Dataset Information

Impact of a clinical decision model for febrile children at risk for serious bacterial infections at the emergency department: a randomized controlled trial.

ABSTRACT:

Objectives

To assess the impact of a clinical decision model for febrile children at risk for serious bacterial infections (SBI) attending the emergency department (ED).

Methods

Randomized controlled trial with 439 febrile children, aged 1 month-16 years, attending the pediatric ED of a Dutch university hospital during 2010-2012. Febrile children were randomly assigned to the intervention (clinical decision model; n = 219) or the control group (usual care; n = 220). The clinical decision model included clinical symptoms, vital signs, and C-reactive protein and provided high/low-risks for "pneumonia" and "other SBI". Nurses were guided by the intervention to initiate additional tests for high-risk children. The clinical decision model was evaluated by 1) area-under-the-receiver-operating-characteristic-curve (AUC) to indicate discriminative ability and 2) feasibility, to measure nurses' compliance to model recommendations. Primary patient outcome was defined as correct SBI diagnoses. Secondary process outcomes were defined as length of stay; diagnostic tests; antibiotic treatment; hospital admission; revisits and medical costs.

Results

The decision model had good discriminative ability for both pneumonia (n = 33; AUC 0.83 (95% CI 0.75-0.90)) and other SBI (n = 22; AUC 0.81 (95% CI 0.72-0.90)). Compliance to model recommendations was high (86%). No differences in correct SBI determination were observed. Application of the clinical decision model resulted in less full-blood-counts (14% vs. 22%, p-value < 0.05) and more urine-dipstick testing (71% vs. 61%, p-value < 0.05).

Conclusions

In contrast to our expectations no substantial impact on patient outcome was perceived. The clinical decision model preserved, however, good discriminatory ability to detect SBI, achieved good compliance among nurses and resulted in a more standardized diagnostic approach towards febrile children, with less full blood-counts and more rightfully urine-dipstick testing.

Trial registration

Nederlands Trial Register NTR2381.

SUBMITTER: de Vos-Kerkhof E

PROVIDER: S-EPMC4449197 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Publications

Impact of a clinical decision model for febrile children at risk for serious bacterial infections at the emergency department: a randomized controlled trial.

de Vos-Kerkhof Evelien E Nijman Ruud G RG Vergouwe Yvonne Y Polinder Suzanne S Steyerberg Ewout W EW van der Lei Johan J Moll Henriëtte A HA Oostenbrink Rianne R

PloS one 20150529 5

<h4>Objectives</h4>To assess the impact of a clinical decision model for febrile children at risk for serious bacterial infections (SBI) attending the emergency department (ED).<h4>Methods</h4>Randomized controlled trial with 439 febrile children, aged 1 month-16 years, attending the pediatric ED of a Dutch university hospital during 2010-2012. Febrile children were randomly assigned to the intervention (clinical decision model; n = 219) or the control group (usual care; n = 220). The clinical d ...[more]

PMID: 26024532

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Project description:ObjectiveTo derive, cross validate, and externally validate a clinical prediction model that assesses the risks of different serious bacterial infections in children with fever at the emergency department.DesignProspective observational diagnostic study.SettingThree paediatric emergency care units: two in the Netherlands and one in the United Kingdom.ParticipantsChildren with fever, aged 1 month to 15 years, at three paediatric emergency care units: Rotterdam (n=1750) and the Hague (n=967), the Netherlands, and Coventry (n=487), United Kingdom. A prediction model was constructed using multivariable polytomous logistic regression analysis and included the predefined predictor variables age, duration of fever, tachycardia, temperature, tachypnoea, ill appearance, chest wall retractions, prolonged capillary refill time (>3 seconds), oxygen saturation <94%, and C reactive protein.Main outcome measuresPneumonia, other serious bacterial infections (SBIs, including septicaemia/meningitis, urinary tract infections, and others), and no SBIs.ResultsOxygen saturation <94% and presence of tachypnoea were important predictors of pneumonia. A raised C reactive protein level predicted the presence of both pneumonia and other SBIs, whereas chest wall retractions and oxygen saturation <94% were useful to rule out the presence of other SBIs. Discriminative ability (C statistic) to predict pneumonia was 0.81 (95% confidence interval 0.73 to 0.88); for other SBIs this was even better: 0.86 (0.79 to 0.92). Risk thresholds of 10% or more were useful to identify children with serious bacterial infections; risk thresholds less than 2.5% were useful to rule out the presence of serious bacterial infections. External validation showed good discrimination for the prediction of pneumonia (0.81, 0.69 to 0.93); discriminative ability for the prediction of other SBIs was lower (0.69, 0.53 to 0.86).ConclusionA validated prediction model, including clinical signs, symptoms, and C reactive protein level, was useful for estimating the likelihood of pneumonia and other SBIs in children with fever, such as septicaemia/meningitis and urinary tract infections.

