Performance of International Classification of Disease-10 codes in detecting emergency department patients with opioid misuse.

BACKGROUND AND AIMS: Accurate case discovery is critical for disease surveillance, resource allocation and research. International Classification of Disease (ICD) diagnosis codes are commonly used for this purpose. We aimed to determine the sensitivity, specificity and positive predictive value (PPV) of ICD-10 codes for opioid misuse case discovery in the emergency department (ED) setting. DESIGN AND SETTING: Retrospective cohort study of ED encounters from January 2018 to December 2020 at an urban academic hospital in the United States. A sample of ED encounters enriched for opioid misuse was developed by oversampling ED encounters with positive urine opiate screens or pre-existing opioid-related diagnosis codes in addition to other opioid misuse risk factors. CASES: A total of 1200 randomly selected encounters were annotated by research staff for the presence of opioid misuse within health record documentation using a 5-point scale for likelihood of opioid misuse and dichotomized into cohorts of opioid misuse and no opioid misuse. MEASUREMENTS: Using manual annotation as ground truth, the sensitivity and specificity of ICD-10 codes entered during the encounter were determined with PPV adjusted for oversampled data. Metrics were also determined by disposition subgroup: discharged home or admitted. FINDINGS: There were 541 encounters annotated as opioid misuse and 617 with no opioid misuse. The majority were males (54.4%), average age was 47 years and 68.5% were discharged directly from the ED. The sensitivity of ICD-10 codes was 0.56 (95% confidence interval [CI], 0.51-0.60), specificity 0.99 (95% CI, 0.97-0.99) and adjusted PPV 0.78 (95% CI, 0.65-0.92). The sensitivity was higher for patients discharged from the ED (0.65; 95% CI, 0.60-0.69) than those admitted (0.31; 95% CI, 0.24-0.39). CONCLUSIONS: International Classification of Disease-10 codes appear to have low sensitivity but high specificity and positive predictive value in detecting opioid misuse among emergency department patients in the United States.

Association of Patient English Proficiency and Diagnostic Imaging Acquisition in Emergency Department Patients with Abdominal Symptoms.

BACKGROUND: Patients with limited English proficiency (LEP) are at risk for communication barriers during medical care in the United States compared with English-proficient (EP) patients. It is unknown how EP affects the utilization of advanced diagnostic imaging (ADI) in the emergency department (ED). OBJECTIVE: The aim of this study was to compare the acquisition and findings of ADI in EP and LEP patients with abdominal symptoms. METHODS: We conducted a retrospective analysis of adult ED patients from January 2015 to January 2016. Patients were divided into EP and LEP cohorts. Logistic regression models incorporated language proficiency, interpretation method, and demographic characteristics. We determined crude and adjusted odds ratios (ORs) for the acquisition of ADI, defined as either computed tomography or ultrasound, and the proportion with actionable findings. RESULTS: In 3324 encounters (2134 EP; 1190 LEP), LEP patients were older (46.3 years vs. 43.8 years), more likely to be female (66.7% vs. 51.5%), and preferred Spanish (91.4%). ADI was obtained in 43.5% of EP and 48.1% of LEP. Adjusting for age, sex, and interpretation method, the OR was 1.09 (95% CI 0.90-1.32). There were no significant associations between interpretation type and acquisition of ADI. The proportion with actionable findings were similar in EP and LEP cohorts (29.6% vs. 26.7%). CONCLUSIONS: Accounting for demographic differences, ADI acquisition was similar for ED patients with and without LEP. Further research is needed to determine optimal interpretation modalities in this setting to prevent unnecessary imaging.

Previous exposure to violence among emergency department patients without trauma-related complaints: A cross sectional analysis.

