Using nurses’ natural language entries to build a concept-oriented terminology for patients’ chief complaints in the emergency department

Information about the chief complaint (CC), also known as the patient's reason for seeking emergency care, is critical for patient prioritization for treatment and determination of patient flow through the emergency department (ED). Triage nurses document the CC at the start of the ED visit, and the data are increasingly available in electronic form. Despite the clinical and operational significance of the CC to the ED, there is no standard CC terminology. We propose the construction of concept-oriented nursing terminologies from the actual language used by experts. We use text analysis to extract CC concepts from triage nurses' natural language entries. Our methodology for building the nursing terminology utilizes natural language processing techniques and the Unified Medical Language System

Evaluation of preprocessing techniques for chief complaint classification

OBJECTIVE: To determine whether preprocessing chief complaints before automatically classifying them into syndromic categories improves classification performance. METHODS: We preprocessed chief complaints using two preprocessors (CCP and EMT-P) and evaluated whether classification performance increased for a probabilistic classifier (CoCo) or for a keyword-based classifier (modification of the NYC Department of Health and Mental Hygiene chief complaint coder (KC)). RESULTS: CCP exhibited high accuracy (85%) in preprocessing chief complaints but only slightly improved CoCo's classification performance for a few syndromes. EMT-P, which splits chief complaints into multiple problems, substantially increased CoCo's sensitivity for all syndromes. Preprocessing with CCP or EMT-P only improved KC's sensitivity for the Constitutional syndrome. CONCLUSION: Evaluation of preprocessing systems should not be limited to accuracy of the preprocessor but should include the effect of preprocessing on syndromic classification. Splitting chief complaints into multiple problems before classification is important for CoCo, but other preprocessing steps only slightly improved classification performance for CoCo and a keyword-based classifier

Evaluation of preprocessing techniques for chief complaint classification

OBJECTIVE: To determine whether preprocessing chief complaints before automatically classifying them into syndromic categories improves classification performance. METHODS: We preprocessed chief complaints using two preprocessors (CCP and EMT-P) and evaluated whether classification performance increased for a probabilistic classifier (CoCo) or for a keyword-based classifier (modification of the NYC Department of Health and Mental Hygiene chief complaint coder (KC)). RESULTS: CCP exhibited high accuracy (85%) in preprocessing chief complaints but only slightly improved CoCo's classification performance for a few syndromes. EMT-P, which splits chief complaints into multiple problems, substantially increased CoCo's sensitivity for all syndromes. Preprocessing with CCP or EMT-P only improved KC's sensitivity for the Constitutional syndrome. CONCLUSION: Evaluation of preprocessing systems should not be limited to accuracy of the preprocessor but should include the effect of preprocessing on syndromic classification. Splitting chief complaints into multiple problems before classification is important for CoCo, but other preprocessing steps only slightly improved classification performance for CoCo and a keyword-based classifier

Why Do Cancer Patients Die in the Emergency Department?: An Analysis of 283 Deaths in NC EDs

Emergency department (ED) visits are made by cancer patients for symptom management, treatment effects, oncologic emergencies, or end of life care. While most patients prefer to die at home, many die in health care institutions. The purpose of this study is to describe visit characteristics of cancer patients who died in the ED and their most common chief complaints using 2008 ED visit data from the North Carolina Disease Event Tracking and Epidemiologic Collection Tool (NC DETECT). Of the 37,760 cancer-related ED visits, 283 resulted in death. For lung cancer patients, 104 died in the ED with 70.9% dying on their first ED visit. Research on factors precipitating ED visits by cancer patients is needed to address end of life care needs

Accurate detection of sepsis at ED triage using machine learning with clinical natural language processing

Sepsis is a life-threatening condition with organ dysfunction and is a leading cause of death and critical illness worldwide. Accurate detection of sepsis during emergency department triage would allow early initiation of lab analysis, antibiotic administration, and other sepsis treatment protocols. The purpose of this study was to determine whether EHR data can be extracted and synthesized with the latest machine learning algorithms (KATE Sepsis) and clinical natural language processing to produce accurate sepsis models, and compare KATE Sepsis performance with existing sepsis screening protocols, such as SIRS and qSOFA. A machine learning model (KATE Sepsis) was developed using patient encounters with triage data from 16 participating hospitals. KATE Sepsis, SIRS, standard screening (SIRS with source of infection) and qSOFA were tested in three settings. Cohort-A was a retrospective analysis on medical records from a single Site 1. Cohort-B was a prospective analysis of Site 1. Cohort-C was a retrospective analysis on Site 1 with 15 additional sites. Across all cohorts, KATE Sepsis demonstrates an AUC of 0.94-0.963 with 73-74.87% TPR and 3.76-7.17% FPR. Standard screening demonstrates an AUC of 0.682-0.726 with 39.39-51.19% TPR and 2.9-6.02% FPR. The qSOFA protocol demonstrates an AUC of 0.544-0.56, with 10.52-13.18% TPR and 1.22-1.68% FPR. For severe sepsis, across all cohorts, KATE Sepsis demonstrates an AUC of 0.935-0.972 with 70-82.26% TPR and 4.64-8.62% FPR. For septic shock, across all cohorts, KATE Sepsis demonstrates an AUC of 0.96-0.981 with 85.71-89.66% TPR and 4.85-8.8% FPR. SIRS, standard screening, and qSOFA demonstrate low AUC and TPR for severe sepsis and septic shock detection. KATE Sepsis provided substantially better sepsis detection performance in triage than commonly used screening protocols.Comment: 35 pages, 1 figure, 6 tables, 7 supplementary table

