Some intensive care unit (ICU) patients develop an extremely deep and sustained immunosuppression that increases the risk of secondary infections and can ultimately compromise survival. Thanks to an easily accessible and simplified immune monitoring to identify immunological failure, a personalised immune restoration approach is now feasible. Among the different therapeutic strategies in this field, interferon gamma (IFN-) is probably the most interesting drug to reduce the burden of secondary infections in the ICU.

This is a two parallel group multicentre blinded add-on randomised trial comparing immunorestoration by subcutaneous injection of IFN- to standard of care in targeted ICU patients. The study will be performed in 23 ICUs in France. Patients hospitalised in the ICU for a week, with multiple organ failure defined by a sequential organ failure assessment score ≥6 during this first week, will be enrolled. If within 96 hours after inclusion, these patients express immunosuppressed features defined by a low absolute lymphocyte count (x10⁹/L) and low expression of human leucocyte antigen-DR (HLA-DR) on monocytes (13 500 antibodies bound per cell and an absolute lymphocyte count >1200 x10⁹/L) at day 10, healthcare costs at day 90 and rate of serious adverse reactions and suspected unexpected serious adverse reaction at day 90. We plan to randomise 326 patients.

The study will be implemented in accordance with European regulations and was independently reviewed and approved by the French Ethics Committee Comité de Protection des Personnes Ile de France III (EUCT number: 2024-516780-93-00). The results will be reported in international peer-reviewed journals and presented at international and national conferences.

NCT06774235.

To develop and validate a risk prediction model for preterm premature rupture of membranes (PPROM) to enable early identification of at-risk women and support clinical decision-making in North Wollo Zone, Ethiopia.

A hospital-based retrospective cross-sectional study.

Six public hospitals in the North Wollo Zone, Northern Ethiopia.

A total of 1098 pregnant women were included in the study using systematic random sampling.

Occurrence of PPROM.

Data were collected between 20 November 2023 and 20 March 2024, using structured interviews and medical record reviews. A risk prediction model was developed using Least Absolute Shrinkage and Selection Operator and logistic regression. Model performance was assessed through area under the curve (AUC), calibration plots and the Hosmer-Lemeshow test. Internal validation was conducted via bootstrap resampling. A simplified risk score was created to categorise women into high-risk and low-risk groups, and its clinical utility was evaluated using decision curve analysis.

Among the 1098 participants (100% response rate), the mean age was 21.54 years (IQR: 18–26), with 57.2% aged 20–34 years. The prevalence of PPROM was 10.75% (95% CI 9.01% to 12.77%). Seven significant predictors were identified: maternal age

PPROM remains a significant obstetric complication in the study area. The validated risk prediction model showed moderate to good performance and can be used to support early screening and risk-based management in antenatal care (ANC). Integrating the tool into routine ANC services, along with health education and management of modifiable risk factors, may help reduce PPROM-related adverse outcomes. Further external validation is recommended.

This study employs structural equation modelling to explore the inter-relationships among optimal antenatal care (ANC), health facility delivery and early postnatal care (EPNC) in Ethiopia. By identifying both direct and indirect influencing factors, the study offers valuable insights to support integrated maternal health strategies and guide informed decision-making by policymakers and women alike.

The secondary analysis of the Ethiopian Demographic and Health Survey 2016 was performed to investigate inter-relationships between optimal ANC, health facility delivery and postnatal care (PNC) among women in Ethiopia. Data were analysed with R software V.4.3.2. The study used binary logistic regression to examine differences in optimal ANC, health facility delivery and EPNC, focusing on variables with a p value of 0.1 or less. Selected variables were incorporated into a generalised structural equation model (GSEM) using the LAVAAN package to explore both direct and indirect effects. The GSEM method assessed the impact of exogenous variables on endogenous variables, all binary, using a logistic link and binomial family. Missing data were handled with the multiple imputation by chained equations package, and sampling weights were applied to ensure national and regional representativeness.

The source population comprised all women of reproductive age (15–49 years) who gave birth in the 5 years preceding the survey. From 16 650 interviewed households (98% response rate), we identified 7590 eligible women with recent births. Finally, we included 2415 women who had attended four or more ANC visits.

Media exposure significantly boosts the likelihood of using ANC (OR=1.8, 95% CI (1.04 to 3.23), p=0.04), health facility delivery (OR=1.7, 95% CI (1.23 to 2.45), p=0.05) and PNC (OR=2.0, 95% CI (1.6 to 4.01), p=0.01). Urban residence and secondary education also enhance ANC (OR=1.2, 95% CI (1.01 to 2.88), p=0.022; OR=1.3, 95% CI (1.20 to 3.01), p=0.018), health facility delivery (OR=1.1, 95% CI (1.01 to 3.24), p=0.035; OR=1.5, 95% CI (1.22 to 3.45), p=0.03) and PNC (OR=1.6, 95% CI (1.01 to 4.32), p=0.03). ANC directly affects health facility delivery (OR=1.4, 95% CI (1.28 to 3.09), p=0.01) and PNC (OR=1.6, 95% CI (1.01 to 3.80), p=0.03). Additionally, women aged 20–34 years and those from male-headed households positively impact health facility delivery (OR=1.5, 95% CI (1.20 to 4.80), p=0.01; OR=1.3, 95% CI (1.07 to 3.45), p=0.014) and PNC (OR=1.4, 95% CI (1.10 to 2.90), p=0.01; OR=1.2, 95% CI (1.07 to 3.08), p=0.025).

Optimal ANC is vital for encouraging health facility delivery and EPNC. To enhance maternal and neonatal health, policies should integrate these services. Key predictors include being aged 20–34, having secondary and higher education, media exposure, male-headed households and living in urban areas. Improving education and media exposure can boost maternal healthcare service use.