Supporting decisions to increase the safe discharge of children with febrile illness from the emergency department: a systematic review and meta-analysis

2015 ◽  
Vol 101 (3) ◽  
pp. 259-266 ◽  
Author(s):  
A D Irwin ◽  
J Wickenden ◽  
K Le Doare ◽  
S Ladhani ◽  
M Sharland
Keyword(s):  
Systematic Review ◽  
Emergency Department ◽  
Prediction Models ◽  
Decision Rules ◽  
Febrile Illness ◽  
Low Risk ◽  
Prediction Rules ◽  
Serious Infections ◽  
Empirical Antibiotics ◽  
Rule Out

BackgroundDespite fewer serious infections presenting to the children's emergency department (ED), hospital admissions of children with febrile illness have increased. We review evidence for the use of decision rules to increase the safe discharge of these children from the ED.MethodsA systematic review of prospective studies of decision rules for the discharge of children with febrile illness, and prediction rules for the diagnosis of serious infections in children presenting to ED. We reviewed the MEDLINE database, Cochrane Library and hand searched the bibliographies of related studies. The search was limited to the English language.ResultsThirty-three studies were identified. Fourteen reported low-risk criteria to rule out serious bacterial infection (SBI) in infants less than 3 months of age. In this group, clinical tools such as the Rochester and Philadelphia criteria support the safe discharge of low-risk infants without empirical antibiotics. Seventeen studies reported prediction rules in older children, though only four included children over 3 years. Two impact studies based upon multivariable prediction models failed to demonstrate any impact on rates of discharge from ED.ConclusionsThe use of clinical prediction models can improve discrimination between serious and self-limiting infections in children. The application of low-risk thresholds may help to rule out serious infections and discharge children from the ED without empirical antibiotics. A growing evidence base for prediction rules has so far failed to translate into validated rules to aid decision-making. Future work should evaluate decision rules in well designed impact studies, focusing on the need for hospital admission and antibiotic therapy.

The Use of Prognostic Prediction Models for Mortality or Clinical Deterioration among Hospitalized and Non-hospitalized Adults with COVID-19: A Systematic Review

2021 ◽  
Author(s):  
Patricia Pauline M. Remalante-Rayco ◽  
Evelyn Osio-Salido
Keyword(s):  
Systematic Review ◽  
Emergency Department ◽  
Early Warning ◽  
Prediction Models ◽  
External Validation ◽  
Risk Of Bias ◽  
Clinical Deterioration ◽  
Early Warning Score ◽  
Good Prediction ◽  
Prediction Of Mortality

Objective. To assess the performance of prognostic models in predicting mortality or clinical deterioration among patients with COVID-19, both hospitalized and non-hospitalized Methods. We conducted a systematic review of the literature until March 8, 2021. We included models for the prediction of mortality or clinical deterioration in COVID-19 with external validation. We used the Prediction model Risk Of Bias ASsessment Tool (PROBAST) and the GRADEpro Guideline Development Tool (GDT) to assess the evidence obtained. Results. We reviewed 33 cohort studies. Two studies had a low risk of bias, four unclear risks, and 27 with a high risk of bias due to participant selection and analysis. For the outcome of mortality, the QCOVID model had excellent prediction with high certainty of evidence but was specific for use in England. The COVID Outcome Prediction in the Emergency Department (COPE) model, the 4C Mortality Score, the Age, BUN, number of comorbidities, CRP, SpO2/FiO2 ratio, platelet count, heart rate (ABC2-SPH) risk score, the Confusion Urea Respiration Blood Pressure (CURB-65) severity score, the Rapid Emergency Medicine Score (REMS), and the Risk Stratification in the Emergency Department in Acutely Ill Older Patients (RISE UP) score had fair to good prediction of death among inpatients, while the quick Sepsis-related Organ Failure Assessment (qSOFA) score had poor to fair prediction. The certainty of evidence for these models was very low to low. For the outcome of clinical deterioration, the 4C Deterioration Score had fair prediction, the National Early Warning Score 2 (NEWS2) score poor to good, and the Modified Early Warning Score (MEWS) had poor prediction. The certainty of evidence for these three models was also very low to low. None of these models had been validated in the Philippine setting. Conclusion. The QCOVID, COPE, ABC2-SPH, 4C, CURB-65, REMS, RISE-UP models for prediction of mortality and the 4C Deterioration and NEWS2 models for prediction of clinical deterioration are potentially useful but need to be validated among patients with COVID-19 of varying severity in the Philippine setting.