Availability of Advance Care Planning Documentation for Older Emergency Department Patients: A Cross-Sectional Study

Introduction: Increasing advance care planning (ACP) among older adults is a national priority. Documentation of ACP in the electronic health record (EHR) is particularly important during emergency care

Defining Emergency Department Asthma Visits for Public Health Surveillance, North Carolina, 2008–2009

IntroductionWhen using emergency department (ED) data sets for public health surveillance, a standard approach is needed to define visits attributable to asthma. Asthma can be the first (primary) or a subsequent (2nd through 11th) diagnosis. Our study objective was to develop a definition of ED visits attributable to asthma for public health surveillance. We evaluated the effect of including visits with an asthma diagnosis in primary-only versus subsequent positions.MethodsThe study was a cross-sectional analysis of population-level ED surveillance data. Of the 114 North Carolina EDs eligible to participate in a statewide surveillance system in 2008–2009, we used data from the 111 (97%) that participated during those years. Included were all ED visits with an ICD-9-CM diagnosis code for asthma in any diagnosis position (1 through 11). We formed 11 strata based on the diagnosis position of asthma and described common chief complaint and primary diagnosis categories for each. Prevalence ratios compared each category’s proportion of visits that received either asthma- or cardiac-related procedure codes.ResultsRespiratory diagnoses were most common in records of ED visits in which asthma was the first or second diagnosis, while primary diagnoses of injury and heart disease were more common when asthma appeared in positions 3–11. Asthma-related chief complaints and procedures were most common when asthma was the first or second diagnosis, whereas cardiac procedures were more common in records with asthma in positions 3–11.ConclusionED visits should be defined as asthma-related when asthma is in the first or second diagnosis position

Population-based burden of COPD-related visits in the ED: Return ED visits, hospital admissions, and comorbidity risks

Little is known about the population-based burden of ED care for COPD

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms

Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes.

OBJECTIVE: Proinsulin is a precursor of mature insulin and C-peptide. Higher circulating proinsulin levels are associated with impaired β-cell function, raised glucose levels, insulin resistance, and type 2 diabetes (T2D). Studies of the insulin processing pathway could provide new insights about T2D pathophysiology. RESEARCH DESIGN AND METHODS: We have conducted a meta-analysis of genome-wide association tests of ∼2.5 million genotyped or imputed single nucleotide polymorphisms (SNPs) and fasting proinsulin levels in 10,701 nondiabetic adults of European ancestry, with follow-up of 23 loci in up to 16,378 individuals, using additive genetic models adjusted for age, sex, fasting insulin, and study-specific covariates. RESULTS: Nine SNPs at eight loci were associated with proinsulin levels (P < 5 × 10(-8)). Two loci (LARP6 and SGSM2) have not been previously related to metabolic traits, one (MADD) has been associated with fasting glucose, one (PCSK1) has been implicated in obesity, and four (TCF7L2, SLC30A8, VPS13C/C2CD4A/B, and ARAP1, formerly CENTD2) increase T2D risk. The proinsulin-raising allele of ARAP1 was associated with a lower fasting glucose (P = 1.7 × 10(-4)), improved β-cell function (P = 1.1 × 10(-5)), and lower risk of T2D (odds ratio 0.88; P = 7.8 × 10(-6)). Notably, PCSK1 encodes the protein prohormone convertase 1/3, the first enzyme in the insulin processing pathway. A genotype score composed of the nine proinsulin-raising alleles was not associated with coronary disease in two large case-control datasets. CONCLUSIONS: We have identified nine genetic variants associated with fasting proinsulin. Our findings illuminate the biology underlying glucose homeostasis and T2D development in humans and argue against a direct role of proinsulin in coronary artery disease pathogenesis