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BackgroundPatients with kidney diseases are at risk of severe complications from COVID-19, yet little is known about the effectiveness of COVID-19 vaccines in children and adolescents with kidney diseases. MethodsWe investigated the immunogenicity and safety of an accelerated, 3-dose primary series of COVID-19 vaccines among 64 pediatric chronic kidney disease patients (mean age 12.2; 32 male) with or without immunosuppression, dialysis, or kidney transplant. CoronaVac was given to those aged <5 years, 0.1ml BNT162b2 to those aged 5-11 years, and 0.3ml BNT162b2 to those aged 11-18 years. ResultsAntibody responses including S-RBD IgG (90.9-100% seropositive) and surrogate virus neutralization (geometric mean sVNT% level, 78.6-94.0%) were significantly elicited by 3 doses of any vaccine. T cell responses were also elicited. Weaker neutralization responses were observed among kidney transplant recipients and non-dialysis children receiving rituximab for glomerular diseases. Neutralization was reduced against Omicron BA.1 compared to wild-type (post-dose 3 sVNT% level; 84% vs 27.2%; p<0.0001). However, T cell response against Omicron BA.1 was preserved, which likely confer protection against severe COVID-19. Hybrid immunity was observed after vaccination in infected patients, as evidenced by higher Omicron BA.1 neutralization response among infected patients receiving 2 doses than those uninfected. Generally mild or moderate adverse reactions following vaccines were reported. ConclusionsOur findings support that an accelerated 3-dose primary series with CoronaVac and BNT162b2 is safe and immunogenic in young children and adolescents with kidney diseases. TRIAL REGISTRATIONClinicaltrials.gov NCT04800133 SIGNIFICANCE STATEMENTLittle is known about the effectiveness of COVID-19 vaccines in children and adolescents with kidney diseases. This paper describes the antibody and T cell responses of 3 doses of CoronaVac or BNT162b2, the top 2 COVID-19 vaccines distributed worldwide, by an accelerated regimen in patients with kidney diseases aged 1-18 years. Antibody and T cell responses were significantly elicited by either vaccine. Neutralization was reduced against Omicron while T cell response was preserved, which likely confer protection against severe COVID-19. Rate of severe adverse reactions was low in the study. Results confirm that accelerated 3-dose primary series with CoronaVac and BNT162b2 is safe and immunogenic in young children and adolescents with kidney diseases.
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BACKGROUND: Prediction of early outcomes of nontraumatic out-of-hospital cardiac arrest (OHCA) by emergency physicians is inaccurate. OBJECTIVE: Our aim was to develop and validate practical machine learning (ML)-based models to predict early outcomes of nontraumatic OHCA for use in the emergency department (ED). We compared their discrimination and calibration performances with the traditional logistic regression (LR) approach. METHODS: Between October 1, 2017 and March 31, 2020, prehospital resuscitation was performed on 17,166 OHCA patients. There were 8157 patients 18 years or older with nontraumatic OHCA who received continued resuscitation in the ED included for analysis. Eleven demographic and resuscitation predictor variables were extracted to predict survived events, defined as any sustained return of spontaneous circulation until in-hospital transfer of care. Prediction models based on random forest (RF), multilayer perceptron (MLP), and LR were created with hyperparameter optimization. Model performances on internal and external validation were compared using discrimination and calibration statistics. RESULTS: The three models showed similar discrimination performances with c-statistics values of 0.712 (95% confidence interval [CI] 0.711-0.713) for LR, 0.714 (95% CI 0.712-0.717) for RF, and 0.712 (95% CI 0.710-0.713) for MLP models on external validation. For calibration, MLP model had a better performance (slope of calibration regression line = 1.10, intercept = -0.09) than LR (slope = 1.17, intercept = -0.11) and RF (slope = 1.16, intercept= -0.10). CONCLUSIONS: Two practical ML-based and one regression-based clinical prediction models of nontraumatic OHCA for survived events were developed and validated. The ML-based models did not outperform LR in discrimination, but the MLP model showed a better calibration performance.