Impact of extreme prematurity, chorioamnionitis and sepsis on neonatal monocyte characteristics and functions.

INTRODUCTION: The innate branch of the immune system is important in early life, in particular for infants born preterm. METHODS: We performed a longitudinal analysis of the peripheral monocyte compartment in extremely preterm children from a randomized placebo-controlled study of probiotic supplementation. PBMCs and fecal samples were collected at several timepoints during the first months of life. Monocyte characteristics were analyzed by flow cytometry and LPS-stimulated PBMC culture supernatants were analyzed by Luminex or ELISA. Plasma cytokines and gut microbiota composition were analyzed by ELISA and 16S rRNA-sequencing, respectively. RESULTS: The extremely preterm infants had persistent alterations in their monocyte characteristics, that were further aggravated in chorioamnionitis cases. They showed a markedly reduced TLR4 expression and hampered LPS-stimulated cytokine responses 14 days after birth. Notably, at later time-points, TLR4 expression and LPS-responses no longer correlated. Sepsis during the first weeks of life strongly associated with increased proinflammatory, and reduced IL-10, responses also at post menstrual week 36. Further, we report a correlation between gut microbiota features and monocyte phenotype and responses, but also that probiotic supplementation associated with distinct monocyte phenotypic characteristics, without significantly influencing their responsiveness. CONCLUSION: Extremely preterm infants have monocyte characteristics and functional features that deviate from infants born full-term. Some of these differences persist until they reach an age corresponding to full-term, potentially making them more vulnerable to microbial exposures during the first months of life.

Predictive analytics for cardio-thoracic surgery duration as a stepstone towards data-driven capacity management.

Effective capacity management of operation rooms is key to avoid surgery cancellations and prevent long waiting lists that negatively affect clinical and financial outcomes as well as patient and staff satisfaction. This requires optimal surgery scheduling, leveraging essential parameters like surgery duration, post-operative bed type and hospital length-of-stay. Common clinical practice is to use the surgeon's average procedure time of the last N patients as a planned surgery duration for the next patient. A discrepancy between the actual and planned surgery duration may lead to suboptimal surgery schedule. We used deidentified data from 2294 cardio-thoracic surgeries to first calculate the discrepancy of the current model and second to develop new predictive models based on linear regression, random forest, and extreme gradient boosting. The new ensamble models reduced the RMSE for elective and acute surgeries by 19% (0.99 vs 0.80, p = 0.002) and 52% (1.87 vs 0.89, p < 0.001), respectively. Also, the elective and acute surgeries "behind schedule" were reduced by 28% (60% vs. 32%, p < 0.001) and 9% (37% vs. 28%, p = 0.003), respectively. These improvements were fueled by the patient and surgery features added to the models. Surgery planners can benefit from these predictive models as a patient flow AI decision support tool to optimize OR utilization.

A guide to sharing open healthcare data under the General Data Protection Regulation.

Sharing healthcare data is increasingly essential for developing data-driven improvements in patient care at the Intensive Care Unit (ICU). However, it is also very challenging under the strict privacy legislation of the European Union (EU). Therefore, we explored four successful open ICU healthcare databases to determine how open healthcare data can be shared appropriately in the EU. A questionnaire was constructed based on the Delphi method. Then, follow-up questions were discussed with experts from the four databases. These experts encountered similar challenges and regarded ethical and legal aspects to be the most challenging. Based on the approaches of the databases, expert opinion, and literature research, we outline four distinct approaches to openly sharing healthcare data, each with varying implications regarding data security, ease of use, sustainability, and implementability. Ultimately, we formulate seven recommendations for sharing open healthcare data to guide future initiatives in sharing open healthcare data to improve patient care and advance healthcare.