Neonatal SARS-CoV-2 antibodies at day 14 of life, 3.5-fold higher than her COVID-19 asymptomatic mother

BackgroundThroughout the COVID-19 pandemic, it has been unclear how SARS-CoV-2 infection (by vertical transmission or natural infection) would affect neonates, with a significant number of case reports and series identifying neonates requiring respiratory support. A single report suggests that paediatric multisystem inflammatory syndrome was demonstrated in a 24-day-old.Together, these concerns justify screening for SARS-CoV-2 RNA and antibodies in unwell neonates without clear infective focus, particularly with high community prevalence. Maternal SARS-CoV-2 antibodies usually match those observed in the neonate.We present a 14-day-old neonate with SARS-CoV-2 anti-nucleocapsid antibodies at a 3.5-fold greater concentration than her mother.ObjectivesTo describe differential SARS-CoV-2 antibody titres in a neonate and her asymptomatic mother.MethodsWe conducted a retrospective review of clinical notes, with the mother’s consent.ResultsA female neonate was admitted on day 14 of life with fever and 1 day of jittery movements noted by her mother. She had a history of bilateral aniridia (partially observed in her father and brother, currently under clinical genetics investigations) and haemolytic anaemia (DAT positive at birth, on folic acid treatment). She had no syndromic features and was otherwise well. Born at term, by spontaneous vaginal delivery with an uneventful antenatal history.Clinical examination was normal, other than a fever up to 39 degrees. She underwent a full septic screen including urinalysis, blood cultures and a lumbar puncture. Bloods demonstrated CRP 106 (maximally 136 at 24 hours), with normal full blood count. CSF microscopy showed significant white blood cells (WBC 347/µL with 65% neutrophils, RBC 105/µL), and no microorganisms. No microorganisms were demonstrated in CSF by BioFire® meningitis/encephalitis panel (including SARS-CoV-2 using BioFire® respiratory panel), or subsequent culture. However, admission urine was leukocyte, nitrite and glucose positive (WBC 21/µL by microscopy), growing E. coli and Enterococcus. US KUB was normal. Chest X-ray demonstrated hazy opacification but nil focal.Given the presentation, the patient was treated with 7 days of IV cefotaxime and amoxicillin, covering for bacterial meningitis, clinically improving by day 2.Due to presentation at the height of the second-wave of the COVID-19 pandemic, there was initial concern of SARS-CoV-2 infection. Her mother was asymptomatic, with negative nasopharyngeal swabs. Nasopharyngeal swabs for SARS-CoV-2 RNA in our patient were negative on day 0, 2 and 3 of admission. However, SARS-CoV-2 anti-nucleocapsid (1.84 AU/mL, positive threshold ≥1.4 AU/mL) IgG was demonstrated. Given the unexpected result, her mother was tested, initially reported as negative, later revised to borderline (0.53 AU/mL). Both were re-rested 10 days from discharge. Anti-nucleocapsid results were reproduced, whereas both mother and patient had significant anti-spike IgG (312.4 and 98.3 AU/mL, respectively, positive threshold ≥50 AU/mL), without vaccination.ConclusionsWe highlight the need to corroborate SARS-CoV-2 antibodies in neonates with paired maternal samples, and to explore both anti-spike serology with discordant anti-nucleocapsid results. Our case results from an asymptomatic infection, likely close to birth, producing differential active transport of anti-nucleocapsid antibodies across the placenta, producing 3.5-fold higher neonatal titres.

Using Machine Learning Algorithms to Develop a Clinical Decision-Making Tool for COVID-19 Inpatients.

BACKGROUND: Within the UK, COVID-19 has contributed towards over 103,000 deaths. Although multiple risk factors for COVID-19 have been identified, using this data to improve clinical care has proven challenging. The main aim of this study is to develop a reliable, multivariable predictive model for COVID-19 in-patient outcomes, thus enabling risk-stratification and earlier clinical decision-making. METHODS: Anonymised data consisting of 44 independent predictor variables from 355 adults diagnosed with COVID-19, at a UK hospital, was manually extracted from electronic patient records for retrospective, case-control analysis. Primary outcomes included inpatient mortality, required ventilatory support, and duration of inpatient treatment. Pulmonary embolism sequala was the only secondary outcome. After balancing data, key variables were feature selected for each outcome using random forests. Predictive models were then learned and constructed using Bayesian networks. RESULTS: The proposed probabilistic models were able to predict, using feature selected risk factors, the probability of the mentioned outcomes. Overall, our findings demonstrate reliable, multivariable, quantitative predictive models for four outcomes, which utilise readily available clinical information for COVID-19 adult inpatients. Further research is required to externally validate our models and demonstrate their utility as risk stratification and clinical decision-making tools.