Development and validation of an early warning tool for sepsis and decompensation in children during emergency department triage.
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ABSTRACT:
SUBMITTER: Ehwerhemuepha L
PROVIDER: S-EPMC8060307 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Around 42,000 children suffer from severe sepsis each year in the US alone, resulting in significant morbidity, mortality and billion dollar expenditures in the US healthcare system. Sepsis recognition is a clinical challenge in children. Biomarkers are needed to tailor appropriate antimicrobial therapies and improve risk stratification. The goal of this study was to determine if gene expression profiles from peripheral blood were associated with pathogen type and sepsis severity in children treated for suspected sepsis.
2020-01-01 | GSE119217 | GEO
Project description:Early recognition and prevention comprise the first ring of the Chain of Survival for in-hospital cardiac arrest (IHCA). We previously developed and internally validated an emergency department (ED) triage tool, Emergency Department In-hospital Cardiac Arrest Score (EDICAS), for predicting ED-based IHCA. We aimed to externally validate this novel tool in another ED population. This retrospective cohort study used electronic clinical warehouse data from a tertiary medical center with approximately 130,000 ED visits per year. We retrieved data from 268,208 ED visits over a 2-year period. We selected one ED visit per person and excluded out-of-hospital cardiac arrest or children. Patient demographics and computerized triage information were retrieved, and the EDICAS was calculated to predict the ED-based IHCA. A total of 145,557 adult ED patients were included. Of them, 240 (0.16%) developed IHCA. The EDICAS showed excellent discrimination with an area under the receiver operating characteristic (AUROC) of 0.88. The AUROC of the EDICAS outperformed those of other early warning scores (0.80 for Modified Early Warning Score [MEWS] and 0.83 for Rapid Emergency Medicine Score [REMS]) in the same ED population. An EDICAS of 6 or above (i.e., high-risk patients) corresponded to a sensitivity of 33%, a specificity of 97%, and a positive likelihood ratio of 12.2. In conclusion, we externally validated a tool for predicting imminent IHCA in the ED and demonstrated its superior performance over other early warning scores. The real-world impact of the EDICAS warning system with appropriate interventions would require a future prospective study.
| S-EPMC9130149 | biostudies-literature
Project description:Seizure is a common neurological presentation in patients visiting the emergency department (ED) that requires time for evaluation and observation. Timely decision and disposition standards for seizure patients need to be established to prevent overcrowding in the ED and achieve patients' safety. Here, we conducted a retrospective cohort study to predict early seizure recurrence in the ED (ES-RED). We randomly assigned 688 patients to the derivation and validation cohorts (2:1 ratio). Prediction equations extracted routine clinical and laboratory information from EDs using logistic regression (Model 1) and machine learning (Model 2) methods. The prediction equations showed good predictive performance, the area under the receiver operating characteristics curve showing 0.808 in Model 1 [95% confidential interval (CI): 0.761-0.853] and 0.805 in Model 2 [95% CI: 0.747-0.857] in the derivation cohort. In the external validation, the models showed strong prediction performance of 0.739 [95% CI: 0.640-0.824] in Model 1 and 0.738 [95% CI: 0.645-0.819] in Model 2. Intriguingly, the lowest quartile group showed no ES-RED after 6 h. The ES-RED calculator, our proposed prediction equation, would provide strong evidence for safe and appropriate disposition of adult resolved seizure patients from EDs, reducing overcrowding and delays and improving patient safety.
| S-EPMC9267812 | biostudies-literature
Project description:BackgroundElder abuse is a worldwide problem with serious consequences for individuals and society. To effectively deal with elder abuse, a timely identification of signals as well as a systematic approach towards (suspected) elder abuse is necessary. This study aimed to develop and test the acceptability and appropriateness of ERASE (EldeR AbuSE) in the emergency department (ED) setting. ERASE is an early warning tool for elder abuse self-administered by the healthcare professional in patients ≥ 70 years.MethodsA systematic literature review was previously conducted to identify potential available instruments on elder abuse for use in the ED. Furthermore, a field consultation in Dutch hospitals was performed to identify practice tools and potential questions on the recognition of elder abuse that were available in clinical practice. Based on this input, in three subsequent rounds the ERASE tool was developed. The ERASE tool was tested in a pilot feasibility study in healthcare professionals (n = 28) working in the ED in three Dutch hospitals. A semi-structured online questionnaire was used to determine acceptability and appropriateness of the ERASE tool.ResultsThe systematic literature review revealed seven screening instruments developed for use in the hospital and/or ED setting. In total n = 32 (44%) hospitals responded to the field search. No suitable and validated instruments for the detection of elder abuse in the ED were identified. The ERASE tool was developed, with a gut feeling awareness question, that encompassed all forms of elder abuse as starting question. Subsequently six signalling questions were developed to collect information on observed signs and symptoms of elder abuse and neglect. The pilot study showed that the ERASE tool raised the recognition of healthcare professionals for elder abuse. The tool was evaluated acceptable and appropriate for use in the ED setting.ConclusionsERASE as early warning tool is guided by an initial gut feeling awareness question and six signalling questions. The ERASE tool raised the recognition of healthcare professionals for elder abuse, and was feasible to use in the ED setting. The next step will be to investigate the reliability and validity of the ERASE early warning tool.
