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Background Respiratory tract infections are often viral and but are frequently treated with antibiotics, providing a significant opportunity for antibiotic de-escalation in patients. We sought to determine whether an automated electronic medical record best practice alert (BPA) based on procalcitonin and respiratory polymerase chain reaction (PCR) results could help reduce inappropriate antibiotic use in patients with likely viral respiratory illness. Methods This multisite, pre–post, quasi-experimental study included patients 18 years and older with a procalcitonin level, In patients with a procalcitonin level

Objective This study aimed to determine if integrating antimicrobial stewardship program (ASP) personnel with rapid diagnostic testing resulted in improved outcomes for patients with positive blood cultures. Method Beginning in 2016, Saint Luke’s Health System (SLHS) implemented a new process where all positive blood cultures were communicated to ASP personnel or SLHS pharmacy staff. Pharmacists then became responsible for interpreting results, assessing patient specific information, and subsequently relaying culture and treatment information to providers. This was a multisite, pre-post, quasi-experimental study (Pre: August to December 2014; Post: August to December 2016). Patients 18 years of age and older with a positive blood culture during admission were included (2014, n = 218; 2016, n = 286). Coprimary outcomes of time to optimal and appropriate therapy were determined from time of culture positivity via gram stain. Secondary outcomes focused on clinical, process, and fiscal endpoints. A pre-post intervention physician survey was conducted to assess the impact on antimicrobial decision making and perceived effect on patient outcomes. Results There was no difference in time to appropriate therapy groups (P = .079). Time to optimal therapy was 9.2 hours shorter in 2016 (P = .004). Provider surveys indicated the process improved communication among clinicians and facilitated a shared decision-making process with a perceived improvement in patient care. Conclusions An ASP-led blood culture communication process for patients with positive blood cultures was shown to improve time to optimal therapy, support physicians in their decision making on critical lab data, and improve the care for hospitalized patients., Integration of antimicrobial stewardship personnel plus rapid identification testing was associated with reduced time to optimal therapy, increased collaboration between clinical staff, and improved care for hospitalized patients with positive blood cultures.

Antimicrobial resistance is recognized as a global health crisis.[1][1] In the United States alone, more than 2 million patients contract antibiotic-resistant infections that result in 23,000 deaths annually.[2][2] One factor contributing to antimicrobial resistance is antimicrobial misuse or

The analysis of neural systems leverages tools from many different fields. Drawing on techniques from the study of critical phenomena in statistical mechanics, several studies have reported signatures of criticality in neural systems, including power-law distributions, shape collapses, and optimized quantities under tuning. Independently, neural complexity - an information theoretic measure - has been introduced in an effort to quantify the strength of correlations across multiple scales in a neural system. This measure represents an important tool in complex systems research because it allows for the quantification of the complexity of a neural system. In this analysis, we studied the relationships between neural complexity and criticality in neural culture data. We analyzed neural avalanches in 435 recordings from dissociated hippocampal cultures produced from rats, as well as neural avalanches from a cortical branching model. We utilized recently developed maximum likelihood estimation power-law fitting methods that account for doubly truncated power-laws, an automated shape collapse algorithm, and neural complexity and branching ratio calculation methods that account for sub-sampling, all of which are implemented in the freely available Neural Complexity and Criticality MATLAB toolbox. We found evidence that neural systems operate at or near a critical point and that neural complexity is optimized in these neural systems at or near the critical point. Surprisingly, we found evidence that complexity in neural systems is dependent upon avalanche profiles and neuron firing rate, but not precise spiking relationships between neurons. In order to facilitate future research, we made all of the culture data utilized in this analysis freely available online.

Neural systems include interactions that occur across many scales. Two divergent methods for characterizing such interactions have drawn on the physical analysis of critical phenomena and the mathematical study of information. Inferring criticality in neural systems has traditionally rested on fitting power laws to the property distributions of ''neural avalanches'' (contiguous bursts of activity), but the fractal nature of avalanche shapes has recently emerged as another signature of criticality. On the other hand, neural complexity, an information theoretic measure, has been used to capture the interplay between the functional localization of brain regions and their integration for higher cognitive functions. Unfortunately, treatments of all three methods - power-law fitting, avalanche shape collapse, and neural complexity – have suffered from shortcomings. Empirical data often contain biases that introduce deviations from true power law in the tail and head of the distribution, but deviations in the tail have often been unconsidered; avalanche shape collapse has required manual parameter tuning; and the estimation of neural complexity has relied on small data sets or statistical assumptions for the sake of computational efficiency. In this paper we present technical advancements in the analysis of criticality and complexity in neural systems. We use maximum-likelihood estimation to automatically fit power laws with left and right cutoffs, present the first automated shape collapse algorithm, and describe new techniques to account for large numbers of neural variables and small data sets in the calculation of neural complexity. In order to facilitate future research in criticality and complexity, we have made the software utilized in this analysis freely available online in the MATLAB NCC (Neural Complexity and Criticality) Toolbox.