Clinical prediction models for serious infections in children: external validation in ambulatory care

Background Early distinction between mild and serious infections (SI) is challenging in children in ambulatory care. Clinical prediction models (CPMs), developed to aid physicians in clinical decision-making, require broad external validation before clinical use. We aimed to externally validate four CPMs, developed in emergency departments, in ambulatory care. Methods We applied the CPMs in a prospective cohort of acutely ill children presenting to general practices, outpatient paediatric practices or emergency departments in Flanders, Belgium. For two multinomial regression models, Feverkidstool and Craig model, discriminative ability and calibration were assessed, and a model update was performed by re-estimation of coefficients with correction for overfitting. For two risk scores, the SBI score and PAWS, the diagnostic test accuracy was assessed. Results A total of 8211 children were included, comprising 498 SI and 276 serious bacterial infections (SBI). Feverkidstool had a C-statistic of 0.80 (95% confidence interval 0.77–0.84) with good calibration for pneumonia and 0.74 (0.70–0.79) with poor calibration for other SBI. The Craig model had a C-statistic of 0.80 (0.77–0.83) for pneumonia, 0.75 (0.70–0.80) for complicated urinary tract infections and 0.63 (0.39–0.88) for bacteraemia, with poor calibration. The model update resulted in improved C-statistics for all outcomes and good overall calibration for Feverkidstool and the Craig model. SBI score and PAWS performed extremely weak with sensitivities of 0.12 (0.09–0.15) and 0.32 (0.28–0.37). Conclusions Feverkidstool and the Craig model show good discriminative ability for predicting SBI and a potential for early recognition of SBI, confirming good external validity in a low prevalence setting of SBI. The SBI score and PAWS showed poor diagnostic performance. Trial registration ClinicalTrials.gov, NCT02024282. Registered on 31 December 2013.