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Triage guideline for immunocompromised children with fever in an emergency centre in Ethiopia.

ABSTRACT: Fever in children with neutropenia often portends life-threatening bacteremia that may be ameliorated with early recognition and the rapid administration of antimicrobial therapy. Studies demonstrating this effect have been done in resource-endowed countries, but not in resource-limited settings. We attempted to decrease the time to antibiotics in patients with fever and neutropenia presenting to a paediatric emergency centre at a tertiary care referral hospital in Ethiopia. In 3 phases we performed baseline data collection, instituted triaging and treatment guidelines, and provided antibiotics. We tracked a variety of outcomes, most importantly time from arrival to initiation of antibiotics. While this time was reduced during the guideline institution phase of our intervention, time reductions were inconsistent and not sustained. This was likely due to competing clinical priorities among providers caring for a high volume of ill children. While in the U.S., fever and neutropenia is easy to prioritise within the paediatric emergency centre, future quality improvement measures in this setting may have a greater benefit on critical presentations such as shock or respiratory failure. Alternative strategies geared towards general efficiency improvement and teamwork, rather than focusing on one patient group may be a higher yield approach for improving care in this paediatric emergency centre.

SUBMITTER: Abebe W

PROVIDER: S-EPMC7721625 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature

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Triage guideline for immunocompromised children with fever in an emergency centre in Ethiopia.

Abebe Workeabeba W Bacha Tigist T Shane Andi L AL Berkowitz Tal T

African journal of emergency medicine : Revue africaine de la medecine d'urgence 20201204 1

Fever in children with neutropenia often portends life-threatening bacteremia that may be ameliorated with early recognition and the rapid administration of antimicrobial therapy. Studies demonstrating this effect have been done in resource-endowed countries, but not in resource-limited settings. We attempted to decrease the time to antibiotics in patients with fever and neutropenia presenting to a paediatric emergency centre at a tertiary care referral hospital in Ethiopia. In 3 phases we perfo ...[more]

PMID: 33312850

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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.

Project description:IntroductionOur goal in this study was to estimate rates of emergency department (ED) visits for fever by children <2 years of age, and evaluate frequencies of testing and treatment during these visits.MethodsWe performed a cross-sectional study of ED encounters from 2007-2017 using the National Hospital Ambulatory Medical Care Survey, a cross-sectional, multi-stage probability sample survey of visits to nonfederal United States EDs. We included encounters with a visit reason of "fever" or recorded fever in the ED. We report demographics and management strategies in two groups: infants ≤90 days in age; and children 91 days to <2 years old. For patients 91 days to <2 years, we compared testing and treatment strategies between general and pediatric EDs using chi-squared tests.ResultsOf 1.5 billion encounters over 11 years, 2.1% (95% confidence interval [CI], 1.9-2.2%) were by children <2 years old with fever. Two million encounters (95% CI, 1.7-2.4 million) were by infants ≤90 days, and 28.4 million (95% CI, 25.5-31.4 million) were by children 91 days to <2 years. Among infants ≤90 days, 27.6% (95% CI, 21.1-34.1%) had blood and 21.3% (95% CI, 13.6-29.1%) had urine cultures; 26.8% (95% CI, 20.9-32.7%) were given antibiotics, and 21.1% (95% CI, 15.3-26.9%) were admitted or transferred. Among patients 91 days to <2 years in age, 6.8% (95% CI, 5.8-7.8%) had blood and 7.7% (95% CI 6.1-9.4%) had urine cultures; 40.5% (95% CI, 40.5-40.5%) were given antibiotics, and 4.4% (95% CI, 3.5-5.3%) were admitted or transferred. Patients 91 days to <2 years who were evaluated in general EDs had higher rates of radiography (27.1% vs 15.2%; P<0.01) and antibiotic utilization (42.3% vs 34.2%; P<0.01), but lower rates of urine culture testing (6.4% vs 11.6%, p = 0.03), compared with patients evaluated in pediatric EDs.ConclusionApproximately 180,000 patients ≤90 days old and 2.6 million patients 91 days to <2 years in age with fever present to US EDs annually. Given existing guidelines, blood and urine culture performance was low for infants ≤90 days old. For children 91 days to <2 years, rates of radiography and antibiotic use were higher in general EDs compared to pediatric EDs. These findings suggest opportunities to improve care among febrile young children in the ED.

Project description:Patients presenting to the emergency department (ED) currently face inacceptable delays in initial treatment, and long, costly hospital stays due to suboptimal initial triage and site-of-care decisions. Accurate ED triage should focus not only on initial treatment priority, but also on prediction of medical risk and nursing needs to improve site-of-care decisions and to simplify early discharge management. Different triage scores have been proposed, such as the Manchester triage system (MTS). Yet, these scores focus only on treatment priority, have suboptimal performance and lack validation in the Swiss health care system. Because the MTS will be introduced into clinical routine at the Kantonsspital Aarau, we propose a large prospective cohort study to optimize initial patient triage. Specifically, the aim of this trial is to derive a three-part triage algorithm to better predict (a) treatment priority; (b) medical risk and thus need for in-hospital treatment; (c) post-acute care needs of patients at the most proximal time point of ED admission.Prospective, observational, multicenter, multi-national cohort study. We will include all consecutive medical patients seeking ED care into this observational registry. There will be no exclusions except for non-adult and non-medical patients. Vital signs will be recorded and left over blood samples will be stored for later batch analysis of blood markers. Upon ED admission, the post-acute care discharge score (PACD) will be recorded. Attending ED physicians will adjudicate triage priority based on all available results at the time of ED discharge to the medical ward. Patients will be reassessed daily during the hospital course for medical stability and readiness for discharge from the nurses and if involved social workers perspective. To assess outcomes, data from electronic medical records will be used and all patients will be contacted 30 days after hospital admission to assess vital and functional status, re-hospitalization, satisfaction with care and quality of life measures. We aim to include between 5000 and 7000 patients over one year of recruitment to derive the three-part triage algorithm. The respective main endpoints were defined as (a) initial triage priority (high vs. low priority) adjudicated by the attending ED physician at ED discharge, (b) adverse 30 day outcome (death or intensive care unit admission) within 30 days following ED admission to assess patients risk and thus need for in-hospital treatment and (c) post acute care needs after hospital discharge, defined as transfer of patients to a post-acute care institution, for early recognition and planning of post-acute care needs. Other outcomes are time to first physician contact, time to initiation of adequate medical therapy, time to social worker involvement, length of hospital stay, reasons for discharge delays, patient's satisfaction with care, overall hospital costs and patients care needs after returning home.Using a reliable initial triage system for estimating initial treatment priority, need for in-hospital treatment and post-acute care needs is an innovative and persuasive approach for a more targeted and efficient management of medical patients in the ED. The proposed interdisciplinary , multi-national project has unprecedented potential to improve initial triage decisions and optimize resource allocation to the sickest patients from admission to discharge. The algorithms derived in this study will be compared in a later randomized controlled trial against a usual care control group in terms of resource use, length of hospital stay, overall costs and patient's outcomes in terms of mortality, re-hospitalization, quality of life and satisfaction with care.ClinicalTrials.gov Identifier, NCT01768494.

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Triage guideline for immunocompromised children with fever in an emergency centre in Ethiopia.

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Triage guideline for immunocompromised children with fever in an emergency centre in Ethiopia.

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