Repurposing cefuroxime for treatment of COVID-19: a scoping review of in silico studies.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the causative agent of Coronavirus disease 19 (COVID-19), is a novel human Coronavirus that is responsible for about 300,000 deaths worldwide. To date, there is no confirmed treatment or vaccine prevention strategy against COVID-19. Due to the urgent need for effective treatment, drug repurposing is regarded as the immediate option. Potential drugs can often be identified via in silico drug screening experiments. Consequently, there has been an explosion of in silico experiments to find drug candidates or investigate anecdotal claims. One drug with several anecdotal accounts of benefit is Cefuroxime. The aim of this study was to identify and summarize in silico evidence for possible activity of Cefuroxime against SARS-CoV-2.To this end, we performed a scoping review of literature of in silico drug repurposing experiments for SARS-CoV-2 using PRISMA-ScR. We searched Medline, Embase, Scopus, Web of Knowledge, and Google Scholar for original studies published between 1st Feb, 2020 and 15th May, 2020 that screened drug libraries, and identified Cefuroxime as a top-ranked potential inhibitor drug against SARS-CoV-2 proteins. Six studies were identified. These studies reported Cefuroxime as a potential inhibitor of 3 key SARS-CoV-2 proteins; main protease, RNA dependent RNA polymerase, and ACE2-Spike complex. We provided a summary of the methodology and findings of the identified studies. Our scoping review identified significant in silico evidence that Cefuroxime may be a potential multi-target inhibitor of SARS-CoV-2. Further in vitro and in vivo studies are required to evaluate the potential of Cefuroxime for COVID-19.Communicated by Ramaswamy H. Sarma.

Evaluation of multidisciplinary collaboration in pediatric trauma care using EHR data.

OBJECTIVES: The study sought to identify collaborative electronic health record (EHR) usage patterns for pediatric trauma patients and determine how the usage patterns are related to patient outcomes. MATERIALS AND METHODS: A process mining-based network analysis was applied to EHR metadata and trauma registry data for a cohort of pediatric trauma patients with minor injuries at a Level I pediatric trauma center. The EHR metadata were processed into an event log that was segmented based on gaps in the temporal continuity of events. A usage pattern was constructed for each encounter by creating edges among functional roles that were captured within the same event log segment. These patterns were classified into groups using graph kernel and unsupervised spectral clustering methods. Demographics, clinical and network characteristics, and emergency department (ED) length of stay (LOS) of the groups were compared. RESULTS: Three distinct usage patterns that differed by network density were discovered: fully connected (clique), partially connected, and disconnected (isolated). Compared with the fully connected pattern, encounters with the partially connected pattern had an adjusted median ED LOS that was significantly longer (242.6 [95% confidence interval, 236.9-246.0] minutes vs 295.2 [95% confidence, 289.2-297.8] minutes), more frequently seen among day shift and weekday arrivals, and involved otolaryngology, ophthalmology services, and child life specialists. DISCUSSION: The clique-like usage pattern was associated with decreased ED LOS for the study cohort, suggesting greater degree of collaboration resulted in shorter stay. CONCLUSIONS: Further investigation to understand and address causal factors can lead to improvement in multidisciplinary collaboration.

Radiology Resident Assessment and Feedback Dashboard.

Assessment of residents is optimally performed through processes and platforms that provide daily feedback, which can be immediately acted on. Given the documentation required by the Accreditation Council for Graduate Medical Education (ACGME), effective data management, integration, and presentation are crucial to ease the burden of manual documentation and increase the timeliness of actionable information. To this end, the authors modeled the learning activities of residents using the Experience Application Programming Interface (xAPI) framework, which is a standard framework for the learning community. On the basis of the xAPI framework and using open-source software to extend their existing infrastructure, the authors developed a Web-based dashboard that provides residents with a more holistic view of their educational experience. The dashboard was designed around the ACGME radiology milestones and provides real-time feedback to residents using various assessment metrics derived from multiple data sources. The purpose of this article is to describe the dashboard's architecture and components, the design and technical considerations, and the lessons learned in implementing the dashboard. ©RSNA, 2018.

