1. Central nervous system infection in the intensive care unit: Development and validation of a multi-parameter diagnostic prediction tool to identify suspected patients
- Author
-
Margareth Catoia Varela, Hugo Boechat Andrade, Justin Lee Sim, Jesus Soares, James J. Sejvar, José Cerbino-Neto, Mayara Secco Torres da Silva, José Henrique Mello-Neto, Ermias D. Belay, Fernando A. Bozza, André M. Japiassú, Grazielle Viana Ramos, Aline Ramos da Silva, Pedro Henrique Nascimento Theodoro, and Ivan Rocha Ferreira da Silva
- Subjects
Central Nervous System ,RNA viruses ,Male ,Physiology ,Epidemiology ,Fevers ,Pathology and Laboratory Medicine ,Logistic regression ,Nervous System ,law.invention ,Medical Conditions ,Central Nervous System Infections ,Immunodeficiency Viruses ,Infectious Diseases of the Nervous System ,law ,Medicine and Health Sciences ,Cerebrospinal Fluid ,Multidisciplinary ,Middle Aged ,Prognosis ,Intensive care unit ,Hospitals ,Body Fluids ,Intensive Care Units ,Infectious Diseases ,Neurology ,Medical Microbiology ,Viral Pathogens ,Viruses ,Medicine ,Encephalitis ,Female ,Anatomy ,Pathogens ,Brazil ,Research Article ,Adult ,medicine.medical_specialty ,Critical Care ,Science ,Microbiology ,External validity ,Signs and Symptoms ,Acquired immunodeficiency syndrome (AIDS) ,Diagnostic Medicine ,Intensive care ,Retroviruses ,medicine ,Humans ,Glasgow Coma Scale ,Microbial Pathogens ,Aged ,Retrospective Studies ,Chicago ,Receiver operating characteristic ,business.industry ,Lentivirus ,Organisms ,Biology and Life Sciences ,HIV ,medicine.disease ,Confidence interval ,Health Care ,Logistic Models ,ROC Curve ,Health Care Facilities ,Medical Risk Factors ,Multivariate Analysis ,Emergency medicine ,Clinical Medicine ,business - Abstract
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 2 cells/mm3, fever (≥38°C/100.4°F), focal neurologic deficit, Glasgow Coma Scale 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 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.
- Published
- 2021