Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype.- Pfizer Pharmaceuticals(undefined

Comparison of Ticagrelor Versus Clopidogrel on Cerebrovascular Microembolic Events and Platelet Inhibition during Transcatheter Aortic Valve Implantation

The impact of the antiplatelet regimen and the extent of associated platelet inhibition on cerebrovascular microembolic events during transcatheter aortic valve implantation (TAVI) are unknown. Our aim was to evaluate the effects of ticagrelor versus clopidogrel and of platelet inhibition on the number of cerebrovascular microembolic events in patients undergoing TAVI. Patients scheduled for TAVI were randomized previous to the procedure to either aspirin and ticagrelor or to aspirin and clopidogrel. Platelet inhibition was expressed in P2Y12 reaction units (PRU) and percentage of inhibition. High intensity transient signals (HITS) were assessed with transcranial Doppler (TCD). Safety outcomes were recorded according to the VARC-2 definitions. Among 90 patients randomized, 6 had an inadequate TCD signal. The total number of procedural HITS was lower in the ticagrelor group (416.5 [324.8, 484.2]) (42 patients) than in the clopidogrel group (723.5 [471.5, 875.0]) (42 patients), p <0.001. After adjusting for the duration of the procedure, diabetes, extra-cardiac arteriopathy, BMI, hypertension, aortic valve calcium content, procedural ACT, and pre-implantation balloon valvuloplasty, patients on ticagrelor had on average 256.8 (95% CI: [-335.7, -176.5]) fewer total procedural HITS than patients on clopidogrel. Platelet inhibition was greater with ticagrelor 26 [10, 74.5] PRU than with clopidogrel 207.5 (120 to 236.2) PRU, p <0.001, and correlated significantly with procedural HITS (r = 0.5, p <0.05). In conclusion, ticagrelor resulted in fewer procedural HITS, compared with clopidogrel, in patients undergoing TAVI, while achieving greater platelet inhibition. (c) 2021 Published by Elsevier Inc

Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks

There is an unmet need of models for early prediction of morbidity and mortality of Coronavirus disease-19 (COVID-19). We aimed to a) identify complement-related genetic variants associated with the clinical outcomes of ICU hospitalization and death, b) develop an artificial neural network (ANN) predicting these outcomes and c) validate whether complement-related variants are associated with an impaired complement phenotype. We prospectively recruited consecutive adult patients of Caucasian origin, hospitalized due to COVID-19. Through targeted next-generation sequencing, we identified variants in complement factor H/CFH, CFB, CFH-related, CFD, CD55, C3, C5, CFI, CD46, thrombomodulin/THBD, and A Disintegrin and Metalloproteinase with Thrombospondin motifs (ADAMTS13). Among 381 variants in 133 patients, we identified 5 critical variants associated with severe COVID-19: rs2547438 (C3), rs2250656 (C3), rs1042580 (THBD), rs800292 (CFH) and rs414628 (CFHR1). Using age, gender and presence or absence of each variant, we developed an ANN predicting morbidity and mortality in 89.47% of the examined population. Furthermore, THBD and C3a levels were significantly increased in severe COVID-19 patients and those harbouring relevant variants. Thus, we reveal for the first time an ANN accurately predicting ICU hospitalization and death in COVID-19 patients, based on genetic variants in complement genes, age and gender. Importantly, we confirm that genetic dysregulation is associated with impaired complement phenotype