Dataset Information

Disentangling etiologies of CNS infections in Singapore using multiple correspondence analysis and random forest.

ABSTRACT: Central nervous system (CNS) infections cause substantial morbidity and mortality worldwide, with mounting concern about new and emerging neurologic infections. Stratifying etiologies based on initial clinical and laboratory data would facilitate etiology-based treatment rather than relying on empirical treatment. Here, we report the epidemiology and clinical outcomes of patients with CNS infections from a prospective surveillance study that took place between 2013 and 2016 in Singapore. Using multiple correspondence analysis and random forest, we analyzed the link between clinical presentation, laboratory results, outcome and etiology. Of 199 patients, etiology was identified as infectious in 110 (55.3%, 95%-CI 48.3-62.0), immune-mediated in 10 (5.0%, 95%-CI 2.8-9.0), and unknown in 79 patients (39.7%, 95%-CI 33.2-46.6). The initial presenting clinical features were associated with the prognosis at 2 weeks, while laboratory-related parameters were related to the etiology of CNS disease. The parameters measured were helpful to stratify etiologies in broad categories, but were not able to discriminate completely between all the etiologies. Our results suggest that while prognosis of CNS is clearly related to the initial clinical presentation, pinpointing etiology remains challenging. Bio-computational methods which identify patterns in complex datasets may help to supplement CNS infection diagnostic and prognostic decisions.

SUBMITTER: Zellweger RM

PROVIDER: S-EPMC7588471 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Disentangling etiologies of CNS infections in Singapore using multiple correspondence analysis and random forest.

Zellweger Raphaël M RM Yacoub Sophie S Chan Yvonne F Z YFZ Soon Derek D Shafi Humaira H Ooi Say Tat ST Chan Monica M Jacobson Leslie L Sessions October M OM Vincent Angela A Low Jenny Guek Hong JGH Ooi Eng Eong EE Wang Linfa L Wijaya Limin L Tan Kevin K

Scientific reports 20201026 1

Central nervous system (CNS) infections cause substantial morbidity and mortality worldwide, with mounting concern about new and emerging neurologic infections. Stratifying etiologies based on initial clinical and laboratory data would facilitate etiology-based treatment rather than relying on empirical treatment. Here, we report the epidemiology and clinical outcomes of patients with CNS infections from a prospective surveillance study that took place between 2013 and 2016 in Singapore. Using m ...[more]

PMID: 33106525

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Disentangling etiologies of CNS infections in Singapore using multiple correspondence analysis and random forest.

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Disentangling etiologies of CNS infections in Singapore using multiple correspondence analysis and random forest.

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