ABSTRACT
OBJECTIVE: To apply the 2019 joint American Academy of Neurology (AAN) and American Headache Society (AHS) quality measures for headache management to a cohort of neurology resident physicians and then assess outcomes related to guideline adherence. BACKGROUND: The optimization of headache management is essential to reduce both the individual and systemic impact of these disorders. In 2014, the AAN developed 10 quality measures for evidence-based management of patients with headache. A workgroup updated and condensed its headache quality measures in 2019, narrowing the set to six measurements, four of which would primarily focus on the management of migraine and two of which would address the management of cluster headache. METHODS: This quality improvement study was conducted using a pretest-posttest study design. A pre-intervention survey based on retrospective analysis of five clinic notes for adherence to the measures was designed and distributed to all neurology residents (n = 32) at a large, academic tertiary referral center. The intervention included the creation of an electronic medical record template to aid residents in following the measures during clinical encounters, as well as the provision of direct feedback based on pre-intervention results. Finally, a post-intervention survey was distributed for completion based on notes written during the intervention period. Analysis was limited to migraine, given the low percentage of cluster headache seen in clinic. RESULTS: An increase in adherence was seen in three of the four migraine-related quality measures, with the Use of Abortive Medications for Migraine and Documentation of Counseling on Modifiable Lifestyle and Chronification Factors demonstrating statistically significant improvements (75.8% to 88.0% [p = 0.013] and 83.9% to 94.0% [p = 0.029] adherence, respectively). For secondary outcomes, the increase in the utilization of appropriate diagnostic criteria (82.6% to 93.2%, p = 0.018) was significant, and the self-assessed confidence rating for adherence to guidelines was significant (p < 0.001). CONCLUSIONS: This study provides evidence that the quality improvement intervention led to increased adherence to the AAN and AHS migraine-related measures. It is anticipated that increased adherence may lead to improved patient outcomes.
ABSTRACT
Antibiotic-resistant bacteria rapidly spread in clinical and natural environments and challenge our modern lifestyle. A major component of defense against antibiotics in Gram-negative bacteria is a drug permeation barrier created by active efflux across the outer membrane. We identified molecular determinants defining the propensity of small peptidomimetic molecules to avoid and inhibit efflux pumps in Pseudomonas aeruginosa, a human pathogen notorious for its antibiotic resistance. Combining experimental and computational protocols, we mapped the fate of the compounds from structure-activity relationships through their dynamic behavior in solution, permeation across both the inner and outer membranes, and interaction with MexB, the major efflux transporter of P. aeruginosa We identified predictors of efflux avoidance and inhibition and demonstrated their power by using a library of traditional antibiotics and compound series and by generating new inhibitors of MexB. The identified predictors will enable the discovery and optimization of antibacterial agents suitable for treatment of P. aeruginosa infections.IMPORTANCE Efflux pump avoidance and inhibition are desired properties for the optimization of antibacterial activities against Gram-negative bacteria. However, molecular and physicochemical interactions defining the interface between compounds and efflux pumps remain poorly understood. We identified properties that correlate with efflux avoidance and inhibition, are predictive of similar features in structurally diverse compounds, and allow researchers to distinguish between efflux substrates, inhibitors, and avoiders in P. aeruginosa The developed predictive models are based on the descriptors representative of different clusters comprising a physically intuitive combination of properties. Molecular shape (represented by acylindricity), amphiphilicity (anisotropic polarizability), aromaticity (number of aromatic rings), and the partition coefficient (LogD) are physicochemical predictors of efflux inhibitors, whereas interactions with Pro668 and Leu674 residues of MexB distinguish between inhibitors/substrates and efflux avoiders. The predictive models and efflux rules are applicable to compounds with unrelated chemical scaffolds and pave the way for development of compounds with the desired efflux interface properties.
