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In Denmark, a nationwide COVID-19 lockdown was implemented on March 12, 2020 and eased on April 14, 2020. The COVID-19 lockdown featured reduced prevalence of extremely preterm or extremely low birth weight births. Here we explore the impact of this COVID-19 lockdown on term birth weights in Denmark.We conducted a nationwide register-based cohort study on 27,870 live singleton infants, born at term (weeks 37-41), between March 12 and April 14, 2015-2020, using data from the Danish Neonatal Screening Biobank. Primary outcomes, corrected for confounders, were birth weight, small-for-gestational-age (SGA), and large-for-gestational-age (LGA), comparing the COVID-19 lockdown to the previous five years. Data were analysed using linear regression to assess associations with birth weight. Multinomial logistic regression was used to assess associations with size-for-gestational-age (xGA) categories.Adjusted mean birth weight was significantly increased by 16.9 g (95% CI = 4.1 – 31.3) during the lockdown period. A dip in mean birth weight was found in gestational weeks 37 and 38 balanced by an increase in weeks 40 and 41. The 2020 lockdown period was associated with an increased LGA prevalence (aOR 1.13, 95% CI = 1.05 - 1.21). No significant changes in proportions of xGA groups were found between 2015 and 2019.ConclusionThe nationwide COVID-19 lockdown resulted in a small but significant increase in birth weight and proportion of LGA infants, driven by an increase in birth weight in gestational weeks 40 and 41.What is knownThe COVID-19 lockdown periods have had profound, albeit variable, effects on pregnant women in various countries.Extremely preterm and stillbirth rates were reduced during COVID-19 lockdown/mitigation periods in DenmarkWhat is newDuring COVID-19 lockdown a moderate, but statistically significant, increase in birth weight was seen.The proportion of large-for-gestational age pregnancies was statistically significantly elevated in the lockdown period.

IntroductionPre-eclampsia (PE) is a leading cause of perinatal morbidity and mortality worldwide. Low-dose aspirin can prevent PE in high risk pregnancies if started early. However, despite intense research into the area, first-trimester screening for PE risk is still not a routine part of pregnancy care. Several studies have described the application of artificial intelligence (AI) and machine learning (ML) in risk prediction of PE and its subtypes. A systematic review of available literature is necessary to catalogue the current applications of AI/ML methods in early pregnancy screening for PE, in order to better inform the development of clinically relevant risk prediction algorithms which will enable timely intervention and the development of new treatment strategies. The aim of this systematic review is to identify and assess studies regarding the application of AI/ML methods in first-trimester screening for PE.MethodsA systematic review of peer-reviewed as well as the grey literature cohort or case-control studies will be conducted. Relevant information will be accessed from the following databases; PubMed, Google Scholar, Web of Science, Arxiv, BioRxiv, and MedRxiv. The studies will be evaluated by two reviewers in a parallel, blind assessment of the literature, a third reviewer will assess any studies in which the first two reviewers did not agree. The free online tool Rayyan, will be used in this literature assessment stage. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 checklist will be used to guide the review process and the methods of the studies will be assessed using the Newcastle-Ottawa scale. Narrative synthesis will be conducted for all included studies. Meta-analysis will also be conducted where data quality and availability allow.Ethics and disseminationThe review will not require ethical approval and the findings will be published in a peer-reviewed journal using the PRISMA guidelines.

ObjectivesPre-eclampsia (PE) is an important cause of perinatal morbidity and mortality. Despite an elusive pathophysiology, PE has been associated with changes in maternal serum concentrations of adipokines in early pregnancy. We hypothesized, that symbolic regression might identify interactions between adipokines and PE, which may have eluded regression and Bayesian models.MethodsIn this nested case-control sub-study, of the Copenhagen First Trimester Screening Study, data regarding maternal weight and serum concentrations of PAPP-A, leptin (Lp), soluble leptin receptor (sLR), adiponectin, and resistin (Re) was available from 423 first trimester pregnancies (gestational week 10+3– 13+6), 126 of which developed PE. Symbolic regression with QLattice/Feyn 2.1 was used to identify models comprising two-interactions between up-to three markers.ResultsThe optimal mathematical model exhibited a non-linear relation between Re and the combined effect of sLR and Lp. The model was dependent, in a Gaussian way, on the level of Re. The receiver operating characteristic (ROC) curve of the model viz. identification of PE cases in first trimester had an AUC of 0.81 and a modelled DR of 40 % for a FPR of 4 %. Symbolic regression outperformed logistic regression of the same parameters with a ROC with AUC = 0.77, and a DR of 7 % for a 3 % FPR.ConclusionsSymbolic regression identified non-linear interactions between Lp, sLR and Re concentrations in first trimester pregnancy serum of pregnancies which later developed PE. Non-linear interactions suggest new pathophysiological pathways and may help in designing more efficient screening protocols for PE.

Background Heart failure is a clinical syndrome characterised by a reduced ability of the heart to pump blood. Patients with heart failure have a high mortality rate, and physicians need reliable prognostic predictions to make informed decisions about the appropriate application of devices, transplantation, medications, and palliative care. In this study, we demonstrate that combining symbolic regression with the Cox proportional hazards model improves the ability to predict death due to heart failure compared to using the Cox proportional hazards model alone. Methods We used a newly invented symbolic regression method called the QLattice to analyse a data set of medical records for 299 Pakistani patients diagnosed with heart failure. The QLattice identified non-linear mathematical transformations of the available covariates, which we then used in a Cox model to predict survival. Results An exponential function of age, the inverse of ejection fraction, and the inverse of serum creatinine were identified as the best risk factors for predicting heart failure deaths. A Cox model fitted on these transformed covariates had improved predictive performance compared with a Cox model on the same covariates without mathematical transformations. Conclusion Symbolic regression is a way to find transformations of covariates from patients’ medical records which can improve the performance of survival regression models. At the same time, these simple functions are intuitive and easy to apply in clinical settings. The direct interpretability of the simple forms may help researchers gain new insights into the actual causal pathways leading to deaths.