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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:Several triage systems have been developed, but little is known about their performance in low-resource settings. Evaluating and comparing novel triage systems to existing triage scales provides essential information about their added value, reliability, safety, and effectiveness before adoption. This study included children aged < 15 years who presented to the emergency departments of two public hospitals in Kenya between February and December 2021. We compared the performance of Emergency Triage Assessment and Treatment (ETAT) guidelines and Smart Triage (ST) models (ST model with independent triggers, and recalibrated ST model with independent triggers) in categorizing children into emergency, priority, and non-urgent triage categories. Sankey diagrams were used to visualize the distribution of children into similar or different triage categories by ETAT and ST models. Sensitivity, specificity, negative and positive predictive values for mortality and admission were calculated. 5618 children were enrolled, and the majority (3113, 55.4%) were aged between one and five years of age. Overall admission and mortality rates were 7% and 0.9%, respectively. ETAT classified 513 (9.2%) children into the emergency category compared to 1163 (20.8%) and 1161 (20.7%) by the ST model with independent triggers and recalibrated model with independent triggers, respectively. ETAT categorized 3089 (55.1%) children as non-urgent compared to 2097 (37.4%) and 2617 (46.7%) for the respective ST models. ETAT classified 191/395 (48.4%) admitted patients as emergencies compared to more than half by all the ST models. ETAT and ST models classified 25/49 (51%) and 39/49 (79.6%) deceased children as emergencies. Sensitivity for admission and mortality was 48.4% and 51% for ETAT and 74.9% and 79.6% for the ST models, respectively. Smart Triage shows potential for identifying critically ill children in low-resource settings, particularly when combined with independent triggers and performs comparably to ETAT. Evaluation of Smart Triage in other contexts and comparison to other triage systems is required.

Project description:ObjectiveTo date, there is no standard diagnostic practice to identify the underlying disease-causing mechanism for paediatric patients suffering from chronic fever without any specific diagnosis, which is one of the leading causes of death in paediatric patients. Therefore, we aimed this retrospective study to analyse medical records of paediatric patients with fever of unknown origin (FUO) to provide a preliminary basis for improving the diagnostic categories and facilitate the treatment outcomes.DesignA retrospective study.SettingBeijing Children's Hospital.ParticipantsClinical data were collected from 1288 children between 1 month and 18 years of age diagnosed with FUO at Beijing Children's Hospital between January 2010 and December 2017.InterventionsAccording to the aetiological composition, age, duration of fever and laboratory examination results, the diagnostic strategies were analysed and formulated.Primary and secondary outcome measuresThe statistical analyses were carried out using SPSS V.24.0 platform along with the χ2 test and analysis of variance (p<0.05).ResultsThe duration of fever ranged from 2 weeks to 2 years, with an average of 6 weeks. There were 656 cases (50.9%) of infectious diseases, 63 cases (4.9%) of non-infectious inflammatory diseases (NIIDs), 86 cases (6.7%) of neoplastic diseases, 343 cases (26.6%) caused by miscellaneous diseases and 140 cases (10.9%) were undiagnosed. With increasing age, the proportion of FUO from infectious diseases gradually decreased from 73.53% to 44.21%. NIID was more common in children over 3 years old, and neoplastic diseases mainly occurred from 1 to 6 years of age. Among miscellaneous diseases, the age distribution was mainly in school-aged children over 6 years. Respiratory tract infection was the most common cause of FUO in children, followed by bloodstream infections. Bacterial infection was the most common cause in children with less than 1 year old, while the virus was the main pathogen in children over 1 year old.ConclusionsThe diagnosis of neoplastic diseases and miscellaneous diseases-related diseases still depends mainly on invasive examination. According to our clinical experience, the diagnostic process was formulated based on fever duration and the type of disease. This process can provide a guide for the diagnosis and treatment of paediatric FUO in the future.

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.

Dataset Information

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

Publications

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

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