hospital cardiac arrest patients who had received targeted temperature management. Measurements and main results We divided all 1,111 postcardiac arrest syndrome patients treated with targeted temperature management into two groups: those who received targeted temperature management at a lower target temperature (33-34°C) and those who received targeted temperature management at a higher target temperature (35-36°C). In regard to classification of the patients, we divided the patients into three categories of severity (low, moderate, and high severities) using the risk classification tool, post-Cardiac Arrest Syndrome for Therapeutic hypothermia, which was previously validated. The primary outcome was the percentage of patients with a good neurologic outcome at 30 days, and the secondary outcome was the survival rate at 30 days. Multivariate analysis showed that targeted temperature management at 33-34°C was significantly associated with a good neurologic outcome and survival at 30 days in the moderate severity (odds ratio, 1.70 [95% CI, 1.03-2.83] and 1.90 [95% CI, 1.15-3.16], respectively), but not in the patients of low or high severity (pinteraction = 0.033). Propensity score analysis also showed that targeted temperature management at 33-34°C was associated with a good neurologic outcome in the moderate-severity group (p = 0.022). Conclusions Targeted temperature management at 33-34°C was associated with a significantly higher rate of a good neurologic outcome in the moderate-severity postcardiac arrest syndrome group, but not in the low- or high-severity group.

Background At lower ambient temperature, patients with out-of-hospital cardiac arrest (OHCA) easily experience hypothermia. Hypothermia has shown to improve the rate of successful return of spontaneous circulation (ROSC) in animal models. We hypothesized that lower temperature affects the impact of bystander cardiopulmonary resuscitation (CPR) on the increased odds of a favorable neurological outcome post-OHCA. Methods This study used information collected by the prospective, nationwide, Utstein registry to examine data from 352,689 adult patients who experienced OHCA from 2012 to 2016 in Japan. The primary outcome was a 1-month favorable neurological outcomes. Multivariable logistic regression analyses were conducted to test the impact of bystander CPR according to the temperature on the favorable outcome. Results A total of 201,111 patients with OHCA were included in the complete case analysis. The lower temperature group had lower proportions of receiving bystander CPR (46.5 vs. 47.9%) and having favorable outcome (2.1 vs 2.8%) than those in the higher group. Multivariable analysis revealed that bystander CPR at lower temperatures was significantly associated with favorable outcomes (adjusted odds ratio, 1.22; 95% CI, 1.09–1.37), whereas bystander CPR at higher temperatures was not associated with favorable outcomes (1.02; 0.92–1.13). The nonlinear relationship using a spline curve in the multivariable model revealed that odds ratio of favorable neurological outcomes associated with bystander CPR increased as the temperature decreased. Conclusion Bystander CPR was associated with favorable neurological outcomes at lower temperatures. The odds of a favorable outcome associated with bystander CPR increased as the temperature decreased.

In disease-association studies using neuroimaging data, evaluating the biological or clinical significance of individual associations requires not only detection of disease-associated areas of the brain but also estimation of the magnitudes of the associations or effect sizes for individual brain areas. In this paper, we propose a model-based framework for voxel-based inferences under spatial dependency in neuroimaging data. Specifically, we employ hierarchical mixture models with a hidden Markov random field structure to incorporate the spatial dependency between voxels. A nonparametric specification is proposed for the effect size distribution to flexibly estimate the underlying effect size distribution. Simulation experiments demonstrate that compared with a naive estimation method, the proposed methods can substantially reduce the selection bias in the effect size estimates of the selected voxels with the greatest observed associations. An application to neuroimaging data from an Alzheimer’s disease study is provided.

In disease-association studies using neuroimaging data, the detection of disease-associations and the estimation of the magnitudes of the associations or effect sizes are essential statistical tasks. In this paper, we propose a framework for modeling effect sizes of whole-brain voxels. Specifically, we assume a semi-parametric hierarchical mixture model to flexibly estimate the underlying effect size distribution for the whole-brain voxels and a hidden Markov random field model to incorporate the spatial dependency among contiguous voxels. We then derive a multiple testing procedure for controlling the false discovery rate (FDR) and a procedure for effect size estimation, for both individual voxels and prespecified regions that have structural or functional features. The proposed method for the estimation of effect sizes reduces the selection bias caused by selecting voxels or regions with the greatest observed effect sizes, possibly due to random errors. An application to neuroimaging data from an Alzheimer’s disease study is provided.

Identification of genetic components that are either shared by or differentiate multiple disorders is one of the most crucial issues for understanding the underlying biology of disorders and improving diagnosis and treatment based on genetic features. Although one of the most promising types of studies for this purpose is the genome-wide association study (GWAS) across disorders, only a modest fraction of the total heritability can be explained by loci identified through GWASs, partly due to a lack of sufficient statistical power. To search for relevant components more effectively, we developed an alternative efficient multi-subgroup gene screening method using multidimensional semiparametric hierarchical mixture models to analyze GWAS data involving multiple subgroups. This method reveals the underlying effect size distributions of relevant variants from summary association data to evaluate the existence of variant subgroups and their effect sizes rather than identifying individual variants via single-variant association tests with a conservative genome-wide significance criterion. Here we demonstrate the strength of the newly developed method by applying it to a recently published large-scale GWAS dataset for schizophrenia and bipolar disorder.