| S-EPMC10988976 | biostudies-literature
Project description:ObjectiveTriage is a tool developed to identify patients who need immediate care and those who can safely wait. The aim of this study was to assess the validity and interrater reliability of a modified version of the pediatric South African triage scale (pSATS) in a single-center tertiary pediatric emergency department in Norway.MethodsThis prospective, observational study included all patients with medical conditions, referred to the pediatric emergency department of a tertiary hospital in Norway from September 1, 2015, to November 17, 2015. Their assigned triage priority was compared with rate of hospitalization and resource utilization. Validity parameters were sensitivity, specificity, positive and negative predictive value, and percentage of over- and undertriage. Interrater agreement and accuracy of the triage ratings were calculated from triage performed by nurses on written case scenarios.ResultsDuring the study period, 1171 patients arrived at the hospital for emergency assessment. A total of 790 patients (67 %) were triaged and included in the study. The percentage of hospital admission increased with increasing level of urgency, from 30 % of the patients triaged to priority green to 81 % of those triaged to priority red. The sensitivity was 74 %, the specificity was 48 %, the positive predictive value was 52 %, and the negative predictive value was 70 % for predicting hospitalization. The level of over- and undertriage was 52 % and 26 %, respectively. Resource utilization correlated with higher triage priority. The interrater agreement had an intraclass correlation coefficient of 0.99 by Cronbach's alpha, and the accuracy was 92 %.ConclusionsThe modified pSATS had a moderate sensitivity and specificity but showed good correlation with resource utilization. The nurses demonstrated excellent interrater agreement and accuracy when triaging written case scenarios.
| S-EPMC6205310 | biostudies-literature
Project description:BackgroundThe aim of the emergency department (ED) triage is to recognize critically ill patients and to allocate resources. No strong evidence for accuracy of the current triage instruments, especially for the older adults, exists. We evaluated the National Early Warning Score 2 (NEWS2) and a 3-level triage assessment as risk predictors for frail older adults visiting the ED.MethodsThis prospective, observational study was performed in a Finnish ED. The data were collected in a six-month period and included were ≥ 75-year-old residents with Clinical Frailty Scale score of at least four. We analyzed the predictive values of NEWS2 and the three-level triage scale for 30-day mortality, hospital admission, high dependency unit (HDU) and intensive care unit (ICU) admissions, a count of 72-h and 30-day revisits, and ED length-of-stay (LOS).ResultsA total of 1711 ED visits were included. Median for age, CFS, LOS and NEWS2 were 85 years, 6 points, 6.2 h and 1 point, respectively. 30-day mortality was 96/1711. At triage, 69, 356 and 1278 of patients were assessed as red, yellow and green, respectively. There were 1103 admissions, of them 31 to an HDU facility, none to ICU. With NEWS2 and triage score, AUCs for 30-day mortality prediction were 0.70 (0.64-0.76) and 0.62 (0.56-0.68); for hospital admission prediction 0.62 (0.60-0.65) and 0.55 (0.52-0.56), and for HDU admission 0.72 (0.61-0.83) and 0.80 (0.70-0.90), respectively. The NEWS2 divided into risk groups of low, medium and high did not predict the ED LOS (p = 0.095). There was a difference in ED LOS between the red/yellow and as red/green patient groups (p < 0.001) but not between the yellow/green groups (p = 0.59). There were 48 and 351 revisits within 72 h and 30 days, respectively. With NEWS2 AUCs for 72-h and 30-day revisit prediction were 0.48 (95% CI 0.40-0.56) and 0.47 (0.44-0.51), respectively; with triage score 0.48 (0.40-0.56) and 0.49 (0.46-0.52), respectively.ConclusionsThe NEWS2 and a local 3-level triage scale are statistically significant, but poor in accuracy, in predicting 30-day mortality, and HDU admission but not ED LOS or revisit rates for frail older adults. NEWS2 also seems to predict hospital admission.