Analysis of emergency department prediction tools in evaluating febrile young infants at risk for serious infections

2019 ◽  
pp. emermed-2018-208210 ◽  
Author(s):  
Sarah Hui Wen Yao ◽  
Gene Yong-Kwang Ong ◽  
Ian K Maconochie ◽  
Khai Pin Lee ◽  
Shu-Ling Chong
Keyword(s):  
At Risk ◽  
Emergency Department ◽  
Clinical Assessment ◽  
Low Risk ◽  
Assessment Tools ◽  
Predictive Values ◽  
Nice Guideline ◽  
Risk Criteria ◽  
Young Infants ◽  
Serious Infections

ObjectiveFebrile infants≤3 months old constitute a vulnerable group at risk of serious infections (SI). We aimed to (1) study the test performance of two clinical assessment tools—the National Institute for Health and Care Excellence (NICE) Traffic Light System and Severity Index Score (SIS) in predicting SI among all febrile young infants and (2) evaluate the performance of three low-risk criteria—the Rochester Criteria (RC), Philadelphia Criteria (PC) and Boston Criteria (BC) among well-looking febrile infants.MethodsA retrospective validation study was conducted. Serious illness included both bacterial and serious viral illness such as meningitis and encephalitis. We included febrile infants≤3 months old presenting to a paediatric emergency department in Singapore between March 2015 and February 2016. Infants were assigned to high-risk and low-risk groups for SI according to each of the five tools. We compared the performance of the NICE guideline and SIS at initial clinical assessment for all infants and the low-risk criteria—RC, PC and BC—among well-looking infants. We presented their performance using sensitivity, specificity, positive, negative predictive values and likelihood ratios.ResultsOf 1057 infants analysed, 326 (30.8%) were diagnosed with SI. The NICE guideline had an overall sensitivity of 93.3% (95% CI 90.0 to 95.7), while the SIS had a sensitivity of 79.1% (95% CI 74.3 to 83.4). The incidence of SI was similar among infants who were well-looking and those who were not. Among the low-risk criteria, the RC performed with the highest sensitivity in infants aged 0–28 days (98.2%, 95% CI 90.3% to 100.0%) and 29–60 days (92.4%, 95% CI 86.0% to 96.5%), while the PC performed best in infants aged 61–90 days (100.0%, 95% CI 95.4% to 100.0%).ConclusionsThe NICE guideline achieved high sensitivity in our study population, and the RC had the highest sensitivity in predicting for SI among well-appearing febrile infants. Prospective validation is required.

scholarly journals The HEART score for early rule out of acute coronary syndromes in the emergency department: a systematic review and meta-analysis

2017 ◽  
Vol 7 (2) ◽  
pp. 111-119 ◽  
Author(s):  
Patricia Van Den Berg ◽  
Richard Body
Keyword(s):  
Systematic Review ◽  
Emergency Department ◽  
Diagnostic Accuracy ◽  
Meta Analysis ◽  
Low Risk ◽  
Major Adverse Cardiac Events ◽  
Current Evidence ◽  
Cardiac Events ◽  
Heart Score ◽  
Adverse Cardiac Events

