Timely diagnostic tools are needed to improve antibiotic treatment. Pairing metagenomic sequencing with genomic neighbor typing algorithms may support rapid clinically actionable results. We created resistance-associated sequence elements (RASE) databases for and . and used them to predict antibiotic susceptibility in directly sequenced (Oxford Nanopore) urine specimens from critically ill patients. RASE analysis was performed on pathogen-specific reads from metagenomic sequencing. We evaluated the ability to predict (i) multi-locus sequence type (MLST) and (ii) susceptibility profiles. We used neighbor typing to predict MLST and susceptibility phenotype of (64/80) and . (16/80) from urine samples. When optimized by lineage score, MLST predictions were concordant for 73% of samples. Similarly, a RASE-susceptible prediction for a given isolate was associated with a specificity and a positive likelihood ratio (LR+) for susceptibility of 0.65 (95% CI, 0.54-0.76) and 2.26 (95% CI, 1.75-2.92), respectively, with an increase in the probability of susceptibility of 10%. A RASE-non-susceptible prediction was associated with a sensitivity and a negative likelihood ratio (LR-) for susceptibility of 0.79 (95% CI, 0.74-0.84) and 0.32 (95% CI, 0.24-0.43) respectively, with a decrease in the probability of susceptibility of 20%. Numerous antibiotic classes could reasonably be reconsidered empiric therapy by shifting empiric probabilities of susceptibility across relevant treatment thresholds. Moreover, these predictions can be available within 6 h. Metagenomic sequencing of urine specimens with neighbor typing provides rapid and informative predictions of lineage and antibiotic susceptibility with the potential to impact clinical decision-making.

Rapid digitalization of health care and a dearth of digital health education for medical students and junior physicians worldwide means there is an imperative for more training in this dynamic and evolving field.

With the rapid expansion of veterans’ access to community care under the Veterans Affairs Maintaining Internal Systems and Strengthening Integrated Outside Networks (VA MISSION) Act of 2018, ensuring that veterans receive high-quality community care has become a national priority. Using Veterans Health Administration (VHA) data and Medicare performance scores, we assessed how clinicians’ performance on quality measures differed between those who treated veterans within the VHA Community Care Network and those who did not. We found that in 2022, 66.0 percent of community-based clinicians treated VHA enrollees. These clinicians were more likely to be male, have less practice experience, be affiliated with group practices, and be based in rural and socially vulnerable areas compared with clinicians who did not treat VHA enrollees. Notably, clinicians in the lowest quartile of quality performance measures were 8.8 percentage points more likely to treat VHA enrollees than those in the highest quartile. This pattern was most pronounced among primary care and mental health clinicians, and it persisted across VHA Community Care Network regions. These results underscore the need for federal efforts to ensure that veterans receive care from high-performing community clinicians.

Although interventions to change nutrition policies, systems, and environments (PSE) for children are generally cost-effective for preventing childhood obesity, existing evidence suggests that nutrition education curricula, without accompanying PSE changes, are more commonly implemented.