Patient-Oriented Evidence That Matters

Laboratory-Based Prediction Model Can Rule Out Serious Bacterial Infections in Febrile Infants


Am Fam Physician. 2019 Oct 1;100(7):440.

Clinical Question

Can a laboratory-based prediction model rule out serious bacterial infections in infants?

Bottom Line

In febrile infants up to 60 days of age, the combination of a normal urinalysis result, an absolute neutrophil count of less than 4,090 per mL (4.1 × 109 per L), and a serum procalcitonin level of less than 1.71 ng per mL is accurate at ruling out serious bacterial infections. (Level of Evidence = 1b)


Most of us believe that clinical signs are unreliable in identifying serious illness in febrile infants, which results in extensive and invasive septic work-ups. The authors recruited a convenience sample of febrile infants (rectal temperature of at least 100.4°F [38°C]) up to 60 days of age who showed up in emergency departments during times when research staff were available. They excluded infants who appeared critically ill, those born prematurely, and those with chronic conditions. All infants had standardized clinical assessments, and blood and urine cultures and lumbar punctures were done at the discretion of the treating physician. Of 3,230 eligible infants, 1,821 had a procalcitonin sample drawn.

The presence of a serious bacterial infection, as defined by bacterial meningitis, bacteremia, or a urinary tract infection, was detected in 170 infants (9%). The researchers performed a variety of statistical gymnastics to derive a prediction model on a split sample of the infants and then validated the model on the rest. Using the validation sample, the combination of a negative urinalysis, an absolute neutrophil count less than 4,090 per mL, and a procalcitonin level of less than 1.71 ng per mL was accurate at ruling out serious infections: 97.7% sensitivity (95% CI, 91.3 to 99.6) and 60.0% specificity (56.6 to 63.3), with a positive likelihood ratio of 2.4 (2.1 to 2.7) and a negative likelihood ratio of 0.04 (0.006 to 0.15). The clinicians were asked to predict the likelihood of a serious infection and they were not particularly accurate.


POEMs (patient-oriented evidence that matters) are provided by Essential Evidence Plus, a point-of-care clinical decision support system published by Wiley-Blackwell. For more information, see http://www.essentialevidenceplus.com. Copyright Wiley-Blackwell. Used with permission.

For definitions of levels of evidence used in POEMs, see http://www.essentialevidenceplus.com/product/ebm_loe.cfm?show=oxford.

To subscribe to a free podcast of these and other POEMs that appear in AFP, search in iTunes for “POEM of the Week” or go to http://goo.gl/3niWXb.

This series is coordinated by Sumi Sexton, MD, editor-in-chief.

A collection of POEMs published in AFP is available at https://www.aafp.org/afp/poems.



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