Post-neurosurgical meningitis diagnosis
Post-neurosurgical meningitis (PNM) often leads to serious consequences; unfortunately, the commonly used clinical diagnostic methods of PNM are time-consuming or have low specificity.
Due to the excessive neuroinflammation which are evoked by the primary brain disease or the craniotomy operation, the symptoms derived from the infection and aseptic may not be easily distinguished. On the other hand, the low positive rate and time-consuming character restrict the clinical practical values of microbiological culture. Therefore, it is always difficult to make a definite diagnosis of post-neurosurgical bacterial meningitis. Bao et al. reviewed the established literature about the diagnostic biomarkers for the post-neurosurgical bacterial meningitis (PNBM) and analyzed the potential obstacles in both clinical and scientific studies. Given the obstacle which has negative impacts on further investigation about the biology of PNBM, they only find relatively small numbers of study on PNBM 1)
Suspected internal external ventricular drain (EVD)-related infections could represent sterile inflammation or lower bacterial load leading to false-negative cultures. There is a need for improved microbiology diagnostics and biomarkers of bacterial infection to permit accurate discrimination and improve antimicrobial stewardship 2).
To realize the accurate and convenient diagnosis of PNM, Xie et al. proposed a comprehensive strategy for cerebrospinal fluid analysis based on a machine-learning-aided Cross-Reactive Chemical Sensor Array. The sensor array involves three Eu3+-doped metal-organic frameworks (MOFs), which can generate specific fluorescence responding patterns after reacting with potential targets in CSF. Then, the responding pattern is used as learning data to train the machine learning algorithms. The discrimination confidence for artificial CSF containing different components of molecules, proteins, and cells is from 81.3 to 100%. Furthermore, the machine-learning-aided sensing array was applied in the analysis of CSF samples from post-neurosurgical patients. Only 25 μL of CSF samples was needed, and the samples could be robustly classified into “normal,” “mild,” or “severe” groups within 40 min. It is believed that the combination of machine learning algorithms with robust data processing capability and a lanthanide luminescent sensor array will provide a reliable alternative for more comprehensive, convenient, and rapid diagnosis of PNM 3).
A study suggests that raised cerebrospinal fluid tumor necrosis factor -α, interleukin-1β, and interleukin-8 in a temporal manner may indicate early bacterial meningitis development in neurosurgical patients, enabling earlier diagnostic certainty and improved patient outcomes 4).