Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
Add filters

Main subject
Language
Document Type
Year range
1.
Hamostaseologie ; 43(Supplement 1):S77, 2023.
Article in English | EMBASE | ID: covidwho-2280218

ABSTRACT

Introduction Vaccine-induced immune thrombotic thrombocytopenia (VITT) is a rare, but severe side effect after Covid-19 and other vaccinations. First cases of VITT-mimicking antibodies in unvaccinated patients with recurrent thrombosis have been described. Differentiation between heparin-induced thrombocytopenia (HIT) and VITT is difficult in some patients. Widely used enzyme-linked immunoassays (EIA) cannot differentiate between the two, some of them even fail to detect VITT antibodies. So far, differentiation between HIT-like and VITT-like anti-PF4 antibodies can only be performed in specialized laboratories by functional tests using the heparin-induced platelet activation (HIPA) or PF4-induced platelet activation (PIPA) test. We have developed an assay, which can distinguish between HIT and VITT antibodies and can be used in any hospital laboratory. Method Confirming platelet-activation assays (HIPA and PIPA) were performed as described.[1] We defined 3 cohorts: 1) Negative controls (n = 112, including 35 healthy donors from before 2020, 46 clinical patients suspected for HIT but with negative EIA and HIPA and 31 non-thrombotic patients);2) classical HIT-patients with positive EIA and HIPA (n = 121);3) typical VITT patients (n = 63;presenting after vaccination with adenoviral vector-based Covid-19 vaccine and positive EIA and PIPA). Samples were analyzed by an automated coagulation analyzer ACL AcuStar (Werfen / IL Inc., Bedford, MA, USA) using HemosIL AcuStar HIT-IgG(PF4-H) and a prototype of VITT-IgG(PF4) assay according to the manufacturer's protocol. For both assays, raw data was analyzed as relative light units (RLU). Results All VITT samples were positive in the prototype VITT-assay (Fig. 1);only a few (n = 9;14.3 %) also showed weakly positive results in the HIT-assay. On the other hand, most of the HIT samples showed positive results in the HIT-assay (113;93.4 %), 34 of them (30.1 %) also reacted positive in the prototype VITT-assay (12 of them strongly;10.6 %), and three demonstrated an antibody pattern like autoimmune VITT. Negative control samples where all non-reactive in the HITassay and served to adjust the cutoff for the prototype VITT-assay. Conclusion The different reaction pattern of samples of HIT and VITT patients using HemosIL AcuStar HIT-IgG(PF4-H) and a VITT prototype assay was able to distinguish between the two antibody entities for the first time. The combination of assays can facilitate a rapid decision whether heparin may be used for treatment and also identify patients with autoimmune-VITT as a cause of recurrent thrombosis. (Table Presented).

2.
Nat Commun ; 12(1): 5173, 2021 08 27.
Article in English | MEDLINE | ID: covidwho-1376196

ABSTRACT

Disease modelling has had considerable policy impact during the ongoing COVID-19 pandemic, and it is increasingly acknowledged that combining multiple models can improve the reliability of outputs. Here we report insights from ten weeks of collaborative short-term forecasting of COVID-19 in Germany and Poland (12 October-19 December 2020). The study period covers the onset of the second wave in both countries, with tightening non-pharmaceutical interventions (NPIs) and subsequently a decay (Poland) or plateau and renewed increase (Germany) in reported cases. Thirteen independent teams provided probabilistic real-time forecasts of COVID-19 cases and deaths. These were reported for lead times of one to four weeks, with evaluation focused on one- and two-week horizons, which are less affected by changing NPIs. Heterogeneity between forecasts was considerable both in terms of point predictions and forecast spread. Ensemble forecasts showed good relative performance, in particular in terms of coverage, but did not clearly dominate single-model predictions. The study was preregistered and will be followed up in future phases of the pandemic.


Subject(s)
COVID-19/epidemiology , COVID-19/virology , Forecasting , Germany/epidemiology , Humans , Models, Statistical , Pandemics/statistics & numerical data , Poland/epidemiology , SARS-CoV-2/physiology , Seasons
SELECTION OF CITATIONS
SEARCH DETAIL