Distinct patterns of postoperative fever in paediatric neurosurgery patients.

PURPOSE: Postoperative fever is a common problem following neurosurgery but data on the causes among paediatric patients is sparse. In this report, we determined the incidence, causes, and outcomes of postoperative fever in paediatric neurosurgical patients (< 18 years), and contrasted the findings with an adult cohort published recently from our unit. METHODS: We recruited 61 patients who underwent 73 surgeries for non-traumatic neurosurgical indications over 12 months. A standard protocol was followed for the evaluation and management of postoperative fever. We prospectively collected data pertaining to operative details, daily maximal temperature, clinical features, and use of surgical drains, urinary catheters, and other adjuncts. Elevated body temperature of > 99.9 °F or 37.7 °C for > 48 h or associated with clinical deterioration or localising features was considered as "fever"; elevated temperature not meeting these criteria was classified as transient elevation in temperature (TET). RESULTS: Twenty-six patients (35.6%) had postoperative fever, more frequent than in adult patients. TET occurred in 12 patients (16.4%). The most common causes of fever were aseptic meningitis (34.6%), followed by urinary tract infections (15.4%), pyogenic meningitis, COVID-19, and wound infections. Postoperative fever was associated with significantly longer duration of hospital admission and was the commonest cause of readmission. CONCLUSION: In contrast to adults, early temperature elevations in paediatric patients may portend infectious and serious non-infectious causes of fever, including delayed presentation with aseptic meningitis, a novel association among paediatric patients. Investigation guided by clinical assessment and conservative antibiotic policy in keeping with the institutional microbiological profile provides the most appropriate strategy in managing paediatric postoperative fever.

Is radiomics a useful addition to magnetic resonance imaging in the preoperative classification of PitNETs?

BACKGROUND: The WHO 2021 introduced the term pituitary neuroendocrine tumours (PitNETs) for pituitary adenomas and incorporated transcription factors for subtyping, prompting the need for fresh diagnostic methods. Current biomarkers struggle to distinguish between high- and low-risk non-functioning PitNETs. We explored if radiomics can enhance preoperative decision-making. METHODS: Pre-treatment magnetic resonance (MR) images of patients who underwent surgery between 2015 and 2019 with available WHO 2021 classification were used. The tumours were manually segmented on the T1w, T1-contrast enhanced, and T2w images using 3D Slicer. One hundred Pyradiomic features were extracted from each MR sequence. Models were built to classify (1) somatotroph and gonadotroph PitNETs and (2) high- and low-risk subtypes of non-functioning PitNETs. Feature were selected independently from the MR sequences and multi-sequence (combining data from more than one MR sequence) using Boruta and Pearson correlation. Support vector machine (SVM), logistic regression (LR), random forest (RF), and multi-layer perceptron (MLP) were the classifiers used. Data imbalance was addressed using the Synthetic Minority Oversampling TEchnique (SMOTE). Performance of the models were evaluated using area under the receiver operating curve (AUC), accuracy, sensitivity, and specificity. RESULTS: A total of 222 PitNET patients (train, n = 149; test, n = 73) were enrolled in this retrospective study. Multi-sequence-based LR model discriminated best between somatotroph and gonadotroph PitNETs, with a test AUC of 0.84, accuracy of 0.74, specificity of 0.81, and sensitivity of 0.70. Multi-sequence-based MLP model perfomed best for the high- and low-risk non-functioning PitNETs, achieving a test AUC of 0.76, accuracy of 0.67, specificity of 0.72, and sensitivity of 0.66. CONCLUSIONS: Utilizing pre-treatment MRI and radiomics holds promise for distinguishing high-risk from low-risk non-functioning PitNETs based on the latest WHO classification. This could assist neurosurgeons in making critical decisions regarding surgery or alternative management strategies for PitNETs after further clinical validation.