Identifying signs and symptoms of urinary tract infection from emergency department clinical notes using large language models

BACKGROUND

Natural language processing (NLP) tools including recently developed large language models (LLMs) have myriad potential applications in medical care and research, including the efficient labeling and classification of unstructured text such as electronic health record (EHR) notes. This opens the door to large-scale projects that rely on variables that are not typically recorded in a structured form, such as patient signs and symptoms.

OBJECTIVES

This study is designed to acquaint the emergency medicine research community with the foundational elements of NLP, highlighting essential terminology, annotation methodologies, and the intricacies involved in training and evaluating NLP models. Symptom characterization is critical to urinary tract infection (UTI) diagnosis, but identification of symptoms from the EHR has historically been challenging, limiting large-scale research, public health surveillance, and EHR-based clinical decision support. We therefore developed and compared two NLP models to identify UTI symptoms from unstructured emergency department (ED) notes.

METHODS

The study population consisted of patients aged ≥ 18 who presented to an ED in a northeastern U.S. health system between June 2013 and August 2021 and had a urinalysis performed. We annotated a random subset of 1250 ED clinician notes from these visits for a list of 17 UTI symptoms. We then developed two task-specific LLMs to perform the task of named entity recognition: a convolutional neural network-based model (SpaCy) and a transformer-based model designed to process longer documents (Clinical Longformer). Models were trained on 1000 notes and tested on a holdout set of 250 notes. We compared model performance (precision, recall, F1 measure) at identifying the presence or absence of UTI symptoms at the note level.

RESULTS

A total of 8135 entities were identified in 1250 notes; 83.6% of notes included at least one entity. Overall F1 measure for note-level symptom identification weighted by entity frequency was 0.84 for the SpaCy model and 0.88 for the Longformer model. F1 measure for identifying presence or absence of any UTI symptom in a clinical note was 0.96 (232/250 correctly classified) for the SpaCy model and 0.98 (240/250 correctly classified) for the Longformer model.

CONCLUSIONS

The study demonstrated the utility of LLMs and transformer-based models in particular for extracting UTI symptoms from unstructured ED clinical notes; models were highly accurate for detecting the presence or absence of any UTI symptom on the note level, with variable performance for individual symptoms.

Automated HEART score determination via ChatGPT: Honing a framework for iterative prompt development

Objectives

This study presents a design framework to enhance the accuracy by which large language models (LLMs), like ChatGPT can extract insights from clinical notes. We highlight this framework via prompt refinement for the automated determination of HEART (History, ECG, Age, Risk factors, Troponin risk algorithm) scores in chest pain evaluation.

Methods

We developed a pipeline for LLM prompt testing, employing stochastic repeat testing and quantifying response errors relative to physician assessment. We evaluated the pipeline for automated HEART score determination across a limited set of 24 synthetic clinical notes representing four simulated patients. To assess whether iterative prompt design could improve the LLMs' ability to extract complex clinical concepts and apply rule-based logic to translate them to HEART subscores, we monitored diagnostic performance during prompt iteration.

Results

Validation included three iterative rounds of prompt improvement for three HEART subscores with 25 repeat trials totaling 1200 queries each for GPT-3.5 and GPT-4. For both LLM models, from initial to final prompt design, there was a decrease in the rate of responses with erroneous, non-numerical subscore answers. Accuracy of numerical responses for HEART subscores (discrete 0-2 point scale) improved for GPT-4 from the initial to final prompt iteration, decreasing from a mean error of 0.16-0.10 (95% confidence interval: 0.07-0.14) points.

Conclusion

We established a framework for iterative prompt design in the clinical space. Although the results indicate potential for integrating LLMs in structured clinical note analysis, translation to real, large-scale clinical data with appropriate data privacy safeguards is needed.

Disparities Associated With Electronic Behavioral Alerts for Safety and Violence Concerns in the Emergency Department

STUDY OBJECTIVE

Although electronic behavioral alerts are placed as an alert flag in the electronic health record to notify staff of previous behavioral and/or violent incidents in emergency departments (EDs), they have the potential to reinforce negative perceptions of patients and contribute to bias. We provide characterization of ED electronic behavioral alerts using electronic health record data across a large, regional health care system.

METHODS

We conducted a retrospective cross-sectional study of adult patients presenting to 10 adult EDs within a Northeastern United States health care system from 2013 to 2022. Electronic behavioral alerts were manually screened for safety concerns and then categorized by the type of concern. In our patient-level analyses, we included patient data at the time of the first ED visit where an electronic behavioral alert was triggered or, if a patient had no electronic behavioral alerts, the earliest visit in the study period. We performed a mixed-effects regression analysis to identify patient-level risk factors associated with safety-related electronic behavioral alert deployment.

RESULTS

Of the 2,932,870 ED visits, 6,775 (0.2%) had associated electronic behavioral alerts across 789 unique patients and 1,364 unique electronic behavioral alerts. Of the encounters with electronic behavioral alerts, 5,945 (88%) were adjudicated as having a safety concern involving 653 patients. In our patient-level analysis, the median age for patients with safety-related electronic behavioral alerts was 44 years (interquartile range 33 to 55 years), 66% were men, and 37% were Black. Visits with safety-related electronic behavioral alerts had higher rates of discontinuance of care (7.8% vs 1.5% with no alert; P<.001) as defined by the patient-directed discharge, left-without-being-seen, or elopement-type dispositions. The most common topics in the electronic behavioral alerts were physical (41%) or verbal (36%) incidents with staff or other patients. In the mixed-effects logistic analysis, Black non-Hispanic patients (vs White non-Hispanic patients: adjusted odds ratio 2.60; 95% confidence interval [CI] 2.13 to 3.17), aged younger than 45 (vs aged 45-64 years: adjusted odds ratio 1.41; 95% CI 1.17 to 1.70), male (vs female: adjusted odds ratio 2.09; 95% CI 1.76 to 2.49), and publicly insured patients (Medicaid: adjusted odds ratio 6.18; 95% CI 4.58 to 8.36; Medicare: adjusted odds ratio 5.63; 95% CI 3.96 to 8.00 vs commercial) were associated with a higher risk of a patient having at least 1 safety-related electronic behavioral alert deployment during the study period.

CONCLUSION

In our analysis, younger, Black non-Hispanic, publicly insured, and male patients were at a higher risk of having an ED electronic behavioral alert. Although our study is not designed to reflect causality, electronic behavioral alerts may disproportionately affect care delivery and medical decisions for historically marginalized populations presenting to the ED, contribute to structural racism, and perpetuate systemic inequities.

Dementia risk analysis using temporal event modeling on a large real-world dataset

The objective of the study is to identify healthcare events leading to a diagnosis of dementia from a large real-world dataset. This study uses a data-driven approach to identify temporally ordered pairs and trajectories of healthcare codes in the electronic health record (EHR). This allows for discovery of novel temporal risk factors leading to an outcome of interest that may otherwise be unobvious. We identified several known (Down syndrome RR = 116.1, thiamine deficiency RR = 76.1, and Parkinson's disease RR = 41.1) and unknown (Brief psychotic disorder RR = 68.6, Toxic effect of metals RR = 40.4, and Schizoaffective disorders RR = 40.0) factors for a specific dementia diagnosis. The associations with the greatest risk for any dementia diagnosis were found to be primarily related to mental health (Brief psychotic disorder RR = 266.5, Dissociative and conversion disorders RR = 169.8), or neurologic conditions or procedures (Dystonia RR = 121.9, Lumbar Puncture RR = 119.0). Trajectory and clustering analysis identified factors related to cerebrovascular disorders, as well as diagnoses which increase the risk of toxic imbalances. The results of this study have the ability to provide valuable insights into potential patient progression towards dementia and improve recognition of patients at risk for developing dementia.

