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

• Published in: PloS one Vol. 16; no. 11; p. e0260551
• Main Authors: Andrade, Hugo Boechat, Ferreira da Silva, Ivan Rocha, Sim, Justin Lee, Mello-Neto, José Henrique, Theodoro, Pedro Henrique Nascimento, Torres da Silva, Mayara Secco, Varela, Margareth Catoia, Ramos, Grazielle Viana, Ramos da Silva, Aline, Bozza, Fernando Augusto, Soares, Jesus, Belay, Ermias D, Sejvar, James J, Cerbino-Neto, José, Japiassú, André Miguel
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 29-11-2021; Public Library of Science (PLoS)
• Subjects: Adult; Aged; Biology and Life Sciences; Brain research; Brazil; Care and treatment; Central nervous system; Central nervous system diseases; Central Nervous System Infections - diagnosis; Cerebrospinal fluid; Chicago; Confidence intervals; Convulsions & seizures; Critical Care; Critical care medicine; Critically ill; Data collection; Diagnosis; Disease control; Disease prevention; Education; Encephalitis; Female; Fever; Glasgow Coma Scale; Health risks; Health surveillance; HIV; Human immunodeficiency virus; Humans; Immune system; Infections; Infectious diseases; Intensive care; Intensive Care Units; Laboratories; Leukocytes; Logistic Models; Male; Medical diagnosis; Medical imaging; Medical records; Medicine and Health Sciences; Meningitis; Methods; Middle Aged; Morbidity; Multivariate Analysis; Nervous system; Parameter identification; Pathogens; Pathology; Patients; Prediction models; Prognosis; Public health; Regression analysis; Regression models; Retrospective Studies; Risk factors; ROC Curve; Seizures; Statistical analysis; Zoonoses; Brazil; Chicago; Atlanta Georgia; Rio de Janeiro Brazil; Chicago Illinois; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0260551

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.
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/mm3, 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).
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).
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.