Abstract
BACKGROUND: Sepsis affects 25 million children and neonates annually, causing significant mortality and morbidity. Early identification and treatment are crucial for improving outcomes. Identifying children at risk is challenging due to clinical heterogeneity and overlap with other conditions. Current evaluations of sepsis criteria adopt a variable-centred approach, evaluating each criterion independently. The objective of this study was to explore associations between patterns of sepsis screening criteria and sepsis risk in children screened in the emergency department (ED) to identify distinct profiles that describe the clinical heterogeneity of suspected sepsis. METHODS: This secondary analysis involved 3473 children screened for sepsis across 12 EDs in Queensland, Australia. Bayesian profile regression was used to construct data-driven clinical profiles derived from sepsis screening criteria and their association with suspected sepsis, defined as senior medical officer diagnosis and antibiotic administration in the ED. Posterior risk probabilities (Prs) with 95% credible intervals (CIs) were calculated for each profile. Profiles were internally validated by assessing their association with sepsis, septic shock, organ dysfunction and infection sources, in both adjusted and unadjusted models. RESULTS: Seven distinct clinical profiles were identified. Two profiles were labelled as high risk of suspected sepsis (profile 1, n=22: Pr 0.73, 95% CI 0.55, 0.89; profile 2, n=150: Pr 0.69, 95% CI 0.59, 0.80), four as moderate risk and one as low risk. High-risk profiles were characterised by severe illness indicators and elevated lactate levels. Moderate-risk profiles included criteria such as altered behaviour, young age (<3 months) and respiratory distress. High-risk profiles had strong associations with all clinical outcomes. CONCLUSIONS: Seven clinical profiles were identified that varied in their risk of suspected sepsis and associated outcomes. Validation of these profiles in diverse populations and identification of which profiles are likely to benefit from certain interventions is needed.