Adoption of Emergency Department-Initiated Buprenorphine for Patients With Opioid Use Disorder: Secondary Analysis of a Cluster Randomized Trial

Importance

Emergency department (ED) initiation of buprenorphine is safe and effective but underutilized in practice. Understanding the factors affecting adoption of this practice could inform more effective interventions.

Objective

To quantify the factors, including social contagion, associated with the adoption of the practice of ED initiation of buprenorphine for patients with opioid use disorder.

Design, Setting, and Participants

This is a secondary analysis of the EMBED (Emergency Department-Initiated Buprenorphine For Opioid Use Disorder) trial, a multicentered, cluster randomized trial of a clinical decision support intervention targeting ED initiation of buprenorphine. The trial occurred from November 2019 to May 2021. The study was conducted at ED clusters across health care systems from the northeast, southeast, and western regions of the US and included attending physicians, resident physicians, and advanced practice practitioners. Data analysis was performed from August 2022 to June 2023.

Exposures

This analysis included both the intervention and nonintervention groups of the EMBED trial. Graph methods were used to construct the network of clinicians who shared in the care of patients for whom buprenorphine was initiated during the trial before initiating the practice themselves, termed exposure.

Main Outcomes and Measures

Cox proportional hazard modeling with time-dependent covariates was performed to assess the association of the number of these exposures with self-adoption of the practice of ED initiation of buprenorphine while adjusting for clinician role, health care system, and intervention site status.

Results

A total of 1026 unique clinicians in 18 ED clusters across 5 health care systems were included. Analysis showed associations of the cumulative number of exposures to others initiating buprenorphine with the self-practice of buprenorphine initiation. This increased in a dose-dependent manner (1 exposure: hazard ratio [HR], 1.31; 95% CI, 1.16-1.48; 5 exposures: HR, 2.85; 95% CI, 1.66-4.89; 10 exposures: HR, 3.55; 95% CI, 1.47-8.58). Intervention site status was associated with practice adoption (HR, 1.50; 95% CI, 1.04-2.18). Health care system and clinician role were also associated with practice adoption.

Conclusions and Relevance

In this secondary analysis of a multicenter, cluster randomized trial of a clinical decision support tool for buprenorphine initiation, the number of exposures to ED initiation of buprenorphine and the trial intervention were associated with uptake of ED initiation of buprenorphine. Although systems-level approaches are necessary to increase the rate of buprenorphine initiation, individual clinicians may change practice of those around them.

Trial Registration

ClinicalTrials.gov Identifier: NCT03658642.

Computational phenotypes for patients with opioid-related disorders presenting to the emergency department

OBJECTIVE

We aimed to discover computationally-derived phenotypes of opioid-related patient presentations to the ED via clinical notes and structured electronic health record (EHR) data.

METHODS

This was a retrospective study of ED visits from 2013-2020 across ten sites within a regional healthcare network. We derived phenotypes from visits for patients ≥18 years of age with at least one prior or current documentation of an opioid-related diagnosis. Natural language processing was used to extract clinical entities from notes, which were combined with structured data within the EHR to create a set of features. We performed latent dirichlet allocation to identify topics within these features. Groups of patient presentations with similar attributes were identified by cluster analysis.

RESULTS

In total 82,577 ED visits met inclusion criteria. The 30 topics were discovered ranging from those related to substance use disorder, chronic conditions, mental health, and medical management. Clustering on these topics identified nine unique cohorts with one-year survivals ranging from 84.2-96.8%, rates of one-year ED returns from 9-34%, rates of one-year opioid event 10-17%, rates of medications for opioid use disorder from 17-43%, and a median Carlson comorbidity index of 2-8. Two cohorts of phenotypes were identified related to chronic substance use disorder, or acute overdose.

CONCLUSIONS

Our results indicate distinct phenotypic clusters with varying patient-oriented outcomes which provide future targets better allocation of resources and therapeutics. This highlights the heterogeneity of the overall population, and the need to develop targeted interventions for each population.

Low-dose carboplatin modifies the tumor microenvironment to augment CAR T cell efficacy in human prostate cancer models

Chimeric antigen receptor (CAR) T cells have transformed the treatment landscape for hematological malignancies. However, CAR T cells are less efficient against solid tumors, largely due to poor infiltration resulting from the immunosuppressive nature of the tumor microenvironment (TME). Here, we assessed the efficacy of Lewis Y antigen (LeY)-specific CAR T cells in patient-derived xenograft (PDX) models of prostate cancer. In vitro, LeY CAR T cells directly killed organoids derived from androgen receptor (AR)-positive or AR-null PDXs. In vivo, although LeY CAR T cells alone did not reduce tumor growth, a single prior dose of carboplatin reduced tumor burden. Carboplatin had a pro-inflammatory effect on the TME that facilitated early and durable CAR T cell infiltration, including an altered cancer-associated fibroblast phenotype, enhanced extracellular matrix degradation and re-oriented M1 macrophage differentiation. In a PDX less sensitive to carboplatin, CAR T cell infiltration was dampened; however, a reduction in tumor burden was still observed with increased T cell activation. These findings indicate that carboplatin improves the efficacy of CAR T cell treatment, with the extent of the response dependent on changes induced within the TME.

Automatable end-of-life screening for older adults in the emergency department using electronic health records

BACKGROUND

Emergency department (ED) visits are common at the end-of-life, but the identification of patients with life-limiting illness remains a key challenge in providing timely and resource-sensitive advance care planning (ACP) and palliative care services. To date, there are no validated, automatable instruments for ED end-of-life screening. Here, we developed a novel electronic health record (EHR) prognostic model to screen older ED patients at high risk for 6-month mortality and compare its performance to validated comorbidity indices.

METHODS

This was a retrospective, observational cohort study of ED visits from adults aged ≥65 years who visited any of 9 EDs across a large regional health system between 2014 and 2019. Multivariable logistic regression that included clinical and demographic variables, vital signs, and laboratory data was used to develop a 6-month mortality predictive model-the Geriatric End-of-life Screening Tool (GEST) using five-fold cross-validation on data from 8 EDs. Performance was compared to the Charlson and Elixhauser comorbidity indices using area under the receiver-operating characteristic curve (AUROC), calibration, and decision curve analyses. Reproducibility was tested against data from the remaining independent ED within the health system. We then used GEST to investigate rates of ACP documentation availability and code status orders in the EHR across risk strata.

RESULTS

A total of 431,179 encounters by 123,128 adults were included in this study with a 6-month mortality rate of 12.2%. Charlson (AUROC (95% CI): 0.65 (0.64-0.69)) and Elixhauser indices (0.69 (0.68-0.70)) were outperformed by GEST (0.82 (0.82-0.83)). GEST displayed robust performance across demographic subgroups and in our independent validation site. Among patients with a greater than 30% mortality risk using GEST, only 5.0% had ACP documentation; 79.0% had a code status previously ordered, of which 70.7% were full code. In decision curve analysis, GEST provided greater net benefit than the Charlson and Elixhauser scores.

CONCLUSIONS

Prognostic models using EHR data robustly identify high mortality risk older adults in the ED for whom code status, ACP, or palliative care interventions may be of benefit. Although all tested methods identified patients approaching the end-of-life, GEST was most performant. These tools may enable resource-sensitive end-of-life screening in the ED.