INTRODUCTION: The Emergency Department (ED) is a critical setting for the treatment of acute violence-related complaints and violent victimization is associated with numerous long-term negative health outcomes. A trauma-informed care framework can prevent re-traumatization of victims within the healthcare setting, but currently there are insufficient mechanisms to detect previous exposures to community violence within the ED. The current study sought to determine the prevalence of community violence and characterize the types of violence exposures among adult ED patients without trauma-related complaints to determine if there may be a need for expanded screening for previous exposure to violence for ED patients. METHODS: This was a prospective cross-sectional observational study of adult ED patients without a trauma-related chief complaint at an urban public hospital. Adult patients were approached by trained research staff and answered questions adapted from the Survey of Exposure to Community Violence (SECV), which measures lifetime exposure to community violence, including both witnessing and victimization. The SECV was modified for clarity, brevity, and to assess exposure to violence within the previous 3 months and 3 years from enrollment, in addition to lifetime exposure. Enrollment occurred from June 2019 to September 2022 with a 19-month gap due to the COVID-19 pandemic. Demographics and results within SECV domains were analyzed using descriptive statistics. Comparisons between males and females in types of violence experienced during one's lifetime were made by fitting logistic regression models adjusting for age. RESULTS: A total of 222 respondents completed some or all of the modified SECV. Ages ranged from 19 to 88, with 47.7% of respondents identifying as female and 50.9% as male. Of all respondents, 43.7% reported directly witnessing violence during their lifetime, 69.4% being personally victimized by violence, and 55.4% personally knowing someone killed by a violent act. Of those personally victimized, 47.4% occurred within the preceding 3 years and 20.1% within 3 months. Among all respondents, lifetime victimization was reported in the following domains: slapping, hitting, or punching (45.9%); robbings or muggings (33.8%); physical threats (28.8%); verbal or emotional abuse (27.5%); being shot at (18.0%); uncomfortable physical touch (15.3%); forced entry while home (14.4%); sexual assault, molestation, or rape (13.5%); or being attacked with a knife (13.5%). Comparing male and female respondents, 63.5% of females and 76.6% of males reported any violent victimization over their lifetime (aOR 1.88; 95% CI 1.04-3.43). Additionally, 31.7% of females and 56.3% of males reported witnessing violence (aOR 2.86; 95% CI 1.64-5.06). Males were more commonly exposed to physical violence, violence with weapons, and threats while females more commonly reported sexual assault, molestation, and rape. CONCLUSION: Both lifetime and recent exposure to community violence was common among adult ED patients without trauma-related complaints. Broader adoption of a trauma-informed care framework and the development of efficient ED screening tools for previous exposure to trauma is reasonable in areas where community violence exposure is highly prevalent.

Applying Project ECHO (Extension for Community Health Care Outcomes) to improve addiction care in rural emergency departments.

Background: Advancements in research and legislation have improved emergency provider ability to treat opioid use disorder (OUD), but dissemination into rural emergency departments (EDs) is limited. Project Extension for Community Healthcare Outcomes (ECHO) allows community generalists to learn from specialists through telementoring. We aimed to use ECHO to facilitate knowledge translation, increase confidence, and change behavior of rural ED providers treating patients with OUD. Methods: Stakeholder interviews were conducted with rural ED providers. A group of ED addiction experts created an ECHO curriculum with eight OUD topics. ED health professionals were recruited and completed pre/post surveys centered around knowledge and comfort with treating OUD in the ED, with focus on clinical practice and stigma. Following the ECHO model, sessions included a 20-min didactic followed by two cases presented by participants, with discussion facilitated by faculty. Results: Twenty-seven participants registered; seven attended ≥75% of sessions and completed both surveys. Of the seven, three were physicians, two advanced practice providers, one nurse, and one clinical pharmacist. Eight 1-hour sessions were conducted in two cohorts between January and December 2021. On a 5-point Likert scale, respondents on average agreed with questions evaluating acceptability (mean ± SD 3.96 ± 0.64), appropriateness (mean ± SD 4.18 ± 1.18), and feasibility (mean ± SD 4.00 ± 1.17). Participants had a 1.09-point increase (paired t-test = 2.43, p = 0.05) on 7-point Likert-scale questions measuring self-efficacy and a 0.13-point change (paired t-test = 2.64, p = 0.04) on 4-point Likert scale questions measuring stigmatizing attitudes (reduction of attitudes). A total of 71% (5/7) reported changes in clinical practice and 57% (4/7) in departmental protocols after participation. Conclusions: Our ED OUD ECHO course successfully created a model for rural ED providers to learn from ED addiction experts. It was well received and impacted self-reported provider stigmatizing attitudes, patient-facing behavior, and departmental initiatives. Recruitment was challenging and participation was limited. Future efforts will target maximizing recruitment.

