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Multidrug resistant organisms are a serious threat to human health.sup.1,2. Fast, accurate antibiotic susceptibility testing (AST) is a critical need in addressing escalating antibiotic resistance, since delays in identifying multidrug resistant organisms increase mortality.sup.3,4 and use of broad-spectrum antibiotics, further selecting for resistant organisms. Yet current growth-based AST assays, such as broth microdilution.sup.5, require several days before informing key clinical decisions. Rapid AST would transform the care of patients with infection while ensuring that our antibiotic arsenal is deployed as efficiently as possible. Growth-based assays are fundamentally constrained in speed by doubling time of the pathogen, and genotypic assays are limited by the ever-growing diversity and complexity of bacterial antibiotic resistance mechanisms. Here we describe a rapid assay for combined genotypic and phenotypic AST through RNA detection, GoPhAST-R, that classifies strains with 94-99% accuracy by coupling machine learning analysis of early antibiotic-induced transcriptional changes with simultaneous detection of key genetic resistance determinants to increase accuracy of resistance detection, facilitate molecular epidemiology and enable early detection of emerging resistance mechanisms. This two-pronged approach provides phenotypic AST 24-36 h faster than standard workflows, with A new assay combining genotypic and phenotypic information accelerates accurate clinical antibiotic susceptibility testing., Author(s): Roby P. Bhattacharyya [sup.1] [sup.2] , Nirmalya Bandyopadhyay [sup.1] , Peijun Ma [sup.1] , Sophie S. Son [sup.1] , Jamin Liu [sup.1] , Lorrie L. He [sup.1] , Lidan [...]