Analysis of Health Trajectories Leading to Adverse Opioid-Related Events

Identifying patient risk factors leading to adverse opioid-related events (AOEs) may enable targeted risk-based interventions, uncover potential causal mechanisms, and enhance prognosis. In this article, we aim to discover patient diagnosis, procedure, and medication event trajectories associated with AOEs using large-scale data mining methods. The individual temporally preceding factors associated with the highest relative risk (RR) for AOEs were opioid withdrawal therapy agents, toxic encephalopathy, problems related to housing and economic circumstances, and unspecified viral hepatitis, with RR of 33.4, 26.1, 19.9, and 18.7, respectively. Patient cohorts with a socioeconomic or mental health code had a larger RR for over 75% of all identified trajectories compared to the average population. By analyzing health trajectories leading to AOEs, we discover novel, temporally-connected combinations of diagnoses and health service events that significantly increase risk of AOEs, including natural histories marked by socioeconomic and mental health diagnoses.

The design and conduct of a randomized clinical trial comparing emergency department initiation of sublingual versus a 7-day extended-release injection formulation of buprenorphine for opioid use disorder: Project ED Innovation

ED-INNOVATION (Emergency Department-INitiated bupreNOrphine VAlidaTION) is a Hybrid Type-1 Implementation-Effectiveness multisite emergency department (ED) study funded through The Helping to End Addiction Long-term^SM Initiative, or NIH HEAL Initiative^SM efforts to increase access to medications for opioid use disorder (OUD). We use components of Implementation Facilitation to enhance adoption of ED-initiated buprenorphine (BUP) at approximately 30 sites. Subsequently we compare the effectiveness of two BUP formulations, sublingual (SL-BUP) and 7-day extended-release injectable (CAM2038, XR-BUP) in a randomized clinical trial (RCT) of approximately 2000 patients with OUD on the primary outcome of engagement in formal addiction treatment at 7 days. Secondary outcomes assessed at 7 and 30 days include self-reported opioid use, craving and satisfaction, health service utilization, overdose events, and engagement in formal addiction treatment (30 days) and receipt of medications for OUD (at 7 and 30 days). A sample size of 1000 per group provides 90% power at the 2-sided significance level to detect a difference in the primary outcome of 8% and accommodates a 15% dropout rate. We will compare the cost effectiveness of the two treatments on the primary outcome using the incremental cost-effectiveness ratio. We will also conduct an ancillary study in approximately 75 patients experiencing minimal to no opioid withdrawal who will undergo XR-BUP initiation. If the ancillary study demonstrates safety, we will expand the eligibility criteria for the RCT to include individuals with minimal to no opioid withdrawal. The results of these studies will inform implementation of ED-initiated BUP in diverse EDs which has the potential to improve treatment access.

Delirium in Older Patients With COVID-19 Presenting to the Emergency Department

IMPORTANCE

Delirium is common among older emergency department (ED) patients, is associated with high morbidity and mortality, and frequently goes unrecognized. Anecdotal evidence has described atypical presentations of coronavirus disease 2019 (COVID-19) in older adults; however, the frequency of and outcomes associated with delirium in older ED patients with COVID-19 infection have not been well described.

OBJECTIVE

To determine how frequently older adults with COVID-19 present to the ED with delirium and their associated hospital outcomes.

DESIGN, SETTING, AND PARTICIPANTS

This multicenter cohort study was conducted at 7 sites in the US. Participants included consecutive older adults with COVID-19 presenting to the ED on or after March 13, 2020.

EXPOSURE

COVID-19 was diagnosed by positive nasal swab for severe acute respiratory syndrome coronavirus 2 (99% of cases) or classic radiological findings (1% of cases).

MAIN OUTCOMES AND MEASURES

The primary outcome was delirium as identified from the medical record according to a validated record review approach.

RESULTS

A total of 817 older patients with COVID-19 were included, of whom 386 (47%) were male, 493 (62%) were White, 215 (27%) were Black, and 54 (7%) were Hispanic or Latinx. The mean (SD) age of patients was 77.7 (8.2) years. Of included patients, 226 (28%) had delirium at presentation, and delirium was the sixth most common of all presenting symptoms and signs. Among the patients with delirium, 37 (16%) had delirium as a primary symptom and 84 (37%) had no typical COVID-19 symptoms or signs, such as fever or shortness of breath. Factors associated with delirium were age older than 75 years (adjusted relative risk [aRR], 1.51; 95% CI, 1.17-1.95), living in a nursing home or assisted living (aRR, 1.23; 95% CI, 0.98-1.55), prior use of psychoactive medication (aRR, 1.42; 95% CI, 1.11-1.81), vision impairment (aRR, 1.98; 95% CI, 1.54-2.54), hearing impairment (aRR, 1.10; 95% CI 0.78-1.55), stroke (aRR, 1.47; 95% CI, 1.15-1.88), and Parkinson disease (aRR, 1.88; 95% CI, 1.30-2.58). Delirium was associated with intensive care unit stay (aRR, 1.67; 95% CI, 1.30-2.15) and death (aRR, 1.24; 95% CI, 1.00-1.55).

CONCLUSIONS AND RELEVANCE

In this cohort study of 817 older adults with COVID-19 presenting to US emergency departments, delirium was common and often was seen without other typical symptoms or signs. In addition, delirium was associated with poor hospital outcomes and death. These findings suggest the clinical importance of including delirium on checklists of presenting signs and symptoms of COVID-19 that guide screening, testing, and evaluation.

Association between patient-physician gender concordance and patient experience scores. Is there gender bias?

BACKGROUND

Patient satisfaction, a commonly measured indicator of quality of care and patient experience, is often used in physician performance reviews and promotion decisions. Patient satisfaction surveys may introduce gender-related bias.

OBJECTIVE

Examine the effect of patient and physician gender concordance on patient satisfaction with emergency care.

METHODS

We performed a cross-sectional analysis of electronic health record and Press Ganey patient satisfaction survey data of adult patients discharged from the emergency department (2015-2018). Logistic regression models were used to examine relationships between physician gender, patient gender, and physician-patient gender dyads. Binary outcomes included: perfect care provider score and perfect overall assessment score.

RESULTS

Female patients returned surveys more often (n=7 612; 61.55%) and accounted for more visits (n=232 024; 55.26%). Female patients had lower odds of perfect scores for provider score and overall assessment score (OR: 0.852, 95% CI: 0.790, 0.918; OR: 0.782, 95% CI: 0.723, 0.846). Female physicians had 1.102 (95% CI: 1.001, 1.213) times the odds of receiving a perfect provider score. Physician gender did not influence male patients' odds of reporting a perfect care provider score (95% CI: 0.916, 1.158) whereas female patients treated by female physicians had 1.146 times the odds (95% CI: 1.019, 1.289) of a perfect provider score.

CONCLUSION

Female patients prefer female emergency physicians but were less satisfied with their physician and emergency department visit overall. Over-representation of female patients on patient satisfaction surveys introduces bias. Patient satisfaction surveys should be deemphasized from physician compensation and promotion decisions.

Forty years of emergency medicine research: Uncovering research themes and trends through topic modeling

STUDY OBJECTIVE

Topic identification can facilitate knowledge curation, discover thematic relationships, trends, and predict future direction. We aimed to determine through an unsupervised, machine learning approach to topic modeling the most common research themes in emergency medicine over the last 40 years and summarize their trends and characteristics.