Machine Learning Techniques to Explore Clinical Presentations of COVID-19 Severity and to Test the Association With Unhealthy Opioid Use: Retrospective Cross-sectional Cohort Study.

BACKGROUND: The COVID-19 pandemic has exacerbated health inequities in the United States. People with unhealthy opioid use (UOU) may face disproportionate challenges with COVID-19 precautions, and the pandemic has disrupted access to opioids and UOU treatments. UOU impairs the immunological, cardiovascular, pulmonary, renal, and neurological systems and may increase severity of outcomes for COVID-19. OBJECTIVE: We applied machine learning techniques to explore clinical presentations of hospitalized patients with UOU and COVID-19 and to test the association between UOU and COVID-19 disease severity. METHODS: This retrospective, cross-sectional cohort study was conducted based on data from 4110 electronic health record patient encounters at an academic health center in Chicago between January 1, 2020, and December 31, 2020. The inclusion criterion was an unplanned admission of a patient aged ≥18 years; encounters were counted as COVID-19-positive if there was a positive test for COVID-19 or 2 COVID-19 International Classification of Disease, Tenth Revision codes. Using a predefined cutoff with optimal sensitivity and specificity to identify UOU, we ran a machine learning UOU classifier on the data for patients with COVID-19 to estimate the subcohort of patients with UOU. Topic modeling was used to explore and compare the clinical presentations documented for 2 subgroups: encounters with UOU and COVID-19 and those with no UOU and COVID-19. Mixed effects logistic regression accounted for multiple encounters for some patients and tested the association between UOU and COVID-19 outcome severity. Severity was measured with 3 utilization metrics: low-severity unplanned admission, medium-severity unplanned admission and receiving mechanical ventilation, and high-severity unplanned admission with in-hospital death. All models controlled for age, sex, race/ethnicity, insurance status, and BMI. RESULTS: Topic modeling yielded 10 topics per subgroup and highlighted unique comorbidities associated with UOU and COVID-19 (eg, HIV) and no UOU and COVID-19 (eg, diabetes). In the regression analysis, each incremental increase in the classifier's predicted probability of UOU was associated with 1.16 higher odds of COVID-19 outcome severity (odds ratio 1.16, 95% CI 1.04-1.29; P=.009). CONCLUSIONS: Among patients hospitalized with COVID-19, UOU is an independent risk factor associated with greater outcome severity, including in-hospital death. Social determinants of health and opioid-related overdose are unique comorbidities in the clinical presentation of the UOU patient subgroup. Additional research is needed on the role of COVID-19 therapeutics and inpatient management of acute COVID-19 pneumonia for patients with UOU. Further research is needed to test associations between expanded evidence-based harm reduction strategies for UOU and vaccination rates, hospitalizations, and risks for overdose and death among people with UOU and COVID-19. Machine learning techniques may offer more exhaustive means for cohort discovery and a novel mixed methods approach to population health.

The Identification of Subphenotypes and Associations with Health Outcomes in Patients with Opioid-Related Emergency Department Encounters Using Latent Class Analysis.

The emergency department (ED) is a critical setting for the treatment of patients with opioid misuse. Detecting relevant clinical profiles allows for tailored treatment approaches. We sought to identify and characterize subphenotypes of ED patients with opioid-related encounters. A latent class analysis was conducted using 14,057,302 opioid-related encounters from 2016 through 2017 using the National Emergency Department Sample (NEDS), the largest all-payer ED database in the United States. The optimal model was determined by face validity and information criteria-based metrics. A three-step approach assessed class structure, assigned individuals to classes, and examined characteristics between classes. Class associations were determined for hospitalization, in-hospital death, and ED charges. The final five-class model consisted of the following subphenotypes: Chronic pain (class 1); Alcohol use (class 2); Depression and pain (class 3); Psychosis, liver disease, and polysubstance use (class 4); and Pregnancy (class 5). Using class 1 as the reference, the greatest odds for hospitalization occurred in classes 3 and 4 (Ors 5.24 and 5.33, p < 0.001) and for in-hospital death in class 4 (OR 3.44, p < 0.001). Median ED charges ranged from USD 2177 (class 1) to USD 2881 (class 4). These subphenotypes provide a basis for examining patient-tailored approaches for this patient population.

Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study.

BACKGROUND: Substance misuse is a heterogeneous and complex set of behavioural conditions that are highly prevalent in hospital settings and frequently co-occur. Few hospital-wide solutions exist to comprehensively and reliably identify these conditions to prioritise care and guide treatment. The aim of this study was to apply natural language processing (NLP) to clinical notes collected in the electronic health record (EHR) to accurately screen for substance misuse. METHODS: The model was trained and developed on a reference dataset derived from a hospital-wide programme at Rush University Medical Center (RUMC), Chicago, IL, USA, that used structured diagnostic interviews to manually screen admitted patients over 27 months (between Oct 1, 2017, and Dec 31, 2019; n=54 915). The Alcohol Use Disorder Identification Test and Drug Abuse Screening Tool served as reference standards. The first 24 h of notes in the EHR were mapped to standardised medical vocabulary and fed into single-label, multilabel, and multilabel with auxillary-task neural network models. Temporal validation of the model was done using data from the subsequent 12 months on a subset of RUMC patients (n=16 917). External validation was done using data from Loyola University Medical Center, Chicago, IL, USA between Jan 1, 2007, and Sept 30, 2017 (n=1991 adult patients). The primary outcome was discrimination for alcohol misuse, opioid misuse, or non-opioid drug misuse. Discrimination was assessed by the area under the receiver operating characteristic curve (AUROC). Calibration slope and intercept were measured with the unreliability index. Bias assessments were performed across demographic subgroups. FINDINGS: The model was trained on a cohort that had 3·5% misuse (n=1 921) with any type of substance. 220 (11%) of 1921 patients with substance misuse had more than one type of misuse. The multilabel convolutional neural network classifier had a mean AUROC of 0·97 (95% CI 0·96-0·98) during temporal validation for all types of substance misuse. The model was well calibrated and showed good face validity with model features containing explicit mentions of aberrant drug-taking behaviour. A false-negative rate of 0·18-0·19 and a false-positive rate of 0·03 between non-Hispanic Black and non-Hispanic White groups occurred. In external validation, the AUROCs for alcohol and opioid misuse were 0·88 (95% CI 0·86-0·90) and 0·94 (0·92-0·95), respectively. INTERPRETATION: We developed a novel and accurate approach to leveraging the first 24 h of EHR notes for screening multiple types of substance misuse. FUNDING: National Institute On Drug Abuse, National Institutes of Health.

Student Doctor Network: Fake News or Facts for Emergency Medicine Applicants?

INTRODUCTION: Residency applicants use multiple resources to guide their application process including the Student Doctor Network (SDN), a publicly available online forum for the discussion of various topics in medical education. In recent years, specialty-specific forums for residency applicants to self-report their own application information have become popular. These forums allow other applicants to review self-reported data from their peers to inform their own application process. The accuracy of this resource is unknown. To determine whether the SDN is an accurate source of information for emergency medicine (EM) applicants, we compared self-reported SDN data to objective data from the National Resident Matching Program (NRMP). METHODS: We retrospectively reviewed self-reported SDN data by DO and MD candidates from EM forums for the 2014, 2016, and 2018 residency application cycles. These data were compared to the NRMP charting outcomes for each respective year. RESULTS: A total of 360 EM applicants self-reported data on the SDN during the years reviewed. The majority of these applicants (79%) posted for the 2018 application cycle following transition to a Google Docs spreadsheet. For the first two years of analysis, mean United States Medical Licensing Examination (USMLE) scores were similar to SDN reports. For the most recent year studied, applicants who posted to SDN reported higher mean (USMLE) Step 1 (234, 95% confidence interval [CI], 233-236) and Step 2 scores (250, 95% CI, 248-251) when compared to NRMP data (231 and 241). Reported contiguous residency program ranks were similar to NRMP in all years, and the proportion indicating Alpha Omega Alpha Honor Medical Society membership was similar to NRMP only for the most recent year studied. CONCLUSION: Self-reporting on SDN showed a slight bias toward higher USMLE step scores in the most recent year when compared to objective NRMP data. Self-reporting on SDN has increased in recent years, but it is unknown whether this increase will lead to more accurate information for EM applicants. Given the self-reported nature of the SDN, applicants should use SDN forums with caution.

