Diagnostic delay for giant cell arteritis – a systematic review and meta-analysis

Background Giant cell arteritis (GCA), if untreated, can lead to blindness and stroke. The study’s objectives were to (1) determine a new evidence-based benchmark of the extent of diagnostic delay for GCA and (2) examine the role of GCA-specific characteristics on diagnostic delay. Methods Medical literature databases were searched from inception to November 2015. Articles were included if reporting a time-period of diagnostic delay between onset of GCA symptoms and diagnosis. Two reviewers assessed the quality of the final articles and extracted data from these. Random-effects meta-analysis was used to pool the mean time-period (95% confidence interval (CI)) between GCA symptom onset and diagnosis, and the delay observed for GCA-specific characteristics. Heterogeneity was assessed by I 2 and by 95% prediction interval (PI). Results Of 4128 articles initially identified, 16 provided data for meta-analysis. Mean diagnostic delay was 9.0 weeks (95% CI, 6.5 to 11.4) between symptom onset and GCA diagnosis (I 2 = 96.0%; P < 0.001; 95% PI, 0 to 19.2 weeks). Patients with a cranial presentation of GCA received a diagnosis after 7.7 (95% CI, 2.7 to 12.8) weeks (I 2 = 98.4%; P < 0.001; 95% PI, 0 to 27.6 weeks) and those with non-cranial GCA after 17.6 (95% CI, 9.7 to 25.5) weeks (I 2 = 96.6%; P < 0.001; 95% PI, 0 to 46.1 weeks). Conclusions The mean delay from symptom onset to GCA diagnosis was 9 weeks, or longer when cranial symptoms were absent. Our research provides an evidence-based benchmark for diagnostic delay of GCA and supports the need for improved public awareness and fast-track diagnostic pathways

Thrombosis in vasculitis: from pathogenesis to treatment

In recent years, the relationship between inflammation and thrombosis has been deeply investigated and it is now clear that immune and coagulation systems are functionally interconnected. Inflammation-induced thrombosis is by now considered a feature not only of autoimmune rheumatic diseases, but also of systemic vasculitides such as Behçet’s syndrome, ANCA-associated vasculitis or giant cells arteritis, especially during active disease. These findings have important consequences in terms of management and treatment. Indeed, Behçet’syndrome requires immunosuppressive agents for vascular involvement rather than anticoagulation or antiplatelet therapy, and it is conceivable that also in ANCA-associated vasculitis or large vessel-vasculitis an aggressive anti-inflammatory treatment during active disease could reduce the risk of thrombotic events in early stages. In this review we discuss thrombosis in vasculitides, especially in Behçet’s syndrome, ANCA-associated vasculitis and large-vessel vasculitis, and provide pathogenetic and clinical clues for the different specialists involved in the care of these patients

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Association of HLA-DRB1 amino acid residues with giant cell arteritis: genetic association study, meta-analysis and geo-epidemiological investigation

Introduction: Giant cell arteritis (GCA) is an autoimmune disease commonest in Northern Europe and Scandinavia. Previous studies report various associations with HLA-DRB1*04 and HLA-DRB1*01; HLA-DRB1 alleles show a gradient in population prevalence within Europe. Our aims were (1) to determine which amino acid residues within HLA-DRB1 best explained HLA-DRB1 allele susceptibility and protective effects in GCA, seen in UK data combined in meta-analysis with previously published data, and (2) to determine whether the incidence of GCA in different countries is associated with the population prevalence of the HLA-DRB1 alleles that we identified in our meta-analysis. Methods: GCA patients from the UK GCA Consortium were genotyped by using single-strand oligonucleotide polymerization, allele-specific polymerase chain reaction, and direct sequencing. Meta-analysis was used to compare and combine our results with published data, and public databases were used to identify amino acid residues that may explain observed susceptibility/protective effects. Finally, we determined the relationship of HLA-DRB1*04 population carrier frequency and latitude to GCA incidence reported in different countries. Results: In our UK data (225 cases and 1378 controls), HLA-DRB1*04 carriage was associated with GCA susceptibility (odds ratio (OR) = 2.69, P = 1.5×10 −11 ), but HLA-DRB1*01 was protective (adjusted OR = 0.55, P = 0.0046). In meta-analysis combined with 14 published studies (an additional 691 cases and 4038 controls), protective effects were seen from HLA-DR2, which comprises HLA-DRB1*15 and HLA-DRB1*16 (OR = 0.65, P = 8.2×10 −6 ) and possibly from HLA-DRB1*01 (OR = 0.73, P = 0.037). GCA incidence (n = 17 countries) was associated with population HLA-DRB1*04 allele frequency (P = 0.008; adjusted R 2 = 0.51 on univariable analysis, adjusted R 2 = 0.62 after also including latitude); latitude also made an independent contribution. Conclusions: We confirm that HLA-DRB1*04 is a GCA susceptibility allele. The susceptibility data are best explained by amino acid risk residues V, H, and H at positions 11, 13, and 33, contrary to previous suggestions of amino acids in the second hypervariable region. Worldwide, GCA incidence was independently associated both with population frequency of HLA-DRB1*04 and with latitude itself. We conclude that variation in population HLA-DRB1*04 frequency may partly explain variations in GCA incidence and that HLA-DRB1*04 may warrant investigation as a potential prognostic or predictive biomarker

Non-standard errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants