Using Artificial Intelligence to Predict Intracranial Hypertension in Patients After Traumatic Brain Injury: A Systematic Review.

Intracranial hypertension (IH) is a key driver of secondary brain injury in patients with traumatic brain injury. Lowering intracranial pressure (ICP) as soon as IH occurs is important, but a preemptive approach would be more beneficial. We systematically reviewed the artificial intelligence (AI) models, variables, performances, risks of bias, and clinical machine learning (ML) readiness levels of IH prediction models using AI. We conducted a systematic search until 12-03-2023 in three databases. Only studies predicting IH or ICP in patients with traumatic brain injury with a validation of the AI model were included. We extracted type of AI model, prediction variables, model performance, validation type, and prediction window length. Risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool, and we determined the clinical ML readiness level. Eleven out of 399 nonduplicate publications were included. A gaussian processes model using ICP and mean arterial pressure was most common. The maximum reported area under the receiver operating characteristic curve was 0.94. Four studies conducted external validation, and one study a prospective clinical validation. The prediction window length preceding IH varied between 30 and 60 min. Most studies (73%) had high risk of bias. The highest clinical ML readiness level was 6 of 9, indicating "real-time model testing" stage in one study. Several IH prediction models using AI performed well, were externally validated, and appeared ready to be tested in the clinical workflow (clinical ML readiness level 5 of 9). A Gaussian processes model was most used, and ICP and mean arterial pressure were frequently used variables. However, most studies showed a high risk of bias. Our findings may help position AI for IH prediction on the path to ultimate clinical integration and thereby guide researchers plan and design future studies.

Investigation and response to an outbreak of Neisseria meningitidis serogroup Y ST-1466 urogenital infections, Australia.

Abstract: In 2023, an increased number of urogenital and anorectal infections with Neisseria meningitis serogroup Y (MenY) were reported in New South Wales (NSW). Whole genome sequencing (WGS) found a common sequence type (ST-1466), with limited sequence diversity. Confirmed outbreak cases were NSW residents with a N. meningitidis isolate matching the cluster sequence type; probable cases were NSW residents with MenY isolated from a urogenital or anorectal site from 1 July 2023 without WGS testing. Of the 41 cases, most were men (n = 27), of whom six reported recent contact with a female sex worker. Five cases were men who have sex with men and two were female sex workers. Laboratory alerts regarding the outbreak were sent to all Australian jurisdictions through the laboratories in the National Neisseria Network. Two additional states identified urogenital MenY ST-1466 infections detected in late 2023. Genomic analysis showed all MenY ST-1466 sequences were interspersed, suggestive of an Australia-wide outbreak. The incidence of these infections remains unknown, due to varied testing and reporting practices both within and across jurisdictions. Isolates causing invasive meningococcal disease (IMD) in Australia are typed, and there has been no MenY ST-1466 IMD recorded in Australia to end of March 2024. Concerns remain regarding the risk of IMD, given the similarity of these sequences with a MenY ST-1466 IMD strain causing a concurrent outbreak in the United States of America.