| S-EPMC7594283 | biostudies-literature
Project description:BackgroundThe Interagency Integrated Triage Tool (IITT) is a three-tier triage system designed for resource-limited emergency care (EC) settings. This study sought to assess the validity and reliability of a pilot version of the tool in an urban emergency department (ED) in Papua New Guinea.MethodsA pragmatic observational study was conducted at Gerehu General Hospital in Port Moresby, commencing eight weeks after IITT implementation. All ED patients presenting within the subsequent two-month period were included. Triage assessments were performed by a variety of ED clinicians, including community health workers, nurses and doctors. The primary outcome was sensitivity for the detection of time-critical illness, defined by ten pre-specified diagnoses. The association between triage category and ED outcomes was examined using Cramer's V correlation coefficient. Reliability was assessed by inter-rater agreement between a local and an experienced, external triage officer.FindingsAmong 4512 presentations during the study period, 58 (1.3%) were classified as category one (emergency), 967 (21.6%) as category two (priority) and 3478 (77.1%) as category three (non-urgent). The tool's sensitivity for detecting the pre-specified set of time-sensitive conditions was 70.8% (95%CI 58.2-81.4%), with negative predictive values of 97.3% (95%CI 96.7 - 97.8%) for admission/transfer and 99.9% (95%CI 99.7 - 100.0%) for death. The admission/transfer rate was 44.8% (26/58) among emergency patients, 22.9% (223/976) among priority patients and 2.7% (94/3478) among non-urgent patients (Cramer's V=0.351, p=0.00). Four of 58 (6.9%) emergency patients, 19/976 (2.0%) priority patients and 3/3478 (0.1%) non-urgent patients died in the ED (Cramer's V=0.14, p=0.00). The under-triage rate was 2.7% (94/3477) and the over-triage rate 48.2% (28/58), both within pre-specified limits of acceptability. On average, it took staff 3 minutes 34 seconds (SD 1:06) to determine and document a triage category. Among 70 observed assessments, weighted κ was 0.84 (excellent agreement).InterpretationThe pilot version of the IITT demonstrated acceptable performance characteristics, and validation in other EC settings is warranted.FundingThis project was funded through a Friendship Grant from the Australian Government Department of Foreign Affairs and Trade and an International Development Fund Grant from the Australasian College for Emergency Medicine Foundation.
| S-EPMC8358156 | biostudies-literature
Project description:Background: Sepsis is the leading cause of death and disability in children. Every hour of delay in treatment is associated with an escalating risk of morbidity and mortality. The burden of sepsis is greatest in low- and middle-income countries where timely treatment may not occur due to delays in diagnosis and prioritization of critically ill children. To circumvent these challenges, we propose the development and clinical evaluation of a digital triage tool that will identify high risk children and reduce time to treatment. We will also implement and clinically validate a Radio-Frequency Identification system to automate tracking of patients. The mobile platform (mobile device and dashboard) and automated patient tracking system will create a low cost, highly scalable solution for critically ill children, including those with sepsis.Methods: This is pre-post intervention study consisting of three phases. Phase I will be a baseline period where data is collected on key predictors and outcomes before implementation of the digital triage tool. In Phase I, there will be no changes to healthcare delivery processes in place at the study hospitals. Phase II will involve model derivation, technology development, and usability testing. Phase III will be the intervention period where data is collected on key predictors and outcomes after implementation of the digital triage tool. The primary outcome, time to treatment initiation, will be compared to assess effectiveness of the digital health intervention.Discussion: Smart technology has the potential to overcome the barrier of limited clinical expertise in the identification of the child at risk. This mobile health platform, with sensors and data-driven applications, will provide real-time individualized risk prediction to rapidly triage patients and facilitate timely access to life-saving treatments for children in low- and middle-income countries, where specialists are not regularly available and deaths from sepsis are common.Trial registration: Clinical Trials.gov Identifier: NCT04304235, Registered 11 March 2020.
