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Pregnant patients have increased morbidity and mortality in the setting of SARS-CoV-2 infection. The exposure of pregnant patients in New York City to SARS-CoV-2 is not well understood due to early lack of access to testing and the presence of asymptomatic COVID-19 infections. Before the availability of vaccinations, preventative (shielding) measures, including but not limited to wearing a mask and quarantining at home to limit contact, were recommended for pregnant patients. Using universal testing data from 2196 patients who gave birth from April through December 2020 from one institution in New York City, and in comparison, with infection data of the general population in New York City, we estimated the exposure and real-world effectiveness of shielding in pregnant patients. Our Bayesian model shows that patients already pregnant at the onset of the pandemic had a 50% decrease in exposure compared to those who became pregnant after the onset of the pandemic and to the general population.
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IntroductionSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and influenza virus are contagious respiratory pathogens with similar symptoms but require different treatment and management strategies. This study investigated whether laboratory blood tests can discriminate between SARS-CoV-2 and influenza infections at emergency department (ED) presentation. Methods723 influenza A/B positive (2018/1/1 to 2020/3/15) and 1,281 SARS-CoV-2 positive (2020/3/11 to 2020/6/30) ED patients were retrospectively analyzed. Laboratory test results completed within 48 hours prior to reporting of virus RT-PCR results, as well as patient demographics were included to train and validate a random forest (RF) model. The dataset was randomly divided into training (2/3) and testing (1/3) sets with the same SARS-CoV-2/influenza ratio. The Shapley Additive Explanations technique was employed to visualize the impact of each laboratory test on the differentiation. ResultsThe RF model incorporating results from 15 laboratory tests and demographic characteristics discriminated SARS-CoV-2 and influenza infections, with an area under the ROC curve value 0.90 in the independent testing set. The overall agreement with the RT-PCR results was 83% (95% CI: 80-86%). The test with the greatest impact on the differentiation was serum total calcium level. Further, the model achieved an AUC of 0.82 in a new dataset including 519 SARS-CoV-2 ED patients (2020/12/1 to 2021/2/28) and the previous 723 influenza positive patients. Serum calcium level remained the most impactful feature on the differentiation. ConclusionWe identified characteristic laboratory test profiles differentiating SARS-CoV-2 and influenza infections, which may be useful for the preparedness of overlapping COVID-19 resurgence and future seasonal influenza.
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Longitudinal studies are needed to evaluate the SARS-CoV-2 mRNA vaccine antibody response under "real-world" conditions. This longitudinal study investigated the quantity and quality of SARS-CoV-2 antibody response in 846 specimens from 350 subjects: comparing BNT162b2-vaccinated individuals (19 previously diagnosed with COVID-19 [RecoVax]; 49 never been diagnosed [NaiveVax]) to 122 hospitalized unvaccinated (HospNoVax) and 160 outpatient unvaccinated (OutPtNoVax) COVID-19 patients. NaiveVax experienced a delay in generating SARS-CoV-2 total antibody levels (TAb) and neutralizing antibodies (SNAb) after the 1st vaccine dose (D1), but a rapid increase in antibody levels was observed after the 2nd dose (D2). However, these never reached the robust levels observed in RecoVax. In fact, NaiveVax TAb and SNAb levels decreased 4-weeks post-D2 (p=0.003;p<0.001). For the most part, RecoVax TAb persisted throughout this study, after reaching maximal levels 2-weeks post-D2; but SNAb decreased significantly [~]6-months post-D1 (p=0.002). Although NaiveVax avidity lagged behind that of RecoVax for most of the follow-up periods, NaiveVax did reach similar avidity by [~]6-months post-D1. These data suggest that one vaccine dose elicits maximal antibody response in RecoVax and may be sufficient. Also, despite decreasing levels in TAb and SNAb overtime, long-term avidity maybe a measure worth evaluating and possibly correlating to vaccine efficacy.
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The response of the immune system to COVID-19 in end stage kidney disease patients who undergo kidney transplantation has yet to be described. We report data on 72 patients who underwent SARS-CoV-2 antibody testing both before and after kidney transplantation and were followed for a median of 186 days (range 83, 277). Of the 25 patients with a positive antibody test at the time of transplant, 17 (68%) remained positive after transplantation. Patients were significantly more likely to have a persistently positive test if they reported a symptomatic COVID-19 infection prior to transplant (p=0.01). SARS-CoV-2 IgG index values were measured in a subset of kidney transplant recipients and compared to wait -listed dialysis patients. These assays demonstrated a more significant decline in IgG (58% versus 14% p = 0.008) in transplant recipients when compared to dialysis patients tested during the same time period. Additional analysis of the quality of the immune response measuring the binding of SARS-CoV-2 antibodies to the receptor-binding domain (RBD binding), the antibody neutralizing capability, and the antibody avidity demonstrated a more pronounced effect when comparing pre-transplant values to post-induction therapy/post transplant values. The attenuated IgG response seen in transplant patients compared to dialysis patients after induction therapy requires further study. These data have important implications for post-transplant management of vaccinated dialysis patients.
