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Prediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population

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dc.contributor.authorAmorim, Robson Luis
dc.contributor.authorOliveira, Louise Makarem
dc.contributor.authorMalbouisson, Luis Marcelo
dc.contributor.authorNagumo, Marcia Mitie
dc.contributor.authorSimoes, Marcela
dc.contributor.authorMiranda, Leandro
dc.contributor.authorBor-Seng-Shu, Edson
dc.contributor.authorBeer-Furlan, Andre
dc.contributor.authorDe Andrade, Almir Ferreira
dc.contributor.authorRubiano, Andres M.
dc.contributor.authorTeixeira, Manoel Jacobsen
dc.contributor.authorKolias, Angelos G.
dc.contributor.authorPaiva, Wellingson Silva
dc.date.accessioned2020-02-07T06:11:45Z
dc.date.available2020-02-07T06:11:45Z
dc.date.issued2020-01-24
dc.date.submitted2019-06-17
dc.date.updated2020-02-07T06:11:44Z
dc.description.abstractBackground: In a time when the incidence of severe traumatic brain injury (TBI) is increasing in low- to middle-income countries (LMICs), it is important to understand the behavior of predictive variables in an LMIC's population. There are few previous attempts to generate prediction models for TBI outcomes from local data in LMICs. Our study aim is to design and compare a series of predictive models for mortality on a new cohort in TBI patients in Brazil using Machine Learning. Methods: A prospective registry was set in São Paulo, Brazil, enrolling all patients with a diagnosis of TBI that require admission to the intensive care unit. We evaluated the following predictors: gender, age, pupil reactivity at admission, Glasgow Coma Scale (GCS), presence of hypoxia and hypotension, computed tomography findings, trauma severity score, and laboratory results. Results: Overall mortality at 14 days was 22.8%. Models had a high prediction performance, with the best prediction for overall mortality achieved through Naive Bayes (area under the curve = 0.906). The most significant predictors were the GCS at admission and prehospital GCS, age, and pupil reaction. When predicting the length of stay at the intensive care unit, the Conditional Inference Tree model had the best performance (root mean square error = 1.011), with the most important variable across all models being the GCS at scene. Conclusions: Models for early mortality and hospital length of stay using Machine Learning can achieve high performance when based on registry data even in LMICs. These models have the potential to inform treatment decisions and counsel family members. Level of evidence: This observational study provides a level IV evidence on prognosis after TBI.
dc.identifier.doi10.17863/CAM.48883
dc.identifier.eissn1664-2295
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/301814
dc.languageen
dc.publisherFrontiers Media S.A.
dc.rightsAttribution 4.0 International (CC BY 4.0)en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectNeurology
dc.subjectprognostic
dc.subjecttraumatic brain injury
dc.subjectmachine learning
dc.subjectmortality
dc.subjectLMICs
dc.titlePrediction of Early TBI Mortality Using a Machine Learning Approach in a LMIC Population
dc.typeArticle
dcterms.dateAccepted2019-12-10
prism.publicationNameFrontiers in Neurology
prism.volume10
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
rioxxterms.versionVoR
rioxxterms.versionofrecord10.3389/fneur.2019.01366

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