BACKGROUND: To analyze early changes in white blood cells (WBCs), C-reactive protein (CRP) and procalcitonin (PCT) in children with multiple trauma, before secondary inflammation develops.

METHODS: This single-center retrospective study collected data from patients with blunt traumatic injury admitted to the pediatric intensive care unit (PICU). According to the prognostic outcome of 28 d after admission to the PICU, patients were divided into survival group (n=141) and non-survival group (n=36). Characteristics between the two groups were compared. Receiver operation characteristic (ROC) curve analysis was conducted to evaluate the capacity of different biomarkers as predictors of mortality.

RESULTS: The percentages of children with elevated WBC, CRP, and PCT levels were 81.36%, 31.07%, and 95.48%, respectively. Patients in the non-survival group presented a statistically significantly higher injury severity score (ISS) than those in the survival group: 37.17±16.11 vs. 22.23±11.24 (t=6.47, P<0.01). WBCs were also higher in non-survival group than in the survival group ([18.70±8.42]×10⁹/L vs. [15.89±6.98] ×10⁹/L, t=2.065, P=0.040). There was no significant difference between the survival and non-survival groups in PCT or CRP. The areas under the ROC curves of PCT, WBC and ISS for predicting 28-day mortality were 0.548 (P=0.376), 0.607 (P=0.047) and 0.799 (P<0.01), respectively.

CONCLUSIONS: Secondary to multiple trauma, PCT levels increased in more patients, even if their WBC and CRP levels remained unchanged. However, early rising WBC and ISS were superior to PCT at predicting the mortality of multiple trauma patients in the PICU.

BACKGROUND: The latest sepsis definition includes both infection and organ failure, as evidenced by the sequential organ failure assessment (SOFA) score. However, the applicability of the pediatric SOFA score (pSOFA) is not yet determined. This study evaluated the effectiveness of both pSOFA and system inflammatory reaction syndrome (SIRS) scores in predicting sepsis-related pediatric deaths.

METHODS: This is a retrospective multi-center cohort study including hospitalized patients <18 years old with diagnosed or not-yet-diagnosed infections. Multivariate analyses were carried out to evaluate risk factors for in-hospital mortality. According to Youden index (YI), three sub-categories of pSOFA were screened out and a new simplified pSOFA score (spSOFA) was formed. The effectiveness and accuracy of prediction of pSOFA, SIRS and spSOFA was retrieved from the area under the receiver operating characteristic curve (AUROC) and Delong’s test.

RESULTS: A total of 1,092 participants were eligible for this study, and carried a 23.4% in-hospital mortality rate. The 24-h elevated pSOFA score (24 h-pSOFA), bloodstream infection, and mechanical ventilation (MV) requirement were major risk factors associated with sepsis-related deaths. The AUROC analysis confirmed that the spSOFA provided good predictive capability in sepsis-related pediatric deaths, relative to the 24 h-pSOFA and SIRS.

CONCLUSIONS: The pSOFA score performed better than SIRS in diagnosing infected children with high mortality risk. However, it is both costly and cumbersome. We, therefore, proposed spSOFA to accurately predict patient outcome, without the disadvantages. Nevertheless, additional investigations, involving a large sample population, are warranted to confirm the conclusion of this study.

BACKGROUND: To promote the shared decision-making (SDM) between patients and doctors in pediatric outpatient departments, this study was designed to validate artificial intelligence (AI) -initiated medical tests for children with fever.

METHODS: We designed an AI model, named Xiaoyi, to suggest necessary tests for a febrile child before visiting a pediatric outpatient clinic. We calculated the sensitivity, specificity, and F1 score to evaluate the efficacy of Xiaoyi's recommendations. The patients were divided into the rejection and acceptance groups. Then we analyzed the rejected examination items in order to obtain the corresponding reasons.

RESULTS: We recruited a total of 11,867 children with fever who had used Xiaoyi in outpatient clinics. The recommended examinations given by Xiaoyi for 10,636 (89.6%) patients were qualified. The average F1 score reached 0.94. A total of 58.4% of the patients accepted Xiaoyi’s suggestions (acceptance group), and 41.6% refused (rejection group). Imaging examinations were rejected by most patients (46.7%). The tests being time-consuming were rejected by 2,133 patients (43.2%), including rejecting pathogen studies in 1,347 patients (68.5%) and image studies in 732 patients (31.8%). The difficulty of sampling was the main reason for rejecting routine tests (41.9%).

CONCLUSION: Our model has high accuracy and acceptability in recommending medical tests to febrile pediatric patients, and is worth promoting in facilitating SDM.