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dc.contributor.authorBhattacharyay, Shubhayu
dc.contributor.authorMilosevic, Ioan
dc.contributor.authorWilson, Lindsay
dc.contributor.authorMenon, David
dc.contributor.authorStevens, Robert D
dc.contributor.authorSteyerberg, Ewout W
dc.contributor.authorNelson, David W
dc.contributor.authorErcole, Ari
dc.contributor.authorCENTER-TBI investigators participants
dc.date.accessioned2022-07-05T19:00:14Z
dc.date.available2022-07-05T19:00:14Z
dc.date.issued2022
dc.date.submitted2022-02-20
dc.identifier.citationPLOS ONE, volume 17, issue 7, article-number e0270973
dc.identifier.issn1932-6203
dc.identifier.otherpone-d-22-05175
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/338819
dc.descriptionFunder: ZNS - Hannelore Kohl Stiftung; funder-id: http://dx.doi.org/10.13039/501100007731
dc.descriptionFunder: One Mind
dc.descriptionFunder: Integra LifeSciences; funder-id: http://dx.doi.org/10.13039/100009006
dc.description.abstractWhen a patient is admitted to the intensive care unit (ICU) after a traumatic brain injury (TBI), an early prognosis is essential for baseline risk adjustment and shared decision making. TBI outcomes are commonly categorised by the Glasgow Outcome Scale-Extended (GOSE) into eight, ordered levels of functional recovery at 6 months after injury. Existing ICU prognostic models predict binary outcomes at a certain threshold of GOSE (e.g., prediction of survival [GOSE > 1]). We aimed to develop ordinal prediction models that concurrently predict probabilities of each GOSE score. From a prospective cohort (n = 1,550, 65 centres) in the ICU stratum of the Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI) patient dataset, we extracted all clinical information within 24 hours of ICU admission (1,151 predictors) and 6-month GOSE scores. We analysed the effect of two design elements on ordinal model performance: (1) the baseline predictor set, ranging from a concise set of ten validated predictors to a token-embedded representation of all possible predictors, and (2) the modelling strategy, from ordinal logistic regression to multinomial deep learning. With repeated k-fold cross-validation, we found that expanding the baseline predictor set significantly improved ordinal prediction performance while increasing analytical complexity did not. Half of these gains could be achieved with the addition of eight high-impact predictors to the concise set. At best, ordinal models achieved 0.76 (95% CI: 0.74-0.77) ordinal discrimination ability (ordinal c-index) and 57% (95% CI: 54%- 60%) explanation of ordinal variation in 6-month GOSE (Somers' Dxy). Model performance and the effect of expanding the predictor set decreased at higher GOSE thresholds, indicating the difficulty of predicting better functional outcomes shortly after ICU admission. Our results motivate the search for informative predictors that improve confidence in prognosis of higher GOSE and the development of ordinal dynamic prediction models.
dc.languageen
dc.publisherPublic Library of Science (PLoS)
dc.subjectResearch Article
dc.subjectResearch and analysis methods
dc.subjectPhysical sciences
dc.subjectMedicine and health sciences
dc.subjectPeople and places
dc.subjectComputer and information sciences
dc.subjectBiology and life sciences
dc.titleThe leap to ordinal: Detailed functional prognosis after traumatic brain injury with a flexible modelling approach.
dc.typeArticle
dc.date.updated2022-07-05T19:00:13Z
prism.publicationNamePLoS One
dc.identifier.doi10.17863/CAM.86226
dcterms.dateAccepted2022-06-21
rioxxterms.versionofrecord10.1371/journal.pone.0270973
rioxxterms.versionVoR
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
datacite.contributor.supervisoreditor: Park, Soojin
dc.contributor.orcidBhattacharyay, Shubhayu [0000-0001-7428-5588]
dc.contributor.orcidWilson, Lindsay [0000-0003-4113-2328]
dc.contributor.orcidMenon, David [0000-0002-3228-9692]
dc.contributor.orcidErcole, Ari [0000-0001-8350-8093]
dc.identifier.eissn1932-6203
dc.publisher.urlhttp://dx.doi.org/10.1371/journal.pone.0270973
pubs.funder-project-idEuropean Commission (602150)
pubs.funder-project-idEPSRC (EP/T022159/1)
cam.issuedOnline2022-07-05


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