METHODS

We retrieved the complete reference entries including article abstracts from Ovid for all original research articles from 1980 to 2019 within emergency medicine for six widely-cited journals. Abstracts were processed through a natural language pipeline and analyzed by a latent Dirichlet allocation topic modeling algorithm for unsupervised topic discovery. Topics were further examined through trend analysis, word associations, co-occurrence metrics, and two-dimensional embeddings.

RESULTS

We retrieved 47,158 articles during the defined time period that were filtered to 20,528 articles for further analysis. Forty topics covering methodologic and clinical areas were discovered. These topics separated into distinct clusters when embedded in two-dimensional space and exhibited consistent patterns of interaction. We observed the greatest increase in popularity in research themes involving risk factors (0.4% to 5.2%), health utilization (1.2% to 5.0%), and ultrasound (0.7% to 3.3%), and a relative decline in research involving basic science (8.9% to 1.1%), cardiac arrest (6.5% to 2.2%), and vitals (6.3% to 1.3%) over the past 40 years. Our data show only very modest growth in mental health and substance abuse research (1.0% to 1.6%), despite ongoing crises.

CONCLUSIONS

Topic modeling via unsupervised machine learning applied to emergency medicine abstracts discovered coherent topics, trends, and patterns of interaction.

Development and Validation of the Quick COVID-19 Severity Index: A Prognostic Tool for Early Clinical Decompensation

STUDY OBJECTIVE

The goal of this study is to create a predictive, interpretable model of early hospital respiratory failure among emergency department (ED) patients admitted with coronavirus disease 2019 (COVID-19).

METHODS

This was an observational, retrospective, cohort study from a 9-ED health system of admitted adult patients with severe acute respiratory syndrome coronavirus 2 (COVID-19) and an oxygen requirement less than or equal to 6 L/min. We sought to predict respiratory failure within 24 hours of admission as defined by oxygen requirement of greater than 10 L/min by low-flow device, high-flow device, noninvasive or invasive ventilation, or death. Predictive models were compared with the Elixhauser Comorbidity Index, quick Sequential [Sepsis-related] Organ Failure Assessment, and the CURB-65 pneumonia severity score.

RESULTS

During the study period, from March 1 to April 27, 2020, 1,792 patients were admitted with COVID-19, 620 (35%) of whom had respiratory failure in the ED. Of the remaining 1,172 admitted patients, 144 (12.3%) met the composite endpoint within the first 24 hours of hospitalization. On the independent test cohort, both a novel bedside scoring system, the quick COVID-19 Severity Index (area under receiver operating characteristic curve mean 0.81 [95% confidence interval {CI} 0.73 to 0.89]), and a machine-learning model, the COVID-19 Severity Index (mean 0.76 [95% CI 0.65 to 0.86]), outperformed the Elixhauser mortality index (mean 0.61 [95% CI 0.51 to 0.70]), CURB-65 (0.50 [95% CI 0.40 to 0.60]), and quick Sequential [Sepsis-related] Organ Failure Assessment (0.59 [95% CI 0.50 to 0.68]). A low quick COVID-19 Severity Index score was associated with a less than 5% risk of respiratory decompensation in the validation cohort.

CONCLUSION

A significant proportion of admitted COVID-19 patients progress to respiratory failure within 24 hours of admission. These events are accurately predicted with bedside respiratory examination findings within a simple scoring system.

US-Pro: An Application Enabling Efficient, High-Throughput Ultrasound Video Processing

We describe a new graphical user interface-based application, US-Pro, designed to enable customized, high-throughput ultrasound video anonymization and dynamic cropping before output to video or high-efficiency disk storage. This application is distributed in a Docker container environment, which supports facile software installation on the most commonly used operating systems, as well as local processing of data sets, precluding the external transfer of electronic protected health information. The US-Pro application will facilitate the reproducible production of large-scale ultrasound video data sets for varied applications, including machine-learning analysis, educational distribution, and quality assurance review.

Sparse Embedding for Interpretable Hospital Admission Prediction

This paper introduces a sparse embedding for electronic health record (EHR) data in order to predict hospital admission. We use a k-sparse autoencoder to embed the original registry data into a much lower dimension, with sparsity as a goal. Then, t-SNE is used to show the embedding of each patient's data in a 2D plot. We then demonstrate the predictive accuracy in different existing machine learning algorithms. Our sparse embedding performs competitively against the original data and traditional embedding vectors with an AUROC of 0.878. In addition, we demonstrate the expressive power of our sparse embedding, i.e. interpretability. Sparse embedding can discover more phenotypes in t-SNE visualization than original data or traditional embedding. The discovered phenotypes can be regarded as different risk groups, through which we can study the driving risk factors for each patient phenotype.

Mitigating the photocurrent persistence of single ZnO nanowires for low noise photodetection applications

In this work, we investigate the optoelectronic properties of zinc oxide (ZnO) nanowires, which are good candidates for applications based on integrated optics. Single ZnO nanowire photodetectors were fabricated with ohmic contacts. By taking current transient measurements in different atmospheres (oxygen, air, vacuum and argon), and at various temperatures, we point out the importance of surface effects on the electrical behaviour. Results confirm that oxygen chemisorption is responsible for the existence of a high photoconductive gain in these devices, and for the first time a two step process in the photocurrent rise transient is reported. A maximum gain of G = 7.8 × 10⁷ is achieved. However, under certain conditions, the persistence of the photocurrent can last up to several hours and as such may prevent the device from operating at useful rates. From a knowledge of the photocurrent response mechanisms, we establish a method to restore the photodetector to its initial state, with very low dark current, by applying an appropriate gate voltage sequence. This advances the state of the art for these detectors towards commercial applications.

Physical Restraint Use in Adult Patients Presenting to a General Emergency Department

STUDY OBJECTIVE

The prevalence of agitation among emergency department (ED) patients is increasing. Physical restraints are routinely used to prevent self-harm and to protect staff, but are associated with serious safety risks. To date, characterization of physical restraint use in the emergency setting has been limited. We thus aim to describe restraint patterns in the general ED to guide future investigation in the management of behavioral disorders.

METHODS

We conducted a cross-sectional study of adult patients presenting to 5 adult EDs within a large regional health system for 2013 to 2015, and with a physical restraint order during their visit. We undertook descriptive analyses and cluster analysis to determine unique meaningful groups within our sample.

RESULTS

In 956,153 total ED visits, 4,661 patients (0.5%) had associated restraint orders, representing 3,739 unique patients. The median age was 47 years (interquartile range 32 to 59 years), 66.7% of patients were men, 61.9% had a psychiatric history, and 91.1% arrived by ambulance. For chief complaints, 33.7% were alcohol or drug use, 45.4% medical, 12.3% psychiatric, and 8.5% trauma. Cluster analysis identified 2 distinct cohorts. A younger, predominantly male population presented with alcohol or drug use, whereas an older group arrived with medical complaints.

CONCLUSION

Our data found strong association of alcohol or drug use with physical restraints and identified a unique elderly population with behavioral disturbances in the ED. Further characterization of causal links and safer practices to manage agitation for these vulnerable populations are needed.

Predicting hospital admission at emergency department triage using machine learning

OBJECTIVE

To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage.

METHODS

This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model.

RESULTS

A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91).

CONCLUSION

Machine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.