Project description:BackgroundHealth-workers in developing countries rely on clinical algorithms, such as the Integrated Management of Childhood Illnesses (IMCI), for the management of patients, including diagnosis of serious bacterial infections (SBI). The diagnostic accuracy of IMCI in detecting children with SBI is unknown. Prediction rules and guidelines for SBI from well-resourced countries at outpatient level may help to improve current guidelines; however, their diagnostic performance has not been evaluated in resource-limited countries, where clinical conditions, access to care, and diagnostic capacity differ. The aim of this study was to estimate the diagnostic accuracy of existing prediction rules and clinical guidelines in identifying children with SBI in a cohort of febrile children attending outpatient health facilities in Tanzania.MethodsStructured literature review to identify available prediction rules and guidelines aimed at detecting SBI and retrospective, external validation on a dataset containing 1005 febrile Tanzanian children with acute infections. The reference standard, SBI, was established based on rigorous clinical and microbiological criteria.ResultsFour prediction rules and five guidelines, including IMCI, could be validated. All examined rules and guidelines had insufficient diagnostic accuracy for ruling-in or ruling-out SBI with positive and negative likelihood ratios ranging from 1.04-1.87 to 0.47-0.92, respectively. IMCI had a sensitivity of 36.7% (95% CI 29.4-44.6%) at a specificity of 70.3% (67.1-73.4%). Rules that use a combination of clinical and laboratory testing had better performance compared to rules and guidelines using only clinical and or laboratory elements.ConclusionsCurrently applied guidelines for managing children with febrile illness have insufficient diagnostic accuracy in detecting children with SBI. Revised clinical algorithms including simple point-of-care tests with improved accuracy for detecting SBI targeting in tropical resource-poor settings are needed. They should undergo careful external validation against clinical outcome before implementation, given the inherent limitations of gold standards for SBI.

Project description:ObjectiveDevelopment and validation of clinical prediction model (CPM) for serious bacterial infections (SBIs) in children presenting to the emergency department (ED) with febrile illness, based on clinical variables, clinician's "gut feeling," and "sense of reassurance.Materials and methodsFebrile children presenting to the ED of Children's Clinical University Hospital (CCUH) between April 1, 2017 and December 31, 2018 were enrolled in a prospective observational study. Data on clinical signs and symptoms at presentation, together with clinician's "gut feeling" of something wrong and "sense of reassurance" were collected as candidate variables for CPM. Variable selection for the CPM was performed using stepwise logistic regression (forward, backward, and bidirectional); Akaike information criterion was used to limit the number of parameters and simplify the model. Bootstrapping was applied for internal validation. For external validation, the model was tested in a separate dataset of patients presenting to six regional hospitals between January 1 and March 31, 2019.ResultsThe derivation cohort consisted of 517; 54% (n = 279) were boys, and the median age was 58 months. SBI was diagnosed in 26.7% (n = 138). Validation cohort included 188 patients; the median age was 28 months, and 26.6% (n = 50) developed SBI. Two CPMs were created, namely, CPM1 consisting of six clinical variables and CPM2 with four clinical variables plus "gut feeling" and "sense of reassurance." The area under the curve (AUC) for receiver operating characteristics (ROC) curve of CPM1 was 0.744 (95% CI, 0.683-0.805) in the derivation cohort and 0.692 (95% CI, 0.604-0.780) in the validation cohort. AUC for CPM2 was 0.783 (0.727-0.839) and 0.752 (0.674-0.830) in derivation and validation cohorts, respectively. AUC of CPM2 in validation population was significantly higher than that of CPM1 [p = 0.037, 95% CI (-0.129; -0.004)]. A clinical evaluation score was derived from CPM2 to stratify patients in "low risk," "gray area," and "high risk" for SBI.ConclusionBoth CPMs had moderate ability to predict SBI and acceptable performance in the validation cohort. Adding variables "gut feeling" and "sense of reassurance" in CPM2 improved its ability to predict SBI. More validation studies are needed for the assessment of applicability to all febrile patients presenting to ED.

