A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Machine learning-based dynamic mortality prediction after traumatic brain injury
Tekijät: Rahul Raj, Teemu Luostarinen, Eetu Pursiainen, Jussi P. Posti, Riikka S. K. Takala, Stepani Bendel, Teijo Konttila, Miikka Korja
Kustantaja: NATURE PUBLISHING GROUP
Julkaisuvuosi: 2019
Journal: Scientific Reports
Tietokannassa oleva lehden nimi: SCIENTIFIC REPORTS
Lehden akronyymi: SCI REP-UK
Artikkelin numero: ARTN 17672
Vuosikerta: 9
Sivujen määrä: 13
ISSN: 2045-2322
eISSN: 2045-2322
DOI: https://doi.org/10.1038/s41598-019-53889-6
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/44334525
Our aim was to create simple and largely scalable machine learning-based algorithms that could predict mortality in a real-time fashion during intensive care after traumatic brain injury. We performed an observational multicenter study including adult TBI patients that were monitored for intracranial pressure (ICP) for at least 24 h in three ICUs. We used machine learning-based logistic regression modeling to create two algorithms (based on ICP, mean arterial pressure [MAP], cerebral perfusion pressure [CPP] and Glasgow Coma Scale [GCS]) to predict 30-day mortality. We used a stratified crossvalidation technique for internal validation. Of 472 included patients, 92 patients (19%) died within 30 days. Following cross-validation, the ICP-MAP-CPP algorithm's area under the receiver operating characteristic curve (AUC) increased from 0.67 (95% confidence interval [CI] 0.60-0.74) on day 1 to 0.81 (95% CI 0.75-0.87) on day 5. The ICP-MAP-CPP-GCS algorithm's AUC increased from 0.72 (95% CI 0.64-0.78) on day 1 to 0.84 (95% CI 0.78-0.90) on day 5. Algorithm misclassification was seen among patients undergoing decompressive craniectomy. In conclusion, we present a new concept of dynamic prognostication for patients with TBI treated in the ICU. Our simple algorithms, based on only three and four main variables, discriminated between survivors and non-survivors with accuracies up to 81% and 84%. These open-sourced simple algorithms can likely be further developed, also in low and middleincome countries.
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