Central nervous system infection in the intensive care unit: Development and validation of a multi-parameter diagnostic prediction tool to identify suspected patients

BACKGROUND: Central nervous system infections (CNSI) are diseases with high morbidity and mortality, and their diagnosis in the intensive care environment can be challenging. Objective: To develop and validate a diagnostic model to quickly screen intensive care patients with suspected CNSI using readily available clinical data. METHODS: Derivation cohort: 783 patients admitted to an infectious diseases intensive care unit (ICU) in Oswaldo Cruz Foundation, Rio de Janeiro RJ, Brazil, for any reason, between 01/01/2012 and 06/30/2019, with a prevalence of 97 (12.4%) CNSI cases. Validation cohort 1: 163 patients prospectively collected, between 07/01/2019 and 07/01/2020, from the same ICU, with 15 (9.2%) CNSI cases. Validation cohort 2: 7,270 patients with 88 CNSI (1.21%) admitted to a neuro ICU in Chicago, IL, USA between 01/01/2014 and 06/30/2019. Prediction model: Multivariate logistic regression analysis was performed to construct the model, and Receiver Operating Characteristic (ROC) curve analysis was used for model validation. Eight predictors—age <56 years old, cerebrospinal fluid white blood cell count >2 cells/mm(3), fever (≥38°C/100.4°F), focal neurologic deficit, Glasgow Coma Scale <14 points, AIDS/HIV, and seizure—were included in the development diagnostic model (P<0.05). RESULTS: The pool data’s model had an Area Under the Receiver Operating Characteristics (AUC) curve of 0.892 (95% confidence interval 0.864–0.921, P<0.0001). CONCLUSIONS: A promising and straightforward screening tool for central nervous system infections, with few and readily available clinical variables, was developed and had good accuracy, with internal and external validity.