Risk-stratification in febrile infants 29 to 60 days old: a cost-effectiveness analysis

Abstract

Abstract Background Multiple clinical prediction rules have been published to risk-stratify febrile infants ≤60 days of age for serious bacterial infections (SBI), which is present in 8-13% of infants. We evaluate the cost-effectiveness of strategies to identify infants with SBI in the emergency department. Methods We developed a Markov decision model to estimate outcomes in well-appearing, febrile term infants, using the following strategies: Boston, Rochester, Philadelphia, Modified Philadelphia, Pediatric Emergency Care Applied Research Network (PECARN), Step-by-Step, Aronson, and clinical suspicion. Infants were categorized as low risk or not low risk using each strategy. Simulated cohorts were followed for 1 year from a healthcare perspective. Our primary model focused on bacteremia, with secondary models for urinary tract infection and bacterial meningitis. One-way, structural, and probabilistic sensitivity analyses were performed. The main outcomes were SBI correctly diagnosed and incremental cost per quality-adjusted life-year (QALY) gained. Results In the bacteremia model, the PECARN strategy was the least expensive strategy ($3671, 0.779 QALYs). The Boston strategy was the most cost-effective strategy and cost $9799/QALY gained. All other strategies were less effective and more costly. Despite low initial costs, clinical suspicion was among the most expensive and least effective strategies. Results were sensitive to the specificity of selected strategies. In probabilistic sensitivity analyses, the Boston strategy was most likely to be favored at a willingness-to-pay threshold of $100,000/QALY. In the urinary tract infection model, PECARN was preferred compared to other strategies and the Boston strategy was preferred in the bacterial meningitis model. Conclusions The Boston clinical prediction rule offers an economically reasonable strategy compared to alternatives for identification of SBI.

Keywords

• Infant fever
• Economic analysis
• Clinical prediction rules
• Serious bacterial infection
• Neonatal sepsis