Project description:It is unknown whether febrile infants 29 to 60 days old with positive urinalysis results require routine lumbar punctures for evaluation of bacterial meningitis.ObjectiveTo determine the prevalence of bacteremia and/or bacterial meningitis in febrile infants ≤60 days of age with positive urinalysis (UA) results.MethodsSecondary analysis of a prospective observational study of noncritical febrile infants ≤60 days between 2011 and 2019 conducted in the Pediatric Emergency Care Applied Research Network emergency departments. Participants had temperatures ≥38°C and were evaluated with blood cultures and had UAs available for analysis. We report the prevalence of bacteremia and bacterial meningitis in those with and without positive UA results.ResultsAmong 7180 infants, 1090 (15.2%) had positive UA results. The risk of bacteremia was higher in those with positive versus negative UA results (63/1090 [5.8%] vs 69/6090 [1.1%], difference 4.7% [3.3% to 6.1%]). There was no difference in the prevalence of bacterial meningitis in infants ≤28 days of age with positive versus negative UA results (∼1% in both groups). However, among 697 infants aged 29 to 60 days with positive UA results, there were no cases of bacterial meningitis in comparison to 9 of 4153 with negative UA results (0.2%, difference -0.2% [-0.4% to -0.1%]). In addition, there were no cases of bacteremia and/or bacterial meningitis in the 148 infants ≤60 days of age with positive UA results who had the Pediatric Emergency Care Applied Research Network low-risk blood thresholds of absolute neutrophil count <4 × 103 cells/mm3 and procalcitonin <0.5 ng/mL.ConclusionsAmong noncritical febrile infants ≤60 days of age with positive UA results, there were no cases of bacterial meningitis in those aged 29 to 60 days and no cases of bacteremia and/or bacterial meningitis in any low-risk infants based on low-risk blood thresholds in both months of life. These findings can guide lumbar puncture use and other clinical decision making.

Project description:Serious bacterial infection (SBI) in children, such as bacterial meningitis or sepsis, is an important condition that can lead to fatal outcomes. Therefore, since it is very important to accurately diagnose SBI, SBI prediction tools such as 'Refined Lab-score' or 'clinical prediction rule' have been developed and used. However, these tools can predict SBI only when there are values of all factors used in the tool, and if even one of them is missing, the tools become useless. Therefore, the purpose of this study was to develop and validate a machine learning-driven model to predict SBIs among febrile children, even with missing values. This was a multicenter retrospective observational study including febrile children <6 years of age who visited Emergency departments (EDs) of 3 different tertiary hospitals from 2016 to 2018. The SBI prediction model was trained with a derivation cohort (data from two hospitals) and externally tested with a validation cohort (data from a third hospital). A total of 11,973 and 2,858 patient records were included in the derivation and validation cohorts, respectively. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) of the RF model was 0.964 (95% confidence interval [CI], 0.943-0.986), and the area under the precision-recall curve (AUPRC) was 0.753 (95% CI, 0.681-0.824). The conventional LR (CLR) model showed corresponding values of 0.902 (95% CI, 0.894-0.910) and 0.573 (95% CI, 0.560-0.586), respectively. In the validation cohort, the AUROC (95% CI) of the RF model was 0.950 (95% CI, 0.945-0.956), the AUPRC was 0.605 (95% CI, 0.593-0.616), and the CLR presented corresponding values of 0.815 (95% CI, 0.789-0.841) and 0.586 (95% CI, 0.553-0.619), respectively. We developed a machine learning-driven prediction model for SBI among febrile children, which works robustly despite missing values. And it showed superior performance compared to CLR in both internal validation and external validation.

Dataset Information

Impact of a clinical decision model for febrile children at risk for serious bacterial infections at the emergency department: a randomized controlled trial.

Objectives

Methods

Results

Conclusions

Trial registration

Publications

Impact of a clinical decision model for febrile children at risk for serious bacterial infections at the emergency department: a randomized controlled trial.

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