Predicting urinary tract infections in the emergency department with machine learning

BACKGROUND

Urinary tract infection (UTI) is a common emergency department (ED) diagnosis with reported high diagnostic error rates. Because a urine culture, part of the gold standard for diagnosis of UTI, is usually not available for 24-48 hours after an ED visit, diagnosis and treatment decisions are based on symptoms, physical findings, and other laboratory results, potentially leading to overutilization, antibiotic resistance, and delayed treatment. Previous research has demonstrated inadequate diagnostic performance for both individual laboratory tests and prediction tools.

OBJECTIVE

Our aim, was to train, validate, and compare machine-learning based predictive models for UTI in a large diverse set of ED patients.

METHODS

Single-center, multi-site, retrospective cohort analysis of 80,387 adult ED visits with urine culture results and UTI symptoms. We developed models for UTI prediction with six machine learning algorithms using demographic information, vitals, laboratory results, medications, past medical history, chief complaint, and structured historical and physical exam findings. Models were developed with both the full set of 211 variables and a reduced set of 10 variables. UTI predictions were compared between models and to proxies of provider judgment (documentation of UTI diagnosis and antibiotic administration).

RESULTS

The machine learning models had an area under the curve ranging from 0.826-0.904, with extreme gradient boosting (XGBoost) the top performing algorithm for both full and reduced models. The XGBoost full and reduced models demonstrated greatly improved specificity when compared to the provider judgment proxy of UTI diagnosis OR antibiotic administration with specificity differences of 33.3 (31.3-34.3) and 29.6 (28.5-30.6), while also demonstrating superior sensitivity when compared to documentation of UTI diagnosis with sensitivity differences of 38.7 (38.1-39.4) and 33.2 (32.5-33.9). In the admission and discharge cohorts using the full XGboost model, approximately 1 in 4 patients (4109/15855) would be re-categorized from a false positive to a true negative and approximately 1 in 11 patients (1372/15855) would be re-categorized from a false negative to a true positive.

CONCLUSION

The best performing machine learning algorithm, XGBoost, accurately diagnosed positive urine culture results, and outperformed previously developed models in the literature and several proxies for provider judgment. Future prospective validation is warranted.

Polarisation-controlled single photon emission at high temperatures from InGaN quantum dots

Solid-state single photon sources with polarisation control operating beyond the Peltier cooling barrier of 200 K are desirable for a variety of applications in quantum technology. Using a non-polar InGaN system, we report the successful realisation of single photon emission with a g⁽²⁾(0) of 0.21, a high polarisation degree of 0.80, a fixed polarisation axis determined by the underlying crystallography, and a GHz repetition rate with a radiative lifetime of 357 ps at 220 K in semiconductor quantum dots. The temperature insensitivity of these properties, together with the simple planar epitaxial growth method and absence of complex device geometries, demonstrates that fast single photon emission with polarisation control can be achieved in solid-state quantum dots above the Peltier temperature threshold, making this system a potential candidate for future on-chip applications in integrated systems.

Use of Point-of-Care Ultrasound in the Emergency Department: Insights From the 2012 Medicare National Payment Data Set

OBJECTIVES

Point-of-care ultrasound is a valuable tool with potential to expedite diagnoses and improve patient outcomes in the emergency department. However, little is known about national patterns of adoption. This study examined nationwide point-of-care ultrasound reimbursement among emergency medicine (EM) practitioners and examined regional and practitioner level variations.

METHODS

Data from the 2012 Center for Medicare and Medicaid Services Fee-for-Service Provider Utilization and Payment Data include all practitioners who received more than 10 Medicare Part B fee-for-service reimbursements for any Healthcare Common Procedure Coding System code in 2012. Odds ratios (ORs) and descriptive statistics were calculated to assess relationships between ultrasound reimbursement and practice location, nearby presence of an EM residency, and time elapsed since practitioner graduation.

RESULTS

Of 52,928 unique EM practitioners, 391 (0.7%) received limited ultrasound reimbursements for a total of 16,389 scans in 2012. Urban counties had an OR of 5.4 (95% confidence interval, 3.8-7.8) for receiving point-of-care ultrasound reimbursements compared to rural counties. Counties with an EM residency had an OR of 84.7 (95% confidence interval, 42.6-178.8) for reimbursement compared to counties without. The OR for receiving reimbursement was independent of medical school graduation year (P = .83); however, recent graduates performed more scans (P = .02).

CONCLUSIONS

A small minority of EM practitioners received reimbursements for point-of-care ultrasound from Medicare beneficiaries. These practitioners were more likely to reside in urban and academic settings. Future efforts should assess the degree to which our findings reflect either low point-of-care ultrasound use or low rates of billing for ultrasound examinations that are performed.

The Association Between Physician Empathy and Variation in Imaging Use

BACKGROUND

Variation in emergency physician computed tomography (CT) imaging utilization is well described, but little is known about what drives it. Physician empathy has been proposed as a potential characteristic affecting CT utilization.

OBJECTIVES

The objective was to describe empathy in a cohort of emergency physicians and evaluate its association with CT utilization. We also sought to compare emergency physician performance on an empathy psychometric test with performance on other psychometric tests previously proposed as predictors of CT utilization.

METHODS

This cross-sectional study included two parts: 1) a secondary analysis of emergency department (ED) CT imaging utilization data in a large health system from July 2013 to June 2014 and 2) a survey study of the cohort of physicians responsible for this imaging using four psychometric scales: the Jefferson Scale of Empathy (JSE), a risk-taking subset of the Jackson Personality Index (RTS), the Stress from Uncertainty Scale (SUS), and the Malpractice Fear Scale (MFS). The study included data and physicians from four EDs: one urban, academic ED, two community, and one free-standing. A hierarchical, mixed-effects regression model was used to evaluate the association between emergency physician performance on the four scales and risk-adjusted CT imaging utilization. The model incorporated physician-specific CT utilization rates adjusted for propensity scores that were calculated using over 500 patient-level variables via random forest methods, physician demographics, and a random provider effect to account for the clustering of observations.

RESULTS

CT variation analysis included 113,517 patients seen during the study period by the 74 eligible emergency physician survey respondents; 20,972 (18.5%) of these patients had at least one CT. The survey response rate was 74 of 82 (90.2%). Correlation coefficients between JSE and the other scales were not statistically significant. In subset analysis, there was a trend toward a physician's number of years in practice and RTS score contributing to CT utilization for traumatic head CT. There were no significant associations between performance on any of the psychometric scales and CT utilization.

CONCLUSIONS

Performance on the JSE, RTS, SUS, or MFS was not predictive of risk-adjusted CT utilization in the ED. The underlying physician-based factors that mediate interphysician variation remain to be clearly identified.

Cost-effectiveness of the Cardiac Component of the Focused Assessment of Sonography in Trauma Examination in Blunt Trauma

BACKGROUND

Blunt cardiac injury severe enough to require surgical intervention (sBCI) is an exceedingly rare event occurring in approximately 1 out of every 1600 trauma patients. While performing the cardiac component of the Focused Assessment of Sonography in Trauma (cFAST) exam is effective in penetrating trauma, it is unclear whether it is of value in blunt trauma given the low prevalence of sBCI, the imperfect test characteristics of the FAST exam, and the rate of incidental pericardial effusion.

OBJECTIVE

The objective was to determine through decision analysis whether performing the cFAST exam is cost-effective in the evaluation of hypotensive and normotensive blunt trauma patients.

METHODS

We created two decision analytic models using commercially available software (TreeAgePro2011) to evaluate the cost-effectiveness of the cFAST in hypotensive (systolic blood pressure <90 mm Hg) and normotensive blunt trauma patients. Clinical probabilities were obtained from published data. Costs were estimated from Medicare reimbursement and charge data. The willingness-to-pay threshold was $50,000/quality-adjusted life-years (QALYs). Sensitivity analyses were performed over plausible ranges using available literature.

