Prevalence and Healthcare Burden of Fungal Infections in the United States, 2018

Background Fungal infections are responsible for >1.5 million deaths globally per year, primarily in those with compromised immune function. This is concerning as the number of immunocompromised patients, especially in those without human immunodeficiency virus (HIV), has risen in the past decade. The purpose of this analysis was to provide the current prevalence and impact of fungal disease in the United States. Methods We analyzed hospital discharge data from the most recent (2018) Healthcare Cost and Utilization Project National Inpatient Sample, and outpatient visit data from the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey. Costs are presented in 2018 United States (US) dollars. Results In the 35.5 million inpatient visits documented in 2018 in the US, approximately 666 235 fungal infections were diagnosed, with an estimated attributable cost of $6.7 billion. Aspergillus, Pneumocystis, and Candida infections accounted for 76.3% of fungal infections diagnosed, and 81.1% of associated costs. Most fungal disease occurred in patients with elevated risk of infection. The visit costs, lengths of stay, and risks of mortality in this population were more than twice that of those without fungal diagnoses. A further 6.6 million fungal infections were diagnosed during outpatient visits. Conclusions Fungal disease is a serious clinical concern with substantial healthcare costs and significant increases in morbidity and mortality, particularly among predisposed patients. Increased surveillance, standardized treatment guidelines, and improvement in diagnostics and therapeutics are needed to support the rising numbers of at-risk patients.

A Novel Deep Learning–Based System for Triage in the Emergency Department Using Electronic Medical Records: Retrospective Cohort Study

Background Emergency department (ED) crowding has resulted in delayed patient treatment and has become a universal health care problem. Although a triage system, such as the 5-level emergency severity index, somewhat improves the process of ED treatment, it still heavily relies on the nurse’s subjective judgment and triages too many patients to emergency severity index level 3 in current practice. Hence, a system that can help clinicians accurately triage a patient’s condition is imperative. Objective This study aims to develop a deep learning–based triage system using patients’ ED electronic medical records to predict clinical outcomes after ED treatments. Methods We conducted a retrospective study using data from an open data set from the National Hospital Ambulatory Medical Care Survey from 2012 to 2016 and data from a local data set from the National Taiwan University Hospital from 2009 to 2015. In this study, we transformed structured data into text form and used convolutional neural networks combined with recurrent neural networks and attention mechanisms to accomplish the classification task. We evaluated our performance using area under the receiver operating characteristic curve (AUROC). Results A total of 118,602 patients from the National Hospital Ambulatory Medical Care Survey were included in this study for predicting hospitalization, and the accuracy and AUROC were 0.83 and 0.87, respectively. On the other hand, an external experiment was to use our own data set from the National Taiwan University Hospital that included 745,441 patients, where the accuracy and AUROC were similar, that is, 0.83 and 0.88, respectively. Moreover, to effectively evaluate the prediction quality of our proposed system, we also applied the model to other clinical outcomes, including mortality and admission to the intensive care unit, and the results showed that our proposed method was approximately 3% to 5% higher in accuracy than other conventional methods. Conclusions Our proposed method achieved better performance than the traditional method, and its implementation is relatively easy, it includes commonly used variables, and it is better suited for real-world clinical settings. It is our future work to validate our novel deep learning–based triage algorithm with prospective clinical trials, and we hope to use it to guide resource allocation in a busy ED once the validation succeeds.

Trends in Sexually Transmitted Infections in United States Ambulatory Care Clinics from 2005–2016

We examined the prevalence trends of non-human immunodeficiency virus (HIV) sexually transmitted infections (STI) and associated patient characteristics in U.S. ambulatory-care settings from 2005–2016. We conducted a retrospective repeated cross-sectional analysis using data from the National Ambulatory Medical Care Survey (NAMCS) for individuals aged 15–64 with a non-HIV STI-related visit. Data were combined into three periods (2005–2008, 2009–2012, and 2013–2016) to obtain reliable estimates. Logistic regression was used for analysis. A total of 19.5 million weighted, non-HIV STI-related ambulatory visits from 2005–2016 were identified. STI-related visits per 100,000 ambulatory care visits increased significantly over the study period: 206 (95% CI = 153–259), 343 (95% CI = 279–407), and 361 (95% CI = 277–446) in 2005–2008, 2009–2012, and 2013–2016, respectively (Ptrend = 0.003). These increases were mainly driven by increases in HPV-related visits (56 to 163 per 100,000 visits) from 2005–2008 to 2009–2012, followed by syphilis- or gonorrhea-related visits (30 to 67 per 100,000 visits) from 2009–2012 to 2013–2016. Higher odds of having STI-related visit were associated with younger age (aged 15–24: aOR = 4.45; 95% CI = 3.19–6.20 and aged 25–44: aOR = 3.59; 95% CI = 2.71–4.77) vs. 45–64-year-olds, Black race (aOR = 2.41; 95% CI = 1.78–3.25) vs. White, and HIV diagnosis (aOR = 10.60; 95% CI = 5.50–20.27) vs. no HIV diagnosis. STI-related office visits increased by over 75% from 2005–2016, and were largely driven by HPV-related STIs and syphilis- or gonorrhea-related STIs.

Abstract 10975: National Study of In-Hospital Cardiac Arrest in the Emergency Department, 2010-2018

Introduction: Little is known about in-hospital cardiac arrest (IHCA) in the U.S. emergency department (ED). This study aimed to describe the incidence and mortality of ED-based IHCA visits and to investigate factors associated with higher incidence and poor outcome of IHCA. Hypothesis: We hypothesized that ED-based IHCA contributes to a stable proportion of ED visits and remains a higher mortality rate after the event. Methods: Data were obtained from the National Hospital Ambulatory Medical Care Survey (NHAMCS) during 2010 to 2018. Adult ED visits with IHCA were identified using the cardiopulmonary resuscitation code, excluding those with out-of-hospital cardiac arrest. Analysis used descriptive statistics and multivariable logistic regression accounting for NHAMCS's complex survey design. Results: Over the 9-year study period, there was an estimated 1,114,000 ED visits with IHCA and the proportion of IHCA visits in the entire ED population (1.8 per 1,000 ED visits) appeared stable. The mean age of the IHCA visits was 60 years, and 65% were men. Older age, male sex, arrival by ambulance, and being uninsured independently predicted a higher incidence of ED-based IHCA. About 51% of ED-based IHCA died in the ED, and the trend remained stable. Arrival by ambulance, nighttime or weekend arrival, and being in the non-Northeast location were independently associated with a higher mortality rate after IHCA. Conclusions: The high burden of ED visits with IHCA persisted and the outcome remained poor. Some patients were disproportionately affected and certain contextual factors were associated with a poorer outcome.