Routine blood tests predict infection type better than CRP in children
Routine Blood Tests Outperform CRP for Distinguishing Bacterial From Viral Infection in Children
Machine Learning
Summary
Distinguishing whether a child's infection is caused by bacteria or viruses is hard, leading doctors to often prescribe antibiotics just in case. The authors found that routine blood tests, specifically complete blood count (CBC), combined with age and CRP levels, predict whether the infection is bacterial or viral more accurately than CRP levels alone. Their best method used a machine learning model that improved detection accuracy and could help reduce unnecessary antibiotic use. This suggests that using multiple blood test markers together is more helpful for diagnosis than relying on one marker.
What this means in practice
- •For hospital laboratory teams: Improve infection type diagnosis by integrating CBC data with CRP using machine learning models for better clinical decision support.
- •For clinical decision support developers: Develop diagnostic software that combines routine blood count and CRP data to help distinguish bacterial from viral infections in children.$Commercial implications: Enables creation of software tools sold to hospitals and clinics for improved antibiotic prescribing guidance.
Authors
Mihaela Demireva, Zhecho Mitev, Djuna Chinareva-Klimentova, Svetoslav Ivanov, Georgi Nalbantov, Dimitar Mitev
Abstract
Acute infectious diseases are among the leading causes of medical consultations and hospitalizations in children worldwide. These infections are predominantly caused by viruses or bacteria, yet differentiating between the two remains a common clinical challenge. As a result, pediatricians often default to the safer option of prescribing antibiotics contributing to the growing problem of antimicrobial resistance. The objective is to assess the additional predictive value of CBC towards determining the current infection. This retrospective study used data from 906 pediatric patients aged between 2 and 14 years who were tested positive either for viral or bacterial infection between 2022 and 2026. Inclusion criteria further required availability of CBC results and CRP level measurements. These laboratory parameters as well as age were used as input features for several supervised classification models. Model performance was evaluated using AUC, sensitivity and specificity. The best performing model is XGBoost, which included all features, achieving out of-sample performance of AUC of 81.7% and sensitivity of 70.8%, specificity of 79.2%. All trained models outperform a CRP-based only decision-rule model in terms of AUC. We suggest that the decision to prescribe antibiotics should be based on a number of factors, including but not limited to CBC, some of which are not currently incorporated into routine practice.