BackgroundSimple and inexpensive non-invasive fibrosis tests are highly needed but have been poorly studied in sub-Saharan Africa.MethodsUsing liver histology as a gold standard, we developed a novel index using routine laboratory tests to predict significant fibrosis in patients with chronic HBV infection in The Gambia, West Africa. We prospectively assessed the diagnostic accuracy of the novel index, Fibroscan, aspartate transaminase-to-platelet ratio index (APRI), and Fib-4 in Gambian patients with CHB (training set) and also in French and Senegalese CHB cohorts (validation sets).ResultsOf 135 consecutive treatment-naïve patients with CHB who had liver biopsy, 39% had significant fibrosis (Metavir fibrosis stage ≥F2) and 15% had cirrhosis (F4). In multivariable analysis, gamma-glutamyl transpeptidase (GGT) and platelet count were independent predictors of significant fibrosis. Consequently, GGT-to-platelet ratio (GPR) was developed. In The Gambia, the area under the receiver operating characteristic curve (AUROC) of the GPR was significantly higher than that of APRI and Fib-4 to predict ≥F2, ≥F3 and F4. In Senegal, the AUROC of GPR was significantly better than Fib-4 and APRI for ≥F2 (0.73, 95% CI 0.59 to 0.86) and better than Fib-4 and Fibroscan for ≥F3 (0.93, 0.87 to 0.99). In France, the AUROC of GPR to diagnose ≥F2 (0.72, 95% CI 0.59 to 0.85) and F4 (0.87, 0.76 to 0.98) was equivalent to that of APRI and Fib-4.ConclusionsThe GPR is a more accurate routine laboratory marker than APRI and Fib-4 to stage liver fibrosis in patients with CHB in West Africa. The GPR represents a simple and inexpensive alternative to liver biopsy and Fibroscan in sub-Saharan Africa.

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EULAR standardised operating procedures for the elaboration, evaluation, dissemination, and implementation of recommendations endorsed by the EULAR standing committees I t is the objective of the EULAR executive committee to promote actions and/or projects aimed at improving the knowledge and/or the recognition of musculoskeletal disorders.The chief aim is to contribute to the improvement of outcome of patients with rheumatic disorders. Apart from the projects devoted to education and research, projects aimed at facilitating the conduct of clinical studies or at improving the management of musculoskeletal disorders are welcome. Such studies can be categorised in four sections:

A formal consensus process integrating evidence and expert opinion based on the ICF framework and classification led to the definition of ICF Core Sets for rheumatoid arthritis. Both the Comprehensive ICF Core Set and the Brief ICF Core Set were defined.

Background & Aims Liver biopsy is the reference standard for staging and grading nonalcoholic fatty liver disease (NAFLD), but histologic scoring systems are semiquantitative with marked interobserver and intraobserver variation. We used machine learning to develop fully automated software for quantification of steatosis, inflammation, ballooning, and fibrosis in biopsy specimens from patients with NAFLD and validated the technology in a separate group of patients. Methods We collected data from 246 consecutive patients with biopsy-proven NAFLD and followed up in London from January 2010 through December 2016. Biopsy specimens from the first 100 patients were used to derive the algorithm and biopsy specimens from the following 146 were used to validate it. Biopsy specimens were scored independently by pathologists using the Nonalcoholic Steatohepatitis Clinical Research Network criteria and digitalized. Areas of steatosis, inflammation, ballooning, and fibrosis were annotated on biopsy specimens by 2 hepatobiliary histopathologists to facilitate machine learning. Images of biopsies from the derivation and validation sets then were analyzed by the algorithm to compute percentages of fat, inflammation, ballooning, and fibrosis, as well as the collagen proportionate area, and compared with findings from pathologists’ manual annotations and conventional scoring systems. Results In the derivation group, results from manual annotation and the software had an interclass correlation coefficient (ICC) of 0.97 for steatosis (95% CI, 0.95–0.99; P < .001); ICC of 0.96 for inflammation (95% CI, 0.9–0.98; P < .001); ICC of 0.94 for ballooning (95% CI, 0.87–0.98; P < .001); and ICC of 0.92 for fibrosis (95% CI, 0.88–0.96; P = .001). Percentages of fat, inflammation, ballooning, and the collagen proportionate area from the derivation group were confirmed in the validation cohort. The software identified histologic features of NAFLD with levels of interobserver and intraobserver agreement ranging from 0.95 to 0.99; this value was higher than that of semiquantitative scoring systems, which ranged from 0.58 to 0.88. In a subgroup of paired liver biopsy specimens, quantitative analysis was more sensitive in detecting differences compared with the nonalcoholic steatohepatitis Clinical Research Network scoring system. Conclusions We used machine learning to develop software to rapidly and objectively analyze liver biopsy specimens for histologic features of NAFLD. The results from the software correlate with those from histopathologists, with high levels of interobserver and intraobserver agreement. Findings were validated in a separate group of patients. This tool might be used for objective assessment of response to therapy for NAFLD in practice and clinical trials.