Dataset Information

Derivation of a clinical-based model to detect invasive bacterial infections in febrile infants.

ABSTRACT:

Background

Febrile infants are at risk for invasive bacterial infections (IBIs) (i.e., bacteremia and bacterial meningitis), which, when undiagnosed, may have devastating consequences. Current IBI predictive models rely on serum biomarkers, which may not provide timely results and may be difficult to obtain in low-resource settings.

Objective

The aim of this study was to derive a clinical-based IBI predictive model for febrile infants.

Designs, setting, and participants

This is a cross-sectional study of infants brought to two pediatric emergency departments from January 2011 to December 2018. Inclusion criteria were age 0-90 days, temperature ≥38°C, and documented gestational age, fever duration, and illness duration.

Main outcome and measures

To detect IBIs, we used regression and ensemble machine learning models and evidence-based predictors (i.e., sex, age, chronic medical condition, gestational age, appearance, maximum temperature, fever duration, illness duration, cough status, and urinary tract inflammation). We up-weighted infants with IBIs 8-fold and used 10-fold cross-validation to avoid overfitting. We calculated the area under the receiver operating characteristic curve (AUC), prioritizing a high sensitivity to identify the optimal cut-point to estimate sensitivity and specificity.

Results

Of 2311 febrile infants, 39 had an IBI (1.7%); the median age was 54 days (interquartile range: 35-71). The AUC was 0.819 (95% confidence interval: 0.762, 0.868). The predictive model achieved a sensitivity of 0.974 (0.800, 1.00) and a specificity of 0.530 (0.484, 0.575). Findings suggest that a clinical-based model can detect IBIs in febrile infants, performing similarly to serum biomarker-based models. This model may improve health equity by enabling clinicians to estimate IBI risk in any setting. Future studies should prospectively validate findings across multiple sites and investigate performance by age.

SUBMITTER: Yaeger JP

PROVIDER: S-EPMC9633417 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature

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Publications

Derivation of a clinical-based model to detect invasive bacterial infections in febrile infants.

Yaeger Jeffrey P JP Jones Jeremiah J Ertefaie Ashkan A Caserta Mary T MT Fiscella Kevin A KA

Journal of hospital medicine 20220829 11

<h4>Background</h4>Febrile infants are at risk for invasive bacterial infections (IBIs) (i.e., bacteremia and bacterial meningitis), which, when undiagnosed, may have devastating consequences. Current IBI predictive models rely on serum biomarkers, which may not provide timely results and may be difficult to obtain in low-resource settings.<h4>Objective</h4>The aim of this study was to derive a clinical-based IBI predictive model for febrile infants.<h4>Designs, setting, and participants</h4>Thi ...[more]

PMID: 36036211

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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.

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: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:ImportanceYoung 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.ObjectiveTo assess whether RNA biosignatures can distinguish febrile infants aged 60 days or younger with and without serious bacterial infections.Design, setting, and participantsProspective 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.ExposureRNA biosignatures compared with cultures for discriminating febrile infants with and without bacterial infections and infants with bacteremia from those without bacterial infections.Main outcomes and measuresBacterial infection confirmed by culture. Performance of RNA biosignatures was compared with routine laboratory screening tests and Yale Observation Scale (YOS) scores.ResultsOf 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).Conclusions and relevanceIn 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: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.

Dataset Information

Derivation of a clinical-based model to detect invasive bacterial infections in febrile infants.

Background

Objective

Designs, setting, and participants

Main outcome and measures

Results

Publications

Derivation of a clinical-based model to detect invasive bacterial infections in febrile infants.

Similar Datasets

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