| S-EPMC7268489 | biostudies-literature
Project description:ImportanceWhile machine learning approaches may enhance prediction ability, little is known about their utility in emergency department (ED) triage.ObjectivesTo examine the performance of machine learning approaches to predict clinical outcomes and disposition in children in the ED and to compare their performance with conventional triage approaches.Design, setting, and participantsPrognostic study of ED data from the National Hospital Ambulatory Medical Care Survey from January 1, 2007, through December 31, 2015. A nationally representative sample of 52 037 children aged 18 years or younger who presented to the ED were included. Data analysis was performed in August 2018.Main outcomes and measuresThe outcomes were critical care (admission to an intensive care unit and/or in-hospital death) and hospitalization (direct hospital admission or transfer). In the training set (70% random sample), using routinely available triage data as predictors (eg, demographic characteristics and vital signs), we derived 4 machine learning-based models: lasso regression, random forest, gradient-boosted decision tree, and deep neural network. In the test set (the remaining 30% of the sample), we measured the models' prediction performance by computing C statistics, prospective prediction results, and decision curves. These machine learning models were built for each outcome and compared with the reference model using the conventional triage classification information.ResultsOf 52 037 eligible ED visits by children (median [interquartile range] age, 6 [2-14] years; 24 929 [48.0%] female), 163 (0.3%) had the critical care outcome and 2352 (4.5%) had the hospitalization outcome. For the critical care prediction, all machine learning approaches had higher discriminative ability compared with the reference model, although the difference was not statistically significant (eg, C statistics of 0.85 [95% CI, 0.78-0.92] for the deep neural network vs 0.78 [95% CI, 0.71-0.85] for the reference; P = .16), and lower number of undertriaged critically ill children in the conventional triage levels 3 to 5 (urgent to nonurgent). For the hospitalization prediction, all machine learning approaches had significantly higher discrimination ability (eg, C statistic, 0.80 [95% CI, 0.78-0.81] for the deep neural network vs 0.73 [95% CI, 0.71-0.75] for the reference; P < .001) and fewer overtriaged children who did not require inpatient management in the conventional triage levels 1 to 3 (immediate to urgent). The decision curve analysis demonstrated a greater net benefit of machine learning models over ranges of clinical thresholds.Conclusions and relevanceMachine learning-based triage had better discrimination ability to predict clinical outcomes and disposition, with reduction in undertriaging critically ill children and overtriaging children who are less ill.
| S-EPMC6484561 | biostudies-literature
Project description:ObjectiveTo demonstrate the incremental benefit of using free text data in addition to vital sign and demographic data to identify patients with suspected infection in the emergency department.MethodsThis was a retrospective, observational cohort study performed at a tertiary academic teaching hospital. All consecutive ED patient visits between 12/17/08 and 2/17/13 were included. No patients were excluded. The primary outcome measure was infection diagnosed in the emergency department defined as a patient having an infection related ED ICD-9-CM discharge diagnosis. Patients were randomly allocated to train (64%), validate (20%), and test (16%) data sets. After preprocessing the free text using bigram and negation detection, we built four models to predict infection, incrementally adding vital signs, chief complaint, and free text nursing assessment. We used two different methods to represent free text: a bag of words model and a topic model. We then used a support vector machine to build the prediction model. We calculated the area under the receiver operating characteristic curve to compare the discriminatory power of each model.ResultsA total of 230,936 patient visits were included in the study. Approximately 14% of patients had the primary outcome of diagnosed infection. The area under the ROC curve (AUC) for the vitals model, which used only vital signs and demographic data, was 0.67 for the training data set, 0.67 for the validation data set, and 0.67 (95% CI 0.65-0.69) for the test data set. The AUC for the chief complaint model which also included demographic and vital sign data was 0.84 for the training data set, 0.83 for the validation data set, and 0.83 (95% CI 0.81-0.84) for the test data set. The best performing methods made use of all of the free text. In particular, the AUC for the bag-of-words model was 0.89 for training data set, 0.86 for the validation data set, and 0.86 (95% CI 0.85-0.87) for the test data set. The AUC for the topic model was 0.86 for the training data set, 0.86 for the validation data set, and 0.85 (95% CI 0.84-0.86) for the test data set.ConclusionCompared to previous work that only used structured data such as vital signs and demographic information, utilizing free text drastically improves the discriminatory ability (increase in AUC from 0.67 to 0.86) of identifying infection.
| S-EPMC5383046 | biostudies-literature