External validation of a machine learning classifier to identify unhealthy alcohol use in hospitalized patients.

BACKGROUND AND AIMS: Unhealthy alcohol use (UAU) is one of the leading causes of global morbidity. A machine learning approach to alcohol screening could accelerate best practices when integrated into electronic health record (EHR) systems. This study aimed to validate externally a natural language processing (NLP) classifier developed at an independent medical center. DESIGN: Retrospective cohort study. SETTING: The site for validation was a midwestern United States tertiary-care, urban medical center that has an inpatient structured universal screening model for unhealthy substance use and an active addiction consult service. PARTICIPANTS/CASES: Unplanned admissions of adult patients between October 23, 2017 and December 31, 2019, with EHR documentation of manual alcohol screening were included in the cohort (n = 57 605). MEASUREMENTS: The Alcohol Use Disorders Identification Test (AUDIT) served as the reference standard. AUDIT scores ≥5 for females and ≥8 for males served as cases for UAU. To examine error in manual screening or under-reporting, a post hoc error analysis was conducted, reviewing discordance between the NLP classifier and AUDIT-derived reference. All clinical notes excluding the manual screening and AUDIT documentation from the EHR were included in the NLP analysis. FINDINGS: Using clinical notes from the first 24 hours of each encounter, the NLP classifier demonstrated an area under the receiver operating characteristic curve (AUCROC) and precision-recall area under the curve (PRAUC) of 0.91 (95% CI = 0.89-0.92) and 0.56 (95% CI = 0.53-0.60), respectively. At the optimal cut point of 0.5, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.66 (95% CI = 0.62-0.69), 0.98 (95% CI = 0.98-0.98), 0.35 (95% CI = 0.33-0.38), and 1.0 (95% CI = 1.0-1.0), respectively. CONCLUSIONS: External validation of a publicly available alcohol misuse classifier demonstrates adequate sensitivity and specificity for routine clinical use as an automated screening tool for identifying at-risk patients.

A Review of Cancer Immunotherapy Toxicity II: Adoptive Cellular Therapies, Kinase Inhibitors, Monoclonal Antibodies, and Oncolytic Viruses.

Immunotherapy for cancer has undergone a rapid expansion in classes, agents, and indications. By utilizing aspects of the body's innate immune system, immunotherapy has improved life expectancy and quality of life for patients with several types of cancer. Adoptive cellular therapies, including chimeric antigen receptor T (CAR T) cell therapy, involve the genetic engineering of patient T cells to allow for targeting of neoplastic cells. Monitoring of patients during the lymphodepletion prior to therapy and following CAR T cell infusion is necessary to detect toxicity of therapy. Specific toxicities include cytokine release syndrome and neurologic toxicity, both of which may be life-threatening. Tocilizumab and/or corticosteroids should be considered for moderate to severe toxicity. Kinase inhibitor toxicity can occur as "on target" effects or "off target" effects to multiple organ systems due to shared protein epitopes. Treatments are organ-specific. Infusion reactions are common during treatment with monoclonal antibodies and treatment is largely supportive. Clinical experience with oncolytic viruses is limited, but local reactions including cellulitis as well as systemic influenza-like syndromes have been seen but are typically mild. Although clinical experience with adverse effects due to newer immunotherapy agents is growing, an up-to-date understanding of their mechanisms and potential toxicities is critical.

A cross-sectional analysis of fentanyl analog exposures among living patients.

