Association of Cough Status With Bacterial Infections in Febrile Infants

Respiratory viral testing for young febrile infants presenting to emergency care: a planned secondary analysis of the Febrile Infants Diagnostic assessment and Outcome (FIDO) prospective observational cohort study

OBJECTIVE

To describe the association of respiratory viral test results and the risk of invasive bacterial infection (IBI) for febrile young infants presenting to emergency care.

DESIGN

A planned secondary analysis within the Febrile Infants Diagnostic assessment and Outcome (FIDO) study, a prospective multicentre observational cohort study conducted across the UK and Ireland.

SETTING

35 paediatric emergency departments and assessment units across the UK and Ireland between 6 July 2022 and 31 August 2023.

PATIENTS

Febrile infants aged 90 days and under presenting to emergency care.

MAIN OUTCOME MEASURES

IBI (meningitis or bacteraemia) among febrile infants, undergoing respiratory viral testing for respiratory syncytial virus (RSV), influenza and SARS-CoV-2.

RESULTS

1395 out of 1821 participants underwent respiratory viral testing, of those tested 339 (24.5%) tested positive for at least one of, SARS-CoV-2, RSV or influenza. A total of 45 infants (3.2%) were diagnosed with IBI. Of these, IBI occurred in 40 out of 1056 (3.8%) participants with a negative viral test and 5 out of 339 (1.5%) occurred in participants with a positive viral respiratory test (p=0.034). Infants aged 29 days and older with a positive respiratory viral test had a significantly lower rate of IBI (0.7%) compared with those with a negative test (3.2%) (p=0.015).

CONCLUSIONS

Young febrile infants with a positive respiratory viral test for SARS-CoV-2, RSV or influenza are at lower risk of IBI. Infants over 28 days of age with a positive viral test represent the lowest risk cohort.

TRIAL REGISTRATION NUMBER

NCT05259683.

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

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.

Refinement and Validation of a Clinical-Based Approach to Evaluate Young Febrile Infants

BACKGROUND AND OBJECTIVE

For febrile infants, predictive models to detect bacterial infections are available, but clinical adoption remains limited by implementation barriers. There is a need for predictive models using widely available predictors. Thus, we previously derived 2 novel predictive models (machine learning and regression) by using demographic and clinical factors, plus urine studies. The objective of this study is to refine and externally validate the predictive models.

METHODS

This is a cross-sectional study of infants initially evaluated at one pediatric emergency department from January 2011 to December 2018. Inclusion criteria were age 0 to 90 days, temperature ≥38°C, documented gestational age, and insurance type. To reduce potential biases, we derived models again by using derivation data without insurance status and tested the ability of the refined models to detect bacterial infections (ie, urinary tract infection, bacteremia, and meningitis) in the separate validation sample, calculating areas-under-the-receiver operating characteristic curve, sensitivities, and specificities.

RESULTS

Of 1419 febrile infants (median age 53 days, interquartile range = 32-69), 99 (7%) had a bacterial infection. Areas-under-the-receiver operating characteristic curve of machine learning and regression models were 0.92 (95% confidence interval [CI] 0.89-0.94) and 0.90 (0.86-0.93) compared with 0.95 (0.91-0.98) and 0.96 (0.94-0.98) in the derivation study. Sensitivities and specificities of machine learning and regression models were 98.0% (94.7%-100%) and 54.2% (51.5%-56.9%) and 96.0% (91.5%-99.1%) and 50.0% (47.4%-52.7%).

CONCLUSIONS

Compared with the derivation study, the machine learning and regression models performed similarly. Findings suggest a clinical-based model can estimate bacterial infection risk. Future studies should prospectively test the models and investigate strategies to optimize clinical adoption.

Derivation of a natural language processing algorithm to identify febrile infants

BACKGROUND

Diagnostic codes can retrospectively identify samples of febrile infants, but sensitivity is low, resulting in many febrile infants eluding detection. To ensure study samples are representative, an improved approach is needed.

OBJECTIVE

To derive and internally validate a natural language processing algorithm to identify febrile infants and compare its performance to diagnostic codes.