RESULTS

In hypotensive patients, for the base case scenario of a 34-year-old with blunt trauma, the cFAST strategy had a cost of $42,882.70 and an effectiveness of 25.3597 QALYs, whereas the no cFAST strategy had a cost of $42,753.52 and an effectiveness of 25.3532 QALYs. The incremental cost-effectiveness ratio (ICER) was $19,918/QALY. For normotensive patients the cFAST strategy had a cost of $18,331.03 and an effectiveness of 23.2817 QALYs, whereas the no cFAST strategy had a cost of $18,207.58 and an effectiveness of 23.2814 QALYs. The ICER was $465,867/QALY. In the sensitivity analyses, age, probability of death from sBCI with prompt treatment, and probability of sBCI were the main drivers of variability in the model outcomes.

CONCLUSIONS

The cFAST for blunt trauma is cost-effective for hypotensive but not for normotensive patients. The ICER for hypotensive patients was more than 20 times higher than the ICER for normotensive patients. Our results suggest that performing the cFAST exam may not be an effective use of resources in normotensive blunt trauma patients.

Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach

OBJECTIVES

Predictive analytics in emergency care has mostly been limited to the use of clinical decision rules (CDRs) in the form of simple heuristics and scoring systems. In the development of CDRs, limitations in analytic methods and concerns with usability have generally constrained models to a preselected small set of variables judged to be clinically relevant and to rules that are easily calculated. Furthermore, CDRs frequently suffer from questions of generalizability, take years to develop, and lack the ability to be updated as new information becomes available. Newer analytic and machine learning techniques capable of harnessing the large number of variables that are already available through electronic health records (EHRs) may better predict patient outcomes and facilitate automation and deployment within clinical decision support systems. In this proof-of-concept study, a local, big data-driven, machine learning approach is compared to existing CDRs and traditional analytic methods using the prediction of sepsis in-hospital mortality as the use case.

METHODS

This was a retrospective study of adult ED visits admitted to the hospital meeting criteria for sepsis from October 2013 to October 2014. Sepsis was defined as meeting criteria for systemic inflammatory response syndrome with an infectious admitting diagnosis in the ED. ED visits were randomly partitioned into an 80%/20% split for training and validation. A random forest model (machine learning approach) was constructed using over 500 clinical variables from data available within the EHRs of four hospitals to predict in-hospital mortality. The machine learning prediction model was then compared to a classification and regression tree (CART) model, logistic regression model, and previously developed prediction tools on the validation data set using area under the receiver operating characteristic curve (AUC) and chi-square statistics.

RESULTS

There were 5,278 visits among 4,676 unique patients who met criteria for sepsis. Of the 4,222 patients in the training group, 210 (5.0%) died during hospitalization, and of the 1,056 patients in the validation group, 50 (4.7%) died during hospitalization. The AUCs with 95% confidence intervals (CIs) for the different models were as follows: random forest model, 0.86 (95% CI = 0.82 to 0.90); CART model, 0.69 (95% CI = 0.62 to 0.77); logistic regression model, 0.76 (95% CI = 0.69 to 0.82); CURB-65, 0.73 (95% CI = 0.67 to 0.80); MEDS, 0.71 (95% CI = 0.63 to 0.77); and mREMS, 0.72 (95% CI = 0.65 to 0.79). The random forest model AUC was statistically different from all other models (p ≤ 0.003 for all comparisons).

CONCLUSIONS

In this proof-of-concept study, a local big data-driven, machine learning approach outperformed existing CDRs as well as traditional analytic techniques for predicting in-hospital mortality of ED patients with sepsis. Future research should prospectively evaluate the effectiveness of this approach and whether it translates into improved clinical outcomes for high-risk sepsis patients. The methods developed serve as an example of a new model for predictive analytics in emergency care that can be automated, applied to other clinical outcomes of interest, and deployed in EHRs to enable locally relevant clinical predictions.

Abstract P6-11-02: Hospitalizations and costs during Implementation of a lay navigation program for older patients with breast cancer in the deep south

Background: Patient-centered strategies are needed to enhance the value of cancer care particularly at the end of life. Lay navigators (LN) can be trained to provide an extra layer of support for cancer patients from diagnosis through survivorship or end of life. We hypothesized that integrating LNs into the care team would reduce healthcare utilization and cost for patients with cancer, including those with breast cancer.

Methods: A prospective, observational study of Medicare claims data was conducted of beneficiaries ≥ 65 years old diagnosed with cancer after 2008 who received care within the UAB Health System Cancer Community Network (12 cancer centers of varying size located in AL, MS, TN, GA, and FL). The first breast cancer (BC) patient was enrolled in navigation in April 2013, and ∼18% of BC patients were navigated by the end of 2014. For this analysis, we report on the subset of patients with BC. The outcomes of interest were calculated per quarter from 2012-2014: (1) the proportion of patients with at least 1 hospitalization, (2) the proportion of the 492 deceased BC patients with a hospitalization in the last 30 and 14 days of life and (3) the Total costs for Medicare, excluding prescription drug costs. We used general linear models to evaluate changes in both health care utilization and cost over time, adjusting for age, sex, cancer stage, phase of care, and navigation group. Differential effects for navigated and non-navigated groups were tested with a group*time interaction. Healthcare utilization estimates are presented as Incidence Rate Ratios (IRR), and costs for Medicare as parameter estimates (β) in terms of dollar amounts.

Results: 4835 BC patients received care from 2012-2014: 622 received navigation services. 14.2 % of navigated BC patients were stage III/IV, compared to 9.33% of non-navigated patients. The proportion of hospitalizations trended downward from 7.9% in quarter 1 (Q1) 2012 to 5.7% in Q4 of 2014 (IRR 0.965, p =0.14), with similar decreases for navigated and non-navigated patients (IRR= 1.00, p &gt; 0.05). Hospitalization in the last 30 days and last 14 days of life were 49.7% and 29.3%, respectively, with no between groups difference. Costs per beneficiary per quarter decreased overall from $4,161 in Q1 2012 to $3,010 in Q4 2014 (p &lt;0.0001). In adjusted analysis, the navigated patients had an average $577 greater decline per quarter than the non-navigated patients (βNavigated=-$636; βnon-Navigated=-$59; p&lt;0.0001).

Conclusions: Medicare costs declined during implementation of a lay navigation program, with greater reductions for navigated patients than non-navigated BC patients. Overall hospitalizations also declined, yet rates remain high for breast cancer patients at the end of life. Integration of LNs should be considered by health systems aiming to transition to value-based healthcare delivery.

The project described was supported by Grant Number 1C1CMS331023 from the Department of Health and Human Services, Centers for Medicare & Medicaid Services. The contents of this abstract are solely the responsibility of the authors and do not necessarily represent the official views of the U.S. Department of Health and Human Services or any of its agencies.

Citation Format: Rocque GB, Kvale EA, Jackson BE, Kenzik K, Lisovicz N, Demark-Wahnefried W, Meneses KM, Taylor RA, Acemgil A, Chambless C, Li Y, Martin M, Fouad M, Pisu M, Partridge EE. Hospitalizations and costs during Implementation of a lay navigation program for older patients with breast cancer in the deep south. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P6-11-02.

