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BIBTEX RIS

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

2021; Public Library of Science; Volume: 16; Issue: 11 Linguagem: Inglês

10.1371/journal.pone.0260551

ISSN

1932-6203

Autores

Hugo Boechat Andrade, Ivan Da Silva, Justin Sim, José Henrique Mello-Neto, Pedro Henrique Nascimento Theodoro, Mayara Secco Torres Silva, Margareth Catoia Varela, Grazielle Viana Ramos, Aline Ramos da Silva, Fernando A. Bozza, Jesus Soares, Ermias D. Belay, James J. Sejvar, José Cerbino-Neto, André Miguel Japiassú,

Tópico(s)

Neonatal and Maternal Infections

Resumo

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.

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