The risk stratification of adverse neonatal outcomes in women with gestational diabetes (STRONG) study

Aims: To assess the risk of adverse neonatal outcomes in women with gestational diabetes (GDM) by identifying subgroups of women at higher risk to recognize the characteristics most associated with an excess of risk. Methods: Observational, retrospective, multicenter study involving consecutive women with GDM. To identify distinct and homogeneous subgroups of women at a higher risk, the RECursive Partitioning and AMalgamation (RECPAM) method was used. Overall, 2736 pregnancies complicated by GDM were analyzed. The main outcome measure was the occurrence of adverse neonatal outcomes in pregnancies complicated by GDM. Results: Among study participants (median age 36.8 years, pre-gestational BMI 24.8 kg/m2), six miscarriages, one neonatal death, but no maternal death was recorded. The occurrence of the cumulative adverse outcome (OR 2.48, 95% CI 1.59–3.87), large for gestational age (OR 3.99, 95% CI 2.40–6.63), fetal malformation (OR 2.66, 95% CI 1.00–7.18), and respiratory distress (OR 4.33, 95% CI 1.33–14.12) was associated with previous macrosomia. Large for gestational age was also associated with obesity (OR 1.46, 95% CI 1.00–2.15). Small for gestational age was associated with first trimester glucose levels (OR 1.96, 95% CI 1.04–3.69). Neonatal hypoglycemia was associated with overweight (OR 1.52, 95% CI 1.02–2.27) and obesity (OR 1.62, 95% CI 1.04–2.51). The RECPAM analysis identified high-risk subgroups mainly characterized by high pre-pregnancy BMI (OR 1.68, 95% CI 1.21–2.33 for obese; OR 1.38 95% CI 1.03–1.87 for overweight). Conclusions: A deep investigation on the factors associated with adverse neonatal outcomes requires a risk stratification. In particular, great attention must be paid to the prevention and treatment of obesity

Robust and subject-independent driving manoeuvre anticipation through Domain-Adversarial Recurrent Neural Networks

Through deep learning and computer vision techniques, driving manoeuvres can be predicted accurately a few seconds in advance. Even though adapting a learned model to new drivers and different vehicles is key for robust driver-assistance systems, this problem has received little attention so far. This work proposes to tackle this challenge through domain adaptation, a technique closely related to transfer learning. A proof of concept for the application of a Domain-Adversarial Recurrent Neural Network (DA-RNN) to multi-modal time series driving data is presented, in which domain-invariant features are learned by maximising the loss of an auxiliary domain classifier. Our implementation is evaluated using a leave-one-driver-out approach on individual drivers from the Brain4Cars dataset, as well as using a new dataset acquired through driving simulations, yielding an average increase in performance of 30% and 114% respectively compared to no adaptation. We also show the importance of fine-tuning sections of the network to optimise the extraction of domain-independent features. The results demonstrate the applicability of the approach to driver-assistance systems as well as training and simulation environments

Coeliac disease in infants: antibodies to deamidated gliadin peptide come first!

Abstract Background The onset of coeliac disease (CD) in the first year of life is uncommon and the diagnosis can be challenging due to the suboptimal sensitivity of tissue transglutaminase antibodies (tTG) at this age and the many other possible causes of malabsorption in infants. Antibodies to deamidated gliadin peptides (anti-DGPs), especially IgG, may appear earlier than IgA anti-tTG in very young children with CD. Case presentation We report here on an 8-month-old child who was evaluated for failure to thrive, constipation and developmental delay. The symptoms started following gluten introduction in the diet. Laboratory tests showed high fecal elastase concentration, normal serum IgA levels with positive IgG and IgA anti-DGPs, whereas anti-tTG were not detected. The duodenal biopsy revealed a complete villous atrophy (Marsh-Oberhuber 3C). The culture of biopsy fragments in the presence of gliadin peptides did not stimulate the production of IgA anti-endomysial antibodies. Genetic testing proved the child was positive for HLA-DQ2 (DQA1*05; DQB1*02) and HLA-DQ8 (DQA1*03, DQB1*0302). Having initiated the gluten-free diet, the symptoms disappeared and the infant experienced rapid catch-up growth with normalization of psychomotor development. Conclusions This case report highlights the utility of anti-DGPs for screening infants with suspected CD. The pattern with positivity for IgG and IgA anti-DGPs only is rare in IgA-competent children with biopsy-proven CD. It could be explained in infancy as immaturity of the adaptive immune system

The role of technology in minimally invasive surgery: state of the art, recent developments and future directions

The diffusion of minimally invasive surgery has thrived in recent years, providing substantial benefits over traditional techniques for a number of surgical interventions. This rapid growth has been possible due to significant advancements in medical technology, which partly solved some of the technical and clinical challenges associated with minimally invasive techniques. The issues that still limit its widespread adoption for some applications include the limited field of view; reduced manoeuvrability of the tools; lack of haptic feedback; loss of depth perception; extended learning curve; prolonged operative times and higher financial costs. The present review discusses some of the main recent technological advancements that fuelled the uptake of minimally invasive surgery, focussing especially on the areas of imaging, instrumentation, cameras and robotics. The current limitations of state-of-the-art technology are identified and addressed, proposing future research directions necessary to overcome them

Explainable Machine Learning on AmsterdamUMCdb for ICU Discharge Decision Support: Uniting Intensivists and Data Scientists

Unexpected ICU readmission is associated with longer length of stay and increased mortality. To prevent ICU readmission and death after ICU discharge, our team of intensivists and data scientists aimed to use AmsterdamUMCdb to develop an explainable machine learning-based real-time bedside decision support tool. Derivation Cohort: Data from patients admitted to a mixed surgical-medical academic medical center ICU from 2004 to 2016. Validation Cohort: Data from 2016 to 2019 from the same center. Prediction Model: Patient characteristics, clinical observations, physiologic measurements, laboratory studies, and treatment data were considered as model features. Different supervised learning algorithms were trained to predict ICU readmission and/or death, both within 7 days from ICU discharge, using 10-fold cross-validation. Feature importance was determined using SHapley Additive exPlanations, and readmission probability-time curves were constructed to identify subgroups. Explainability was established by presenting individualized risk trends and feature importance. Results: Our final derivation dataset included 14,105 admissions. The combined readmission/mortality rate within 7 days of ICU discharge was 5.3%. Using Gradient Boosting, the model achieved an area under the receiver operating characteristic curve of 0.78 (95% CI, 0.75-0.81) and an area under the precision-recall curve of 0.19 on the validation cohort (n = 3,929). The most predictive features included common physiologic parameters but also less apparent variables like nutritional support. At a 6% risk threshold, the model showed a sensitivity (recall) of 0.72, specificity of 0.70, and a positive predictive value (precision) of 0.15. Impact analysis using probability-time curves and the 6% risk threshold identified specific patient groups at risk and the potential of a change in discharge management to reduce relative risk by 14%. Conclusions: We developed an explainable machine learning model that may aid in identifying patients at high risk for readmission and mortality after ICU discharge using the first freely available European critical care database, AmsterdamUMCdb. Impact analysis showed that a relative risk reduction of 14% could be achievable, which might have significant impact on patients and society. ICU data sharing facilitates collaboration between intensivists and data scientists to accelerate model development