Mapping the Flow of Pediatric Trauma Patients Using Process Mining.

BACKGROUND: Inhospital pediatric trauma care typically spans multiple locations, which influences the use of resources, that could be improved by gaining a better understanding of the inhospital flow of patients and identifying opportunities for improvement. OBJECTIVES: To describe a process mining approach for mapping the inhospital flow of pediatric trauma patients, to identify and characterize the major patient pathways and care transitions, and to identify opportunities for patient flow and triage improvement. METHODS: From the trauma registry of a level I pediatric trauma center, data were extracted regarding the two highest trauma activation levels, Alpha (n = 228) and Bravo (n = 1,713). An event log was generated from the admission, discharge, and transfer data from which patient pathways and care transitions were identified and described. The Flexible Heuristics Miner algorithm was used to generate a process map for the cohort, and separate process maps for Alpha and Bravo encounters, which were assessed for conformance when fitness value was less than 0.950, with the identification and comparison of conforming and nonconforming encounters. RESULTS: The process map for the cohort was similar to a validated process map derived through qualitative methods. The process map for Bravo encounters had a relatively low fitness of 0.887, and 96 (5.6%) encounters were identified as nonconforming with characteristics comparable to Alpha encounters. In total, 28 patient pathways and 20 care transitions were identified. The top five patient pathways were traversed by 92.1% of patients, whereas the top five care transitions accounted for 87.5% of all care transitions. A larger-than-expected number of discharges from the pediatric intensive care unit (PICU) were identified, with 84.2% involving discharge to home without the need for home care services. CONCLUSION: Process mining was successfully applied to derive process maps from trauma registry data and to identify opportunities for trauma triage improvement and optimization of PICU use.

Linking Electronic Health Record and Trauma Registry Data: Assessing the Value of Probabilistic Linkage.

BACKGROUND: Electronic health record (EHR) systems contain large volumes of novel heterogeneous data that can be linked to trauma registry data to enable innovative research not possible with either data source alone. OBJECTIVE: This article describes an approach for linking electronically extracted EHR data to trauma registry data at the institutional level and assesses the value of probabilistic linkage. METHODS: Encounter data were independently obtained from the EHR data warehouse (n = 1,632) and the pediatric trauma registry (n = 1,829) at a Level I pediatric trauma center. Deterministic linkage was attempted using nine different combinations of medical record number (MRN), encounter identity (ID) (visit ID), age, gender, and emergency department (ED) arrival date. True matches from the best performing variable combination were used to create a gold standard, which was used to evaluate the performance of each variable combination, and to train a probabilistic algorithm that was separately used to link records unmatched by deterministic linkage and the entire cohort. Additional records that matched probabilistically were investigated via chart review and compared against records that matched deterministically. RESULTS: Deterministic linkage with exact matching on any three of MRN, encounter ID, age, gender, and ED arrival date gave the best yield of 1,276 true matches while an additional probabilistic linkage step following deterministic linkage yielded 110 true matches. These records contained a significantly higher number of boys compared to records that matched deterministically and etiology was attributable to mismatch between MRNs in the two data sets. Probabilistic linkage of the entire cohort yielded 1,363 true matches. CONCLUSION: The combination of deterministic and an additional probabilistic method represents a robust approach for linking EHR data to trauma registry data. This approach may be generalizable to studies involving other registries and databases.

Characterizing the Utilization of the Problem List for Pediatric Trauma Care.

The EHR problem list has the potential to support care coordination among the multidisciplinary care team that cares for pediatric trauma patients. To realize this potential, the need exists to ensure appropriate utilization by formulating acceptable usage and management policy. In this regard, understanding the prevailing utilization pattern is pivotal. To this end, we analyzed EHR in tandem with trauma registry data at a Level I pediatric trauma center. Almost all (97.8%) of the actions executed on the problem list were addition of items. Among the 517 patient encounters in the cohort, 263 (48.9%) encounters involving sicker patients had at least one problem list item added, mostly within the first 4 hours of arrival, while in the emergency department, and by providers in the service of record. This represents a foundation to build upon. Subsequent research will explore completeness, accuracy, and the impact of the utilization on patient outcomes.