Background: Synthetic opioids, including fentanyl analogs, contribute to an increasing proportion of opioid-related deaths. Highly potent analogs pose an increased risk for fatal overdose. The prevalence of fentanyl analog exposures in patients with known opioid exposure is unknown.Objective: The purpose of this study was to determine the exposure prevalence for fentanyl analogs in living patients with positive urine screens for opiates or fentanyl.Methods: This was a cross-sectional analysis of urine high performance liquid chromatography/tandem mass spectroscopy (HPLC-MS/MS) results from patients with a positive urine screen for opiates or fentanyl at a large public healthcare system in Chicago, Illinois. Samples with positive screens were non-continuously tested by HPLC-MS/MS for 5 selected months in 2018 and 2019.Results: A total of 219 urine samples which screened positive for fentanyl or opiates underwent HPLC-MS/MS testing. At least one fentanyl analog was detected in 65.3% (n = 143) of samples with 26.0% (n = 57) testing positive for multiple analogs. The most common analogs, intermediates, or metabolites were: 4-ANPP (n = 131); 2-furanylfentanyl (n = 22); acryl fentanyl (n = 21); butyrylfentanyl (n = 15); cyclopropylfentanyl (n = 15); and carfentanil (n = 13). Of samples which screened positive for fentanyl (n = 188), 70.2% (132) tested positive for at least one fentanyl analog. Of samples which screened negative for fentanyl but positive for opiates (n = 31), 35.5% (n = 11) tested positive for fentanyl analogsConclusion: Fentanyl analog exposure is common in patients with positive urine screens for fentanyl or opiates. Screening living patient samples for synthetic opioids has future toxicosurveillance implications and these data underscore the increased risks from illicit opioid use.

A Review of Cancer Immunotherapy Toxicity: Immune Checkpoint Inhibitors.

Cancer immunotherapy, which leverages features of the immune system to target neoplastic cells, has revolutionized the treatment of cancer. The use of these therapies has rapidly expanded in the past two decades. Immune checkpoint inhibitors represent one drug class within immunotherapy with its first agent FDA-approved in 2011. Immune checkpoint inhibitors act by disrupting inhibitory signals from neoplastic cells to immune effector cells, allowing activated T-cells to target these neoplastic cells. Unique adverse effects associated with immune checkpoint inhibitors are termed immune-related adverse effects (irAEs) and are usually immunostimulatory in nature. Almost all organ systems may be affected by irAEs including the dermatologic, gastrointestinal, pulmonary, endocrine, and cardiovascular systems. These effects range from mild to life-threatening, and their onset can be delayed several weeks or months. For mild irAEs, symptomatic care is usually sufficient. For higher grade irAEs, discontinuation of therapy and initiation of immunosuppressive therapy may be necessary. The management of patients with irAEs involves multidisciplinary care coordination with respect to the long-term goals the individual patient. Clinicians must be aware of the unique and sometimes fatal toxicologic profiles associated with immunotherapies to ensure prompt diagnosis and appropriate management.

Mock Oral Board Examinations via Web-based Video Teleconferencing in the Era of COVID-19.

Owing to infection control measures necessitated by the COVID-19 pandemic, many educational didactic components within residency programs have needed to adapt to virtual formats. We describe and evaluate the transition of an in-person mock oral board certification examination to an entirely virtual format. Oral board cases consisting of two single cases and one triple case were adapted to a virtual format using a Web-based video teleconferencing platform. Faculty examiners underwent 20-minute training sessions prior to the examination. Both resident examinees and faculty examiners completed postexamination surveys. The system usability scale (SUS) was used to assess the usability of the new format by the faculty examiners. Fifteen resident examinees completed the mock virtual oral board examination with eight faculty examiners. All faculty members completed the postexamination survey. The mean (±SD) SUS score was 90.6 (±11.5) out of a maximum of 100. Eleven of 15 (73%) resident examinees completed the postexamination survey. All respondents agreed or strongly agreed that examiner instructions were easy to understand and that examiners were adept at using the Web-based platform. Some technical issues were encountered including audio difficulties for some examiners. Respondents were split regarding personal preference for virtual or in-person mock oral board formats. Utilizing video teleconferencing software to provide a virtual, Web-based alternative to in-person mock oral board examination was feasible, and the virtual format was shown to have high usability despite minimal training time for experienced faculty examiners.

Short-Term Graft Failure of Organs Procured from Drug-Related Deaths Compared with Other Causes of Death.

