The accuracy of clinical symptoms and signs for the diagnosis of serious bacterial infection in young febrile children: prospective cohort study of 15 781 febrile illnesses

Abstract

Objectives: To evaluate current processes by which young children presenting with a febrile illness but suspected of having serious bacterial infection are diagnosed and treated, and to develop and test a multivariable model to distinguish serious bacterial infections from self limiting non-bacterial illnesses. Design Two year prospective cohort study. Setting The emergency department of The Children's Hospital at Westmead, Westmead, Australia.

Participants: Children aged less than 5 years presenting with a febrile illness between 1 July 2004 and 30 June 2006.

Intervention: A standardised clinical evaluation that included mandatory entry of 40 clinical features into the hospital's electronic record keeping system was performed by physicians. Serious bacterial infections were confirmed or excluded using standard radiological and microbiological tests and follow-up. Main outcome measures Diagnosis of one of three key types of serious bacterial infection (urinary tract infection, pneumonia, and bacteraemia), and the accuracy of both our clinical decision making model and clinician judgment in making these diagnoses.

Results: We had follow-up data for 93% of the 15 781 instances of febrile illnesses recorded during the study period. The combined prevalence of any of the three infections of interest (urinary tract infection, pneumonia, or bacteraemia) was 7.2% (1120/15 781, 95% confidence interval (CI) 6.7% to 7.5%), with urinary tract infection the diagnosis in 543 (3.4%) cases of febrile illness (95% CI 3.2% to 3.7%), pneumonia in 533 (3.4%) cases (95% CI 3.1% to 3.7%), and bacteraemia in 64 (0.4%) cases (95% CI 0.3% to 0.5%). Almost all (>94%) of the children with serious bacterial infections had the appropriate test (urine culture, chest radiograph, or blood culture). Antibiotics were prescribed acutely in 66% (359/543) of children with urinary tract infection, 69% (366/533) with pneumonia, and 81% (52/64) with bacteraemia. However, 20% (2686/13 557) of children without bacterial infection were also prescribed antibiotics. On the basis of the data from the clinical evaluations and the confirmed diagnosis, a diagnostic model was developed using multinomial logistic regression methods. Physicians' diagnoses of bacterial infection had low sensitivity (10-50%) and high specificity (90-100%), whereas the clinical diagnostic model provided a broad range of values for sensitivity and specificity.

Conclusions: Emergency department physicians tend to underestimate the likelihood of serious bacterial infection in young children with fever, leading to undertreatment with antibiotics. A clinical diagnostic model could improve decision making by increasing sensitivity for detecting serious bacterial infection, thereby improving early treatment.

Fig 1 Patient flow chart

Fig 2 Frequency of testing and antibiotic administration in the emergency department among children less than 5 years of age with febrile illness. *Children with more than one reference test are counted in each test specific box

Fig 3 Clinical indicators of urinary tract infection, displayed as logs of the odds ratios from the multinomial model

Fig 4 Clinical indicators of pneumonia, displayed as logs of the odds ratios from the multinomial model

Fig 5 Clinical indicators of bacteraemia, displayed as logs of the odds ratios from the multinomial model

Fig 6 Receiver operating characteristic curve of the model (solid line) compared with clinician estimation for urinary tract infection (circles (data points) and broken line). Cumulative percentage displays the distribution of the false positive rates

Fig 7 Receiver operating characteristic curve of the model (solid line) compared with the clinician estimation for pneumonia (circles and broken line). Cumulative percentage displays the distribution of the false positive rates

Fig 8 Receiver operating characteristic curve of the model (solid line) compared with the clinician estimation for bacteraemia (circles and broken line). Cumulative percentage displays the distribution of the false positive rates