Emergency physician focused cardiac ultrasound improves diagnosis of ascending aortic dissection

STUDY OBJECTIVE

Ascending aortic dissection (AAD) is an uncommon, time-sensitive, and deadly diagnosis with a nonspecific presentation. Ascending aortic dissection is associated with aortic dilation, which can be determined by emergency physician focused cardiac ultrasound (EP FOCUS). We seek to determine if patients who receive EP FOCUS have reduced time to diagnosis for AAD.

METHODS

We performed a retrospective review of patients treated at 1 of 3 affiliated emergency departments, March 1, 2013, to May 1, 2015, diagnosed as having AAD. All autopsies were reviewed for missed cases. Primary outcome measure was time to diagnosis. Secondary outcomes were time to disposition, misdiagnosis rate, and mortality.

RESULTS

Of 386547 ED visits, targeted review of 123 medical records and 194 autopsy reports identified 32 patients for inclusion. Sixteen patients received EP FOCUS and 16 did not. Median time to diagnosis in the EP FOCUS group was 80 (interquartile range [IQR], 46-157) minutes vs 226 (IQR, 109-1449) minutes in the non-EP FOCUS group (P = .023). Misdiagnosis was 0% (0/16) in the EP FOCUS group vs 43.8% (7/16) in the non-EP FOCUS group (P = .028). Mortality, adjusted for do-not-resuscitate status, for EP FOCUS vs non-EP FOCUS was 15.4% vs 37.5% (P = .24). Median rooming time to disposition was 134 (IQR, 101-195) minutes for EP FOCUS vs 205 (IQR, 114-342) minutes for non-EP FOCUS (P = .27).

CONCLUSIONS

Patients who receive EP FOCUS are diagnosed faster and misdiagnosed less compared with patients who do not receive EP FOCUS. We recommend assessment of the thoracic aorta be performed routinely during cardiac ultrasound in the emergency department.

Towards witnessing quantum effects in complex molecules

Whether many-body objects like organic molecules can exhibit full quantum behaviour, including entanglement, is an open fundamental question. We present a generic theoretical protocol for entangling two organic molecules, such as dibenzoterrylene in anthracene. The availability of organic dye molecules with two-level energy structures characterised by sharp and intense emission lines are characteristics that position them favourably as candidates for quantum information processing technologies involving single-photons. Quantum entanglement can in principle be generated between several organic molecules by carefully interfering their photoluminescence spectra. Major milestones have been achieved in the last 10 years showcasing entanglement in diverse systems including ions, cold atoms, superconductors, photons, quantum dots and NV-centres in diamond, but not yet in molecules.

Improving emergency physician performance using audit and feedback: a systematic review

BACKGROUND

Audit and feedback can decrease variation and improve the quality of care in a variety of health care settings. There is a growing literature on audit and feedback in the emergency department (ED) setting. Because most studies have been small and not focused on a single clinical process, systematic assessment could determine the effectiveness of audit and feedback interventions in the ED and which specific characteristics improve the quality of emergency care.

OBJECTIVE

The objective of the study is to assess the effect of audit and feedback on emergency physician performance and identify features critical to success.

METHODS

We adhered to the PRISMA statement to conduct a systematic review of the literature from January 1994 to January 2014 related to audit and feedback of physicians in the ED. We searched Medline, EMBASE, PsycINFO, and PubMed databases. We included studies that were conducted in the ED and reported quantitative outcomes with interventions using both audit and feedback. For included studies, 2 reviewers independently assessed methodological quality using the validated Downs and Black checklist for nonrandomized studies. Treatment effect and heterogeneity were to be reported via meta-analysis and the I2 inconsistency index.

RESULTS

The search yielded 4332 articles, all of which underwent title review; 780 abstracts and 131 full-text articles were reviewed. Of these, 24 studies met inclusion criteria with an average Downs and Black score of 15.6 of 30 (range, 6-22). Improved performance was reported in 23 of the 24 studies. Six studies reported sufficient outcome data to conduct summary analysis. Pooled data from studies that included 41,124 patients yielded an average treatment effect among physicians of 36% (SD, 16%) with high heterogeneity (I2=83%).

CONCLUSION

The literature on audit and feedback in the ED reports positive results for interventions across numerous clinical conditions but without standardized reporting sufficient for meta-analysis. Characteristics of audit and feedback interventions that were used in a majority of studies were feedback that targeted errors of omission and that was explicit with measurable instruction and a plan for change delivered in the clinical setting greater than 1 week after the audited performance using a combination of media and types at both the individual and group levels. Future work should use standardized reporting to identify the specific aspects of audit or feedback that drive effectiveness in the ED.

The "5Es" of emergency physician-performed focused cardiac ultrasound: a protocol for rapid identification of effusion, ejection, equality, exit, and entrance

Emergency physician (EP)-performed focused cardiac ultrasound (EP FOCUS) has been increasingly recognized as a crucial tool to help clinicians diagnose and treat potentially life-threatening conditions. The existing literature demonstrates a variety of EP FOCUS applications and protocols; however, EP FOCUS is not taught, practiced, or interpreted consistently between institutions. Drawing on over 12 years of experience in a large-volume, high-acuity academic emergency department, we have developed a protocol for teaching and performing EP FOCUS known as "The 5Es," where each E represents a specific assessment for immediately relevant clinical information. These include pericardial effusion, qualitative left ventricular ejection, ventricular equality, exit (aortic root diameter), and entrance (inferior vena cava diameter and respirophasic variation). Each of these assessments has been well described in the emergency medicine literature and is within the scope of EP-performed echocardiography. This approach provides a reliable and easily recalled framework for assessing, teaching, and communicating EP FOCUS findings that are essential in caring for the patient in the emergency setting.

Temperature dependence of the radiative recombination time in ZnO nanorods under an external magnetic field of 6 T

The Temperature dependence of the exciton radiative decay time in ZnO nanorods has been investigated, which is associated with the density of states for the intra-relaxation of thermally excited excitons. The photoluminescence decay time was calibrated by using the photoluminescence intensity in order to obtain the radiative decay time. In the absence of an external magnetic field, we have confirmed that the radiative decay time increased with temperature in a similar manner to that seen in bulk material (∼ T1.5). Under an external magnetic field of 6 T parallel to the c-axis, we found that the power coefficient of the radiative decay time with temperature decreased (∼ T1.3) when compared to that in the absence of a magnetic field. This result can be attributed to an enhancement of the effective mass perpendicular to the magnetic field and a redshift of the center-of-mass exciton as a consequence of perturbation effects in the weak-field regime.

Confocal microphotoluminescence mapping of coupled and detuned states in photonic molecules

We study the coupling of cavities defined by the local modulation of the waveguide width using confocal photoluminescence microscopy. We are able to spatially map the profile of the antisymmetric (antibonding) and symmetric (bonding) modes of a pair of strongly coupled cavities (photonic molecule) and follow the coupled cavity system from the strong coupling to the weak coupling regime in the presence of structural disorder. The effect of disorder on this photonic molecule is also investigated numerically with a finite-difference time-domain method and a semi-analytical approach, which enables us to quantify the light localization observed in either cavity as a function of detuning.

Posterior reversible encephalopathy syndrome induced by pazopanib for renal cell carcinoma

WHAT IS KNOWN AND OBJECTIVE

Posterior reversible encephalopathy syndrome (PRES) can be the result of acute hypertension, eclampsia, renal failure and the use of immunosuppressive or cytotoxic agents. We report a case of PRES as a result of the use of pazopanib, a vascular endothelial growth factor inhibitor used for renal cell carcinoma (RCC).