BACKGROUND: A gap exists between the number of patients on the national organ transplant waiting list and the number of transplants performed. Victims of drug and overdose-related death are increasingly utilized as organ donors. We sought to evaluate the suitability of organs from drug and overdose-related death for organ transplantation. This study compares the proportion of short-term allograft failure of organs procured from patients with drug-related deaths with those without drug-related deaths. METHODS: Organ donations after drug-related deaths (DDD) were compared with organ donations from non-drug-related donations after brain deaths (DBD) and donations after circulatory deaths (DCD) utilizing the Gift of Hope Organ & Tissue Donor Network for a total of 15 months. RESULTS: Eighty-one donors were identified from each of the DDD, DBD, and DCD groups with 264, 234, and 181 organs transplanted, respectively. The proportions of short-term graft failures were 1.15% in the DDD group compared with 2.14% in the DBD group (p = NS) and 5.52% in the DCD group (p = 0.01). The US Public Health Service increased-risk features for transmission of infectious diseases were present in 70.3% of the DDD cases. Donors from the DDD group were younger on average than those in other groups (33 to 42 years). CONCLUSIONS: The proportion of graft failures in the drug-related deaths (DDD) group was equal to or less than those from other causes of death on short-term follow-up. Drug-related death does not appear to be a contraindication for organ procurement despite increased risk features for infectious disease transmission.

Utilizing Analytics to Identify Trends in Residency Program Website Visits.

Introduction Most medical residency training programs maintain websites to provide content for audiences including current residents, prospective residents, and medical students. This study seeks to characterize when and how a residency program website is being accessed in order to identify the primary audiences to provide appropriate and timely content. Methods The authors examined website analytics at a large urban Accreditation Council for Graduate Medical Education (ACGME)-accredited post-graduate year (PGY)1-PGY4 emergency medicine residency training program website. Analytics were performed from July 1, 2016 to June 30, 2018 with daily traffic cataloged along with referral sources, page views, and device type used to access the website. The top five dates by daily traffic were further analyzed with daily traffic trends during the residency interview season. Results There was an average of 45.8 unique visitors daily with 261.5 daily page views. Computer (67.2%), mobile device (29.6%), and tablet (3.1%) were the most common devices used for viewing. The most popular content areas by page-view were "people" (68,987 visits), "home" (38,569), "clinical curriculum" (35,556), and "medical students" (14,461). The five most-visited dates were all related to application processes including the opening of the Visiting Student Application Service (VSAS), the Electronic Residency Application Service (ERAS), and Match Day. During the interview season, peak visits occurred the dates immediately preceding interview dates. Conclusion Residency program websites appear to be accessed most commonly by medical students and prospective residency applicants. Website managers should take the needs of these audiences into account and provide appropriate content to maximally inform prospective residency program candidates.

Applicant Attitudes Toward the Association of American Medical Colleges' Standardized Video Interview.

BACKGROUND: The Standardized Video Interview (SVI) was developed by the American Association of Medical Colleges to allow applicants to include objective data about professional behaviors and interpersonal and communication skills. Although the SVI pilot was administered to individuals applying to emergency medicine (EM) residency programs during the 2018 Electronic Residency Application Service (ERAS) cycle, little data have been published evaluating the applicant's perceptions. This survey aims to assess EM residency applicant attitudes toward the SVI. METHODS: During the 2018 ERAS application season an anonymous survey was administered to interviewees at one urban Accreditation Council for Graduate Medical Education-approved EM residency. Respondents were asked questions regarding the production of their video interviews, thoughts regarding the additive value of the SVI, and individual demographic data such as ethnicity and sex. Participation was optional. RESULTS: A total of 219 of 238 candidates completed the survey representing a 92% response rate. While the majority of applicants did not feel that their ethnicity impacted their application, 58.1% of those who did self-identified as African American or Asian. A total of 8.7% of respondents felt the SVI added information about their professional behaviors and 11% felt that it added information about interpersonal and communication skills. Only 2.8% of survey respondents felt the SVI should remain a portion of the ERAS application. CONCLUSIONS: Most respondents felt that the SVI was not an accurate representation of their interpersonal and communication skills or their professionalism and that it did not add value to their applications. While most cohorts were not concerned about bias regarding sex, ethnicity, sex, or age, a small subset felt that there was a potential for the SVI to bias the party reviewing their applications. Very few applicants felt the SVI should remain a part of the ERAS application. Applicant attitudes toward the SVI are largely negative and require further investigation prior to becoming a standard part of applicants' ERAS files.