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The leap to ordinal: Detailed functional prognosis after traumatic brain injury with a flexible modelling approach

Author

Listed:
  • Shubhayu Bhattacharyay
  • Ioan Milosevic
  • Lindsay Wilson
  • David K Menon
  • Robert D Stevens
  • Ewout W Steyerberg
  • David W Nelson
  • Ari Ercole
  • the CENTER-TBI investigators participants

Abstract

When 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.

Suggested Citation

  • Shubhayu Bhattacharyay & Ioan Milosevic & Lindsay Wilson & David K Menon & Robert D Stevens & Ewout W Steyerberg & David W Nelson & Ari Ercole & the CENTER-TBI investigators participants, 2022. "The leap to ordinal: Detailed functional prognosis after traumatic brain injury with a flexible modelling approach," PLOS ONE, Public Library of Science, vol. 17(7), pages 1-29, July.
    • Handle: RePEc:plo:pone00:0270973
    DOI: 10.1371/journal.pone.0270973
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    References listed on IDEAS

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    1. Ari Ercole & Abhishek Dixit & David W Nelson & Shubhayu Bhattacharyay & Frederick A Zeiler & Daan Nieboer & Omar Bouamra & David K Menon & Andrew I R Maas & Simone A Dijkland & Hester F Lingsma & Lind, 2021. "Imputation strategies for missing baseline neurological assessment covariates after traumatic brain injury: A CENTER-TBI study," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-20, August.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. Christopher Meiring & Abhishek Dixit & Steve Harris & Niall S MacCallum & David A Brealey & Peter J Watkinson & Andrew Jones & Simon Ashworth & Richard Beale & Stephen J Brett & Mervyn Singer & Ari Er, 2018. "Optimal intensive care outcome prediction over time using machine learning," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-19, November.
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