Dataset Information

The Yale Observation Scale Score and the Risk of Serious Bacterial Infections in Febrile Infants.

ABSTRACT:

Objectives

To assess the performance of the Yale Observation Scale (YOS) score and unstructured clinician suspicion to identify febrile infants ?60 days of age with and without serious bacterial infections (SBIs).

Methods

We performed a planned secondary analysis of a prospective cohort of non-critically ill, febrile, full-term infants ?60 days of age presenting to 1 of 26 participating emergency departments in the Pediatric Emergency Care Applied Research Network. We defined SBIs as urinary tract infections, bacteremia, or bacterial meningitis, with the latter 2 considered invasive bacterial infections. Emergency department clinicians applied the YOS (range: 6-30; normal score: ?10) and estimated the risk of SBI using unstructured clinician suspicion (<1%, 1%-5%, 6%-10%, 11%-50%, or >50%).

Results

Of the 4591 eligible infants, 444 (9.7%) had SBIs and 97 (2.1%) had invasive bacterial infections. Of the 4058 infants with YOS scores of ?10, 388 (9.6%) had SBIs (sensitivity: 51/439 [11.6%]; 95% confidence interval [CI]: 8.8%-15.0%; negative predictive value: 3670/4058 [90.4%]; 95% CI: 89.5%-91.3%) and 72 (1.8%) had invasive bacterial infections (sensitivity 23/95 [24.2%], 95% CI: 16.0%-34.1%; negative predictive value: 3983/4055 [98.2%], 95% CI: 97.8%-98.6%). Of the infants with clinician suspicion of <1%, 106 had SBIs (6.4%) and 16 (1.0%) had invasive bacterial infections.

Conclusions

In this large prospective cohort of febrile infants ?60 days of age, neither the YOS score nor unstructured clinician suspicion reliably identified those with invasive bacterial infections. More accurate clinical and laboratory predictors are needed to risk stratify febrile infants.

SUBMITTER: Nigrovic LE

PROVIDER: S-EPMC5495524 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Publications

The Yale Observation Scale Score and the Risk of Serious Bacterial Infections in Febrile Infants.

Nigrovic Lise E LE Mahajan Prashant V PV Blumberg Stephen M SM Browne Lorin R LR Linakis James G JG Ruddy Richard M RM Bennett Jonathan E JE Rogers Alexander J AJ Tzimenatos Leah L Powell Elizabeth C EC Alpern Elizabeth R ER Casper T Charles TC Ramilo Octavio O Kuppermann Nathan N

Pediatrics 20170606 1

<h4>Objectives</h4>To assess the performance of the Yale Observation Scale (YOS) score and unstructured clinician suspicion to identify febrile infants ≤60 days of age with and without serious bacterial infections (SBIs).<h4>Methods</h4>We performed a planned secondary analysis of a prospective cohort of non-critically ill, febrile, full-term infants ≤60 days of age presenting to 1 of 26 participating emergency departments in the Pediatric Emergency Care Applied Research Network. We defined SBIs ...[more]

PMID: 28759413

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Project description:ImportanceIn young febrile infants, serious bacterial infections (SBIs), including urinary tract infections, bacteremia, and meningitis, may lead to dangerous complications. However, lumbar punctures and hospitalizations involve risks and costs. Clinical prediction rules using biomarkers beyond the white blood cell count (WBC) may accurately identify febrile infants at low risk for SBIs.ObjectiveTo derive and validate a prediction rule to identify febrile infants 60 days and younger at low risk for SBIs.Design, setting, and participantsProspective, observational study between March 2011 and May 2013 at 26 emergency departments. Convenience sample of previously healthy febrile infants 60 days and younger who were evaluated for SBIs. Data were analyzed between April 2014 and April 2018.ExposuresClinical and laboratory data (blood and urine) including patient demographics, fever height and duration, clinical appearance, WBC, absolute neutrophil count (ANC), serum procalcitonin, and urinalysis. We derived and validated a prediction rule based on these variables using binary recursive partitioning analysis.Main outcomes and measuresSerious bacterial infection, defined as urinary tract infection, bacteremia, or bacterial meningitis.ResultsWe derived the prediction rule on a random sample of 908 infants and validated it on 913 infants (mean age was 36 days, 765 were girls [42%], 781 were white and non-Hispanic [43%], 366 were black [20%], and 535 were Hispanic [29%]). Serious bacterial infections were present in 170 of 1821 infants (9.3%), including 26 (1.4%) with bacteremia, 151 (8.3%) with urinary tract infections, and 10 (0.5%) with bacterial meningitis; 16 (0.9%) had concurrent SBIs. The prediction rule identified infants at low risk of SBI using a negative urinalysis result, an ANC of 4090/µL or less (to convert to ×109 per liter, multiply by 0.001), and serum procalcitonin of 1.71 ng/mL or less. In the validation cohort, the rule sensitivity was 97.7% (95% CI, 91.3-99.6), specificity was 60.0% (95% CI, 56.6-63.3), negative predictive value was 99.6% (95% CI, 98.4-99.9), and negative likelihood ratio was 0.04 (95% CI, 0.01-0.15). One infant with bacteremia and 2 infants with urinary tract infections were misclassified. No patients with bacterial meningitis were missed by the rule. The rule performance was nearly identical when the outcome was restricted to bacteremia and/or bacterial meningitis, missing the same infant with bacteremia.Conclusions and relevanceWe derived and validated an accurate prediction rule to identify febrile infants 60 days and younger at low risk for SBIs using the urinalysis, ANC, and procalcitonin levels. Once further validated on an independent cohort, clinical application of the rule has the potential to decrease unnecessary lumbar punctures, antibiotic administration, and hospitalizations.

