INTRODUCTION: We investigated the factors associated with readmission in patients with congestive heart failure (HF) receiving long-term administration of tolvaptan (TLV) to support treatment decisions for HF. METHODS: This retrospective cohort study included 181 patients with congestive HF who received long-term administration of TLV. Long-term administration of TLV was defined as the administration of TLV for 60 days or longer. The outcome was a readmission event for worsening HF within 1 year after discharge. Significant factors associated with readmission were selected using multivariate analysis. To compare the time to readmission using significant factors extracted in a multivariate analysis, readmission curves were constructed using the Kaplan-Meier method and analysed using the log-rank test. RESULTS: The median age was 78 years (range, 38-96 years), 117 patients (64.6%) were males, and 77 patients (42.5%) had a hospitalisation history of HF. Readmission for worsening HF within 1 year after long-term TLV treatment occurred in 62 patients (34.3%). In the multivariate analysis, estimated glomerular filtration rate (eGFR) <30 mL/min/1.73 m 2 (odds ratio, 3.22; 95% confidence interval, 1.661-6.249; P = 0.001) was an independent significant factor. When eGFR at discharge was divided into two groups (eGFR < 30 vs. eGFR ≥ 30), readmission rates within 1 year were 53.3% vs. 25.4%, respectively ( P = 0.001). CONCLUSION We revealed that eGFR was strongly associated with readmission in patients with HF who received long-term administration of TLV. Furthermore, we showed that eGFR is an important indicator in guiding treatment of HF in patients receiving TLV.
Humans ; Tolvaptan/therapeutic use* ; Heart Failure/drug therapy* ; Male ; Female ; Patient Readmission/statistics & numerical data* ; Aged ; Retrospective Studies ; Aged, 80 and over ; Middle Aged ; Glomerular Filtration Rate ; Adult ; Antidiuretic Hormone Receptor Antagonists/therapeutic use* ; Risk Factors ; Kaplan-Meier Estimate ; Multivariate Analysis

Online databases are crucial infrastructures to facilitate the wide effective and efficient use of mouse mutant resources in life sciences. The number and types of mouse resources have been rapidly growing due to the development of genetic modification technology with associated information of genomic sequence and phenotypes. Therefore, data integration technologies to improve the findability, accessibility, interoperability, and reusability of mouse strain data becomes essential for mouse strain repositories. In 2020, the RIKEN BioResource Research Center released an integrated database of bioresources including, experimental mouse strains, Arabidopsis thaliana as a laboratory plant, cell lines, microorganisms, and genetic materials using Resource Description Framework-related technologies. The integrated database shows multiple advanced features for the dissemination of bioresource information. The current version of our online catalog of mouse strains which functions as a part of the integrated database of bioresources is available from search bars on the page of the Center (https://brc.riken.jp) and the Experimental Animal Division (https://mus.brc.riken.jp/) websites. The BioResource Research Center also released a genomic variation database of mouse strains established in Japan and Western Europe, MoG+ (https://molossinus.brc.riken.jp/mogplus/), and a database for phenotype-phenotype associations across the mouse phenome using data from the International Mouse Phenotyping Platform. In this review, we describe features of current version of databases related to mouse strain resources in RIKEN BioResource Research Center and discuss future views.

Online databases are crucial infrastructures to facilitate the wide effective and efficient use of mouse mutant resources in life sciences. The number and types of mouse resources have been rapidly growing due to the development of genetic modification technology with associated information of genomic sequence and phenotypes. Therefore, data integration technologies to improve the findability, accessibility, interoperability, and reusability of mouse strain data becomes essential for mouse strain repositories. In 2020, the RIKEN BioResource Research Center released an integrated database of bioresources including, experimental mouse strains, Arabidopsis thaliana as a laboratory plant, cell lines, microorganisms, and genetic materials using Resource Description Framework-related technologies. The integrated database shows multiple advanced features for the dissemination of bioresource information. The current version of our online catalog of mouse strains which functions as a part of the integrated database of bioresources is available from search bars on the page of the Center (https://brc.riken.jp) and the Experimental Animal Division (https://mus.brc.riken.jp/) websites. The BioResource Research Center also released a genomic variation database of mouse strains established in Japan and Western Europe, MoG+ (https://molossinus.brc.riken.jp/mogplus/), and a database for phenotype-phenotype associations across the mouse phenome using data from the International Mouse Phenotyping Platform. In this review, we describe features of current version of databases related to mouse strain resources in RIKEN BioResource Research Center and discuss future views.

To explore the efficacy and safety of switching from once-daily basal insulin therapy to once-daily pre-meal injection insulin degludec/insulin aspart (IDegAsp) with respect to the glycemic control of participants with type 2 diabetes mellitus (T2DM).

BACKGROUND AND PURPOSE: Valproate is used as a prophylactic drug for migraine, but it is not be effective in all patients. We used medical records to investigate which clinical factors affected the response to valproate in patients with migraine as an original headache, and established a scoring system for predicting the clinical response to prophylactic therapy. METHODS: We investigated clinical factors from the medical records of 95 consistent responders (CRs) and 24 inconsistent responders (IRs) to valproate. RESULTS: Multivariate stepwise logistic regression analysis revealed that a history of hyperlipidemia and hay fever and the complication of depression or other psychiatric disorder were significant factors that independently contributed to a negative response, with odds ratios of 6.024 [no vs. yes; 95% confidence interval (CI)=1.616–22.222], 2.825 (no vs. yes; 95% CI=1.046–7.634), and 2.825 (no vs. yes; 95% CI=1.052–7.576), respectively. A predictive index (PI) of the clinical response to valproate in patients with migraine was calculated using the regression coefficients of these three factors as an integer, and the index was significantly higher for IRs than for CRs (1.46±1.10 vs. 0.69±0.74, mean±SD, p<0.001). CONCLUSIONS: The obtained PI may represent an appropriate scoring system for predicting the responses in these patients.
Depression ; Headache ; Humans ; Hyperlipidemias ; Logistic Models ; Medical Records ; Migraine Disorders* ; Odds Ratio ; Rhinitis, Allergic, Seasonal ; Risk Factors ; Valproic Acid*