CASE SUMMARY

A 76-year-old man treated with RCC develops PRES shortly after the initiation of pazopanib.

WHAT IS NEW AND CONCLUSION

There are no known reports of the association between PRES and pazopanib. We postulate that pazopanib can disrupt the normal endothelial function of the brain leading to the development of PRES.

Breaking through a roadblock in prostate cancer research: an update on human model systems

Prostate cancer is a prevalent disease that affects the aging male population. Whilst there have been significant advances of our biological understanding of the disease, clinical translation of promising agents continues to lag behind. In part, this is due to a paucity of relevant experimental and pre-clinical models required to further develop effective prevention and therapeutic strategies. Genetically modified cell lines fail to entirely represent the genetic and molecular diversity of primary human specimens, particularly from localised disease. Furthermore, primary prostate cancer tissues are extremely difficult to grow in the laboratory and virtually all human models, whether they grow as xenografts in immune-deficient animals or as cell cultures, are genetically modified by the investigator or derived from patients with advanced metastatic disease. In this review, we discuss the latest advances and improvements to current methods of xenografting human primary prostate cancer, and their potential application to translational research.

Point-of-care focused cardiac ultrasound for the assessment of thoracic aortic dimensions, dilation, and aneurysmal disease

OBJECTIVES

  Thoracic aortic aneurysm and thoracic aortic dissection are related and potentially deadly diseases that present with nonspecific symptoms. Transthoracic echocardiography (TTE) may detect thoracic aortic pathology and is being increasingly performed by the emergency physician at the bedside; however, the accuracy of point-of-care (POC) focused cardiac ultrasound (FOCUS) for thoracic aortic aneurysm and thoracic aortic dissection has not been studied. The objective of this pilot study was to explore the agreement, sensitivity, and specificity of FOCUS for thoracic aortic dimensions, dilation, and aneurysm compared with CT angiography (CTA) as the reference standard.

METHODS

  This study was a retrospective pilot analysis of image and chart data on consecutive patients presenting to an urban, academic emergency department (ED) between January 2008 and June 2010, who had both a FOCUS and a CTA for suspicion of thoracic aorta pathology. Thoracic aorta dimensions were measured from recordings by three ultrasound-trained emergency physicians blinded to any initial FOCUS and CTA results. CTA measurements were obtained by a radiologist blinded to the FOCUS results. Using cutoffs of 40 and 45 mm, we calculated the sensitivity and specificity of FOCUS for aortic dilation and aneurysm with the largest measurement on CT as the reference standard. Bland-Altman plots with 95% limits of agreement were used to demonstrate agreement for aortic measurements, kappa statistics to assess the degree of agreement between tests for aortic dilation, and intraclass correlation for interobserver and intraobserver variability.

RESULTS

  Ninety-two patients underwent both FOCUS and CTA during the study period. Ten FOCUS studies had inadequate visualization for all measurements areas. Eighty-two patients were included in the final analysis. Mean (±SD) age was 58.1 (±16.6) years and 58.5% were male. Sensitivity, specificity, and the observed kappa value (95% confidence interval [CI]) between FOCUS and CTA for the presence of aortic dilation at the 40-mm cutoff were 0.77 (95% CI = 0.58 to 0.98), 0.95 (95% CI = 0.84 to 0.99), and 0.74 (95% CI = 0.58 to 0.90), respectively. The mean difference (95% limits of agreement) for the Bland-Altman plots was 0.6 mm (-5.3 to 6.5) for the sinuses of Valsalva, 4 mm (-2.7 to 10.7) for the sinotubular junction, 1.5 mm (-5.8 to 8.8) for the ascending aorta, and 2.2 mm (-5.9 to 10.3) for the descending aorta.

CONCLUSIONS

  In this retrospective pilot study, FOCUS demonstrated good agreement with CTA measurements of maximal thoracic aortic diameter. FOCUS appears to be specific for aortic dilation and aneurysm when compared to CTA, but requires further prospective study.

Acutely ruptured intracranial saccular aneurysms treated with stent assisted coiling: complications and outcomes in 42 consecutive patients

BACKGROUND

Antiplatelet agents are required to prevent thromboembolic complications from recently deployed intracranial stents, yet they carry a risk of bleeding complications that may be serious in patients with recent subarachnoid hemorrhage.

METHOD

Consecutive patients at a single institution who had ruptured intracranial saccular aneurysms treated with stent assisted coiling were retrospectively reviewed. Our primary outcomes were ischemic stroke related to the stent and bleeding complications possibly related to antithrombotic therapy. Secondary outcomes included 3 month follow-up National Institute of Health Stroke Scale (NIHSS) scores and modified Rankin Scale (mRS) scores.

RESULTS

44 aneurysms in 42 patients were treated. Seven patients experienced ischemic strokes during their hospitalization. Five ischemic strokes were secondary to vasospasm; one was definitely related to thrombus formation within the stent and one was possibly related to the stent. Two patients had asymptomatic intracranial hemorrhages and one patient had a symptomatic intracranial hemorrhage. Patients with Hunt and Hess grades I-II (n=25) experienced no stent associated ischemic strokes or symptomatic intracranial hemorrhages. The two stent associated ischemic strokes and one symptomatic intracranial hemorrhage occurred in patients with Hunt and Hess grades III-V (n=17) and patients with external ventricular drains (EVDs) (n=17). Only one patient had disability at the 3 month follow-up that was possibly related to the stent (mRS score of 3 and NIHSS score of 2).

CONCLUSION

These data suggest that higher grade hemorrhage patients, especially those with EVDs, are at greater risk for ischemic stroke and/or bleeding complications than lower grade patients. However, the complications had a small impact on mid-term disability outcomes in this cohort.

Abnormal photoluminescence properties of GaN nanorods grown on Si(111) by molecular-beam epitaxy

We have studied the photoluminescence properties of GaN nanorods grown on Si(111) substrates by radio-frequency plasma-assisted molecular-beam epitaxy. The hexagonal shaped nanorods with lateral average diameters from 30 to 150 nm are obtained by controlling the Ga flux with a fixed amount of nitrogen. As the diameters decrease, the main emission lines assigned as donor bound excitons are blueshifted, causing a spectral overlap of this emission line with that of the free exciton at 10 K due to the quantum size effect in the GaN nanorods. The temperature-dependent photoluminescence spectra show an abnormal behaviour with an 'S-like' shape for higher diameter nanorods. The activation energy of the free exciton for GaN nanorods with different diameters was also evaluated.

Prostatic tumor stroma: a key player in cancer progression

Although it is evident that prostatic epithelial stem cells are responsible for maintaining normal and malignant tissues, it is well recognized that epithelial cells do not exist independently, but act in concert with the stromal microenvironment. Prostatic stroma is pivotal for normal development and homeostasis. The genetic and morphological changes that occur in prostatic epithelial cells, as they progress from a normal to malignant phenotype, have been well described. However, it is evident that the surrounding microenvironment also plays a major role in cancer cell growth, survival, invasion and metastatic progression. Prostatic tumor stroma provides a niche environment for cancer stem cells and therefore contributes to self-renewal and differentiation. In order to target the tumor microenvironment and develop new therapeutics for prostate cancer, we must understand the role of the tumor stroma, specifically the events mediating the interactions between the cancer stem cell and its immediate microenvironment during cancer initiation and progression. This article presents the rationale and discusses the challenges to targeting prostatic tumor stroma in cancer therapies that will potentially treat prostate cancer.