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:BACKGROUND:Health-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. METHODS:Structured 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. RESULTS:Four 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. CONCLUSIONS:Currently 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: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.

Project description:Young febrile infants are at substantial risk of serious bacterial infections; however, the current culture-based diagnosis has limitations. Analysis of host expression patterns ("RNA biosignatures") in response to infections may provide an alternative diagnostic approach.To assess whether RNA biosignatures can distinguish febrile infants aged 60 days or younger with and without serious bacterial infections.Prospective observational study involving a convenience sample of febrile infants 60 days or younger evaluated for fever (temperature >38° C) in 22 emergency departments from December 2008 to December 2010 who underwent laboratory evaluations including blood cultures. A random sample of infants with and without bacterial infections was selected for RNA biosignature analysis. Afebrile healthy infants served as controls. Blood samples were collected for cultures and RNA biosignatures. Bioinformatics tools were applied to define RNA biosignatures to classify febrile infants by infection type.RNA biosignatures compared with cultures for discriminating febrile infants with and without bacterial infections and infants with bacteremia from those without bacterial infections.Bacterial infection confirmed by culture. Performance of RNA biosignatures was compared with routine laboratory screening tests and Yale Observation Scale (YOS) scores.Of 1883 febrile infants (median age, 37 days; 55.7% boys), RNA biosignatures were measured in 279 randomly selected infants (89 with bacterial infections-including 32 with bacteremia and 15 with urinary tract infections-and 190 without bacterial infections), and 19 afebrile healthy infants. Sixty-six classifier genes were identified that distinguished infants with and without bacterial infections in the test set with 87% (95% CI, 73%-95%) sensitivity and 89% (95% CI, 81%-93%) specificity. Ten classifier genes distinguished infants with bacteremia from those without bacterial infections in the test set with 94% (95% CI, 70%-100%) sensitivity and 95% (95% CI, 88%-98%) specificity. The incremental C statistic for the RNA biosignatures over the YOS score was 0.37 (95% CI, 0.30-0.43).In this preliminary study, RNA biosignatures were defined to distinguish febrile infants aged 60 days or younger with vs without bacterial infections. Further research with larger populations is needed to refine and validate the estimates of test accuracy and to assess the clinical utility of RNA biosignatures in practice.

Project description:ImportanceClinicians often risk stratify young febrile infants for invasive bacterial infections (IBIs), defined as bacteremia and/or bacterial meningitis, using complete blood cell count parameters.ObjectiveTo estimate the accuracy of individual complete blood cell count parameters to identify febrile infants with IBIs.Design, setting, and participantsPlanned secondary analysis of a prospective observational cohort study comprising 26 emergency departments in the Pediatric Emergency Care Applied Research Network from 2008 to 2013. We included febrile (≥38°C), previously healthy, full-term infants younger than 60 days for whom blood cultures were obtained. All infants had either cerebrospinal fluid cultures or 7-day follow-up.Main outcomes and measuresWe tested the accuracy of the white blood cell count, absolute neutrophil count, and platelet count at commonly used thresholds for IBIs. We determined optimal thresholds using receiver operating characteristic curves.ResultsOf 4313 enrolled infants, 1340 (31%; 95% CI, 30% to 32%) were aged 0 to 28 days, 2412 were boys (56%), and 2471 were white (57%). Ninety-seven (2.2%; 95% CI, 1.8% to 2.7%) had IBIs. Sensitivities were low for common complete blood cell count parameter thresholds: white blood cell count less than 5000/µL, 10% (95% CI, 4% to 16%) (to convert to 109 per liter, multiply by 0.001); white blood cell count ≥15 000/µL, 27% (95% CI, 18% to 36%); absolute neutrophil count ≥10 000/µL, 18% (95% CI, 10% to 25%) (to convert to × 109 per liter, multiply by 0.001); and platelets <100 × 103/µL, 7% (95% CI, 2% to 12%) (to convert to × 109 per liter, multiply by 1). Optimal thresholds for white blood cell count (11 600/µL), absolute neutrophil count (4100/µL), and platelet count (362 × 103/µL) were identified in models that had areas under the receiver operating characteristic curves of 0.57 (95% CI, 0.50-0.63), 0.70 (95% CI, 0.64-0.76), and 0.61 (95% CI, 0.55-0.67), respectively.Conclusions and relevanceNo complete blood cell count parameter at commonly used or optimal thresholds identified febrile infants 60 days or younger with IBIs with high accuracy. Better diagnostic tools are needed to risk stratify young febrile infants for IBIs.

Dataset Information

The Yale Observation Scale Score and the Risk of Serious Bacterial Infections in Febrile Infants.

Objectives

Methods

Results

Conclusions

Publications

The Yale Observation Scale Score and the Risk of Serious Bacterial Infections in Febrile Infants.

Similar Datasets

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