METHODS

This cross-sectional study consisted of infants aged 0-90 days brought to one pediatric emergency department from January 2016 to December 2017. We aimed to identify infants with fever, defined as a documented temperature ≥38°C. We used 2017 clinical notes to develop two rule-based algorithms to identify infants with fever and tested them on data from 2016. Using manual abstraction as the gold standard, we compared performance of the two rule-based algorithms (Models 1, 2) to four previously published diagnostic code groups (Models 5-8) using area under the receiver-operating characteristics curve (AUC), sensitivity, and specificity.

RESULTS

For the test set (n = 1190 infants), 184 infants were febrile (15.5%). The AUCs (0.92-0.95) and sensitivities (86%-92%) of Models 1 and 2 were significantly greater than Models 5-8 (0.67-0.74; 20%-74%) with similar specificities (93%-99%). In contrast to Models 5-8, samples from Models 1 and 2 demonstrated similar characteristics to the gold standard, including fever prevalence, median age, and rates of bacterial infections, hospitalizations, and severe outcomes.

CONCLUSIONS

Findings suggest rule-based algorithms can accurately identify febrile infants with greater sensitivity while preserving specificity compared to diagnostic codes. If externally validated, rule-based algorithms may be important tools to create representative study samples, thereby improving generalizability of findings.

Using Clinical History Factors to Identify Bacterial Infections in Young Febrile Infants

OBJECTIVE

To develop a novel predictive model using primarily clinical history factors and compare performance to the widely used Rochester Low Risk (RLR) model.

STUDY DESIGN

In this cross-sectional study, we identified infants brought to one pediatric emergency department from January 2014 to December 2016. We included infants age 0-90 days, with temperature ≥38°C, and documented gestational age and illness duration. The primary outcome was bacterial infection. We used 10 predictors to develop regression and ensemble machine learning models, which we trained and tested using 10-fold cross-validation. We compared areas under the curve (AUCs), sensitivities, and specificities of the RLR, regression, and ensemble models.

RESULTS

Of 877 infants, 67 had a bacterial infection (7.6%). The AUCs of the RLR, regression, and ensemble models were 0.776 (95% CI 0.746, 0.807), 0.945 (0.913, 0.977), and 0.956 (0.935, 0.975), respectively. Using a bacterial infection risk threshold of .01, the sensitivity and specificity of the regression model was 94.6% (87.4%, 100%) and 74.5% (62.4%, 85.4%), compared with 95.5% (87.5%, 99.1%) and 59.6% (56.2%, 63.0%) using the RLR model.

CONCLUSIONS

Compared with the RLR model, sensitivities of the novel predictive models were similar whereas AUCs and specificities were significantly greater. If externally validated, these models, by producing an individualized bacterial infection risk estimate, may offer a targeted approach to young febrile infants that is noninvasive and inexpensive.

Composition characteristics of the gut microbiota in infants and young children of under 6 years old between Beijing and Japan

BACKGROUND

The composition of intestinal flora in Chinese and Japanese has been reported in many studies but that in infants aged 0-6 years old has not been studied yet.

METHODS

The distribution characteristics of the fecal flora of infants in Beijing (n=84) and Japan (n=53) were analyzed using 16S rRNA gene sequencing analysis.

RESULTS

This study showed the higher relative abundance of Erysipelotrichaceae_ UCG-003 and Anaerostipes in male group that of Ruminiclostridium, Eubacterium, Senegalimassilia and Senegalimassilia in female group, especially Senegalimassilia, which was not detected in male group. Defecation trait groups indicated significantly higher relative abundance of Bifidobacterium in abnormal bowel trait group than that in the normal group (P<0.05). The feeding groups' analysis showed significantly higher relative abundance of Bifidobacterium and Enterococcus and lower abundance of Bacteroides and Lacetospirillaceae in the breast-feeding group than that in the formula feeding and mixed-feeding groups. The relative abundance of Parasutterella and Ruminococcaceae_UCG-003 in the halitosis group was significantly higher than that in the normal group. The comparison of cold and fever group and normal group indicated significantly higher relative abundance of Erysipelatoclostridium and lower relative abundance of Lachnospiraceae _UCG-001 in the fever and cold group than that in the normal group (P<0.05). The regional comparison of intestinal flora of Beijing and Japan showed significant increase in the relative abundance of Bacillus, Lactobacillus, Prevotella, megamonas and Veillonella in the intestinal flora of 0-6 years old infants in Beijing.

CONCLUSIONS

These findings improve the understanding of intestinal bacterial and viral communities of infants from the two Asian countries.