Neonatal haemochromatosis, considered to be a gestational alloimmune liver disease (NH-GALD), is a rare but serious disease that results in fulminant hepatic failure. The recurrence rate of NH-GALD in a subsequent infant of a mother with an affected infant is 70%–90%. Recently, antenatal maternal high-dose intravenous immunoglobulin (IVIG) therapy has been reported as being effective for preventing recurrence of NH-GALD in a subsequent infant. However, no clinical trial has been conducted to date.

This is a multicentre open-label, single-arm study of antenatal maternal high-dose IVIG therapy in pregnant women with a history of documented NH in a previous offspring. The objective of this study is to evaluate the efficacy and safety of antenatal maternal high-dose IVIG therapy in preventing or reducing the severity of alloimmune injury to the fetal liver.

The clinical trial is being performed in accordance with the Declaration of Helsinki. The trial protocol was approved by the Clinical Research Review Board at four hospitals. Before enrolment, written informed consent would be obtained from eligible pregnant women. The results are expected to be published in a scientific journal.

28 October 2024, V.8.0.

jRCT1091220353.

Although artificial intelligence (AI) has been widely applied to electronic health record (EHR) data in hospital environments, its use in long-term care (LTC) facilities remains unexplored. Limited information technology infrastructure and unique challenges in LTC settings require a comprehensive examination of AI’s potential to enhance care quality and operational efficiency. With the aim of examining the application of AI to EHR data in LTC facilities, this scoping review will identify current AI applications for EHR in LTC, informing future research and potential care improvements in LTC settings.

This review will follow the scoping review methodological guidelines. The protocol of this scoping review has been registered on the Open Science Framework. The inclusion criteria are EHR (participants), AI (concept) and LTC facilities (context), with no date restrictions, but limited to articles published in English. Studies of any design focusing on AI applications for EHR in LTC settings will be considered. A systematic search will be performed on MEDLINE (Ovid), CINAHL (EBSCOhost), the Cochrane Central Register of Controlled Trials (Ovid), the Cochrane Database of Systematic Reviews (Ovid) and SCOPUS (Elsevier) by an information specialist. Two reviewers will independently screen titles and abstracts for inclusion based on predefined criteria. The same process will be repeated for full-text screening. Discrepancies will be resolved through team meetings with the third, fourth and fifth reviewers. All reasons for exclusion at the full-text stage will be documented and reported, with any discrepancies resolved by a review team.

As the data will be collected from existing literature, ethical approval is not required. The findings will be disseminated through conference presentations and publication in a peer-reviewed journal. The results will map current knowledge on AI applications in LTC facilities, thereby providing a foundation for future research aimed at enhancing the implementation and effectiveness of AI technologies in such settings.

This study aimed to develop risk prediction equations for the 5-year incidence of diabetes among the Japanese population using health check-up data. We hypothesised that demographic and laboratory data from health check-ups could predict diabetes onset with high accuracy.

Retrospective cohort study.

Data from a health examination in Japan between 2008 and 2016.

Data were analysed from 31 084 participants aged 30–69 years. The presence of baseline diabetes and endocrine disease was included in the exclusion criteria, as were participants with missing data for the analysis. The study population was randomly divided into derivation and validation cohorts in a 1:1 ratio.

The primary outcome was the incidence of diabetes at the 5-year follow-up, defined as a fasting blood glucose level ≥126 mg/dL, glycosylated haemoglobin A1c (National Glycohemoglobin Standardization Program (NGSP)) ≥6.5%, or initiation of diabetes treatment. Predictor variables included age, sex, body mass index, blood pressure, underlying diseases, lifestyle factors and laboratory measurements. The primary measure was the area under the receiver operating characteristic curve (AUC) for the predictive equations.

In the derivation cohort, diabetes incidence was 5.0%. The prediction equation incorporating age, sex, body mass index, fasting blood glucose and glycosylated haemoglobin A1c showed good discriminatory ability with an AUC of 0.89, sensitivity of 0.81 and specificity of 0.81 in the validation cohort.

The equation with laboratory measures effectively predicted the 5-year diabetes risk in the general Japanese population. It has potential clinical utility for identifying individuals at high risk of diabetes and guiding preventive interventions.