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

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 Cortegiani, Andrea

PloS one, 2021-11, Vol.16 (11), p.e0260551-e0260551 [Periódico revisado por pares]

United States: Public Library of Science

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Título:
Central nervous system infection in the intensive care unit: Development and validation of a multi-parameter diagnostic prediction tool to identify suspected patients
Autor: 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
Cortegiani, Andrea
Assuntos: 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
É parte de: PloS one, 2021-11, Vol.16 (11), p.e0260551-e0260551
Notas: ObjectType-Article-2
SourceType-Scholarly Journals-1
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Competing Interests: The authors have declared that no competing interests exist.
Descrição: 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.
Editor: United States: Public Library of Science
Idioma: Inglês

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

United States: Public Library of Science

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