Aims: The objective of this systematic review was to summarise the current evidence on the diagnostic accuracy of the HEART score for predicting major adverse cardiac events in patients presenting with undifferentiated chest pain to the emergency department. Methods and results: Two investigators independently searched Medline, Embase and Cochrane databases between 2008 and May 2016 identifying eligible studies providing diagnostic accuracy data on the HEART score for predicting major adverse cardiac events as the primary outcome. For the 12 studies meeting inclusion criteria, study characteristics and diagnostic accuracy measures were systematically extracted and study quality assessed using the QUADAS-2 tool. After quality assessment, nine studies including data from 11,217 patients were combined in the meta-analysis applying a generalised linear mixed model approach with random effects assumption (Stata 13.1). In total, 15.4% of patients (range 7.3–29.1%) developed major adverse cardiac events after a mean of 6 weeks’ follow-up. Among patients categorised as ‘low risk’ and suitable for early discharge (HEART score 0–3), the pooled incidence of ‘missed’ major adverse cardiac events was 1.6%. The pooled sensitivity and specificity of the HEART score for predicting major adverse cardiac events were 96.7% (95% confidence interval (CI) 94.0–98.2%) and 47.0% (95% CI 41.0–53.5%), respectively. Conclusions: Patients with a HEART score of 0–3 are at low risk of incident major adverse cardiac events. As 3.3% of patients with major adverse cardiac events are ‘missed’ by the HEART score, clinicians must ask whether this risk is acceptably low for clinical implementation.

scholarly journals Prediction Models for Severe Manifestations and Mortality due to COVID-19: A Rapid Systematic Review

2021 ◽  
Author(s):  
Jamie L. Miller ◽  
Masafumi Tada ◽  
Michihiko Goto ◽  
Nicholas Mohr ◽  
Sangil Lee
Keyword(s):  
Systematic Review ◽  
Prediction Model ◽  
Prediction Models ◽  
External Validation ◽  
Clinical Signs ◽  
Risk Of Bias ◽  
Low Risk ◽  
Prognostic Models ◽  
Organ Support ◽  
Public Data

ABSTRACTBackgroundThroughout 2020, the coronavirus disease 2019 (COVID-19) has become a threat to public health on national and global level. There has been an immediate need for research to understand the clinical signs and symptoms of COVID-19 that can help predict deterioration including mechanical ventilation, organ support, and death. Studies thus far have addressed the epidemiology of the disease, common presentations, and susceptibility to acquisition and transmission of the virus; however, an accurate prognostic model for severe manifestations of COVID-19 is still needed because of the limited healthcare resources available.ObjectiveThis systematic review aims to evaluate published reports of prediction models for severe illnesses caused COVID-19.MethodsSearches were developed by the primary author and a medical librarian using an iterative process of gathering and evaluating terms. Comprehensive strategies, including both index and keyword methods, were devised for PubMed and EMBASE. The data of confirmed COVID-19 patients from randomized control studies, cohort studies, and case-control studies published between January 2020 and July 2020 were retrieved. Studies were independently assessed for risk of bias and applicability using the Prediction Model Risk Of Bias Assessment Tool (PROBAST). We collected study type, setting, sample size, type of validation, and outcome including intubation, ventilation, any other type of organ support, or death. The combination of the prediction model, scoring system, performance of predictive models, and geographic locations were summarized.ResultsA primary review found 292 articles relevant based on title and abstract. After further review, 246 were excluded based on the defined inclusion and exclusion criteria. Forty-six articles were included in the qualitative analysis. Inter observer agreement on inclusion was 0.86 (95% confidence interval: 0.79 - 0.93). When the PROBAST tool was applied, 44 of the 46 articles were identified to have high or unclear risk of bias, or high or unclear concern for applicability. Two studied reported prediction models, 4C Mortality Score from hospital data and QCOVID from general public data from UK, and were rated as low risk of bias and low concerns for applicability.ConclusionSeveral prognostic models are reported in the literature, but many of them had concerning risks of biases and applicability. For most of the studies, caution is needed before use, as many of them will require external validation before dissemination. However, two articles were found to have low risk of bias and low applicability can be useful tools.

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